CA2288990A1 - Allelic polygene diagnosis of reward deficiency syndrome and treatment - Google Patents

Abstract

Enhancement of attentional processing is attained by administration of an endorphinase inhibitor or enkephalinase inhibitor and optionally, a dopamine precursor, or a serotonin precursor, a GABA precursor, or an endorphin or enkephalinase releaser, or certain herbal compounds including Rhodiola rosea extract (Pharmaline) and/or Huperzine. These components promote restoration of normal neurotransmitter function and the components combined enhance the release of dopamine at the nucleus accumbens and are non-addictive. Use of the dopamine precursors L-phenylalanine, or L-Tyrosine, the enkephalinase inhibitor D-phenylalanine, and/or the serotonin precursor -hydroxytryptophan and a natural acetylcholenesterase inhibitor and chromium salts (i.e.
picolinate, nicotinate, etc.) is especially preferred, but not limited to assist in relieving symptoms associated with brain phenylalanine deficiency.

Description

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~_ ..r i , t Sixth, if the genetic variants involved in polygenic disorders are common they must be fundamentally different than those causing single gene disorders, i.e.
they must result in only moderate changes in gene expression, since if they caused major alterations in gene expression they would cause single gene disorders (Comings, 1998b). Based on these observations the inventors have suggested that by forming variable lengths of Z-DNA the common dinucleotide repeat polymorphisms themselves play a major role in polygenic inheritance (Comings, 1998b). These repeats are plentiful, the alleles are common, and the Z-DNA formed exert a modest effect on gene expression. A meaningful phenotypic effect can only be produced by adding together the effect of variants at many different functionally related genes.
The implication of this for the present study was that when available the inventors were particularly interested in using short tandem repeat polymorphisms, and any single base pair polymorphism was likely to be in linkage disequilibrium with one or more of the alleles of short tandem repeats associated with the gene. Because of the latter the inventors assumed that any single nucleotide polymorphism, even if it was not associated with exons or promoters, could provide valuable information for the MAA technique. Seventh, to examine the additive effect of two or more genes affecting a given neurotransmitter or functional group, a dummy variable called polygenic (PG), was set up based on simply adding the scores for each individual gene. Examining the effect of various combinations of genes within a given functional group on the percent of the variance allowed the identification of the subset of genes that has an additive effect on a quantitative score. This set of genes was then used to form a traps polygenic score (TPG) that examined the additive effect of genes across different functional groups. The higher the PG or TPG score the greater the number of phenotypicaily relevant gene variants an individual possessed.
' Eighth, as in the previous studies (Coming et al., 1996b; Comings et al., 1998a; Comings et al., 1998b) linear regression analysis was used to examine the additive gene scores. This allowed the calculation of r' or the percent of the variance that the genes being examined contributed to the trait, provided F to estimate the magnitude of the effect, and p for the significance of the effect. The fact that a gene accounts for only about I percent of the variance of a trait does not mean the effect is of little importance. Depending on a number of factors, this can provide for up to 10 percent (r) of the predictability of a phenotypic effect (Rosenthai and Rubin, 1982;
Ozer, 1985). Nineth, since the various behavioral scores have different ranges of magnitude, the cumulative r' values rather than the behavioral scores were used to provide precise comparison of the effect of the 29 genes across different phenotypes.
Tenth, the inventors have chosen to primarily examine the attention deficit hyperactivity (ADHD) score based on the DSM-IV (Diagnostic and Statistical lLlanaral of the American Psychiatric Assn. lv 1994) criteria. Rather than emphasizing a dichotomous diagnosis the inventors have used the . approach of utilizing the whole range of the score by comparing the means of the ADHD
score in individuals with different sets of genotypes. Eleventh, there were two potential approaches to use for the regression analyses: univariate and multivariate.
Far the univariate analysis the scores for each gene were added to produce a single score and the correlation between this score and the QTVs determined. This was an extension of the 0, 1, 2 , 3 scoring approach. The advantage of this approach was that the r 1 S. values increased only when one or more of the genes had an additive effect on the QTV. When genes were added that had a subtractive effect, the r decreased.
This was conservative for the determination of final r and r2 values. However, since all the gene scores were combined into a single variable, the degrees of freedom was always 1 despite the number of genes added. This was non-conservative in regard to p values.
For the second approach each individual gene score was evaluated in a multivariate regression equation and correlated with the QTV. Because the degrees of freedom increased as each gene was added, this was conservative for p values.
However, since both additive and subtractive genes contributed to r, this approach was 2S non-conservative for r and r~ values which increased whether the genes were additive or subtractive in their effect.
The inventors have chosen the univariate approach for two reasons. First, it is more conservative since both the r and p values decrease when genes that do not contribute to the QTV are added. Second, because the genes are represented by a single degree of freedom regardless of the number of genes examined it has the i.i potential of simultaneously examining the effect of hundreds of genes without loss a power.
_ Twelfth, to examine the hypothesis that most of the disorders that are comorbid with ADHD share genes in common, the inventors also examined the additive effect of the same set of 29 genes on the quantitative scores for oppositional defiant disorder (ODD), conduct disorder (CD), tics, learning disorders (LD) and other QTVs. For subjects over 14 years of age the inventors also examined QTVs for alcohol abuse/dependence, drug abuse/dependence and smoking. Thirteenth, the examination of these comorbid disorders also allowed the inventors to examine the hypothesis (Coming et al.. 1996b; Comings, 1996a) that in addition to sharing genes, different comorbid disorders utilize some genes and combinations of genes that are unique to specific phenotypes. Fourteenth, to explore whether a level of significance of p < OS for the association between an individual gene and a given phenotype had any bearing on whether that gene had an additive effect, the inventors examined the relationship between the rz and p values of the 145 gene-phenotype associations with regard to whether they had an additive or subtractive effect on the phenotype score.
Finally, after an initial pass using all the candidate genes had identified those with an additive effect, each additive gene was progressively added to a new TPG score and correlated with the QTV in question. This provided an estimate of the total r2 that can be obtained using only those genes identified as contributing to the QTV in question.
Advantages of an Additive Score The use of an additive gene score taps into a number of the most unique aspects of polygenic inheritance -- the additive effect of different genes, the subtractive effect of different genes, the role of heterosis and epistasis, and the fact that while a number of different genes may be contributing to - 25 the same phenotype in any given individual or group of individuals only a subset of those genes may be present. Most importantly, this approach can compensate for the presence of different sets of genes in different individuals. Thus, many times one association study is replicated by some but not all subsequent studies. This is due to the fact that the gene is contributing to only a small percent of the variance and one set of genes may be involved in one group of subjects while another set is involved in a different group of subjects. Rather than implying that one or the other result is WO 98/48785 PCTlUS98/08684 incorrect, an equally valid conclusion is that different sets of genes can produce the same phenotype in the different groups of individuals. Since the additive score measures the effect of the total set of genes involved, the MAA technique may be much more reproducible across different groups of subjects. To give a specific example, while variants of the DRD2 (TagI AI allele) (Coming et al., 1996b;
Blum et al., 1998), DRD=I (48 by 7 repeat) (Lahost et crl., 1995), DBH (TagI B 1 allele) (Coming et al., I 996b), and DATl ( I 0 repeat allele)genes (Coming et al., 1996b;
Cook et al., 1995; Gill et al., 1997; Waldmaqn et al., 1996) have all been implicated in the etiology of ADHD, each may have a significant effect in some groups of subjects but not others. However, if the physiologic effect is similar for each gene (alteration in dopamine metabolism) an additive score for all four might prove to be consistently and significantly higher in all groups of ADHD subjects versus controls, indicating it is a global genetic defect in dopamine metabolism rather than any single gene that is etiologically important in ADHD. The same rationale holds for additive genes across different neurotransmitter groups. A final strength of the additive score is that by using different combinations of genes a maximally informative set can be optimized to the identification of a given phenotype. Unlike the minor effects of individual genes, such an optimized set may provide great predictive and diagnostic value.
Hypotheses The first hypothesis is that, since polygenic disorders involve the additive effect of multiple genes, the MAA technique will provide much greater power in the identification of the genes involved than examination of the genes one-at-a-time, and the inclusion of only those genes that had an additive effect would maximize the r2 and p values. To test this the inventors have examined the effect of 29 genes in 336 Caucasian individuals consisting of 274 with Tourette syndrome and 62 controls rated for the severity of ADHD. The second hypothesis is, if two independent samples are examined the inventors hypothesized that since different samples may utilize different sets of genes many but not all genes that were additive in one sample would be additive in the re-test sample, many but not all genes that were subtractive in one sample would be subtractive in the re-test, some genes would be additive in one sample and subtractive in the re-test sample, and for both samples the additive effect of multiple genes would be significantly greater than the effect of . . ?. _ ~ r , r any single gene. The inventors also expected that the r'' fluctuations would be greater with a smaller N. To test this the 336 subjects were randomly divided into two independent sets of 168 subjects with equal numbers of TS and control subjects and " equal numbers of males and females in each group. The correlations between the PG
and TPG scores for the ADHID score was examined for each set. The third hypothesis is that, when different phenotypes are examined the inventors hypothesized that comorbid disorders would share some ADHD genes while some genes and gene combinations would be unique to comorbid disorders. To test this the inventors examined the additive and subtractive effect of all 29 genes against different QTVs oppositional defiant disorder, conduct disorder, tics, learning and other disorders.
Test Subjects. The study group consisted of 336 unrelated, non-Hispanic Caucasian subjects. Of these 274 had a diagnosis of Tourette syndrome (TS), and 62 were controls. The TS subjects came from the Tourette syndrome Clinic at the City of Hope Medical Center. All meet DSM-IV criteria for TS and all were personally interviewed by the inventor. While these are predominately the same subjects examined in previous studies (Comings, 1995a; Comings, 1990), some new TS
probands were added when the DNA samples of some of the subjects used in the prior studies were depleted. The inventors have previously divided the inventors' TS
subjects into those with mild (grade 1, chronic tics too mild to treat), moderate (grade 2, severe enough to require treatment), and severe (grade 3, very significant effect on some aspect of their life) (Comings and Comings, 1987b). Among the TS subjects 30% were grade 3, 62% were grade 2, and 8% were grade 1. TS and ADHD are similar disorders and the majority of TS subjects that come to clinics have comorbid ADHD (Comings and Comings, 1987b; Comings and Comings, 1984). Of the TS
subjects 54% met DSM-IV criteria for ADHD. The presence of controls, and TS
subjects with and without ADHD make this group particularly well suited to examining the association between the alleles of different genes and ADHD as a continuous variable. The age of the TS subjects averaged 18.0 years (S.D.
13.2).
While the majority were older children and adolescents, 29% were 21 years of age or older. The mean age of the controls was 46.3 years (S.D. I5.38). Both the TS
subjects and controls have been described elsewhere (Comings et al., 1996b;
Comings, 1995a; Comings, 1994b; Comings, 1994a; Comings, 1995b).

WO 98/48785 . PCTIUS98108684 Each control and TS proband was required to fill out a questionnaire based on the Diagnostic Interview Schedule (Robins et al., 1981 ), DSM-1II-R
(Diagnostic and Statistical rLlanual of Mental Disorders, 1987) and DSM-IV (Diagnostic and Stutistical Manual of the American Psychiatric Assn. IV, 1994) criteria for a range of disorders. The questions asked if the DSM-1V ADHD symptoms during childhood and adolescence were never or rarely present (score = 0), occasionally present (score =
1 ) or always present (score = 2). The oppositional defiant disorder (ODD) and conduct disorder (CD) scales were also based on summing the number of DSM-IV
criteria for these disorders. To assess the presence of problems with learning disorders, subjects were asked three questions. 1. Have you ever been placed in an educationally handicapped (EH), learning handicapped (LH) or learning disorder (LD) special class? 2. Have you ever been placed in a resource class? 3. Have you ever been told you had a seaming disorder? Each question was scored no = 0 or yes =
1 and added to form the LD score. In California, placement in any of the above special I 5 classes requires a thorough evaluation by one or more educational psychologists, and the assessment that the student is two years or more behind his peers. The alcohol score (Comings, 1994b) was based on the summation of DSM-IV symptoms of alcohol abuse/dependence. The tic score was based on the summation of the presence of a range of motor and vocal tics (Comings, 1995a). The specific questions used for the behavioral scores have been described in detail elsewhere (Comings et al., 1996b;
Comings et al., 1998a; Comings, 1995a; Comings, 1990; Comings, 1994a: Comings, 1995b; Robins et al., 1981; Comings, 1995c; Comings, 1994c). The questionnaires were reviewed with each subject to ensure their accuracy. The accuracy, utility and sensitivity of a questionnaire based approach to symptom evaluation has been demonstrated by others (Gadow and Sprafkin, 1994; Giayson and Carlson, 1991 ) by comparing the use of such an instrument to an interviewer administration of the same structured instrument.
To examine the possibility that even sets of randomly scored alleles would show a significant effect when only the additive genes were used, each gene was assigned scores of 0 and 2 on the basis of a rounding off random numbers in an algorithm that produced the same final allele frequencies as those reported in Table 65.

The inventors utilized the dstattl0.exe program (http:I/odin.mdacc.tmc.edu/anonftp/) to provide precise p values for the large F values obtained in the linear regression analyses. Linear chi square analysis was used to test for significant differences in the number of genes in subjects without versus those with ADI-ID. The SPSS Statistical Software from SPSS, Inc (Chicago, IL) was used.
Results. Table 65 lists the name of the 29 genes, the type of polymorphism, a a reference to the polymorphism including the technique for genotyping, how the genotypes were scored, the percent of subjects scored as 1 or 2, whether there has been verification of the method of genotype scoring in an independent set of subjects, and when available, the reference for this. To test hypothesis #l, the r, r2, F and p value of the progressive PG and TPG gene scores against the ADHD variable were calculated by linear regression analysis (Table 66). In presenting the results the inventors have divided the genes into groups based on the neurotransmitter or function they affect.

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Dopamine Genes. All five of the dopamine receptor genes and the dopamine transporter gene (DA TI) were examined. Of these, the DRD2 (Coming et al., 1996b;
Comings et al., 1991), DRD=t (Lahoste et al., 1996; Swanson et al., 1998) and DA TI
(Comings et al., 1996b; Gill et al., 1997) genes have already been implicated in ADHD. The DRD~, DRD2. DRD~ and DA TI genes had the greatest effect on the ADHD score, with r2 values ranging from 0.0022 to 0.0088. The respective p values ranged from 0.388 to 0.093. While the DRD.~ gene played no role in ADl-1D in this study, to conform with the prior literature the inventors scored this gene to emphasize the longer 6 to 8 repeat alleles, despite the inventors' own studies indicating that the 2 alleles may also be important (Comings et al., 1997a).
The PG scores showed that the DRDI and DRD2 genes were additive in their effect such that the sum of their individual r2 values (0.0041 + 0.0084 =
0.0120 was the same as the observed PG score for D 1 + D2 (0.0125). The inventors termed these additive genes'. By contrast when the DRD3 gene scores were added to those of the D 1 + D2 PG score, the resultant r2 was less (0.0092) than for the DRDl plus the DRD2 gene. Thus, in regards to the ADHD phenotype, the inventors termed these the DRD3 a subtractive gene. Since the r2 continued to decrease with the addition of the DRD-1 gene (0.0071 ) this was also considered a subtractive gene. Since the r'' values again increased following the addition of the DRD~ and the DA TI genes, these were considered additive genes. When only the additive genes were included in the PG
score. the total r2 was 0.0208, compared to 0.0138 when both the additive and subtractive genes were included in the PG score.
Based on these results the inventors formulated the rule that if adding a gene increased r~, increased F and decreased p, it was playing an additive role in the phenotype, while if it reversed these values, it had a subtractive effect on the phenotype. In the remainder of these studies the inventors have chosen this, rather than an arbitrary p value, as the criteria for including the gene in the PG or TPG
scores. Thus, the inventors chose DRDl, DRD2, DRD~, and DA TI as the set of additive dopaminergic genes (D) for the TPG score. Together these four dopaminergic genes accounted for =2.08 percent of the variance of the ADHD
score (p ~= 10.0081 ).

Serotonin Genes. The r'' values for the six serotonin genes ranged from 0.0106 for the serotonin transporter (HTT) to 0.0002 for the HTR2C gene. When the HTRIA gene was added to the HTT gene there was an increase in the r2 value from 0.0106 to 0.0143. The r'' value dropped when the HTR1D~3 gene was added, increased following addition of the HTR2A gene, decreased with the HTR2C gene, and increased with the TD02 gene. This suggested that the HTT, HTRIA, fITR2A and TD02 genes would be the optimal set of additive serotonergic genes. The r' for these genes was 0.0161. However, since the r~ value for the HTR2A gene (.0016) was the lowest of the four, the inventors also determined the r2 for the HTT, HTRlA
and TDO2. Since this was higher (0.0167) the HTR2A gene was left out of the additive set of serotonergic genes. Leaving out the gene with the next lowest r' value did not further increase the additive r2. Together these four genes accounted for 1.67% of the variance of the ADHD score (p~= 0.018). When the additive dopaminergic and the serotonergic genes were added they accounted for 3.7% of the variance of the ADHD
score (pt= 0.0004).
Norepinephrine Genes. The r2 values for the four norepinephrine genes ranged from 0.0066 to 0.0122. In this case all four genes had a progressively additive effect on the total r'' value. Together they accounted for 4.22% of the variance of the ADHD score (pt= 0.00015), indicating that the norepinephrine genes played a greater role in ADHD than the dopamine or serotonin genes. or the dopamine and serotonin genes combined. In fact, the two noradrenergic a,~ receptor genes themselves accounted for almost as much of the variance of the ADHD score {3.44%) as the dopamine and serotonin genes combined. Further details of the role of these adrenergic genes in ADHD are presented elsewhere {Comings et al., 1998a). When the effect of the dopaminergic plus the serotonergic plus the noradrenergic genes (DSN TPG score) were combined they accounted for 7.41 % of the variance of the ADHD score (pfi= 0.0000040).
Catecholamine Degrading Genes. The r' values for the ADHD score for the two catecholamine degrading genes ranged from 0.0244 for the MA OA gene and 0.0098 for the COMT gene. When added (C) they accounted for 3.33% of the __..~.... ___ .. . . r , , variance of the ADHD score (p J~= 0.00077). When added to the DSN score they accounted for 10.05% of the variance of the ADHD score (p = 2.8 x I 0 8).
GABA Receptor Genes. The r2 values for the ADHD score for the two GABA receptor genes ranged from 0.0082 for the GABRA3 gene to 0.0066 for the GABRB3 gene. The r' value for the two combined was 0.0143 (~y= 0.028). When combined with the DSN and the C scores they accounted for 10.87% of the variance of the ADHD score {p~-= 5.9 x I 0 9 ).
Other Neurotransmitter Genes. The r2 values for the four additional neurotransmitter genes were 0.0017 for the cannabinoid receptor gene CNRI, O.OI43 for the nicotinic cholinergic a4 gene CHNRA~t, 0.0058 for the NMDA receptor gene NMDAR~, 0.0011 for the proenkephalin gene PENK. When each was added to the previous genes the percent of the variance of the ADHD score progressively increased to 11.09%, 11.78%, 12.26%. and 12.34%. For the entire set of neurotransmitter related genes (NT), pt= 3.4 x 10-l0.
Androgen Receptor Gene. Since all of the behaviors the inventors have examined are more common in males, the inventors also examined the AR gene.
The details of the role of this gene in ADHD, ODD and CD are presented elsewhere (Comings et al., 1998b). The r' value for the ADHD score for the AR gene was 0.011 1. When added to the prior genes they accounted for 13.27% of the variance of the ADHD score (p =i 5.5 x 10-t ~}.
Immunity Genes. Because of the recent interest in the potential role of immune factors in ADHD (Warren et al., 1995) and TS (Swedo et al., 1998) the inventors examined two immunity genes, the interferon-y and the CD8A genes.
Both proved to be subtractive genes and were not included in the additive set.
' 25 Other Genes. Two other genes, presenilin-1 (PSl) and the corticotrophin releasing factor gene (CRF), were examined. Although the rz values for both were similar (.0051 and 0.0050), only the CRF gene was additive. When all of the additive genes were combined they accounted for 13.62% of the variance of the ADHD
score (p = 2.8 x 10-t t ).

ADHD Score. FIG. 4 illustrates the effect of increasing numbers of variant additive genes on the ADHD score. It showed a progressive increasing trend from 1.0 for those with only 4 or ~ variant genes, to 25.0 for those carrying 15 variant genes.
The p value for linear chi square test of a progressive increase in the ADHD
score was <10g.
FIG. 5 illustrates the r' values for the progressive addition of all 29 genes tested. The slope of the curve to the immediate left of the gene label provided an indication of the relative contribution of that gene on the ADHD score. When the slope increased the gene contributed to the ADHD score. The greater the angle of the slope the greater the contribution. When the slope decreased the gene was subtractive and in comparison to other genes, played little or no role in the ADHD score, despite that fact the initial gene scoring always showed a greater phenotypic effect for the genotypes scored as 1 or 2. It was determines that the DRDI, DRD2, DRD~ and DA TI dopamine genes contributed to the ADHD score while the DRD3 and DRD-l genes did not. Of the serotonergic genes, the HTT, HTRlA and TD02 genes contributed to the ADHD score while the HTR2A and HTR2C genes did not. The four noradrenergic genes contributed more to the ADHD score than the genes for any other neurotransmitter group. When all 29 of the additive and the subtractive genes were included the final rz value was 0.113, pf= 2.7 x 10 9.
The inventors examined the possibility that if the same set of genes were assigned alleles on a random basis, and only the genes that gave a positive correlation with the ADHD score were used, the resulting r2 would be significant. The results are shown at the bottom of FIG. 5. The progressively additive effect of the rZ
values is shown by empty squares and the additive effect of using only the positive correlations are shown by squares containing an x. The final r2 using both the additive and subtractive genes was 0.0001. The final r2 using only the additive random genes was 0.0004. Neither was significant. In addition, although the commutative r2 was as high as 0.008 at the random PENK gene, this fell back to 0.0004 when the last random additive gene (CDBA) was added.

..._.~~_~ _..__...._.. _ _.w._. .. r i . y.

To examine the potential confounding role of sex, the inventors examined males and females separately. Both groups showed significant elevations of r2 values that were primarily diminished by the decreased power of the smaller numbers of subjects in each group.
Thus, tests of hypothesis # 1 showed that the additive effect of 29 genes gave significantly greater r2 and p values than for any single gene, and the r2 and p values were maximized when only the additive genes were used. The results of the test of hypothesis #2 are shown in FIG. 6. This shows that the observed results matched the hypothesized results. Fourteen genes were additive in both sets, 2 were subtractive in both sets, 13 were different in both sets. In general, the genes that were most additive in the total set, such as DRDS, DATl, HTT, HTRIA, all four adrenergic genes (DBH, ADRA2C, ADRA2C, NT). MA OA, COMT, and AR genes were the genes that were additive in both sets, and the genes that were most subtractive in the total set, DRD3, DRD=~, were subtractive in both sets. The genes that were most strikingly different in the two sets were the CNRl, CNRA=l, NMDARl, and the PENK genes. For example, the NMDARI gene was quite additive in group 2 but subtractive in group 1.
However, in both sets there was a progressive increase in the r2 with final p values for both of <f 5.0 x 10 4 despite the smaller N. When only the additive genes were included, both were significant at p < 10 G. When only those genes that were additive for both groups were used in the total set, r'' = 0.108, F = 40.82, p7'= 5.6 x 10 9.
When those genes that were additive in both or only one set were used in the total set, r~ = 0.11, F
= 43.71, p = 1.5 x 10 9. These results further serve to illustrate the frequent difficulty in replication when single genes are examined. By contrast, the MAA technique produced robust results under a range of conditions. FIG. 4, FIG. 5 and FIG. 6 illustrate the testing of hypothesis #3 that comorbid conditions share genes in common with ADHD and use some unique genes.
ODD and CD The effect of the 29 genes on the r2 values for the ODD and CD scores were examined. When both the additive and subtractive genes were included the maximum r'' for the ODD score was 0.0775, p7'= 3.1 x 10 6, and for the CD score was 0.0225, p = 0.0059. Again, inspection allows the genes that contribute WO 98/48785 . PCT/US98/08684 to the ODD and CD score to be easily identified. When only the additive genes are utilized the maximum r' for the ODD score was 0.10, p7'= 3.2 x I0 8 and for the CD
score was 0.075, p f =-~3.7 x 10 6. The most important genes for ODD were DRDI, DRD2, DRD3, DA TI. MITT, HTRIA, HTR2A, HTR2C, DBH, ADRA2A, ADRA2C, MA OA, GABRA3, GABRB3, CNRI, CHRNA4, N~LIDARI, PENK, AR and CDBA. The most important genes for CD were DRDI, DRD2, HTT, HTRlA, HTR2A, IITR2C, DBt~ ADR2C, CHRNA-~. AR and CDBA. Thus, 20 of the 29 genes played a role in ADHD, 20 genes played a role in ODD, and 10 played a role in CD. The ODD genes were both similar and different than the ADHD genes, while the all the CD
genes were the same as for the ADHD score.
LD and Tics. The effect of the 29 genes on the r~ values for the LD and tic scores were examined. When both the additive and subtractive genes were included the maximum rZ for the LD score was 0.011, p = 0.054, and for the tic score was 0.014, p~= 0.029. When only the additive genes were included the maximum r2 for the LD score was 0.043, p = 0.0005 and for the tic score was 0.061, p =
0.00005.
Again, inspection shows that the genes involved in the tics score were DRDI, DRDS, HTRIA, HTRlD(3, HTR2C, TD02, DBH, ADR2C, COMT, GABRA3. CNRI and CNRNA-~. The genes involved in the LD score were DRDl, HTR2C, TD02, DBH, ADRA2A, ADR2C, MA OA, CNR~ and CNRA4. Thus, 12 genes were involved in tics and 9 were involved in learning disorders.
Alcohol or Drug Abuse/Dependence and Smoking. The results of testing the 29 genes against an alcohol abuse/dependence score in 164 subjects over age 14 were examined. When the N is smaller there is a wider fluctuation in the r2 values.
Thus, when adults only were examined the additive genes are easily distinguished 2~ from the subtractive genes. For the alcohol abuse/dependence QTV the final r' using both sets of genes was 0.049, p/= 0.0044. However, when only the additive genes were used the r2 value was 0.14, p = i 7.5 x 10 b. As discussed below, many of these genes or neurotransmitter systems have been previously implicated as playing a role in alcoholism.

. ~ _ .-~_. .~._._ __... ___ r The results with the drug abuse/dependence QTV (exclusive of smoking) were similar to those for alcohol abuse/dependence. However, since drug abuse was less prevalent than alcohol abuse/dependence the magnitude of the line for the r2 values was lower. The major differences were that the tITRID~3 and HTR2A, ADRA2C genes were subtractive in drug abuse/dependence but additive in alcohol ' abuse/dependence, HTR2C and NT were additive in drug abuse but subtractive in alcohol abuse, and INFG was subtracting in alcohol abuse but neutral in drug abuse.
Smoking was more common and these results were also examined. Here the QTV
was scored as ever smoked for a month more = 1, never smoked = 0. In this study of subjects the DRD2 gene played a major role producing a r2 value of 0.055 by itself.
Other additive genes were NMDARI, PENN; AR and INFG. The final r2 = 0.10, p = 0. 0003 8.
Other QTVs. The inventors also examined several other QTVs for behaviors that are commonly comorbid with ADHD. The total r' values, p values and major additive genes were as follows: mania -- r2f= 0.0721, p~= 5.8 x 10-6, DRDI, DRD2, DRD3, DATl, HTRID/3, HTR2C, TD02, ADR2AC, GABRB3, CNRI, CHRNA:~;
schizoid -- r'' = 0.058, p = 8.3 x 105, DRD2, DRD3. HTR1D(3, ATRIA, HTR2A, HTR2C. DBH, CNRI, CHNRA.~; OCD --.046, 1.0 x 10~, DRDl, DRD2. HTRID(3, GABRB3. CNRI, NMDARl; general anxiety -- r'' = 0.032, p = 0.001, DRDI, DA TI, HTRID~3, CHRNA~, PENK; and major depression -- r' = 0.0271, p = 0.0025, DRDI, DATl, HTT, HTRID(3, TD02. CNRl, CHRNA;I, NMDARI.
Number of Genes Involved. To examine the relative distribution of the number of variant genes in subjects with and without a specific dichotomous phenotype the inventors have plotted the number of additive variant genes in subjects with no DSM-IV criteria for ADHD versus those who fulfill DSM-IV criteria for ADHD (FIG. 7). The mean number of variants was approximately 10 in those with no ADHD symptoms and approximately 12 in those with a diagnosis of ADHD. In those without ADHD symptoms the distribution ranged from 3 to I4 while for those with a diagnosis of ADHD the distribution ranged from 7 to 18.

Additive Effects Versus p Values. From the present study the inventors plotted 145 rz and p value results for all 29 genes versus 5 quantitative scores (ADHD, ODD, CD, LD and tics} and identified those which were additive versus those which were subtractive for the phenotype in question. This shows that when genes are examined one-at-a-time a p value arbitrarily set at <0.05 has little relevance to whether a gene is additive and contributes to a phenotype, or subtractive and does not contribute. When examined individually, the majority of additive genes had p values of greater than 0.05 and many had p values between 0.10 and 0.40. While a p value of greater than 0.45 universally identified subtractive genes, in the range of p values between 0.12 and 0.45 some were additive and some were not.
Discussion. Concern has frequently been voiced about the difficulty of identifying the genes involved in polygenic disorders. In the present study the inventors have attempted to turn the single major characteristic of polygenic disorders, the additive effect of many different genes. to an advantage by examining the additive effect of multiple candidate genes. Since the additive and subtractive effect of multiple association studies was used, the inventors have termed this a Multiple Additive Associations technique. When using linkage techniques to examine complex disorders, it has been suggested that quite stringent levels of significance must be used to avoid false positives (Larder and Krugyak, 1996). Because of this recommendation, in Table 66 and FIG. ~ and FIG. b, the inventors have presented full rather than truncated p values. This shows that using the MAA technique, 29 genes and only 344 subjects, p values of up to I 0 ~ ~ were readily attained.
Since this is a new technique it is reasonable to carefully scrutinize the statistical approach that was used. It is clearly open to several potential criticisms.
The first is that if the studies were only done in a single group of subjects, that when the gene scoring is set to maximize than effect on a given phenotype, simply adding those gene scores together would result in higher and higher r'' values.
Because of this, the inventors were careful to validate the gene scoring in a separate group of subjects studied either by the inventors or by others in the literature. As shown in Table 6~, all of the genes fulfilled this criteria. For some genes there was only one viable scoring. For example. for genes with a two allele polymorphism. when the ~ , ~

frequency of the I allele is low and thus there are very few with a 11 genotype, there is no alternative to a scoring of 22 = 0 and I 1 or 12 = 2.
A second potential criticism is that if enough genes are examined and if only the additive genes are used, it would be possible to obtain significant results on a random basis. To examine this possibility, the inventors set up a second set of scores in which each 'dummy' gene for each individual was randomly assigned a score with - the frequency being set to match those found in the observed results. The effect of progressively adding both the positive and negative results is shown in FIG. 5 (open squares). By the end of the 29th gene the positive versus the negative results had canceled each other and the final r2 was 0.001. More importantly, when only the random genes that gave positive correlations were used, the commutative r2 value increased to a maximum of 0.006 for the PENK gene. However, after the final two positive random genes were added, the r'' value had dropped back to 0.004, p >
0.25.
This subtractive effect was presumably due to the fact that even though the effect of the CDBA and CRF random scores were positive the r2 values were in 0.0000 to 0.0003 range and produced a subtractive effect. While multiple iterations of the same process using only those with a positive r could undoubtedly have eventually produced a significant result, the important point is that when candidate genes are used that have been independently verified in other studies to be associated with a phenotypic effect the final p values are dramatically higher (10 5 to 10 ~~}
than with the random genes.
A third potential criticism is that even though the results are clearly greater than expected from random scoring, using univariate regression and including only the additive genes still inflates the resultant p values. There are two approaches to this quite reasonable criticism. The first is that the gold standard of all gene studies is replication or the examination of multiple independent sets. The inventors suggest that a reasonable standard for the MAA technique is that the additive set should include all the genes shown to be additive in any study with a reasonable N.
For example, in the inventors' own test-retest study. the chosen set of additive genes could include those that were additive in either set. Since some of these would be subtractive in some studies this would help to avoid the artificial inflation of r' and p values. When this was done the final r2 was still a substantial, 0.11, and highly significant, p < 2.0 x 10 ~~.
Reasons for a Subtractive Effect. A moderate subtractive effect can be due to the fact that the percent of the variance accounted for by the gene in question is relatively small. For example, if the 11 genotype of gene A had a very small effect on the ADHD score, another gene could be responsible for non-A11 subjects having a higher ADHD score than A11 subjects. As a result non-A1 1 subjects could still have significant symptoms, resulting in subtractive effect of the gene. A second reason for a subtractive effect can occur when a number of different phenotypes are examined.
For example, the 11 genotype of a gene may be significantly associated with ADHD
while the 22 genotype may be associated with a different phenotype such as depression. As a result, when the scoring is such that the 1 1 genotype = 2, this could result in an additive effect for an ADHD score but a subtractive effect for a depression score.
1 S Implications for Genetics Studies of Complex Disorders. The inventors believe these studies have a number of implications not only for psychiatric genetics but for the genetics of complex polygenic disorders in general.
1. The power or examining marltiple as' opposed to single genes. These studies provide some insight into the problems with the reproducibility of association studies that examine genes one at a time. This is most likely due to the fact that multiple genes are involved, no single gene is critical, and each gene contributes to only a small percent of the variance. As a consequence, when genes are examined one-at-a-time, one gene will reach significance in one study but not another.
Rather than being the source of endless frustration (Moldin, 1997), or even thoughts chat none of the studies are correct, this should be viewed as the expected outcome in the genetics of complex disorders (Comings, 1998x). The percent of the variance and p values for the results of 145 association studies using the genes and behavioral traits the inventors have reported here (except for alcohol abuse/dependence). This shows that the majority of the additive genes would have been rejected if the usual criteria for single gene significance of p < 0.05 was used. It also demonstrates why it has _.__...,_.v.~ _-._..__.._. ._ r . i , r . .

been so difficult for two laboratories to agree when the p < 0.05 cut off is used. If the associations producing an r2 value of 0.0008 to 0.025 were actually independent studies from different laboratories, only 6 or 20% would have been considered to be positive and the remaining 24 would have been considered negative 'non-replications.' However, by the MAA technique all these associations are utilized.
Since the single most distinctive characteristic of polygenic inheritance is the - involvement of multiple genes, the inventors have suggested that it is necessary to take advantage of this characteristic to identify the genes involved in complex disorders (Comings et crl., 1996b; Comings, 1996a; Comings, 1998a; Comings, 1998b). The present studies validate this concept and indicate that the power in polygenic inheritance comes from the examination of the additive effect of multiple genes. The inventors also suggest that the proper criteria for determining whether a gene plays a role in a given disorder is whether it has an additive rather than a subtractive effect on the relevant quantitative score. This can be easily identified by presenting the results as shown in Table 66, or in FIG. S and FIG. 6.
2. Association studies as the optimal approach to polygenic inheritance.
Presently the most popular methods used to identify the genes in complex disorders consists of whole genome screening using lod score linkage, affected sib pair techniques, or haplotype relative risk techniques. While this has great inherent logic, replication is often difficult and many of the above genes that the inventors have found to play a role in ADHD or TS based on the additive gene approach have been 'excluded' on the basis of such studies (Pakstis et al., 1991 ). While association studies using unrelated controls and significantly affected probands uniquely possesses the power to detect the small effects characteristic of polygenic inheritance (Risch and Merikangas, 1996; Comings, 1998a), they are also plagued with problems of non-replication. The MAA technique of examining the additive effect of multiple genes utilizes the power of association studies and potentially avoids the problems of non-replication. This is because it replaces the criteria of single gene significance at p < 0.05 with a less stringent parameter of having an additive effect on the cumulative r''. This approach is also more practical since the identification of single unrelated probands and unrelated controls, examined with the same instruments, is much easier than the identification of affected sib-pairs. In an effort to identify the genes involved in a number of different disorders, DNA banks have been set up consisting exclusively of affected and unaffected sib pairs. Both the speed of case ascertainment and the gene f nding power of these banks could be dramatically increased by including single probands and a comparable number of unrelated controls screened with the same test instruments. The efficiency of this could be maximized by allowing different banks to share the same, publicly available set of screened controls.
As with the CEPH (Centre d'etude du Polymorphisme I-Iumain) (Dausset et crl., 1990) samples, it would be further enhanced by requiring that after a given period of time, all the genotyping results be made public so the results of multiple investigators could be collated.
The results provide an additional reason why association studies are more powerful than family based linkage techniques. This shows that even when these two optimally disparate groups are used the total number of variant genes is only modestly different (mean of 10 versus 12). This suggests that when comparisons are made within families, as occurs with lod score, sib pair, haplotype relative risk, and TDT
analyses (Spielman and Ewens, 1996) which actually or implicitly compare affected to unaffected, the differences will be even less. When the problem is compounded by examining only one gene at a time, extremely large numbers of subjects are required to detect relevant genes even at borderline lod score or p values. As an example, to date, even with very large numbers of families and sib pairs, the majority of the linkage studies of behavioral disorders have identified no specific genes.
This contrasts with the results in FIG. 5 and FIG. 6 where the use of less than 350 subjects provisionally identified 20 specific genes for ADHD and 15 for alcohol abuse/dependence and produced data to allow an estimation of the relative importance of each gene for each phenotype.
3. The problem of hidden ethnic stratification. One of the most frequently voiced concerns about association studies is the potential problem of hidden ethnic stratification. The MAA technique minimizes that concern for the following reason. While hidden : ethnic stratification could play a role when examining single genes, it is unlikely that the frequencies of all the genes will vary in _u....~..._,.__...... ...., r . i the same direction. As the number of genes examined increases the potential effect of hidden ethnic stratif canon decreases.

4. The issue of examining many genes. Another one of the objections often raised in gene searches and association studies is that if one looks at enough genes, some will be significant by chance alone. This is especially valid when genes are examined one at a time and the end point is a p value of < 0.05. However, the - additive multiple associations technique is independent of the p value of individual genes and asks instead whether a gene in the background of other genes contributes to the r' of the phenotype in an additive or a subtractive fashion. By emphasizing this characteristic of polygenic disorders the technique inherently and naturally accommodates the examination of large numbers of candidate genes. This was seen in the up and down fluctuation of the summary r' values for the tic, LD and alcohol abuse/dependence scores. This suggest that hundreds of genes could have been added and the final r2 values would continue to fluctuate. However, the more genes that are examined the greater the number of additive genes that are identified and the greater the final r2 when this subset of genes is utilized. Thus, in contrast to the one-gene-at-a-time approach where power is lost with each additional gene examined, the power of the additive multiple associations technique increases as more genes are examined.

5. Comorbid disorders utilize related sees of genes. Even though ADHD
was the primary phenotype the inventors examined, there was an additive effect of many of the ADHD genes on related phenotypes such as oppositional defiant disorder, conduct disorder, alcohol abuse/dependence, drug abuse/dependence, smoking, OCD, mania, schizoid behaviors and others representative of RDS behaviors.

6. Comorbid disorders utilize different sets of genes. In addition to using similar sets of genes, comorbid disorders may have a different phenotype because they use a different set of the genes. Two observations suggest the latter. First, even when only the additive genes were used the final rz values were lower for the ODD, CD, LD, tic and other scores than for the ADHD scores. If the inventors assume that the total genetic contribution to each of these phenotypes is similar to that for ADHD, then genes other than the ones examined here would have to be involved to bring the rZ values to comparable levels. This indicates that in addition to sharing genes, these comorbid disorders also use unique genes. Second, the slope of the lines for the cumulative r2 values using both the additive and subtractive genes show that the comorbid disorders use distinct sets of genes or distinct genotypes. For example, the MA DA gene was additive for the ADHD score but subtractive for the alcohol abuse/dependence score. This suggests that the MA OA gene was less important for the alcohol abuse/dependence score than for the ADHD score.

7. Different phenotypes may use different genotypes. An alternative to using different sets of genes is that different phenotypes may use different genotypes.
Again using the MA OA gene as an example, instead of it being less important in the alcohol abuseldependence score, it may utilize different genotypes. As a further example, depression and aggression have often been linked to low CNS serotonin levels (Brown et al., 1982; Coccaro et al.. 1989) whtle obsessme-cnmnnlcivP
behaviors have been linked to increased CNS serotonin or receptor sensitivity (Inset et al., 1985). Thus, it would not be surprising if a given genotype was positively associated with depression/aggression but negatively associated with obsessive compulsive behaviors. These observations raise the question of whether gene scoring should be individualized for each phenotype. The inventors suspect that it should, but this would require that each such scoring be validated in an independent set of subjects.

8. Use of different polymorphisms. In this study the inventors used only one polymorphism per gene. The use of other polymorphisms, or the combination of several polymorphisms into haplotypes, could potentially increase the rz values.
Thus, the total rz values observed here may actually significantly underestimate the true contribution of these genes to the respective phenotypes.

9. Comparative effect of different individual genes in different phenotypes. One of the most powerful aspects of the MAA technique is its ability to identify the relative importance of different genes. This is possible because all genes are examined in the same group of subjects. An example is the role of the AR
gene.
The increase in the slope of the r' curve immediately to the left of the AR
gene for the ~ . ~ , t ADHD (FIG. 5), ODD and CD scores indicates the relative importance of this gene in all three conditions. By contrast, the AR gene was subtractive and played no role for the tic and LD scores and was neutral for the alcohol abuseldependence score.

10. Comparative effect of different groZrps of genes in different phenotypes.
Another aspect of the MAA technique is its ability to identify the important role of groups of genes affecting specific neurotransmitters for different phenotypes.
Thus, the set of four adrenergic genes played a significant role in the ADHD score.
Together they accounted for 42% of the variance of the total r2 score based on the additive genes. By comparison the serotonergic genes accounted for only 9.5%
of the I0 total. This is in agreement with the many studies strongly implicating defects in norepinephrine metabolism in ADHD (Halperin et al., 1997; Yliszka et al., 1996;
Arnsten et al., 1996). By comparison, the opposite trend was seen for conduct disorder. Here the serotonergic genes accounted for 30% of the variance of the total r2 based on the additive genes, while the adrenergic genes accounted for only 13%. This is in agreement with tile many studies indicating defects in central serotonin metabolism in antisocial behaviors (Brown et al., 1982; Lidberg et al., 1985;
Coccaro et al., 1997).
ll. ~Llultiple tiers ,for association studies and implications .for the publicatioh of single gene studies. The results of the MAA technique suggest that association studies should be conducted on multiple tiers. In the first tier, there is a need for preliminary studies of one-gene-at-a-time to determine which polymorphisms may be useful and how they should be coded. However, the inventors suggest that the important finding for monogenic studies is how the genotypes of a polymorphism of a given gene should be scored. Depending upon the size of the sample, even with p values of > 0.05, this information can be of value. Since most single association studies that gave an r2 of > 0.005, or an r of > 0.07 were additive, when the N is adequate this could be the criteria for publication rather than p < 0.0~.
The second tier would be the use of the MAA technique utilizing aII
reasonable candidate genes. These results are represented by the line for additive and subtractive genes in FIG. 5 and represent the non selective inclusion of all genes. The third tier would be the development of a 'new model' for a given phenotype based on using only the additive genes. This is represented by the line for the additive genes in FIG. 5. The final tier would be independent replication studies identifying new additive genes and continually testing and retesting the resultant 'new models' based on the accumulating set of additive genes.
12. Genes for alcoholism, drug abuse and smoking. While twin and adoption studies clearly indicate that at least some forms of alcoholism, drug abuse and smoking are strongly genetic, the identification of the specific genes involved, based on one-gene-at-a-time approaches, has not been outstandingly effectively. In fact, the combination of replication and non-replication (Noble, 1993; Blum et al., 1995b) of the reported association between the Taq A1 allele of the DRD2 gene and alcoholism (Blum et al.. 1990b), has been one of the contributors to skepticism about the power of association studies. The results of using the MAA technique for the alcohol abuse/dependence score provides insight into many of the problems concerning the molecular genetics of alcoholism. First, the steep slope of the r'' curve to the left of the DRD2 gene indicates that the DRD2 gene, using the Taq A l polymorphisms does play a role in the alcohol score in this group of subjects.
However, it accounts for only 10% of the total r2 score and since the total r'' score was only 0.14, this gene accounted for only 1.4% of the total variance of the alcohol abuse/dependence score. This is fairly typical of the additive genes and is a major reason why replication using the one-gene-at-a-time approach is so difficult.
Second, the dopaminergic, serotonergic and gabaergic sets of genes all played an important role in the alcohol score. Thirdly, the power of the MAA technique is illustrated by the highly significant final r' value (p~= 7.5 x 10 b). To the inventors' knowledge, for a comparable N, this far exceeds any results ever reported using the one-gene-at-a-time approach. Fourthly, while the focus of the study was on ADHD rather than alcoholism, the numerous genes that were shared by ADHD and alcoholism (DRDI, DRD2. DA TI, HTT, HTRIA. DBH, ADRA2C, GABRB3, CNRl, NMDA and CRF) is consistent with the high frequency of ADHD in alcoholism and alcoholism in ADHD
(Loney et al., 1981; Cantwell, 1972; Alterman et al., 1983; Tarter, 1988;
Biederman et al., 1995).

The results for smoking showed a strong effect of the DRD2 gene. However, since these subjects had Tourette syndrome, which itself is associated with the DRD2 gene (Comings et al., 1991 ), the association with smoking may be enhanced over what it would be for subjects who smokers only. Despite the primary focus on genes for ADHD the results for the alcohol abuse/dependence scores show that many of the genes identified for ADHD have been previously been implicated in the iitcrature as being candidate genes for alcoholism.
l3. Studying multiple phenotypes in a single group of subjects'. A common strategy in genetic studies of behavior is to collect individuals with a 'pure' disorder, with few or no comorbid conditions, and to score subjects dichotomously into those having or not having that diagnosis. Such studies may lose power for two reasons.
First, individuals with many comorbid conditions are more likely to have higher overall levels of genetic loading, thus increasing the power for identifying genes.
Second, screening for a wide range of behaviors allows a number of different disorders to be examined in the same group of subjects. This is illustrated in the present study. Since TS is associated with many comorbid conditions (Comings and Comings, 1987x; Comings, 1995x; Miller et al., 1996) it was possible to test for the genes specifically associated with a number of disorders, all in one group of subjects.
The results indicated it was still possible to identify unique combinations of genes for the different phenotypes. This suggests the use of structured interviews to make multiple DSM-IV diagnoses and allow the development of multiple quantitative traits in a single group of subjects with high rates of comorbidity, may be a much more efficient approach to finding genes for a range of disorders than accumulating many DNA data bases, each for a specific disorder.
l;t. Risk factors verszrs diagnosis and predictability. One of the common assumptions about polygenic inheritance is that the genes involved act only as risk factors and, in contrast to single gene disorders, it will not be possible to use genetic tests in a diagnostic fashion. However, as the percent of the variance explained by the additive effect of genes increases, r also increases, and with it there is an increase in predictability. The final r of 0.37 for the ADHD score indicates that by using this set of genes the predictability of ADHD score was up to 37% over what it would have WO 98/48785 . PCT/US98I08684 been with no genetic information. With the addition of still more genes, r values of twice this magnitude could be obtained. With the availability of DNA chip technology, the number of genetic tests that have to be performed is no longer an issue. As shown in FIG. 7 while the curves for the distribution of the number of relevant genes for individuals with no symptoms of ADHD versus those who meet DSM-IV criteria for ADHD shows much overlap, the differences that are present are highly significant. Doubling or tripling the number of additive genes may eventually produce largely non-overlapping curves. The inventors contemplate that the diagnostic power will increase with added genes.
l~. Replication. Just as replication is the gold standard in linkage and association studies, it should also be the standard for replication of the MAA
technique. Variations in the number of subjects, composition of probands by severity, the ratio of controls versus subjects. the range of the quantitative scores, and other factors would ail be expected to alter the final r2 values, independent of the effect of I S specific genes themselves. Aside from these factors the inventors would expect varying levels of replication. The highest level is one in which the identical sets of genes are additive or subtractive in different groups of subjects and the slopes of the curves are similar. Since the ability of different genes to produce a similar phenotypic effect is one of the characteristics of polygenic disorders, the inventors expect that this level of replication is both unlikely and unexpected, and that different studies will show that different sets of genes and different slopes of the curves are involved. This was verified by the split sample tests (FIG. 6).
A second and more realistic standard of replication is one in which most but not all of the additive genes for one group are also additive for the replication group, and in which the r' values and significance levels progressively increase as more genes are included, but the slopes of the curves for different genes may vary.
At this level, it would also be expected that the relative importance of groups of genes be replicated. Thus, the present studies showing that adrenergic genes are of relatively greater importance in ADIJD while serotonergic genes are of relatively greater importance in conduct disorder should be replicated. This was verified in the split-sample tests where the importance of the noradrenergic genes in ADHD was ~.t replicated in both sample. The ultimate level of replication of the MAA
technique would occur if it accelerated the identification of genes involved in polygenic disorders.
1 G. Technical issues. While most of the important aspects of the additive . 5 multiple associations technique have been covered. there are several aspects that deserve emphasis. The inventors feel a quantitative trait is preferable for several - reasons. It requires that the controls be evaluated for the same trait as the subjects.
This is important because in the inventors' experience, both the controls and subjects may have a wide range of scores. In addition the power of the analysis is greater when the full range of the quantitative trait is examined. For maximum power, the number of controls should approximate the number of subjects, or the number of subjects with low scores should approximate the number with high scores.
Although the final r and r2 values are independent of order, to clearly identify subtractive genes it may be necessary to change the order of entry of adding genes such that those with the greatest positive associations are entered first. If the initial genes do not produce an adequate increase in the r or r'' values, it would be difficult to identify subtractive genes.
~ 7. Therapeutic and pharmacologicul implications. A final aspect of the MAA technique, based on its ability to both identify the genes involved and to weigh their relative importance, is its potential power to guide therapeutic and pharmacological interventions. For example, the observation that four adrenergic genes accounted for 40% of the final r2 value for the ADHD score suggests that drugs acting on the adrenergic a,, receptors would be of value in the treatment of ADHD.
Clinical studies in fact support the effectiveness of clonidine and guanifacine, adrenergic a, receptor agonists, in the treatment of ADHD (Hunt et al., 1985).

CHROMIUM PICOLINATE AND CHROMIUM NICOTINATE DIETARY
SUPPLEMENTATION TO TREAT OBESITY
In this study, chromium picolinate was of primary interest, but it was thought that preliminary data on chromium nicotinate, tested under the best conditions {combined with exercise training), would be valuable as well. The present study of the effects of chromium supplementation on young, obese women had two objectives.
First, to determine if chromium picolinate supplementation alone favorably alters body weight and composition, glucose tolerance, and plasma lipids, and whether these effects could be augmented with exercise training. Second, to provide data on the effectiveness of chromium nicotinate supplementation combined with exercise training.
METHODS. Subjects were forty-three healthy, sedentary, obese females.
Various statistical cut-off definitions are used for obesity, but for the purpose of this investigation obese was defined as higher than the recommended body fat percentage for young women, recommended being 20-25% body fat (Blackburn et al., 1994).
The inventors recognize that a portion of the inventors' population was only mildly obese. Prior to acceptance, questionnaires were used to determine the health status and activity patterns of the subjects. None of the subjects documented any health problems, nor medication for such conditions. Age ranged from 18 to 35 yrs.
with a mean of 24.4 ~ 0.70 yr. Initial weight ranged from 50.8 to 96.1 kg. with a mean of 71.3 ~ 1.9 kg. Percent body fat as determined by hydrostatic weighing ranged from 25.0% to 45.0%, with a mean of 33.0% ~ 0.91. Initial V02max values ranged from 1.53 to 3.30 L/min, with a mean value of 2.38 ~ 0.13 L/min. Each subject was informed of the potential risks and benefits associated with participation before signing an informed consent document. The study was approved by the Institutional Review Board of The University of Texas.
Experimental design. Subjects were randomly assigned to one of four treatment groups: chromium picolinate supplementation without exercise training (CP), exercise training with placebo (E/P), exercise training with chromium picolinate supplementation (E/CP), and exercise training with chromium nicotinate ......... ..,.... .._.... . ... . ,. .~, . ~ ~ ~. f.

supplementation (E/CN). The treatment period was nine wk. The effects of stage of the menstrual cycle were not controlled. Although pre- and post-testing were conducted at different stages of the presumed 28-day menstrual cycle, the effects of this, if any, were randomized among all study participants and thus should not have influenced the data.
Chromium supplements and placebo tablets were prepared by Shaklee, Inc., - USA (San Francisco, CA) and shipped to the investigator in coded bottles of tablets each. Subjects were given 16 tablets each wk containing chromium picolinate (200 pg), chromium nicotinate (200 pg) or placebo (inert ingredients).
Subjects were given verbal instructions to take one tablet each morning and evening throughout the treatment and post-testing period, resulting in a dosage of 400 ~g daily for those receiving chromium supplements. Subjects were asked to return unused tablets each wk. Compliance was measured by counting returned tablets. There were no problems with compliance.
Exercise training consisted of a cross-training program with several components. The first component was step aerobics, which is a type of aerobic dancing utilizing upbeat music, a bench of 12 to 24 inches in height, and an instructor to guide participants through a series of moves designed to provide a full-body aerobic workout. These one-hr classes were attended twice a wk by each exercising subject and were taught by certified instructors. Subjects were allowed to pick their own bench height. Instructors gradually increased exercise intensity as the study progressed.
The second component was cycling. Cycling exercise was conducted twice a wk for 30 min at a target heart rate of 75% - 80% maximal heart rate (determined during the initial VOZmax test). Universal Aerobicycles (Universal Gym Equipment, Inc., Cedar Rapids, Iowa) were used. Target heart rate was programmed into the ergometer at the beginning of each session and monitored during exercise with an onboard computer adjusting resistance to maintain target heart rate.
The third component was resistance training. Resistance training was conducted twice a wk with The Powercise Fitness System (TruTrac Therapy Products, WO 98/48785 PCTlUS98/08684 Inc., Temecuia, California). Five separate machines were used in which resistance is set by an electronic braking device. This was a "double positive" system eliciting concentric-only contractions from agonist/antagonist muscle groups. The five machines used exercised all major muscle groups of the upper and lower body.
Each subject's maximum strength was determined initially by lifting progressively heavier weights with each repetition (10 lb. increments) until the load could no longer be lifted. Once maximal strength had been determined, workout weights were automatically set at 50% maximal strength, and the subject was guided through a workout consisting of three sets of 15 repetitions on each machine. Subjects followed a pacing light which allowed approximately I.5 sec per concentric contraction.
Rest time between sets was 25 to 28 sec. Time between machines was not tightly controlled. Subjects were encouraged throughout the study to increase the weight lifted while still completing three sets of fifteen repetitions, and these increases were documented. Subjects were also instructed to do three sets of twenty "abdominal crunches" (sit-ups).
All training session were supervised by at least one investigator. Attendance, heart rates during cycling exercise, and resistance training parameters (weight lifted and completion of repetitions and sets) were recorded. Subjects were asked not to alter their diet during the course of the study.
Experimental protocol. During the wk prior to initiation of treatment, subjects were weighed on two separate occasions using a Health-o-Meter scale (Continental Scale Corporation, Bridgeview, IL) with a sensitivity of t 200 g., and subjected to body composition analysis via hydrostatic weighing (Behnke et al., 1974). VOZmax was determined on a treadmill using a modified Burke protocol, with 2~ inspired volumes and expired gasses measured as previously described for the inventors' laboratory (Yaspelkis et crl., 1993). Fasting blood samples were drawn on two separate days, and an oral glucose tolerance test (OGTT) was conducted as subsequently described.
During the nine wk treatment period, supplementation and exercise training were administered as detailed above. In the ninth wk of treatment, all pre-test WO 98/48785 PCTlUS98108684 measurements were repeated. Tests were administered in the same order both pre-and post-treatment. Supplementation and exercise training continued through the post-testing period. All subjects involved in exercise training participated in a step aerobics session two days prior to the post-OGTT and rested on the day prior to the test, resulting in approximately 40 h between the last bout of exercise and the OGTT.
Sample collection and analyses. Plasma for hormone and substrate analysis was obtained between 7 and 9 A.M. after a 12 hr fast which included no caffeine or nicotine. Blood (10 ml) was collected via venipuncture of an antecubital vein.
Determination of glucose and insulin response to an oral glucose load was conducted on the same day as one of the pre-treatment blood samples. Subjects ingested a room temperature beverage containing 100 grams of dextrose (Tru-Glu 100 orange/carbonated, 10 oz. Fischer Scientific, Pittsburgh, PA), and blood samples (3 ml) were obtained from a 21-gauge indwelling venous catheter (Baxter Healthcare, Deerfield, IL) in an antecubital vein prior to ingestion and 15, 30, 60, 90, 120 and 180 min post-ingestion.
All blood samples were anti-coagulated with 250 ml ethylenediaminetetraacetic acid (EDTA) (Sigma Chemical Company, St. Louis, MO).
Aliquots of whole blood (200 ml). from basal samples were used for glycosylated hemoglobin determination via an affinity resin column, colorimetric, endpoint procedure {Sigma Diagnostics, St. Louis, Mo.). The remaining samples were centrifuged at 1,000 x g for I S min; plasma was then removed and frozen at -20oC for subsequent analysis. Insulin concentration was determined by radioimmunoassay (ICN Biomedicals, Inc., Costa Mesa, CA). Plasma samples were analyzed by enzymatic assay for glucose, triglycerides and total cholesterol (Sigma Diagnostics, St. Louis, Mo.). HDL-C was determined enzymatically following the precipitation of LDL-C and VLDL-C (Sigma Diagnostics, St. Louis, MO). LDL-C was calculated with the equation: LDL-C = (Total cholesterol) - (HDL-C) - (Triglycerides/5) (Sigma Diagnostics, St. Louis, MO). Standards were run with each assay to verify consistency. All samples were run in duplicate, with each subject's samples run sequentially.

Statistical analyses. A multivariant ANOVA was run for each variable on pre and post values across all treatment groups. Pre and post values, as well as time points in the OGTT, were treated as repeated measures. If a significant F
value was observed (p < 0.05), further analysis was done to determine where these changes occurred. An a priori significance (p < 0.05) was used as a criteria for further tests.
Post-hoc tests were repeated measure ANOVAs or Fishers PSLD (p < 0.05), depending on the nature of the data. These low stringency were used to protect against the chance of committing a type II error because of the small sample size and therefore limited power of the statistical design. Analyses were conducted using Statistic Package for Social Sciences (SPSS) software, version 6.0 for Macintosh.
RESULTS. Subjects participating in exercise training completed 90.0% (~2.2) of the prescribed training sessions. There were no differences in compliance between treatment groups. Heart rates during cycling were consistently between 75 and 80%
of HR Heart rates during step aerobics were very high, ranging from 175 to 190 max' beats per min (85 to 95% of HRmaX). VO~max was not significantly altered in any treatment group.
Following treatment, there was a significant increase in body weight in the CP
group, and a significant decrease in body weight in the E/CN group. There were no significant changes in body fat percentage, fat mass, or fat-free mass. Fat-free mass in the E/P group was significantly higher than in all other groups both pre- and post-treatment. It is noteworthy that the non-significant higher initial weight in the E/P
group can be attributed almost entirely to greater fat-free mass.
There were no significant differences in pre- and post-basal plasma glucose or insulin levels. Glycosylated hemoglobin was also unchanged following treatment.
Pre- and post-treatment glucose tolerance and insulin response curves for each treatment group for a nine wk treatment period were plotted from a three hr oral glucose tolerance test. Subjects received chromium supplementation (CP), exercise training with placebo (E/P), chromium picolinate {E/CP), or exercise training with chromium nicotinate (E/CN).

..w_. ~__. w.w~. .. _. . _..... _ ... ~ . i . ~

Comparisons of glucose tolerance curves or area under the glucose curves from a three hr oral glucose tolerance test, of a pre and post nine wk treatment period in subjects receiving chromium supplementation (CP), exercise training with placebo (E/P), exercise training with chromium picolinate (E/CP), or exercise training with ~ chromium nicotinate (E/CN) indicated no significant treatment effect for any group.
Likewise, there were no significant improvements in the insulin response curves following the CP (p = 0.433), E/P (p = 0.087), or E/CP (p = 0.110) treatments.
However, following the E/CN treatment there was a significant decline in the in the insulin response at 60, 90 and 120 min after the oral glucose load which resulted in a significant decline in the overall insulin response curve (p = 0.041 ). The area under the insulin response curve for the E/CN treatment was also found to be significantly reduced post training. No significant differences were found for triglycerides, total cholesterol, LDL-C, and HDL.-C between pre- and post-treatment samples.
DtsCUSStoIV. The inventors' study compared the effects of chromium picolinate supplementation, exercise training, or both in young, obese women.
Data was also gathered on the effects of chromium nicotinate supplementation combined with exercise training, as in vitro work suggests chromium nicotinate may be effective in altering these risk factors as well.
Body weight increased significantly in the CP group following treatment.
This is an important finding, as chromium picolinate is often promoted as an aid to weight reduction. The inventors' results indicate that without exercise, not only may chromium picoIinate supplementation be ineffective in causing weight loss, but may result in weight gain. Weight gain has not been seen with previous studies of chromium supplementation (Kaats et al., 1991; Page et al., 1991; Riales, 1979), possibly due to uncontrolled diet and activity patterns as well a differential genotype patterns of the subjects (i. e. carriers of DRD2 A 1 vs DRD2 A2 alleles. Also.
the . inventors' subject population (young, obese females) has not previously been studied;
this may account for the difference in findings. Similarly, the amount of chromium - administered in the present study (400 ug/d) was twice the amount previously studied with females. It is possible that at-this concentration chromium has a facilitating effect on weight gain, whereas at lower concentrations it may have an inhibitory effect.
No significant changes in body weight were seen in the E/P or E/CP groups, confirming previous research indicating weight loss is not often seen with nine wk of _5 exercise training (Stefanick, 1993). There was, however, a significant decrease in body weight in the E/CN group. To the inventors' knowledge, this is the first study to suggest that chromium nicotinate supplementation combined with exercise training may be an effective means of weight loss.
There were no significant changes in relative body fat, fat mass, or fat-free mass following treatment. As with body weight, discrepancies between the results of this study and previous research may be due to differences in study populations, uncontrolled diet and activity patterns, and/or the amount of chromium administered.
VOZmax levels did not increase significantly following the training period.
This may have been due to lack of specificity of testing: pre- and post max tests were conducted on a treadmill and training consisted of cycling and aerobics.
Additionally, the resistance component of the training program may have masked or diminished aerobic changes.
Basal glucose levels were not significantly altered in any treatment group.
This result is consistent with other studies of chromium supplementation (Abraham et ul., 1992; Wilson et al., 1995) and exercise training (Wallberg-Henriksson, 1992) in a normo-glycemic population. The response of plasma glucose levels to an oral glucose load was significantly higher in the CP group both pre- and post-treatment when compared to the E/P group. Group means imply an inverse relationship between fat-free mass and glucose area under the curve, suggesting that a greater fat-free mass 2~ allows for more rapid disposal of an absolute amount of glucose. Individual data, however, indicated a poor correlation between these two factors (r = -0.26).
No treatment effect was seen for glucose response to an oral glucose load. As with basal glucose levels, subjects initially exhibited normal glucose levels following an oral glucose load. Glycosylated hemoglobin. an indication of plasma glucose levels over a six wk period, was unchanged following treatment. Basal insulin levels, initially within a normal range, were not significantly altered in any group following treatment.
Insulin response to an oral glucose load was significantly reduced in the E/CN
group following treatment. An improvement in insulin response has been shown previously with hyperglycemic subjects (Djordjevic et al., 1995). No significant - change in insulin response was observed in the other exercise trained groups; this was unexpected as decreases have previously been documented with exercise training (Heath et al., 1983; Mikines et al., 1989; Sharma, 1992; Wallberg-Henriksson, 1992).
However, this decrease has been found to be very short lived (Heath et al., 1983;
Mikines et al., 1989), thus the inventors' inability to detect a decreased insulin response to a glucose challenge in these subjects may have been due to the rapid decay of improved insulin action. Whatever the cause for the lack of an exercise training effect on insulin action in the E/P and E/CP groups, the results do suggest that the combination of exercise training and chromium nicotinate may be beneficial.
Basal plasma lipids did not change with treatment. It was interesting to note that the few subjects with abnormal initial lipid levels moved towards normalization following treatment, although these changes were not significant.
In summary, high levels of chromium picolinate supplementation without concurrent exercise training caused significant weight gain in young, obese females, while exercise training combined with chromium nicotinate supplementation resulted in several potentially beneficial changes, including significant weight loss and a lower insulin response to an oral glucose load. The inventors conclude that high (400 ~g/d) supplemental amounts of chromium picolinate are contraindicated for weight loss in young, obese women, while exercise training combined with chromium nicotinate supplementation may be more beneficial than exercise training alone in providing some protection against CAD and NIDDM through risk factor modification.
Chromium supplementation may effect various risk factors for coronary artery disease and non-insulin diabetes mellitus, including body weight and composition, basal plasma hormone and substrate levels, and response to an oral glucose load. A

study examined the effects of chromium at 400 pg daily, with or without exercise training, on these risk factors in young obese women (Grant et al., Med. and Sci.
Sports and Exercise, 29:992-998, 1997). Chromium picolinate (CP) supplementation resulted in significant weight gain in this population, while exercise training combined with chromium nicotinate (CN) supplementation resulted in significant weight loss and lowered the insulin response to an oral glucose load. The inventors conclude that high levels of chromium picolinate supplementation are contraindicated for weight loss in young, obese women. Moreover, the inventors' results suggest that exercise training combined with chromium nicotinate supplementation may be more beneficial than exercise training alone for modification of certain CAD and NIDDM
risk factors.
After treatment there was a significant increase in body weight in the CP
group and a significant decrease in body weight in the exercised CN group (at least P = 0.05). There were no significant changes in body fat percentage, fat mass, or fat-free mass. Fat-free mass in the exercised-placebo group was significantly higher than in all other groups both pre- and post-treatment. Moreover, unlike the CP
group, the exercised CN treatment resulted in a significant decline in the insulin response at 60, 90 and 120 min. After the oral glucose load which resulted in a significant decline in the overall insulin response curve (P = 0.041 ).
The data from this study suggests that high levels of chromium picolinate supplementation without concurrent exercise training caused significant weight gain in young, obese females, while exercise training combined with chromium nictinate supplementation resulted in several beneficial changes, including significant weight loss, and a lower insulin response to an oral load. This data suggests that the use of high (400 pg d-~) supplemental amounts of chromium picolinate are contraindicated for weight loss in young, obese females, while exercise training combined with chromium nicotinate supplementation may be more beneficial in terms of weight loss, than exercise alone. The combined allelic frequencies of the OB/ gene and the gene account for as much as 22% of the variance of obesity in young obese women and this genotype may influence the effects of chromium salts on weight loss specific for this population bringing about an effect of chromium on weight loss in humans ....~.W,, r . i , t (Comings. 1997). It is contemplated that supplementation of the composition for treating RDS related disorders, as described at Table 4, and specifically those related to obesity as described at Table 6, with chromium salts, .such as chromium nicotinate and/or chromium picolinate will aid in maintenance of weight loss.

CHROMIUM PICOLINATE INDUCES CHANGES IN BODY COMPOSITION
AS A FUNCTION OF TAQI DOPAMINE D, RECEPTOR A2 ALLELES
METHODS. In this study the inventors genotyped 100 subjects for both the dopamine D~ receptor (DRD2) and the dopamine transporter gene (DATI ) utilizing standard PCRTM techniques (Blum et al., 1997). The subjects were assessed for scale weight and for percent body fat using densitometry. The subjects were divided into placebo and chromium picolinate (CrP) groups (400 mg per day), according to methods developed by Kaats, et al., 1998).
RESULTS. In literature controls the TaqI DRD2 A I allele was present in 26%
of 714 subjects (185/714) and was present in 3.3% of 30 {1/30) very well assessed super controls. Chi Square analysis revealed significant differences between these two control groups (p < 0.006).
The DRD2 A1 allele was present in 67.4% (58/86) of the obese subjects with >_ 28% body fat; in 61.5% (8/13) of obese males with >_28% body fat and in 68.5%
(50/73) of obese females with >_28% body fat. The DRD2 AI allele was present in 65.3% (49/75) of the obese subjects with ?34% body fat; in 62.5% (5/8) of obese males with ?34% body fat and in 65.7% (44/67) of obese females with >_34% body fat. The DAT1 10/10 allele was present in 37.4% of 91 control subjects (34/91), 47.7% (41/86) of obese subjects with >_28% body fat, 38.5% (5/13} of obese males with >_28% body fat, and 49.3% (36/73 } obese females with >_28% body fat. The - DAT1 10/10 allele was present 46.7% (35/75) of obese subjects with >_34%
body fat, 37.5% (3/8) of obese males with >_ 34% body fat, and 47.8% (32/67) obese females with >_34% body fat. Chi Square analysis revealed a significant association between the TuqI DRD2 A 1 allele and morbid obesity when compared to either the literature or super controls. Table 67 shows that a significant association was found for both males and females with _< 28% body fat compared with literature controls (Chi Square = 62.b, df = 1, p =<.0001 ); and for super controls (Chi Square = 36.6, df = 1, p = < .000 I ). A significant association was found for both males and females with _< 34% body fat compared with literature controls (Chi Square = 50.6, df= l, p = < .0001 ); and for super controls (Chi Square = 33.0, df = 1, p = < .0001 ). The effect for males with < 28% body fat compared with literature controls (Chi Square = 8.31, df= 1, p = .004); and, for super controls (Chi Square =18.6, df =l , p = < .0001 ); and for females with <_ 28% body fat compared with literature controls (Chi Square = 57.34, df = I , p =<.0001 ) and for super controls (Chi Square =36. i 1, df = 1, p = < .0001 ). The effect for males with <_ 34% body fat compared with literature controls (Chi Square = 5.46, df = I, p = .02); and, for super controls {Chi Square =16.6 , df =1, p = < .0001 ); and for females with _< 34% body fat compared with literature controls (Chi Square = 46.73, df= 1, p =<,0001 ) and for super controls (Chi Square =32.38 , df = I , p =<.0001 ) In contrast no association was found for any I S allelic combinations for the DAT1 gene including the 10/10 genotype in this morbidly obese population. For both males and females with <_ 28% body fat compared with literature controls (Chi Square =2.27, df = 1, p = .132) and for males with <_ 28% body fat compared with literature controls, (Chi Square =.02 , df =l, p = .89 ) and for females with <_ 28% body fat compared with literature controls (Chi Square =2.73, df =l, p = .098 ). For both males and females with <_ 34% body fat compared with literature controls (Chi Square =1.75 , df= I, p =.185) and for males with _<
34% body fat compared with literature controls (Chi Square =.01, df =1, p =.96 ) and for females with <_ 34% body fat compared with literature controls (Chi Square =2.02, df =1, p =
I6 ).

DOPAMINE TRANSPORTER GENE AND BODY FAT
PERCENT DATA(%) CHI-SQUAREb BODY FAT 10/10 R-VALUE (Controls) < 28 41 0.132 Total Population (47.7) (N = 86) _~.-.~.-..-...w_._...~..~.~_~ .. ., a r . . ~ . T

DOPAMINE TRANSPORTER GENE AND BODY FAT
PERCENT DATA(%) CHI-SQUAREb BODY FAT 10/10 P-VALUE (Controls) 0.89 Mates (38.5}

(N=13) <28 36 0.098 Females (49.3) (N=73 ) <34 35 0.185 Total Population {46.7) (N=75 ) <34 3 0.96 Males (37.5) (N-8) <34 32 0.16 Females (47.8) (N=67) a = parentheses indicate percentages b = 34/91=37.4%

PERCENT DRDZ(%) CHI-SQUARE CHI-SQUARE' (Literature (Super Controls) Controls) ~ 28 58 0.0001 0.0001 Total Population(67.7) (N = 86) ~ 28 8 0.004 0.0001 i i n RECEPTOR GENE
AND BODY FAT

PERCENT DRDz(%) CHI-SQUARE CHI-SQUARE' (Literature (Super Controls) Controls) Males (61.5) (N= I 3 ) <28 50 0.0001 0.0001 Females (68.5) (N=?3) <34 49 0.0001 0.0001 Total Population(65.3) (N=75) <34 5 0.02 0.0001 Males (62.5) (N=8) <34 44 0.0001 0.000 t Females (65.7) (N=67) a = parentheses indicate percentages b=
b = 1/30=3.3%
Comparing super controls and all cases with more than 34% body fat.
utilizing a statistical technique called logistic regression analysis, the DRD2 A1 allele accounts for 45.9% of the variance which is statistically significant (Chi Square =43.47, df =1, p < .0001). In contrast, when compared to literature controls (/0110 allele) accounts for 3% of the variance and is not a statistically significant contributor to the variance in this population under study.
In terms of the CrP data, the sample was separated into two independent groups; those with either an A l IA l or A l /A2 allele and those with only the A2/A2 pattern. Each of these groups was tested separately for differences between placebo and treatment means far a variety of measures of weight change. These measures consisted of calculations of the percent of fat weight change, the change in fat weight, the change in body weight. the change in fat free mass, the percent change in fat weight, the body composition index and body weight change in kilograms. Mean differences between placebo and treatment groups were tested statistically using an independent groups t-test. Statistical significance was determined for these comparisons by setting the alpha criterion at p = 0.05. Any p values less than 0.05 are considered to indicate a statistically significant difference between the mean of the placebo and the treatment groups.
T test analysis revealed that carriers of the DRD2 A2 allele were more responsive to the effects of CrP than were the DRD2 A 1 allele carriers. The measures of the change of fat weight (p < 0.032), change in body weight {p < 0.011 ), the percent change in weight {p < 0.035), and the body weight change in kilograms (p < 0.012) were all significant, whereas no significance was found for any parameter for those subjects possessing a DRD2 A1 allele.
DISCUSS101V These results suggest that the dopaminergic system, specifically the density of the D, receptors, confers a significant differential therapeutic effect of CrP in terms of weight loss and change in body fat. This takes on even greater significance when one considers the relationship between overeating and the A
1 alleie of the DRD2 gene (Blum et al., 1995). It is the inventors' contention that the positive responsiveness of the A2 DRD2 carriers retain the positive metabolic effects of CrP, but, in contrast, the A I DR.D2 carriers because increased carbohydrate bingeing masks the effects of CrP on weight loss and change of body fat. These data further suggest that combinations of CrP as other chromium salts with amino acid precursor therapy as even in A1 DRD2 carriers should result in reduced craving and significant weight loss (Bium et al., 1997). Moreover the inventors propose that mixed effects now observed with CrP administration in terms of body composition, may be resolved by genotyping the prospective patient prior to treatment. The reason that Chromium picolinate or nicotinate have the observed effects is that chromium salts effect primary metabolic parameters while the amino acids affects hypodopamine deficiency thereby reducing craving for sweets.

AMINO ACID AND HERBAL COMPOSITIONS FOR ENHANCEMENT OF
ATTENTION PROCESSING: POTENTIAL EFFECTIVENESS IN
ATTENTION-DEFICIT HYPERACTIVITY DISORDER
The inventor has observed an association between polymorphisms of the dopamine D~ receptor gene and brain electrophysiological abnormalities in humans.
In this regard a weighted linear trend analysis revealed a significant worsening effect of event-related potentials (EPs) in the presence of the DRD2 A 1 allele compared to the DRD2 A2 genotype and comorbid Substance Use Disorder (SUD) (p < 0.0001 ].
Decreased amplitude and latency of the F300 wave of evoked related potentials (ERPs) has long been associated with alcohol and drug dependence. The inventor also found that a significant prolongation of P300 latency correlated with three risk factors: ( 1 ) parental SUD, (2 ) chemical dependency (i. e. cocaine dependence), and (3) carbohydrate bingeing. The inventor also found that two copies of the DRD2 allele (A 1 /A 1 ) compared to the A2 form of the gene (A2/A2) significantly correlated with a prolonged latency of the P300 wave. Moreover, the P300 amplitude correlated with family history of alcoholism and SUD and S. Hill (University of Pittsburgh) found a significant association of P300 amplitude with the DRD2 A1 allele as well.
These results suggest a role for the DRD2 A 1 allele in a non-behavioral pathophysiological phenotype involving brain function including attentional processing and potential RDS associated behaviors (i. e. SUD and ADD/ADHD). It is to be noted that P300 abnormalities are well documented but are not specific to drug abuse as once thought. They are also common in mixed ADD, schizophrenia, delirium, obesity, and other psychiatric disorders.
A number of therapeutic interventions have been shown to improve evoked potential abnormalities as well as spectral analysis, such as cholinergics, cholinesterase inhibitors, dopaminergic and serotonergic agents, stimulants, trace elements, diet {low refined carbohydrates), cranial stimulation, biofeedback, as well as others suggesting a different and non-specific approach.
Since dopaminergic function is linked to brain electrophysiological abnormalities potentially through reduced D~ receptors, therapeutic measures may reside in technology developed to enhance brain D, receptor function. Gene product expression in terms of dopamine D2 receptor is significantly reduced with the TaqI
AI, TaqI Bl, as well as other alleles which result in dopaminergic deficiency.
In terms of gene repair, at the current time there is no known technique to restore such receptor deficiency in a permanent fashion, however certain parts of this invention address the potential to overcome this genetically-induced reduction in D~
B",aX. An up-regulation of DZ dopamine receptors by agonists including bromocryptine, and N-n-propylnorapomorphine have been observed (Fitz et al., 1994). Moreover, changes in mRNA did not appear to account for the increase in receptors after agonist treatment. Instead, studies with cycloheximide, a protein-synthesis inhibitor, suggest that increased protein synthesis (up-regulation of gene expression), and not decreased protein degradation, is responsible for up-regulation by dopaminergic agonists.
Utilizing this logic, this invention proposes to couple the use of enhancement of synaptic dopamine release via enkephalinase inhibition (D-phenylalanine) to promote chronic occupancy of DZ receptors with potential D~ receptor proliferation or up-regulation as shown in transfected HEK-293 cells. This further forms the basis of this mvent~on.
EIectrophysiology and Neurotransmitter Function. Brain electrical activity mapping, including QEEG and cortical evoked potentials, has revealed the existence of subtle neurological changes in a wide variety of subjects (Porjesz, et al., 1987), including schizophrenics (Braverman et al., 1990; Christian et al., 1994; BIum et al., 1995), criminals (Lovinger et al., 1995; Seiden et al., 1995), depressives (Hudson, 1995), Alzheimer's (Scourfield, 1996; Lawford, 1995), AIDS (Meiswanger et al., 1995), ADD/ADHD and their response to medication (Kokkevi et al., 1995; Nunes et al.. 1995; Yoshida et al., 1984), and SUD (Gilman et al., 1990; Morrow et al., 1992;
Brown et al., 1994; Comings et al.; 1996; Neshinge et al., 1991 ). It is well known that drugs can induce neurotransmitter deficits in the deep limbic structures (located in the temporal lobes) (Yoshida et al., 1984; (Gilman et al.. 1990), leading to focal electrophysiological abnormalities. Those topographical changes may be an important marker or component which motivates an individual's desire to engage in substance use. It has been suggested that the kindling phenomenon of the limbic system may be a factor in both the craving and withdrawal of SUD subjects (Ballenger et al., 1980). Furthermore, SUD like premorbid depression may premorbidly predispose probands to subsequent Alzheimer's encephalopathy, concomitant ADDIADHD, and other psychiatric diseases which may originate in the temporal lobes. Brain mapping temporal lobes abnormalities correlate to hypometabolism on PET scan, which is similar to interictal temporal lobe seizure disorder patients who also have hypometabolism (Adams et ul., 1993). It has been reported that the DRD2 A1 allele associated with low glucose metabolism as measured by PET scan in frontal lobes (Noble et al., 1997). This suggests that hypometabolism is associated with low dopamine D~ receptors in the frontal lobes of the brain leading to dysfunction. A number of papers suggest that SUD promotes kindling (Goldstein et al., 1994) or electrophysiological instability which may induce aberrant evoked potential and spectral analysis abnormalities. Moreover, both cocaine and ethanol induce a kindling response or electrophysiological instability and on an acute basis temporarily corrects evoked potential abnormalities most likely via dopamine release. Recovering substance abusers still had low P300 even after substance use was discontinued. The P300 activity only partially recovers and also represents a genetic characteristic antedating substance use leading to cocaine or heroin abuse similar to that observed in alcoholics {Gilman et al., 1990).
The inventors theorizes that substance dependence significantly exacerbates a potential premorbid state and strongly suggests a gene-environment interaction. The self medication of substances or behavioral acts which cause a release of dopamine in the nucleus accumbens (i. e. cocaine, alcohol, nicotine, sugar, sex, etc. ), used to relieve these electrophysiological disturbances especially with drugs, unfortunately results in worsening of brain dysfunction.
Additive Effect of Different Dopamine Genes. A summary of a recent study by the inventor involving the three dopaminergic genes DRD2, D~3H and DAT1 genes, in families with both ADHD/ADD and RDS genotyped up to the fourth generation is presented herein. Together these results provide support at a molecular genetic level for the concept that ADHD, TD and other disorders are inherited in a polygenic fashion, part of a spectrum of related disorders (RDS), caused by shared genes, caused by alleles that are common in the population, caused by genes that are additive in their effect, caused by genes that upset the dopamine system (among others}.
Generational Association Studies of Dopaminergic Genes in Attention-Deficit/Hyperactivity(ADHD) Probands and Multiple Family Members Up To Four Generations. Polymorphisms of the dopamine D2 receptor gene are associated with the "Reward Deficiency Syndrome " (RDS) or a number of related impulsive addictive-compulsive behaviors; the VNTR 10/10 genotype of the dopamine transporter gene (DAT1) associated with ADHD and Tourette's Disorder (TD); and the B 1 allele of dopamine-beta-hydroxylase gene (D(iH) also associated with TD and a number of RDS behavioral sub-traits.
The inventors genotyped 51 subjects up to four generations derived from two multiply affected families. The DNA was extracted from buccal swabs according to the PCRTM-based methods (Blum et al., 1997) The two initial probands were carefully diagnosed by a number of standard instruments to have ADHD.
Subsequently, the additional family members were also diagnosed for ADHD and other related RDS behaviors. All subjects were genotyped for the three dopaminergic genes (DRD2, DATI, and D[iH). Eighty percent of all subjects {40/50) carried the DRD2 TagAl. When compared to "super" controls (1/30 or 3.3% carried the DRD2 A 1 allele) a significant association was observed (Chi Square = 41. I , df =
1, p = 0.00000001, Yates corrected) with an odds ratio of 116 [95% confidence limits 13.6-2,575]. The inventors present these data to point out the importance for highly screened controls. A similar but less robust finding was obtained when the inventors compared the data utilizing 714 non-alcoholic and non-drug abusing literature controls ( 185/714 or 26 % carried the DRD2 A 1 allele). A significant association was found (Chi Square = b3.2, df = 1, p = 0.00000001, Yates corrected) with an odds ratio of 11.4 (95% confidence limits 5.38-24.93]. In 91 screened controls the prevalence of the DAT1 10/10 allele was 34191 or 37.4%, as well as in 51 screened controls where the prevalence of the D~iH B 1 allele was 27/51 or 53%. A significant association was also found between the DATI (VNTR 10/10 genotype) in the ADHD-derived two-family members {30150 or 60%) when compared to screened controls (Chi Square = 7.51, df = 1, p = 0.0061 ) with an odds ratio of 2.64 [95% confidence limits 1.31-5.38]. In contrast, non-significance was found with carriers of the D(3H
Bl (32/50 or 64%) compared to screened controls (Chi Square = 1.27, df = 1, p =
0.259) with an odds ratio of 0.63 [95% confidence limits 0.28-1.4]. The inventors believe that the high percent of the DRD2 Al allele in these subjects compared to 40-50%
usually found with single addictive-impulsive-compulsive behavioral sub-traits, is due to the multiple behavioral sub-traits encompassing RDS. In one family, the allele was present in 100% of subjects diagnosed as having ADHD.
When the data are complete linkage analysis will be performed, utilizing at least one RDS behavior present in a family member as a co-variate. It is noteworthy that as the number of RDS behaviors increase in the subjects, the presence of the DRD2 A 1 allele also increases. At first glance it appears that the DRD2 A 1 allele, relative to the other two dopaminergic genes, is more informative in predicting both ADHD and RDS behavior at least in this sample currently tested. The data are currently being processed and additional outcomes will be presented and discussed in terms of the impact these findings have on the biogenetics of impulsive-addictive-compulsive behaviors (RDS) as well as one important sub-trait ADHD.
Cognition, Electrophysiology and Neurotransmitter Function. One important aspect of this invention is that attentional processing is influenced by alterations in neurotransmitter function especially at the brain site (meso-limbic) responsible for "reward" and other related behaviors. Moreover, dysfunction in the "reward cascade" via genetic or environmental elements like drugs, sex, and stress may influence attentional processing. While a number of neurotransmitter pathways are ultimately involved in focusing, memory and cognition in general at least four major pathways are preferred in this invention to be involved: serotonergic, opioidergic, GABAergic and dopaminergic. A brief review of the literature concerning cognition and neurotransmitters will favor the positive relationship r . i , ~

between the dopaminergic system and attentional processing. This relationship fosters the concept that compounds which activate the dopaminergic system and promote agonist interaction at dopamine receptors or release natural dopamine will enhance attentional processing and focus in an individual.
Polymorphisms of the Dopamine D, Receptor Gene Associates with Brain Electrophysiological Abnormalities in Humans. This is the first study known to the inventors which provides evidence for the association between polymorphisms of the dopamine D2 receptor gene and brain electrophysiological abnormalities in humans. In this regard a weighted linear trend analysis revealed a significant worsening effect of event-related potentials (EPs) in the presence of the DRD2 allele compared to the DRD2 A2 genotype and comorbid Substance Use Disorder [SUD] (n < 0.0001). Duncan's Range Test showed SUD with or without DRD2 A1 allele significantly worsened the EPs compared to DRD2 A2 controls. Moreover, the inventors observed a significant association between severe substance use disorder (SUD) and the DRD2 A 1 allele relative to the inventors' "super controls"
(p < 0.0000033) and to a large number of literature controls (p < 0.0021).
Decreased amplitude and latency of the P300 wave of evoked related potentials {ERP) has long been associated with alcohol and drug dependence. In this investigation the inventors found that a significant prolongation of P300 latency correlated with three risk factors (1) parental SUD, (2) chemical dependency (i.e. cocaine dependence), and (3) carbohydrate bingeing (p < 0.03). In this population the inventors also found that decreased P300 amplitude correlated with family history alcoholism and SUD
(p < 0.049), but did not correlate with the DRD2 A 1 allele. These results suggest a role for the DRD2 A 1 allele in a non-behavioral pathophysiological phenotype involving brain function and potential addiction liability.
The aim of this study was to determine whether the Dopamine D2 Receptor gene (DRD2) TagI A1 allele associates with brain electrophysiological abnormalities with or without substance use disorder (SUD) in humans attending a private outpatient clinic. Following the finding by the inventors' laboratories of a strong association between the A1 allele of the D2 dopamine receptor gene and alcoholism (Blum et al., 1990), several groups were unable to replicate the observation (Gelernter et al., 1997). The inventors have suggested two possible reasons: f rst, inadequate screening of controls for alcohol, drug, and tobacco abuse as well as other related behaviors, and second, sampling errors in terms of characterization of alcoholics for chronicity and severity of the disease (Blum et al., 1996). However, review of the literature reveals a number of positive associations between the DRD2 gene, with not only alcoholism but also with a group of impulsive-addictive-compulsive disorders including polysubstance abuse, smoking, attention-deficit/hyperactivity (ADHD), carbohydrate bingeing, Tourette's Disorder, pathological gambling, post-traumatic stress disorder as well as schizoid/avoidant behavior that have been termed "The Reward Deficiency Syndrome" (RDS} (Blum et al., 1996). The variations in DRD2 alleles have been argued as representing variations in a very common latent trait associated with dopaminergic function of which alcoholism is but a single manifestation (Hill and Neiswanger, 1997). Moreover, excluding "other pathology"
from both controls and affecteds already has been accomplished in San Antonio, Los Angeles, Duarte, acid Pittsburgh (Hill et al., 1997; Bium et al., 1996). It is the inventors' contention then, that failure to find linkage or with-in-family association could be due to incomplete understanding of the appropriate phenotype for analysis.
Therefore, in order to reduce spurious results, the inventors decided to utilize a non-behavioral-pathophysiologically based phenotype known as brain-electrophysiological abnormalities, as a marker for subsequent association studies with the DRD2 TaqI A1 allele. Other human association studies with the DRD2 TaqI
A 1 allele suggested this approach (Noble et al., 1994; Blum et al., 1994). To date, correlations exist between abnormalities in both spectral and evoked potentials for a number of behavioral disorders including SUD, ADHD, conduct disorder (CD), pathological violent behavior, Alzheimer's, among other disruptive behavioral disorders (American Psychiatric Association Task Force, 1991).
Since the inventors found significant evoked potential abnormalities with SUD
and obesity, the inventors decided to systematically assess the possibility of a direct correlation of abnormal brain electrical activity and the DRD2 A1 allele in patients attending a neuropsychiatric and medical clinic in Princeton, New Jersey.
Positive r , ~ , r correlations could provide important and relevant clinical information to diagnose premorbid genetically based brain dysfunction.
The relationship between the DRD2 allelic variance, SUD, and spectral analysis was examined. A significant probability map (SPM) of the visual evoked response (VER) in a typical normal subject having a DRD2 TagI A2/A2 allele was determined. Standard deviations {SD) maximum (0.34) and minimum (-1.00) were calculated as SPM, and the inventors' control group is not significantly different from the standardized BEAMTM (brain electrical activity mapping) control. A
characteristic brain map of the VER in a subject with the DRD2 TaqI A1/A2 allele without SUD, with a light frontal temporal excess negativity to 2.92 SD as visualize as bright white-blue. The right frontal temporal abnormality exhibited by the light white blue area is typical of individuals with mood swings, palpitations, anxiety and stress, with or without SUD. A characteristic brain electrical activity map of the VER in a SUD
patient with a DRD2 TagI A 1 /A2 genotype, with left and right frontal temporal excess negative to 6.13 SD was visualized by a bright white light.
The inventors also found a significant prolongation of P300 latency correlated with three risk factors (a) parental SUD, (b) chemical dependency (i.e.
cocaine dependence), and (c) carbohydrate bingeing (p < 0.03). Also the inventors found that decreased P300 amplitude correlated with family history alcoholism and SUD
(p < 0.049), but did not correlate with the DRD2 A1 allele.
Most importantly, weighted linear trend revealed a significant worsening effect of event-related potentials in the presence of the DRD2 A 1 allele compared to the DRD2 A2 genotype and comorbid SUD (p < 0.0001 ). Duncan's Range Test showed SUD with or without DRD2 A1 allele significantly worsened the EPs compared to the DRD2 A2 controls.
It is noteworthy, that in this study, 52% of the severe SUD subjects (N = 29) carried the DRD2 TagI A 1 allele. The percent prevalence is significantly different when compared with the inventors' "super normal" controls (excluded for alcoholism, SUD, smoking behavior, ADD/ADHD, carbohydrate bingeing, pathological gambling, schizoid/avoidant personality disorder behavior, violent behavior, and family history positive for alcoholism, SUD, and obesity) with a DRD2 A1 Allelic prevalence of 3.3% (1/30) [Chi Square = 17.47, df= 1, p < 0.0000033].
Moreover, of 714 non-alcoholic, non-SUD (except tobacco), non-Hispanic Caucasian controls, 25.9% carried the DRD2 A 1 allele. When compared to the non-Hispanic Caucasian SUD probands a very strong association was found (Chi Square = 9.44, df = I , p < 0.0021 ).
To the inventors' knowledge, this is the first study to observe a significant association between the DRD2 A 1 allele with increasing number of brain electrophysiological abnormalities in both VER and auditory evoked responses (AER) in patients attending a neuropsychiatric setting. This genetic evidence along with other studies of electrophysiological disturbances in individuals that may be prone to psychostimulant abuse/dependence further suggest the relationship of dopaminergic-genes and proclivity for stimulant abuse. It has been reported that SUD
exacerbates brain mapping parameters (Braverman et al., 1996; Blum et al., 1995; Lovinger et al., I S 1995; Seiden et al., 1995; Hudson, J., 1995) and when abstinence occurs, there appears to be persistence of some drug-induced brain electrophysiological damage in most cases (Scourfield et al., 1996; Lawford et al., 1995; Neiswanger et al., 1995).
The inventors theorize that comorbid SUD in psychiatric probands significantly exacerbates a potential premorbid state and strongly suggests a gene-environment interaction (Kokkevi et al., 1995; Nunes et al., 1995). The self medication of illegal drugs, like cocaine for example, used to relieve these electrophysiological disturbances, unfortunately results in the worsening of brain dysfunction, especially in the bi-temporal lobes of the brain with chronic repeated use. Furthermore, a decrease in the amplitude and prolongation of the P300 has been associated with alcoholism and drug addiction (Yoshida et al., 1984; Gilman et al., 1990). This is independent of the acute effects of the alcohol and drugs since findings have been in the sons of alcoholics who do not use alcohol themselves (Morrow et al., 1992; Brown et al., 1994). This speaks for the presence of a gene or genes that are involved in decreasing amplitude and prolonging latency of the P300 wave and are associated with an increased risk of SUD.

,,, WO 98/48785 . PCT/US98/08684 In a global sense the inventors believe these observations will have important neurophysiological relevance supporting the further the role of dopamine in brain function (i.e. reward). The linking DRD2 polymorphisms have been linked to dopamine receptor function (Pohjalainen et ul., 1996). In 33 Finnish male volunteers the Tagl A l allele of the DRD2 gene associated with a statistically significant - reduction in the adjusted Bma~ compared to A2/A2 subjects. The Kd was not significant between the groups. This is evidence that while the number of receptors go down with the Al/A2 subjects, there seems to be no change in receptor function, indicating a regulatory role of 3' mediated polymorphisms in receptor synthesis.
Additionally, DRD2 knock out mice had a significant reduction in D., receptor Bmax without any change in Kd (Grandy et al., 1989). Thus the regulatory gene element may be in linkage disequilibrium with the TugI A polymorphism. Therefore, demonstration of specific molecular polymorphisms identified with brain electrophysiological abnormalities such as the DRD2 A I allele, should have profound clinical usefulness. Logically, from these studies it appears that polymorphisms of the dopaminergic system are tied to specific brain electrophysiological dysfunction (i.e.
VER and AER) which seem to mediate abnormal behaviors {sub-traits of RDS).
If these associations continue to be defined, the inventors will be able to provide better prevention strategies, especially in high risk groups.
Additionally, more specific targeted treatment modalities ultimately will derive from these investigations which, in the inventors' opinion, significantly impact human behavioral pathology.
Subjects A total of two-hundred ninety-four subjects were utilized in this study. All of the subjects were non-Hispanic Caucasians who signed an informed consent; the study was approved by the Path Research Foundation Institutional Review Board. The patients were randomly selected for study from approximately 5,000 visits from 800 patients attending the PATH out-patient private clinical practice over a one-year period.
All subjects in the SUD groups were clinically established to have early full (DSM II~ remission of SUD (Brown et al., 1994). The demographic breakdown of the inventors' one-hundred seventy-three sample base is described in Table 69.
Gender and psychiatric diagnoses were not significantly different between all groups tested. The mean age between all groups was assessed and did not vary significantly (p < 0.00001 }. For this investigation of the sexual selection included 53.1 %
percent males and 46.9% percent females this age difference was found in psychiatric diagnosis and not in gender.
Electrophysioiogieal and Genotyping Methods. For selection criteria and assessment instruments to determine SUD phenotype (cocaine abuse, DSM IV Code No. 305.60; cocaine dependence, DSM IV Code No. 304.20; alcoho! abuse, DSM IV
Code No. 305.00; and alcohol dependence, DSM IV Code No. 303.90) the inventors utilized the same methodology as the inventors previously reported (Braverman et al., 1996). The subjects were genotyped for the DRD2 allelic variance (DRD2 TaqI A1 and A2) in accord with Comings et al. (1996). A Nicolette BEAMTM was used to assess; total brain abnormalities, total spectral abnormalities, evoked potentials (EP, AER, VER) and P300. For a detailed description of this methodology to assess brain eiectrophysiological abnormalities (Braverman and Blum, 1996). The inventors also included in this study a non-genotyped P300 control group for matched comparison purposes. The P300 control group included 15 male volunteer subjects who were drug, alcohol, and food addiction free, and free of psychiatric disease.
Statistical analysis. For statistical analysis all brain map data were classified as abnormal or normal. Specifically, EEG was dichotomized as normal or abnormal, spectral analysis was dichotomized at 2.5 SD from standardized BEAMTM controls with recurrence of deficits (at the same loci) following three independent runs. P300 voltage was dichotomized at an established normal voltage at IOdv (Neshinge et al., 1991 ), P300 latency was dichotomized at 350ms. This value was based on an estimate of 300ms. plus mean age of the control group, which is a criterion developed by Lexicor, Inc., Boulder, Colorado, and an approximate 1.25ms. per year increase in P300 after age 18. A complete description of statistical methodology has been previously published (Braverman et al., 1996). Additionally, the inventors employed a Duncan's Range Test for paired comparison of means. The alpha level was set at 0.05 for significance.

Enhancement of Attention Processing in Healthy Humans by KantrollTM~:
A Cocaine Surrogate with Enkephafinase Inhibitory Properties. This is the first report of the effects of daily ingestion of a specific amino acid mixture, KantrollTM, in humans on cognitive event-related potentials (ERPs) associated with performance.
Cognitive ERPs were generated by responses to two computerized visual attention tasks, the Spatial Orientation Task (SOT) and Contingent Continuous Performance Task (CCPT), in normal young adult volunteers, where each subject acted as his own control for testing before, and after, 28-30 days of amino acid ingestion. A
statistically significant amplitude enhancement of the P300 component of the ERPs was seen after KantrollT~' for both tasks. The changes observed in this study, on normal controls. strongly suggested that enhancement of neurophysiologic function may be the basis for the facilitation of recovery of Reward Deficiency Syndrome behaviors (i.e. ADD/ADHD, Substance Use Disorders, Carbohydrate Bingeing, Nicotine Abuse, etc.) following the ingestion of the amino acid supplement, KantrollTM.
One of the most intriguing discoveries in neurobiology was that many neurotransmitters (e.g., dopamine, norepinephrine, epinephrine, serotonin, melatonin and glycine), which play vital roles in brain functioning and in mood regulation, can be dramatically influenced by the circulating levels of their precursor amino acid nutrients (e.g., Wurtman, 1983). The respective precursor amino acids are L-tyrosine (or L-pheny1alanlne), L-trygtophan and L-threonine. All these neurotransmitter synthesis systems exhibit two crucial features. First, the amino acids from which they are synthesized are among the nine essential amino acids: histidine, isoleucine, leucine, lysine, methionine, pheny1alanlne, threonine, tryptophan and valaime {tyrosine is synthesized from pheny1alanlne). Second, the primary step in the synthetic pathway utilizes an enzyme that is not saturated. The consequence of these two characteristics is that dietary intake of these amino acids can drive synthesis of specific neurotransmitters. While this is not necessarily a linear relationship because of numerous regulatory feedback mechanisms; it has been shown to be a highly significant modulatory route (Hernandez-Rodriques and Chagoya, 1986; Wurtman and Fernstrom, 1976; Wurtman et al., 1981 ).

WO 98/48785 PCT/US98t08684 Sophisticated measurements of brain chemical turnover in animals, including microdialysis measurements, have demonstrated changes in neurotransmitter output following precursor amino acid loading {Hernandez et al., 1988). Complementary behavioral changes have been demonstrated in animals following systemic and direct central nervous system delivery of precursor amino acids (Blum et al., 1972).
While certain L-amino acids are neurotransmitter and neuromodulator precursors, their racemates, the D-amino acids also have biological activity. In particular, D-pheny1alanlne and D-leucine decrease the degradation of opioid peptides which are central to regulation of mood and behavior (Blum et al., 1987; Carenzie et al.. 1980;
Della Bella et ul., 1979; Ehrenpreis et al., 1979).
Neurotransmitter actions form the neurochemical basis of behavior, and their perturbation may be central to a variety of psychiatric and behavioral disorders. Their contribution to addictive disorders also has been the focus of considerable comment (Koob and Bloom, 1988; Wise and Bozart, 1985; Blum et al., 1990; Blum and Kozlowski, 1990; Amit and Brown, 1982). Specifically, dopamine, serotonin, norepinephrine, gamma-aminobutyric acid (GABA), glutamine and the opioid peptides are thought to play crucial roles in addictive disorders, particularly regarding alcohol, heroin, and cocaine abuse (Blum et al., 1977; Geller et al., 1972).
Consequently, these observations have led to the idea that ingestion of selected nutrients could affect mood and therefore behavior in humans.
While nutritional strategies have been employed several times in the past (e.~., Williams, 1959), unequivocal quantitative demonstration has been decidedly limited. Recent clinical data, however, suggest a substantive effect of amino acid precursors and enkephalinase inhibitors on recovery from alcohol, cocaine and food addictions (Blum et al., 1988; Halikas et al., 1989}. A relevant study (Blum et al., 1988) examined the use of the amino acid supplement, KantrollTM, in a 30-day inpatient treatment program for cocaine addicts. The only difference between the experimental and control groups was the use of this supplement. Two primary measures were used: leaving the program before completion or against medical advice (AMA) and drug hunger. Drug hunger was a measure of vivid cocaine-related dreams, somatic complaints, requests for medication, drug-related confrontation _. . . . ~ . ~ . 1 WO 98!48785 PCT/US98Ia8684 responses, program compliance, agitation and violence or threats of leaving AMA.
The amino acid supplement group fared significantly better on both measures than did the control group. AMA rates were reduced nine-fold and measured drug hunger was significantly reduced. In addition, staff reported a decided decrease in agitation, outside focus and craving. However, the measures are complex, frequently qualitative, and dependent on numerous input factors (Brown et al., 1990).
_ The present study sought to close part of the gap between clinical experience and basic understanding of the neurobiological consequences of KantrollTM.
Here. the approach was to evaluate quantitative neurophysiological changes associated with the treatment with KantrollT~'. The electrophysiological portion focused on the component of the cognitive event-related potential (ERP), evoked by two visual attention tasks. The advantage of this electrophysiological approach over more conventional EEG analyses is that, by virtue of being anchored to performance tasks, specific systems important to attentional processing are activated. These attention I S probes generate a family of components of the ERPs, each representing a stage of information processing. However, the ERP analysis here focused on cognitive ERPs, specifically on the P300 component. Quantitative ERP changes have recently been shown to vary predictably over a range of clinical disorders; e.g., attentional disorders (Buschbaum et al., 1973; Halliday et al., 1976 Prichep et al., 1976), schizophrenia (Braverman, 1990), depression (DeFrance et ul., 1995a; Vasile et al., 1992), dementia {Duffy et al., 1984), and substance use disorder (Braverman et al., 1990, and Braverman and Blum. 1996).
The focus in this preliminary report was on attentional processing, because in part, alternations in attentionlor concentration both precede and accompany sustained substance abuse (Begleiter and Projesz, 1988; Whipple et al., 1988; Parsons, 1990), and because many transmitter substrates thought to be important in attention are a targeted for manipulation through the administration of KantrollTM. Another goal in the present study was to address the question of whether amino acids, acting as precursors or modulators of neurotransmitters, affect the central nervous system. This study, then, addressed the issue of the electrophysiological and performance correlates of chronic KantrollT" administration on normal subjects, especially as indexed by changes in the P300 component of the cognitive ERP.
Subjects. This study involved 20 normal volunteer subjects; free of psychological, neurological, or psychiatric conditions as determined by DSM IV
criteria by a board certified psychiatrist. All subjects signed an informed consent and each was compensated for participation in the study. Each subject performed the test battery twice as a test-retest paradigm thus acting as their own control.
Initial testing was done on day zero (pre-test) and then again after 28-30 days (post-test).
The subjects consumed six KantroIlTM capsules daily for 28-30 days. The composition is shown in Table 6. The data from two subjects were not included because of poor quality of either the pre-test or the post-test recordings.
Performance Tasks. Two performance paradigms were used as behavioral probes for the electrophysiological studies. The f rst probe was spatial orientation.
This is a reaction time based task (Posner et al. (1988)} where priming cues are 1 S presented in different portions of the visual field. Reaction times are compared for the right and left visual fields when the priming cues are, and are not, available. Through a comparison of reaction times, it allows for an assessment of the individual's ability to smoothly switch attention. The instructions were to focus on a cross in the center of the monitor screen and push the right mouse button when the '*' appears in the right box, and the left button when the '*' appears in the left box. The boxes alternate with respect to which one is the brightest. Response times were recorded, and ERPs were constructed with respect to four categories: ( 1 ) facilitated for the right visual field, (2) non facilitated for the right visual field, (3) facilitated for the left visual field, and (4) non facilitated for the left visual field. The presentation was randomized, but with an equal probability for the four conditions. The facilitated box was brightened msec prior to the presentation of the target. Accuracy scores and reaction times for both the left and right visual field were recorded, as well as the respective A' {a standard signal detection parameter) values. This paradigm samples the orientation to stimuli, fluidity of attention, along with cognitive processing speeds.

The second probe was contingent continuous performance. This task is a variant of a classical theme (Rosvold et al., 1956). The individual was asked to respond, by pressing the left mouse button, to a specific letter order: e.g., 'T' if it was preceded by another 'T'. This version of the task had a constant interstimulus interval (ISI) of 0.8 sec and included 500 trials. The probability of a non-'T' was set at 50%, the probability of the warning 'T' was set at 30%, and the probability of the target'T' was set at 20%. Averaged ERPs were constructed according to one of three conditions: distractor (any letter other than a 'T'), warning signal (the first 'T' in a pair), and target (the second 'T' in a pair). The principal performance measure was the Inconsistency Index (Ringholz, 1989), which is a measure of consistency of performance. It is noteworthy that the prefrontal cortex appears to be most heavily involved in sustaining attention and effort, leading to good performance on this sort of task (Cohen, et al., 1987; Corbetta, et al., 1991 ), and these tasks are very sensitive to stimulant medication (Sostek et al., 1980).
Recording Scheme. The EEG was recorded from 28 active recording sites referenced to linked earlobes (AI-A2). The montage was based on the international 10-20 system, with additional electrodes placed in the fronto-temporal (FTC1, FTC2), centro-parietal (CP 1, CP2), temporo-parietal (TCP 1, TCP2), and parieto-occipital {POI, P02) regions. Electrode impedances were kept at less than 5 KOhm.
Additional electrodes were used to monitor extraocular artifacts. Vertical eye movements and blinks (i. e. VEOG) were recorded via electrodes placed immediately above and below the orbit of the left eye. Horizontal eye movements (i.e. I-iEOG) were monitored with an additional pair of electrodes at the external canthi.
The EEG and EOG were amplified with a 32-channel Neuroscience Brain Imager (0.1-40 Hz, 6 dB/octave lowpass, 36 dB/octave highpass). The raw EEG
was sampled for 2.56 sec by 16-bit analogue-to-digital converters (TECMAR
Labmaster DMA) under the control of the SCAN EP/EEG acquisition and analysis system (NeuroScan, Inc.). To construct the event-related potentials (ERPs), waveform averages were constructed from 256 points spaced over an 800 msec interval.
Sampling began I 00 msec prior to . the stimulus presentation to establish a pre-stimulus baseline, which was used as a part of the correction process. The single-sweep data was baseline corrected by subtracting the mean. The number of sweeps recorded varied according to the behavioral probe. A total of 200 sweeps was recorded for the Spatial Orientation and 500 sweeps were recorded to the Contingent Continuous Performance Test. Each segment of the behavioral paradigm. There were three parameters examined. each of which may vary according to the efficiency of an individual's attentional processing. These parameters were: latency, amplitude, and symmetry (spatial distribution) of components of the ERPs.
Data Analysis. First, T-test statistical maps were generated for all 28 electrode sites, at each point in time. This was a variation on the Statistical Probability Mapping of Duffy et al. (1981). Though exploratory, this procedure did result in an easily visualized map, indicating which electrode sites warranted further analysis. Since the P300 was the component of interest, Pz was selected as the site for the statistical analysis, since from the maps, this is where the focus of the component appeared to reside for these visual tasks. Then, the peak latency and peak amplitude (within a 275-325 msec interval) was determined. The second stage of statistical analysis employed a paired T test model (Statgraphics, 1988), comparing the Baseline and Treatment conditions. Performance data was also analyzed with the same model.
A schema of the electrode array, with ERPs from the CCPT task superimposed for the 28 scalp recording sites for illustration was made. Included also are recordings of Vertical Extra-oculogram (VEOG) and Horizontal Extra-oculogram (HEOG) that were used for artifact correction and rejection.
The results from the SOT will be discussed first. Again; this particular paradigm generates a number of interesting components (DeFrance et al., 1993), including the N2 negativity that developed within the 100-200 msec interval.
The N2 component is a processing negativity, which is an early stage of the orienting response (DeFrance et al., 1993). This component was found enhanced subsequent to treatment, but the amplitude change did not pass the inventors' pre-established threshold for significance [F( 1,17) = 2.30, p = 0.0259]. Nevertheless, late vertex positivity (i. e. P300 component) in the post-test condition was found markedly larger in amplitude for both the left [F(1,17) = 8.531, p = 0.0095] and right [F(I,17) = 16.31 ~ . 1.

p = 0.009] facilitated conditions. Hence, the group averaged P300 component after KantrolITM administration was found to be significantly enhanced when orienting both to the right and left visual fields.
Topographical maps were examined for the Baseline and Treatment conditions . 5 with respect to the facilitated (i.e. 'primed') condition for the left visual field. The patterns for the right visual field mirror those of the left so they will not be presented.
In the 100-200 msec interval, the N2 negativity is indicated over the right temporo-parietal region. Again, the effects of KantrollTM on this component approached statistical significance, but greater changes were associated with the P300 component and can be readily appreciated by comparing the 300-400 msec intervals.
Essentially, the topographical features of the P300 component remained the same, but the peak amplitudes were enhanced by the KantrolITM treatment. With respect to the performance data, the combined reaction times for the left and right visual field stimuli were faster after treatment 232 ~ 0.03 msec versus 238 ~ 0.03msec [F(1,17) = 8.62, p = 0.001]. In sum, there was a marked effect on the P300 component, with a borderline enhancement on the N2 component. A marked effect was also seen on the P300 component associated with a vigilance task, the CCPT.
The various components of the cognitive ERPs associated with the Contingent Continuous Performance Task (COPT) have many similarities to those generated by other continuous performance tasks, featuring a prominent P300 component (e.
g., DeFrance et al., 19956; Hillyard et al., 1973). Three sets of waveforms were constructed for analysis - distractor, warning signal, and target - but only the target waveform need to be discussed. Comparison of the pre-test to the post-test conditions found a significant [F(1,16) = 7.422, p = .015] enhancement of the P300 amplitude.
To determine the effect, the averaged target waveform at Pz for the Baseline and Treatment conditions were compared. The large positive-going potential, peaking around 300 msec is the classic P300 component. Again, the target waveforms were those taken when the subject responded to the second 'T' in a pair - as per the - instructions. The topographical maps for the 300-400 msec interval for the target conditions before (Baseline) and after (Treatment) KantrollTM was determined, and the topographical features of the P300 component remains the same, but that the i i i amplitude shows the enhancement when comparing the pre-test to the post-test condition. That is, in both the Baseline and Treatment conditions, the P300 . component occupied a posteriocentrai locus and was symmetric as was the case for the SOT. Nevertheless, the amplitude of the P300 component was enhanced in the Treatment condition. While there were electrophysiological differences after approximately four wk of treatment, there was no significant difference with respect the main performance variable for this behavioral probe - the Inconsistency Index.
The likely reason for this is that these normal subjects were already near their optimum performance with respect to their accuracy. It might be expected, however, that performance differences would emerge in clinical populations.
KantrollTM was designed as a potential treatment for cocaine abuse, with the recognition that one manifestation of cocaine abuse is altered attentional processing (Robledo et al., 1993). Moreover, human attentional processing and the P300 component of the cognitive ERP are often linked (e.g., Hillyard et al., 1973).
At this point, then, is worthwhile to revisit the neurology of the P300 component because it provides the context for the importance of the findings. As widely known, the P300 is one of several endogenous cognitive ERPs components, whose latency, morphology, and spatial distribution are highly dependent upon the psychological context in which the stimulus is embedded (Sutton et al., 1965). Consequently, the P300 component has been the subject of much study due to its putative role in attention and memory.
Evidence from depth recordings in humans suggests that anterior (e.g., amygdaloid complex) and medial temporal (e.g., hippocampal formation), along with frontal lobe structures may be involved in the regulation of the P300 component, and may actually contribute to its display over the temporal regions (Halgren and Smith, 1987;
Wood et al., 1980). It is not likely, however, that the deep temporal structures are the sole generators of the P300 component, as believed from earlier studies, since temporal lobectomy does not obliterate the component (Johnson and Fedio, 1986; Smith et al., 1985). Nonetheless, the deep portions of the temporal lobe appear to have clear modulatory responsibilities over the P300 component (Halgren and Smith, 1987), as may prefrontal zones (Simon et al., 1977).

i . ~

As the characteristics of any cognitive ERP are very much anchored to the eliciting behavioral paradigm, it is important to keep in mind that the performance probe used in this study has elements of both a sustained and selective attention task.
So certain components (e.g., P300) of the ERP should be referable to both aspects of attention. Interestingly, recent PET studies (Corbetta et al., 1991 ) have indicated that w the orbitofrontal cortex is heavily involved in the selective aspects of attention, whereas sustained attention appears to be more the purview of the medial portions of prefrontal cortex (Cohen et al., 1987), with the right hemispheric portions playing a particularly important role (Pardo et al., 1991; Wilkins et al., 1987). Since the orbitofrontal cortex regulates the activity in the anterior temporal region via the uncinate fascieulus, it should not be surprising then that amygdaloid damage should also affect selective attention. It has been suggested that amygdaloid damage does impair selective attention, perhaps because of a failure to assign sufficient emotional value to the stimuli (LeDoux, 1993). It is also known that attentional performance is I S influenced by the salience and the distinctiveness of the incoming information. There is considerable evidence that it is precisely this role that the hippocampus plays in the overall process of selective attention (e. g., Salzmann et al., 1993; White, 1993).
Therefore, the orbitofrontal-amygdaloid complex-hippocampal formation axis appears to be important in the regulation of selective attention, where the amygdaloid complex likely assigns salience value to a stimulus complex and the hippocampal formation compares salience value among stimuli. Within this framework, then, the P300 behaves as a modality-independent byproduct of the selective attention process - a necessary foundation to subsequent emotional, memory, and cognitive processing.
These performance probes, then, challenge the functionality of pathways along the frontal-temporal axis. It is precisely these forebrain regions implicated in the brain's response to cocaine.
Attentional processing also has been shown to be dependent on biogenic amine regulation (Stanzione e1 al., 1990; Scatton e~ al., 1982). Since the precursors for synthesizing the amines are dependent upon dietary intake, it is possible that dietary supplements can alter available biogenic amine stores in the brain.
This has lead to various clinical strategies that target nutritional improvement of the brain's chemistry for the treatment of specific disorders (Blum. 1989c). This approach makes use of normal cellular control mechanisms and can result in decided improvement in psychological outlook, behavioral performance, and relapse prevention. Such nutritional improvement rests on the administration of amino acid precursors of key neurotransmitters in conjunction with vitamins and minerals central to synthesis of these neurotransmitters. It is noteworthy that all of these chemicals (transmitters, vitamins and minerals) have been found deficient not only in active alcohol and drug abusers but often remain in deficit well into recovery (Blum, 1991a).
This study, then, demonstrates that nutritional supplementation can enhance neurophysiologic function in normal controls, and this may have important ramifications for the utilization of amino acid supplementation in the cocaine recovery process (Blum et al., 1988; Trachtenberg and Blum, 1988). Future projects will investigate how attention and memory functions are affected in recovering cocaine abusers. Since the various neurotransmitters, important in attention are dependent upon nutritional sources for their precursors, it may be possible to minimize the attentional defects, and/or speed recovery, by selective precursor enhancement. The implication of the Reward Cascade model is that each of these neurotransmitters, which can be shown to be functionally altered as a consequence of drug use and/or genetic anomalies (Bium et al., 1990; Noble et al., 1991a; Noble et al., 1991b; Blum et al., 1991a; Blum et al., 1991b; Blum et al., 1992; Noble et al., 1992; Blum et al., 1994a; Blum et al., 1994b; Blum et al., 1995b; Blum et ul., 1995a; Noble et al., 1995), should be manipulated to facilitate improved brain functioning and thus potentially improve feelings, mood, and behavior. This common disease, first termed by Blum et al. (1995a; 1996a) "The Reward Deficiency Syndrome," is a malfunction of the "Reward Cascade." The inventors' results suggest that treatment of the "Reward Deficiency Syndrome" could be accomplished - at least in part - by amino acid loading techniques with enkephalinase inhibitory properties.

~,, CLASSIFYING COCAINE ABUSERS
TYPE A TYPE B
Cause of Abuse Problem more more genetic environmental Gender equal more male male/female Personality low impulsively high impulsively, sensation and sensation seeking seeking, high harm avoidance Childhood Factors fewer early risk conduct disorder factors Age of Onset later earlier Substance Abuse less severe, more more chronic and severe;
Severity episodic polydrug Psychopathology lower severity, higher severity, more more affective antisocial After years of studies, researchers are able to identify factors that classify alcoholics as Type A or Type B. Recent NIDA-funded studies show that, in general, the same multiple criteria are valid in classifying cocaine abusers.
Results may prove useful in explaining different causes of abuse and in designing specific prevention and treatment interventions.

MAA TECHNIQUE FOR OTHER POLYMORPHIC TRAITS -CHOLESTEROL AND LOW-DENSITY LIPOPROTEIN LEVELS.
In the inventors' studies of the role of various genes in cardiovascular diseases the inventors have identified four genes that were associated with cholesterol and lipoprotein metabolism. These were the serotonin transporter (HTT), the oxytocin IO receptor (OXYR), the dopamine DRD2 receptor (DRD2) and the presenilin gene (PSl) genes. The respective polymorphisms were the promoter insertion deletion at the HTT gene (Collier et al., 1996), a dinucleotide polymorphisms of the OXYR gene (Mecheiini et al., 1995), a promoter insertion/deletion polymorphism of the gene (Arinami et al., 1997), and RFLP at the PSI gene (I-Iiguchi et al., 1996). Based on studies of a separate group of subjects the scoring was as follows: HTT
gene - 0 =
SS, LL, 2 = SL; OXYR gene 278/278 = 0, 278/267 = 1, 276/276 = 2; DRD2 11 = 0, I2 = 1, 22 = 2; and the PSl gene 22 = 0, 12 = 1, 11 = 2.
FIG. 8 shows the use of the MAA technique in assessing the role of these four genes in cholesterol and LDL metabolism.
These results demonstrate that the MAA technique can be generalized to any polygenic disorder or trait. These four genes accounted for 16.2 and 11.5% of the variance of cholesterol and LDL levels respectively. The p values for cholesterol were 0.0002, and for LDL were 0.002.

Dopaminergic Genes, Violence and Schizoid/Avoidant Behaviors. With regard to "pathological violence". the inventors genotyped eleven adolescents between the ages of 12-19, who were in a residential treatment program in San Marcos, Texas, for both the DRD2 and DAT1 gene variants. These subjects were selected on the basis of a one-h structured interview diagnosed to have impulsive-aggressive violent behavior. Each subject selected for study had a 2.5 SD abnormal brain electrical activity map measured by Nicolett(TM) interpreted by a board certified neurologist.
While 6 out of 11 subjects had the D2A1 allele (56%), I1 out of 1 I subjects carried the DAT1 (VNTR 10 allele}. When the D2A1 allele in the subjects were compared to "super-controls (1/30 or 3.3%), significant association was observed (X2 =
14.9, df= 1, p < 0.0001). Similarly a significant association was found for the DAT1 allele when compared to literature controls (34/91 or 37.4%, X2 = 7.6, df = 1, p < 0.006), but not for the 10/10 genotype (p = 0.093).
The present invention describes an association between various dopaminergic genes and SAB. For the SAB data set the inventors genotyped a total of 109 subjects ........ .. ~. ..

attending an outpatient clinic in Princeton, NJ for the three dopaminergic genes (DRD2, DATI, D(3H) as well as 172 screened controls. With chi square, the inventors found that the D2A1 allele significantly associated with patients identified by the Millon Clinical Multi-Axial Inventory computerized test to have SAB
(score >
84} compared to D2A2 allele (X2 = 7.6, df = 1, p = 0.006). Carriers of the D2A

allele in this study was found in 11/22 or 50% of schizoid and 12/27 or 44% of avoidant subjects a significant association when compared to super controls (X2 = 16.75, df = 1, p = 0.000044). While chi square analysis failed to reveal association of D(3HB 1 allele and DAT110/10 allele with SAB utilizing that approach, a significant association was found between the DATI 480bp VNTR 10/10 allele in those individuals diagnosed with SAB (18/28 or 68%) when compared to controls (X2 = 6.3, df = 1., p = 0.012). A similar trend was found in the D(3HB 1 allele ( 17/23 or 76%) compared to screened controls (but not super controls) (X2 = 2.9, df =
1, p < 0.09). Linear trend analysis showed increasing frequency of the D2A1 allele with increasing SAB severity (A 1 /A 1 = 83%; A 1 /A2 = 41 %; and A2/A2 = 23%;
p = 0Ø005). Utilizing multiple variable associations, both D2A1 allele and sex were significant predictors of SAB severity. With D2A 1 allele the inventors found an odds ratio of 2.79 (p = 0.018) and with gender 3.6 (p = 0.007). The Hosmer-Lemeshow goodness of fit at p = 0.778 and the combined contribution to the variance was 17.9%.
In SAB it appears that the DRD2 gene is more important than the DATI gene.

EXAMPLES OF SPECIFIC ASSAYS FOR GENETIC POLYMORPIiISMS
The following are examples of specific detection methods for the various genes proposed in the present invention used to diagnose a predisposition of RDS, related behaviors, and other polygenic traits. Many of the specific assays are the same as found in the literature, and exemplify the use of such assays for polymorphisms in the MAA technique. One of skill in the art will recognize that modifications can be made to such and assay to detect a specific allelic polymorphism, and that additional genes and assays can be used in the MAA technique other than the ones described herein.

DRD1. To examine the DRD1 gene the method involves the utilization of the Ddel polymorphism consisting of an A to G change in the 5'UTR, treated by the PCRTM procedure described by Cichon et al. (1994).
DRD2. The detection for this gene comprises obtaining DNA of a subject, subjecting said DNA to digestion by TaqI restriction enzyme, separating resultant DNA fragments, hybridizing said separated DNA fragments to a labeled recombinant phage-hD2G1(ATCC#61354 and 61355) or a fragment thereof specifically binding a 6.6kbA 1 allele of the human dopamine D2 receptor, and determining the presence of said A1 allele of the human D2 receptor. In particular, the fragment of recombinant phage--hD2G1 (ATCC#61354 and 61355) may be a BamHI fragment having an about 1.7kb size. An alternative method of detection involves a PCRT"' technique (Noble et al., 1994).
DRD4. DNA is extracted and PCRTM amplified using VENT polymerase and a high denaturing temperature (98°C for 1 min) with a combined annealing and extension reaction for 5 min at 70°C (Sommer et al., 1993). The primers employed are (Nanko et al., 1993), 5'-AGG TGG CAC GTC GCG CCA AGG TGC A-3' (SEQ
ID N0:23) and D4 = 42: 5'-TCT GCG GTG GAG TCT GGG GTC GGA G-3' (SEQ
ID N0:24).
DAT1. Determination of the DAT1 repeat polymorphism involves VNTR
genotyping. Genomic DNA is extracted from and amplified by PCRTM of the DAT1 40-by VNTR. The alleles at the 3' UTR are determined by PCRTM using the oligmers and PCRTM conditions reported by Vandenbergh et al., 1992a. Following PCRTM
amplification the products are electrophoresed in an 8% acrylamide gel with a set of size markers.
D~3H. D'Amato et al. (1989) reported the presence of two Taq D(3H
polymorphisms entitled A and B. A D(3H cDNA clone All (Lamouroux et al., 1987) is used which consists of a 2.7kb insert at the EcoRI site. To improve labeling the vestor is digested with BamHI and SaII to produce five bands. A 3.5 kb fragment was labeled for testing the B polymorphism. Digestion with TagI restriction endonuclease, electrophoresis in agarose, Southern transfer to a nylon filter, ,,, hybridization with 32P labeled probe, and autoradiography demonstrates fragments of 2.8kb (B 1 ), and 1.4 kb (B2).
MAOA. The MA OA VNTR polymorphism is utilized (Hinds et al., 1992).
This complex polymorphism consists of a GT microsatellite directly adjacent to an . 5 imperfectly duplicate novel 23-by VNTR mofit, with alleles differing in both the number of dinucleotide repeats and VNTR repeats. DNA is extracted by standard methods and then amplified by PCRT"' and each primer is labeled with fluorescent HEX or FAM Amidite(Applied Biosystems, Foster City, CA) primers [ < 320; 320-333; 334; >_335]. A two ~l of the 10 fold diluted PCRTM product is added to 2.51 deionized formamide and o.5 ~.l of ROX 500 standard and denatured foe 2 min at and loaded on 6% polyacryiamide gel in an Applied Biosystems 373 DNA
sequences.
The gel is electrophoresed for S h at 1100 volts and constant 30 W. The gel is then laser scanned and analyzed using the internal ROX 500 standards. The peaks are recognized by Genotypes (version I . I ) [Applied BiosystemsJ based on color fragments sized by base pair length.
Tryptophan 2,3-dioxygenase gene PCRTM amplification of the Mutant Region of Intron 6. The PCRT'" reaction to amplify the target sequence is as follows:
IOmM Tris HCL, pH 8.3, 50mM Kcl, I.SmM, l.SmM MgCl2, 0.05% Tween 20, 0.05% NP-40, 100~.M each dATP, dCTP, DTTP, dGTP, 0.1 p,M primers. The primers are:#116 GACACTTCTGGAATTAGTGGAGG (SEQ ID N0:25), and # 117 GAAGTTAAATCCATGTGGCTC (SEQ ID N0:26). The following is added to 20pL: O.SU AmpliTAq (Perkin-Elmer, Foster City, CA),1~1 (250ng) genomic DNA.
The reactions are run on a PE-9600 thermal cycles (Perkin Elmer) or a PTC-100 programmable thermal controller (MJ Research, Inc., Watertown, MA) using the following protocol: 94 C 5 min, then 30 cycles of 94 C, 30 sec., 60 C 30 sec., min., then 72 C for 5 min. To determine if amplification occurred, 101 of the reaction mixture will be electrophoresed on a 1.5% agarose gel in TBF buffer.
From the above PCRT"' reaction a 10 p.l aliquot is digested using 1,5 units of restriction enzyme BsII and final 1 x buffer (supplied by New England BioLabs, Beverly, MA) and incubated at S5 C overnight. A 10 ~1 aliquot of the digested product was subjected to electrophoresis in a 4% metaphor agarose (F.M.C.
Products, Rockland, ME) gel for I hr. At 100VV in IxTBE. The geI is stained in ethidium bromide. Three sizes of the fragments are expected. When the polymorphic site is GIG, the DNA is completely digested giving 673 by and 359 by fragments. When the polymorphic site is AIA the I032 by fragment is undigested. G/A heterozygotes had three fragments, 1032 bp, 673bp, and 359bp.
The sequence immediately 3' to the G->T mutation is GATA. GATC is the recognition site for the DpnII restriction. endonuclease. The 3' 23 by oligmer is designed to match the ATC sequence immediately 3' to the G->T mutation is as follows (oligomers underlined and the two g sites for the mutations double underlined);
5' TCATTAATCCTCTGGGTATTGTAAATGTGGATTTAGGTTAATATATTAT
ATATAATGCCAAATAATGGCATAGATAAGGAATAGGGAGAAAA.AGGGAATTA-3' (SEQ ID N0:27) TAGTCTTATATCCCTCTTTTTCTTA (SEQ ID N0:28) This mismatch in the third position rarely compromises its effectiveness as a PCRTM primer.
The 5' primer was chosen to provide a product of 29 base pairs. When the G->T mutation is G, the GATC site is cut to produce 22 and a 70bp fragments.
When the G->T mutation is A, only a 92 by fragment is present. The conditions for PCRTM
reaction is as follows: l uM each primer, 0.2 mM each dNTP, SOmM KCL, 1 OmM
Tris HCL, 1.5 mMMgCl2, O.OOi%(w/v) gelatin, 2.5 U/100pL. AmpliTAq(R) DNA
polymerase, 80 ng genomic DNA. The PCRTM cycles are 94 C 4 min: 30 cycles of C 30 sec., 52 C 90 sec., 72 C 120 sec.; followed by 72 C for 5 min. The conditions for the DpnII digestion is I Op.L of PCRTM product, 0.05 p.L of 10 U/p,L of DpnII;
1.5 p.L
of: 1M NACL, 0.5 M Bis HCL, 0.1 M MgCl2, IOmM dithiothreitol, pH 7.9: 3.Sp.L
H20, at 37 C overnight. The products are electrophoresed in 4% Metaphor agarose.

..._ ..._._. . _ ...._~ _w._._ ~ r . _ i HTR1A repeat Polymorphism. The method used to determine this complex polymorphism has been described by Bolos et al., 1993. To label the PCRT"' products 0.1 pM each of fluorescence labeled primers are used in the reactions. The fluorescent dye is FAM amidite (Applied Biosystems, Foster City, CA). Two pl of the 10 fold diluted PCRTM product is added to 2.5p1 deionized formamide and 0.5 pl of ROX 500 labeled standards, denatured for 2 min. At 92°C, and loaded on 6%
polyacrylamide gel in an Applied Biasystems 373 DNA sequences. The gel is electrophoresed for 5 h at 1100 volts and constant 30 W, laser scanned and analyzed using the internal ROX 500 labeled standards. The presence of internal standards in each lane allows very accurate length determinations. The peaks are analyzed by Genotypes (version 1.1, Applied Biosystems) and sized by base pair length. if the computer detected two peaks of similar height, the shorter peak is always placed in the column of a alleles, and the longer peak in the column of h alleles. If the computer concluded there is a single peak, the subject is assumed to be homozygous for the a allele.
HTR2A Gene. The HTR2A is assessed using the single base pair polymorphism described by Williams et al., 1996. The procedures using the Applied Biosystems DNA sequences are as described above.
OB gene. The dinucleotide repeats present on the YAC contig containing the human Ob gene has been described by Green et al., 1995: D7S1873, D7S1875, D7S514, and D7S780. Of these, D7S1875 is closest to the OB gene. Comings refers to this as OB1875.
Cannabinoid Receptor Gene. DNA samples are amplified using the following primers as described by Dawson 1995, 5'-GCTGCTTCTGTTAACCCTGC-3' (SEQ ID N0:29) and 5'-TACATCTCCGTGTGATGTTCC-3' (SEQ ID N0:30). This identified alleles of a (AAT) n triplet repeat. Standard methods are employed to label the PCRTM
products and to determine resultant polymorphisms, (see above) GABRB3 Gene. DNA samples are amplified using the following primers described by Mutirangura et al., 1992. CA

strand:5'-CTCTTGTTCCTGTTGCTTTCAATACAC-3' (SEQ ID N0:31 ) and GT
strand: 5'-CACTGTGCTAGTTTAGATTCAGCTC-3' (SEO ID NW~~I Th;~
identified 11 alleles of a (CA)n repeat varying in length from 181-201bp.
Standard methods are employed to label the PCRT"' products and to determine resultant polymorphisms. (see above).
Neuronal Nitric Oxide Synthase Gene. The nitric oxide synthase gene has recently been implicated in aggressive behavior in mice (Nelson et al.. 1995).
Utilizing the methods of Hall et al. (1994). The inventors examined the relevance of a dinucleotide repeat polymorphism of the neuronal nitric oxide synthase gene (nNosl a). The procedures using the Applied Biosystems DNA sequencer are as described above.
COMT Gene. For analysis of the COMT gene the single base pair polymorphism described by Lachman et al., 1996. This polymorphism has been shown to be associated with different levels of activity of COMT (see Daniels et al., 1995).
Apolipoprotein-D Gene. DNA is extracted and digested with the restriction enzyme TaqI. After agarose gel electrophoresed it is transferred on to a Nylon membrane(Hybrid N. Amersham, UK) and hybridized with 32P-labeled APO-D probe using standard methods. Two alleles, 2.2 and 2.7kb has been identified.
Human Chromosome-2. A microsatellite polymorphism, D2S 1788 which maps to 2p21 (approximately 74cM from the tip of the short arm) has been identified (Comuzzie et al., 1997). DNA is prepared from lymphocytes and used for PCRTM
with fluorescently labeled primers from the MapParis 6a Linkage Screening Set (Research Genetics) containing 169 highly polymorphic microsatelIite markers spaced at approximately 20cM.
UCP-2 Gene. This gene maps to regions of human chromosome I l and mouse chromosome 7 that have been linked to obesity and hyperinsulinaemia. For this gene the forward primer sequence is 5'-CATCTCCTGGGACGTAGC-3' (SEQ ID
N0:33) and the reverse primer sequence is 5'-AGAGAAGGGAAGGAGGGAAG-3' (SEQ ID N0:34). GenBank accession for the human UCP2 coding sequence is U76367.

EXAMPLES THE MAA TECHNIQUE FOR GENETIC POLYMORPHISMS
This example presents number of polygenic traits that are independent of psychiatric disorders and illustrate that the MAA technique can be generalized to all polygenic disorders and all polygenic traits. The following teaches how a variety of examples of different polygenic disorders could be studied using the MAA
technique.
Osteoarthritis Step 1. Identify the polygenic disorder or trait to be studied. Generalized osteoarthritis (GOA) is the most common from of joint disease. It is a major cause of disability and ranks as one of the top three health care problems in the developed world. GOA results in pain and loss of function in 10 to 15% of men and women over age 4~ and up to 70% of those over age 60. The etiology of GOA is complex, I S involving environmental and genetic factors. A twin study in women estimated that additive genes accounted for 54% of GOA (Kaprio et al., 1996). Sporadic GOA is inherited as a polygenic disorder.
Step 2. Set up a scale that measures the severity of the polygenic disorder.
The severity of OA is determined on the basis of x-rays using the Kellgren score (Kellgren and Lawrence, 1957).
Step 3. Identify the candidate genes to be tested. OA is very likely to be due to presence of a threshold number of variant genes that play a role in the synthesis or degradation of cartilage. This concept and some of the potential candidate genes are Table 70. Candidate genes in OA based on their role in regulating the synthesis or degradation of cartilage.
Step 4. Identify one or more polymorphisms associated with each gene. The following is a list of some of the polymorphisms that can be used in the MAA
technique for OA.

I I I

TABLE ~a Gene Chrom. Type poly. Reference Collagen genes COL2A 1 12q 12 VNTR-1 Wu et al. , 1990 COL2A1 12q12 VNTR-2 Priestley et al., 1990 COL2A1 12q12 Mae II RFLP Loughlin et al., 1995 COL9A1 6q12 DN-1 Warmen et ul., 1993 COL9A 1 6q 12 DN-2 - Warmen et al. , 1993 Aggrecan te proteoglycan (chondroitin I ) sulfa AGCI 15q26 RFLP Finkelstein et al.,1989 Insulin-like growth factors and receptors IGF1 12q22 DN-1 Weber et crl. (Weber and May, 1989) IGFI I2q22 DN-2 Polymeropoulos et ul., 1991 IGF1R 15q25 TN Meloni et al., 1992 IGF1R 15q25 insertion Poduslo et al., 1991 IGF2 11p15 DN Rainer et crl., 1993 IGF2R 6q25 TN Ogawa et al., 1993 Transformingrowth r Beta G Facto TGFBI 19q13.2 Leu->Pro Cambien et al., 1996 RFLP

TGFB2 1q41 DN Westson et al., 1991 Interleukin IL 1 A 2q 13 TN Zulini and Hobbs, 1990 IL1B 2q13 C->T RFLP diGiovine et ul., 1992 IL1R1 2q12 DN GDB

IL1RN 2q14 VNTR GDB

Metalloproteinases MMP9 20p11.2 DN St Jean et al., 1995 MMP tissue MP l inhibitors of M

TIMP1 Xp11.3 RFLP-1 Allred and Wright, 1991 _._._..w~. _ ..ww.... . ~

Gene Chrom. Type poly. Reference TIMPI Xp11.3 RFLP-2 Allred and Wright, 1991 Vitamin D3 12q Uitterman RFLP et al., 1997 VNTR = variable tandem repeat.
DN = dinucleotide repeat.
TN = trinucleotide repeat.
RFLP = restriction fragment length polymorphism.
Step 6. Set up a dummy polygenic or PG variable. The scores for the different candidate genes are added to produce the PG scores.
Step 7. Perform univariate regression analysis of PG versus QT or DV.
Univariate regression analysis using any statistical program is performed against PG
with the scores for the first OA (oa) candidate gene (cg) (PG + PG oacg 1 ), then with the addition of the second candidate gene (PG + oacg2). This is continued until "n"
OA candidate genes are added (PG + oaegn), where n is the number of genes added.
Step 8. PIot the results. The results are then graphed as shown for additive +
subtractive genes for the ADHD score in FIG. 4.
I 5 Step 9. Repeat the procedure using only the additive genes. Only the additive genes are plotted as shown for the additive genes for the ADHD score in FIG.
5.
Cholesterol Levels Step 1. Identify the polygenic disorder or trait to be studied. Years of research have shown a correlation between the elevated levels of blood cholesterol and coronary artery disease and stroke. Since genetic factors as well as diet play a role in cholesterol levels, the identification of the genes involved can be important in . identifying individuals at risk, and in the development of new drugs for lowering cholesterol levels.
Step 2. Set up a scale that measures the severity of the polygenic disorder.
The blood cholesterol level based on a fasting sample would provide the best severity scale.

Step 3. Identify the candidate genes to be tested. A number of genes directly involved in cholesterol synthesis pathways have been proposed as playing a role in cholesterol metabolism. To illustrate the power of the MAA technique the inventors used it to assess the role of several genes outside the cholesterol pathway per se, to identify their possible role in the regulation of cholesterol levels. The inventors identified four genes that were associated with cholesterol and lipoprotein metabolism. These were the serotonin transporter (HTT), the oxytocin receptor (OXYR), the dopamine DRD2 receptor (DRD2) and the presenilin gene (PS 1 ) genes.
Step 4 Identify one or more polymorphisms associated with each gene. The respective polymorphisms were the promoter insertion deletion at the HTT gene (Collier et ul., 1996), a dinucleotide polymorphisms of the OXYR
gene(Mechelini et al., 1995}, a promoter insertion/deletion polymorphism of the DRD2 gene (Arinami et al., 1997}, and RFLP at the PS 1 gene (Higuchi et crl., 1996).
Step 5. Assign a score to the genotypes. Based on studies of a separate group of subjects the scoring was as follows: HTT gene - 0 = SS, LL, 2 = SL; OXYR
gene 278/278 = 0, 278/267 = 1, 2761276 = 2; DRD2 11 = 0, 12 = 1, 22 = 2; and the PS
I
gene 22 = 0, 12 = 1, 11 = 2.
Step 6. Set up a dummy polygenic or PG variable. The scores for the different candidate genes are added to produce the PG scores.
Step 7. Perform univariate regression analysis of PG versus QT or DV.
Univariate regression analysis using any statistical program is performed against PG
with the scores for the first cholesterol (c) candidate gene (PG + ccgl), then with the addition of the second candidate gene (PG + ccg2). This is continued until all the cholesterol candidate genes are added (PG + ccgn).
Step 8. Plot the results. The results are then graphed as shown for the ADHD
score in FIG. 5. FIG. 8 shows the use of the MAA technique in assessing the role of these four genes in cholesterol and LDL metabolism.

Step 9. Repeat the procedure using only the additive genes. Only the additive genes are plotted as shown for the additive genes for the ADHD score in FIG.
5. In this case all four genes were additive. .
Longevity -- 5 Step 1. Identify the polygenic disorder or trait to be studied. Aging is a subject of increased interest as the average age of survival increases and as the number of individuals over the age of 65 in the population increases. The identification of a number of genes that can predict low long a person will live has value in identifying individuals at risk to die early. Interventions directed toward the effects of certain critical genes, could be of considerable benefit in prolonging quality of life as well as life itself.
Step 2. Set up a scale that measures the severity of the polygenic disorder. A
natural severity scale for aging is age in years. However, in this case, the higher the score (older the age) the better the phenotype.
i5 Step 3. Identify the candidate genes to be tested. While certain genes, such as superoxide dysmutase, a free radical scavenger, have tong been known to be involved in animal models of aging, as with cholesterol metabolism, the MAA technique has the potential of identifying a number of new genes previously unsuspected genes, that play role in longevity. The inventors chose to examine two of the genes that played a major role in cholesterol levels, since cholesterol levels correlate with death from cardiovascular disease. The inventors also included the APOE gene.
Step 4. Identify one or more polymorphisms associated with each gene.
These genes were scored in the following fashion. The frequency of the different genotypes of the candidate genes was compared by chi square analysis across groups of different aged subjects. If certain genotypes progressively increased in frequency across these age groups, this genotype was scored as = 2. The remaining genotypes were scored as = 0 or = I depending upon their relative frequency in these different aged subjects.

Step ~. Assign a score to the genotypes. The scores for the different candidate genes were added to produce the PG scores. Each gene is progressively added to the polygenic score.
Step 6. Sct up a dummy polygenic or PG variable. The scores for the different candidate genes are added to produce the PG scores.
Step 7 Perform univariate regression analysis of PG versus QT or DV.
Univariate regression analysis using any statistical program is performed against PG
with the scores for the first longevity (1) candidate gene (PG + lcgl), then with the addition of the second candidate gene (PG + lcg2). This is continued until all I 0 longevity candidate genes are added (PG + lcgn).
Step 8. Plot the results. The results are then graphed as shown for the ADHD
score in FIG. 5. When plotting the data for genes vs. longevity, the MAA
technique identified three genes which when combined in the study of 208 subjects, gave highly significant results with p = 1.5 x 10-7.
I S Step 9. Repeat the procedure using only the additive genes. Only the additive genes are plotted as shown for the additive genes for the ADHD score in FIG.
5.
These are just three examples of how the MAA technique can be generalized to any polygenic disorder or trait, using any human gene that is relevant to the disorder or trait to be studied. As the genome project nears its conclusion in the next 20 decade, it is expected polymorphism will have been identified for every human gene, allowing a through testing for every polygenic disorder or trait. The inventors contemplate that the MAA technique is also applicable to polygenic traits in other animals, and plants. and the use of this technique in other species is encompassed by the invention.

_... ~

EXAMPLE OF ANTI-CRAVING COMPOSITION AND ENHANCEMENT OF
COMPLIANCE TO TREXAN° FOLLOWING RAPID DETOXIFICATION
FROM OPIATES IN HARDCORE ADDICTS
Introduction. Over the last decade, a new rapid method to detoxify either methadone or heroin addicts utilizing the narcotic naltrexone (Trexan°, Dupont, Delaware) sparked interest in many treatment centers throughout the United States, Canada, as well as many other countries on a worldwide basis. The dropout rate among hardcore opiate addicts even today approaches 90 percent. The basic concept in this rapid method is to provide the patient with a pure narcotic antagonist to block the opiate-induced euphoriant effects. Utilizing this approach most patients do not comply and the recidivism rate is over 99% (S. Hall, San Antonio Methadone Clinic).
It is the inventors contention that the major reason for non-compliance is due to the fact that while the narcotic antagonist (Trexan'~'} blocks the opiate or alcohol-induced euphoria (O'Malley et al., 1992; Volpicelli et ul., 1992), the drug has little effect on craving behavior. Since the inventors found that amino-acid therapy reduces craving behavior for a number of euphoriants, they decided to test whether the combination of both Trexari and amino-acid therapy prolongs compliance to Trexan° in hardcore addicts, who have used euphoriants up to 30 years.
Method. This study was accomplished at the San Antonio Methadone Clinic.
The criteria for entry into the study included both male and female patients who were considered hardcore addicts as diagnosed utilizing DSM III for heroin/opiate dependence. Each patient was pre-evaluated by first receiving an injection of 0.4-0.8 mg. Narcan and their withdrawal was assessed (if it was too severe, they were not allowed entry into the study). If they passed this first test, they were then administered an oral dose of 12.5 mg. of Dupont's Trexan'~' and then evaluated for withdrawal symptoms over one-and one-half h. If the patient passed this test, they were given 50 mg, of Trexan~ per day. In this study, a total of 12 patients were evaluated. For this study, each patient besides receiving the narcotic antagonist according to the above regiment, also received the following amino-acids daily:
DL-phenylalanine (2,700 mg.), L-tryptophan (450 mg.), L-tyrosine (300 mg.), L-glutamine (150 mg.), Chromium (as picolinate-200 meg.), and pyridoxal-5-phosphate (30 mg.). The number of days without a relapse or self report of refusal to take either the Trexan~ alone or in combination with the amino-acid formula was counted. Each patient was evaluated (with some degree of failure to make contact) on a daily basis via phone or personal contact.
Results. The results were dramatic in terms of significantly enhancing compliance to continue utilizing Trexan'~'. The average number of days of compliance that the San Antonio Methadone Clinic calculated on over hundreds of their patients, without amino-acid therapy, confronted with this rapid detoxification approach is 37 days. In striking contrast, the 12 subjects in this study receiving both the 'rrexan'~' and amino-acid therapy was relapse-free or reported taking the combination for an average of 262 days (p at least p < 0.05).
Conclusion It is suggested that the addition of the anti-craving formula significantly reduced the craving for opiates and, therefore, seems to be important in assisting those hardcore opiate addicts in preventing relapse -- especially in conjunction with the narcotic antagonist Trexan«.

OF REWARD DEFICIENCY SYNDROME
Summary. The dopaminergic system, and in particular the dopamine DZ
receptor, has been profoundly implicated in reward mechanisms in the brain.
Dysfunction of the D2 dopamine receptors leads to aberrant substance seeking behavior (alcohol, drug, tobacco, and food) and other related behaviors (pathological gambling, Tourette's syndrome, and attention deficit hyperactivity disorder).
The W ventors propose that variants of the Dz dopamine receptor gene are important common genetic determinants of the 'reward deficiency syndrome'.
The inventors have used Bayes (Rosner, 1986) theorem as a mathematical method to evaluate the predictive value of the A~ allele of the DRD, gene in impulsive-addictive-compulsive disorders.

_ _.. ....._...~ _~_ _.. ~ . ~ , y.

WO 98/48785 PCTlUS98108684 Bayes' theorem is widely used in medicine to predict the likelihood that a particular event (defect) will result in an another event (disease) here, for example, that possession of the Ai allele of DRDZ will cause abnormal drug and alcohol seeking behavior (Table 72).
_. 5 When a screening test is evaluated, sensitivity is the probability that the test will be positive in a person with the disease in question; and specificity is the probability that the test will be negative in a person who does not have the disease.
For Bayes' theorem The inventors used the following formula:
Predictive value = (Prevalence) (sensitivity) (Prevalence)(sensitivity) + ( 1 - prevalence)( 1- specificity) SUBSTANCE ABUSE/DEPENDENCE
Prevalence Substance Abuse Allele Abusers Controls P value < References Alcoholism DRD~ A 69 20 0.001 Blum et 1 al., Alcoholism DRD~ A 30 19 NS Nakajima, Alcoholism (less severe) DRD2 B~ 17 13 NS Blum et al., 1993 i Alcoholism (less severe) DRD2 C~ 57 33 0.002 Suarez et al. , 19941 Severe alcoholism DRDZ A, 47 17 0.001 Blum et al., 1991 i Prevalence Substance Abuse Allele Abusers Controls P value References <

Severe alcoholism DRDZ
B!

Severe alcoholism DRD,~nb-~"~39 16 0.02 Zhang et haplotype al., Cocaine dependence DRDZ 51 18 0.0001 Noble A, et al., Cocaine dependence DRDz 39 13 0.01 Noble B~ et crl., Polysubstance abuse DRDZ 44 28 0.25 Comings A i et al., Polysubstance abuse DRD~ 33 20 0.001 Smith B, et al., I992i *C, allele denoted only regard to homozygote with genotype. Alcoholics (47182); controls (29/87):= 9.8, df = 1, (x'' P = 0.002) THE DOPAMINE D, RECEPTOR
GENE AS A PREDICTOR

OF COMPULSIVE
DISEASE

Risk Behavior Predictive Vaiue (%) Alcoholism (severe) I4.3 Cocaine dependence (severe) I2_3 Polysubstance abuse I2.8 Chemical dependency 28.3 Risk Behavior Predictive Vaiue (%) Overeating (severe) I8.6 Ingestive behavior 35.0 ADHD 16.0 Smoking 41.5 Pathological gambling 4.G
Tourette's syndrome 5.5 Total impulsive-addictive-compulsive behavior 74.4 The assumptions supporting the data are explained in Blum et al., 1995 To calculate the specificity, the inventors used well-characterized controls, screened for alcohol, drug, and tobacco use in some samples (Table 71 ). No previous study has used rigid exclusion criteria for controls (Blum et al., 1995a), and such efforts are essential because alcoholism per se is not the true phenotype associated with DRD2 gene polymorphisms (Blum et al., 1995b; Neiswagner et al., 1995;
Comings et al., 1991 ). More-over, to calculate the sensitivity of genotyping the inventors took data from studies where the probands were characterized for chronicity or severity of disease (Table 72).
The positive predictive value (PV+) of a test is the percentage of positive results that are true positives when the test is applied to a population containing both healthy and diseased individuals (Galen et al., 1975). With the Taql A, genotype, PV+ was 0.744 or 74%; in other words, positive predictive value was high; but PV-was only 0.548 or 54.8%. The inventors would expect better negative predictive value in studies where individuals with related impulsive-addictive--compulsive behaviors are excluded from the control groups. Pooled data on patients with these disorders point to a strong positive correlation with the DRD2 gene variant (Yates x2=b8.38, df=1, P < 10-').

ATTENTION DEFICIT - HYPERACTIVITY DISORDER
SYMPTOMS ASSESSMENT SCALE {ADHD SAS) Background. Recent literature (over the past fifteen years) indicates the diagnostic criteria for Attention Deficit Disorder and Hyperactivity Disorder are quite variable. The nature of Attention Deficit Disorder (ADD) remains a controversial issue in research on childhood psychopathology (McGee, et. al. 1989).
While parents, teachers, youngsters, pediatricians, family medicine practitioners, psychologists, school counselors, etc., know and have observed Attention Deficit Disorder symptoms and Hyperactivity Disorder symptoms in youngsters, we, in North America, have experienced difficulty in describing the phenomenon. Many studies in the literature indicate clinicians continue to have difficulty deciding if hyperactivity and attention deficit disorders actually exist as diagnostic conditions, if they are coexisting conditions, or if they are separate 1 S diagnostic entities.
Attention deficit disorder with hyperactivity (ADHD) and without hyperactivity (ADD) is a widespread. It afflicts between three million and four million children in the United States, as well as a larger number of adults.
People with ADHD suffer from overload; that is, they have heightened awareness of incoming stimuli, particularly sight, sound and touch. They are so bombarded by the normal stimuli in their environment that they cannot filter out the background "noise" and concentrate on the task before them. They have trouble focusing on a problem or a task. With a short attention span, they forget appointments, forget to pay bills, miss deadlines, and have frequent legal difficulties because they do not take care of problems as they arise. Always in a hurry, they have trouble settling on a goal or objective.
People with ADHD tend to be disorganized. Children have messy rooms;
adults have cluttered desks; daily activities tend to be chaotic. At all ages they have trouble making plans and even more trouble in carrying out plans in an orderly fashion. Because of their inability to focus, those with ADHD have trouble .... . ... ... .. . r. . i i.

WO 98148785 PCT/US98108b84 completing what they start. They leave tasks unfinished, plans unrealized.
Attics and basements are likely to be filled with partly completed sewing projects, woodworking projects, repairs, notebooks; desk drawers are likely to be cluttered with unfinished letters, outlines and project plans.
ADHD has nothing to do with intelligence. Many people with the disorder are highly intelligent, but they tend to be underachievers because they can not concentrate - or sustain interest. As a result, family, friends, teachers and coworkers become impatient and expect them to fail.
People with ADHD have trouble adapting to change. Their life is so full of tumult that even a minor additional change in their routine can be upsetting.
A parent goes away on a trip, a new teacher takes aver a class, the family moves to a new city, a pet dies - any such change can create a crisis for a person with ADHD.
ADHD afflicted people live under stress so severe they can not tolerate frustration; and when they are frustrated, they are likely to become angry.
The anger tends to come suddenly and explosively slamming doors, harsh words, tantrums.
Children get into fights, adults blow up and lose jobs and alienate friends.
Afterwards they are sorry, but the damage is done.
With their high level of frustration, people with ADHD are impatient. They hate to wait in line, and delays of any kind make them frantic. Whatever is going on -a trip, a movie, a class, a discussion - they want it to go quickly and be finished.
Their impatience makes people with ADHD impulsive. As children, they leap into action without thinking of consequences. As adults, they drive too fast, use power tools carelessly, and plunge into activities without thinking of the danger. The result is they often hurt themselves or others.
People with ADHD have trouble with their orientation to time and space.
They may have to stop and think which is their right hand and which is their left; they - may have difficulty following a set of instructions, reading a map or telling time.

Many ADHD afflicted individuals are hyperactive. As babies or children they constantly are on the move, squirming, twisting, and getting into everything.
As adults, they are restless, easily bored, rebellious when asked to follow a routine and always on the move.
Another difference in those with ADHD is that they have abnormal brain wave patterns. Their Beta waves - brain waves associated with concentration - are low, and their Theta waves - associated with relaxation - are high. which has been associated with daydreaming and drowsiness. It is not surprising, therefore, that activities associated with Beta waves, watchful anticipation and problem solving, are difficult for individuals with ADHD to sustain. They like activities that permit them to stay in a Theta state with a minimum of outside stimulation (Lubar et al., 1991 ).
If you look at these symptoms together, a picture emerges: an individual suffering from overload, trying to adjust to a world that is too bright, too loud, too abrasive and too rapidly changing for comfort.
Early speculation about the causes of ADHD focused on such factors as marital disorder, poor parenting, brain damage, psychiatric illness, or alcoholism or drug abuse in the family. Associated behaviors included conduct disorder and anti-social personality. Later these behaviors were shown to be linked hereditarily to Substance Use Disorders (SUDs). Most recently, research has begun to show a significant association between these behavioral disorders, ADHD. and specific genetic anomalies.
What is the cause or basis of ADHD? It is a compulsive disorder, genetic in origin, that results from imbalances of neurotransmitters. It strikes in childhood and continues into adulthood. Its effects can be eased by treatment and counseling. The biological basis for this disorder has been established by a number of investigators (Biederman et al., 1992).
In very simplistic terms, the immediate cause is that those with ADHD are afflicted with a defective filtering system. In other words, their brain stem reticular formation does not block out irrelevant stimuli. These people are aware of every .Y . , , , sound, every object, every touch, and they all mer;=a in a disorganized, unbearable bedlam. Non-essential stimuli get the same attention as these essential to work or relating to other people.
At a deeper level, ADHD is a problem of communication among brain cells, or S neurons, possibly involving the neurotransmitters that carry interneural messages in ADHD people. These brain messengers are in short supply. If the messengers that _ inhibit incoming stimuli are deficient, too many signals get through and create confusion.
At a still deeper level, the problem lies in the genes that lay down the blueprint for manufacturing neurotransmitters. People with ADHD have at least ane defective gene; the DRDZ gene that makes it difficult for neurons to respond to dopamine, the neurotransmitter that is involved in feelings of pleasure and the regulation of attention. Other studies on genetic anomalies have implicated other dopaminergic genes such as the DRD4 receptor gene, the dopamine beta hydroxylase (D(3H) gene, 1 S and the dopamine transporter genes as causative factors in ADHD {Cook et al.. 1995;
Waldman, et al.. 1996).
Genetic Testing. Due to the genetic complexity of the etiology of ADD and ADHD, the inventors contemplate to assess the presence of ADD and/or ADHD
through the use of the Multi-Plex DNA Based Reward Deficiency Gene Test. This genetic test measures for the presence of the polymorphisms present in the genetic structure of the person being tested. The Multi-Plex DNA Based Reward Deficiency Gene Test will indicate if the polymorphisms are present and if so which ones and whether they are homozygotic or heterozygotic. Since there is a relationship between the severity of the ADfID impairment and symptoms and the particular alleles of the 2S genes mentioned above. The Muiti-Plex DNA Based Reward Deficiency Test will be able to predict the severity of the ADHD symptoms and behavior in a person.
The Multi-Plex DNA Based Reward Deficiency Test is a very valuable tool - for the treatment professional. Although the defect cannot be corrected at this time, the compounds disclosed herein as = Anti-Craving Agents have proven clinically effective in curbing craving by stimulating: the brain's production of Dopamine, and the activity level of the Dopamine receptor sites; and. they have been shown to be effective in increasing a person's ability to focus attention more sharply, to facilitate focus shifting, to increase "on task" behaviors, and to increase attention span.
While the measurement of the Reward Deficiency Syndrome behaviors (ADHD is one genetically based disorder which falls with this syndrome) are quite highly reliable and valid for RDS, differential diagnoses are more challenging. The Mufti-Plex DNA Based Reward Deficiency Test will give outstanding confirmatory data and generalized differential diagnoses; however, the inventors currently are outlining a comprehensive (and rather rapidly completed) research study with the University of North Texas Health Sciences Center DNA Identity Laboratory and the University of Tennessee. This study will be a linkage study rather than an association study. The inventors believe that the results of this current study will allow the inventors to: make a confirmed differential diagnosis, reduce false positives in the diagnosis of ADHD {currently, many diagnoses with the current state of the art of 1 S psychometric testing and interview techniques are false positives), enhance true positives, reduce denial in the patient and in his/her families, reduce erroneous diagnoses (mistaking anxiety for ADHD, etc. ) and thereby change faulty prescriptions for Ritalin, enhance treatment plan strategies, and make true differential diagnoses between ADD and AHIHD.
The Multi-Plex DNA Based Reward Deficiency Test Kit has been engineered to detect the dopaminergic genetic defect. The Test is 99.9% accurate and is non-invasive. It requires the use of a buccal swab; therefore, no special training is required to administer the test and the test does not require blood to be drawn (as other DNA
tests require). The swab is then forwarded by mail or courier to a certified DNA
laboratory for processing. The inventors have an exclusive contract for this particular DNA testing with the University of North Texas Health Sciences Center DNA
Identity Laboratory. Results are processed within 72 to 96 hr (if a "scat"
assessment is requested, the time will be on the order of 24 to 48 hr).
In the second edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM II) published by the American Psychiatric Association (1968) the . , diagnostic category was "Hyperkinetic Reaction of Childhood." In the third edition of the Diagnostic and Statistical Manual of Mental Disorders, (DSM lll), published in 1980, this category was broken into two separate diagnoses, "Attention Deficit Disorder with Hyperactivity" (ADD-H) and "Attention Deficit Disorder without S Hyperactivity" (ADD).
The revised, third edition of the Diagnostic and Statistical Manual of Mental _ Disorders (DSM lll-R) was published in 1987. In this edition the American Psychiatric Association chose again to merge these two diagnostic categories.
The diagnostic category at that time was "Attention Deficit - Hyperactivity Disorder"
(ADHD).
The current edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM IV), published in 1994, lists three diagnostic formulations:
"Attention - Deficit I Hyperactivity Disorder, Combined Type;" "Attention -Deficit I
Hyperactivity Disorder, Predominately Inattentive Type;" "Attention - Deficit /
Hyperactivity Disorder; Predominately Hyperactive - Impulsive Type."
There was a significant difference in the "goodness of the fit" between the diagnostic criteria for DSM Ill's "Attention Deficit Disorder with Hyperactivity" and DSM lll-R's Attention-Deficit Hyperactivity Disorder." (Newcorn, et. al., 1989). It would seem that, with the continuing changes in the diagnostic criteria of this condition and with the lack of significant agreement between groups of diagnostic criteria, assessment scales constructed to meet the diagnostic criteria of any of the Diagnostic and Statistical Manual of Mental Disorders editions or revisions would be of limited applicability. This point was made by others "structured diagnostic instruments, such as the "Diagnostic Interview Schedule for Children (DISC)"
(Costello, 1983) and the "Diagnostic Interview for Children and Adolescents (DICA)"
(Herjanic and Campbell, 1977), were based on DSM III criteria and cannot be considered adequate for subject selection in the new diagnostic system."
(Newcorn, et al., 1989).
Attention deficit disorder is the most widely diagnosed childhood disorder.
On the surface, it is rather curious that the diagnostic descriptions of this condition are undergoing so many changes; however, the symptoms of this condition are not manifested constantly and in all surroundings (Brown, 1986}. Only 20 percent of Attention Deficit - Hyperactivity Disorder youngsters demonstrated ADHD
symptoms during a pediatric examination (Sleator and Ullman, 1981). The frequently many youngsters referred for a psychological evaluation due to hyperactivity were reported by the consulting psychologists to be quiet and cooperative in the actual testing environment (Tobiessen and Karowe, 1969). The status of ADD as a disorder would be more assured if there were a unique pattern of attentional or cognitive correlates which discriminated ADD from other disorders (McGee, et. al., 1989).
Due to this variability in the display of symptoms, the ADHD SAS is designed for responses primarily from parents, teachers, or others who are familiar with the behaviors of the youngster. Therefore, those individuals who interact with the youngster most intensely and most frequently are in the best position to evaluate and report the behavior of the youngster. Reports in the literature (Schachar et al., 1986;
Atkins et al., I985} indicate teacher ratings of childhood and adolescent behavior is highly correlated with clinician ratings, neuropsychological assessments, and direct classroom observation data.
The ADHD SAS was developed in order to provide a quick assessment of the Ievel of Attention Deficit - Hyperactivity Disorder symptoms being demonstrated by a youngster over a period of time. This assessment scale is not meant to be the definitive diagnostic tool for use in diagnosing the hyperactive or hyperkinetic youngster. Making an accurate, definitive diagnosis of the extent, expression, and limitations of Attention Deficit - Hyperactivity Disorder requires many in-depth testing sessions with practitioners from several professional areas including developmental pediatrics, pediatric neurology, clinical psychology, neuropsychology, speech and language, special education, and occupational therapy. Also this scale was not developed to replace an in-depth diagnostic work-up. This type of work-up requires the collaborative efforts of the wide variety of professional practitioners who represent the fields listed above. So this scale is not a psychological assessment, neurological assessment, psychiatric =assessment, neuropsychological assessment, pediatric assessment, etc.

r . ~ , t Many professionals who frequently work with children and adolescents require a scale which is rapidly administered, scored, and interpreted which indicates the presence and level of Attention Deficit - Hyperactivity symptoms. Part of this need is evident due to the incidence figures which have been reported. The incidence of this condition range from a low of less than one percent of all school aged children up to over 20 percent of all school aged children (August and Garfinkel, 1989).
According to the DSM IV, the prevalence of Attention Deficit/Hyperactive Disorder is estimated at 3% to 5% of the school-aged children. Data on prevalence in adolescence and adulthood are limited.
IO In many instances parents and teachers note the behaviors characteristic of Attention Deficit Hyperactivity Disorder which might be interpreted as laziness, stubbornness, lack of interest, anger, immaturity, etc. At times teachers, counselors, primary care physicians, and others in the health care and educational fields interpret the ADHD youngster's behaviors as a lack of discipline or the result of faulty 15 discipline practices on the part of the parents. And, since ADHD youngsters do not demonstrate their symptoms consistently, the descriptions of the youngster's behavior by a concerned parent may be interpreted as coming from a parent who is over-loaded with the responsibility of parenthood rather than dysfunction in the child (since the child is so quiet and well behaved in the doctor's office). When parents or teachers act 20 on these assumptions or interpretations, they exacerbate the youngster's reaction to his underlying problems. A significant value of the ADHD SAS is its ready and economical availability to counselors, teachers, family medicine practitioners, pediatricians, neurologists, and other professional practitioners to quickly and easily determine the level of attention deficit and/or hyperactivity symptoms present in a 25 youngster. Therefore, the professional practitioner is able to validate or refute the assumptions or interpretations the parents or others are making relative to a youngster's behaviors.
The ADHD SAS is a forty-three item scale which can be answered by either parents or teachers, scored, analyzed, and interpreted in approximately I S
min. The 30 scale is an objectively scored instrument which asks parents or others familiar with the youngster's behavior to respond regarding the presence and frequency of behavior, attitudes, or feelings. The scale requests parents to respond in an objective fashion on a four point Likert scale. The options on the scale range from "None or a Little of the Time" up through "Most or All of the Time."
The ADHD SAS is an efficient, cost-effective screening device. It is easy to administer and score, and can be used by trained technicians or paraprofessionals under the supervision of a qualified professional. However, the ADHD SAS does not obviate the need for a thorough, skilled clinical assessment of youngsters.
Since the format of this assessment device is a parental report form, it is particularly susceptible to conscious and unconscious distortions. For this reason and because of other specific limitations of the instrument detailed below, the ADHD SAS should be used only as an indication of the presence and level of attention deficit hyperactivity symptoms: it should not be the solely used instrument upon which one would plan a treatment intervention or a course of treatment. The screening device is designed to be a supplement to skilled clinical judgments, not a replacement for them.
1 S Individuals with Attention Deficit Disorder with and without hyperactivity may differ in their core attention deficits (Lahey, et. al., 1985). Both were rated by teachers as exhibiting similar attention deficits compared to controls on items referring globally to attention span, forgetfulness, difficulty following directions, and immaturity. However, compared to both the Attention Deficit Disorder without hyperactivity and the control groups in their study, the Attention Deficit Disorder -Hyperactivity group was described as irresponsible, distractable, impulsive, answering without thinking, and sloppy. When considering their findings with the (DeLamater and Lahey, 1983; Cahey 1994), there emerges a picture of two rather different groups of children with problems of attention.
The ADHD SAS is constructed in such a manner as to assess both of the prevalent types of Attention Deficit - Hyperactivity Disorders (August and Garfinkel, 1989), as well as the overall level of ADHD disorder symptoms. The results of the ADHD SAS reflect the presence and quantified level of: Overall Attention Deficit-Hyperactivity symptoms (or ADHD, Combined Type), Attention Deficit symptoms, and Hyperactivity symptoms.

_.... . .. . ... r WO 98/48785 PCTlUS98/08684 When the scale detects Attention Deficit - Hyperactivity Disorder symptoms, the results also indicate whether the levels of these symptoms are minimal, mild, moderate, severe, or extreme.
The ADHD SAS is intended for use by parents to describe behaviors of children and adolescents from age 4 yr up. The scale is designed to be used in a physician's office, or during a psychotherapist's clinical interview with parents, or during a parent-teacher-counselor conference, etc. There are numerous settings in which this screening instrument will be of value.
However, it can not be used when a parent is non-cooperative, hostile, uncommunicative, prone to distortions, or so disorganized in his or her thinking that responses do not accurately reflect parental perceptions. Additionally, persons with low verbal ability due to lack of a fourth grade reading skills (the reading difficulty of the scale is approximately at a fourth-grade level). a bilingual background with limited English as a second language, a neuropsychological impairment, or moderate to severe mental retardation will nave difficulty completing the scale.
The ADHD SAS can be administered and scored easily by a trained paraprofessional or technician. However, the ultimate responsibility for the use and interpretation of the ADHD SAS should be assumed by a professional with advanced clinical training and experience. Prior to administering the AD>'-iD SAS, potential users should become thoroughly familiar with the scale's theoretical rationale, method of construction, psychometric properties, and specific limitations, as detailed in this manual. In addition users should be prepared to make clinical judgments about the validity of the scale results in the setting in which it is used by supplementing test data with information concerning the youngster's medical status, behaviors at school and behaviors with peers.
To help ensure the appropriate use of the ADHD SAS, potential users also should become familiar with and conform to the standards for the use of tests prescribed by the American Psychological Association ( 1994) or review a basic text in the field of testing and assessment. Users who lack clinical training in the assessment and case management of attention deficit hyperactive disordered youth should review the relevant literature in this area before using the scale. Several excellent texts exist on issues relating to attention deficit hyperactive disordered youth (e.g., Rourke, 1985; Rourke, 1989; Rourke, et al., 1983; Rourke et al., 1986).
Use of the ADHD SAS in both clinical and research settings should conform to the professional and ethical guidelines presented by the American Psychological Association ( 1981 ). As with any assessment procedure focusing on children, the ADHD SAS should not be used without the informed consent of one of the youngster's parents. In addition, users should take the necessary precautions to safeguard the confidentiality of the results and restrict their use to those with a IO professional "need to know." Communication of the scale results to individual youngsters or the youngster's parents should focus on the qualitative aspects of the attention deficit hyperactivity disorder, rather than focusing or reporting on a specific analysis of item responses. Whenever possible the person interpreting the scale results should enlist the aid of the parent in understanding and amplifying the scale results. taking into account the individual's awareness of attention deficit -hyperactivity disorders and his or her current emotional state.
Identif cation of attention deficit - hyperactivity disorders is a complex task requiring clinical sensitivity and a thorough knowledge of the clinical and research literature on neuropsychology and learning disabilities. The ADHD SAS is intended solely as a screening instrument. It should not be used in isolation. Other diagnostic methods such as such as pediatric evaluations, pediatric neurological evaluations, electroencephalographic evaluations, computer assisted E.E.G. or brain electrical activity mapping, neuropsychological evaluations, speech and language evaluations, psychiatric evaluations should be used to supplement, corroborate. and investigate the test results.
The ADHD SAS also has a number of specific limitations which should be kept in mind when interpreting the test results. First, the intent of the scale is not particularly disguised. Thus, the scores are subject to conscious and unconscious distortions by individuals completing the scale. Second, the scale assesses a parent's reported assessment of his or her child's behaviors at one point in time.

. . . T. ... , , The symptoms of Attention Deficit - Hyperactivity Disorder are not manifested constantly and in all surroundings; therefore, the ADHD SAS may not accurately predict temporal changes in the level of ADHD symptoms {Brown, 1986).
Administration of the ADHD SAS. All that is required to administer the - ~ ADHD SAS is a pen or pencil and the rating form. The Rating Form is to be filled out by one or both of the youngster's parents. It asks for identifying information, - including selected sociodemographic factors. The 43 test items are contained on the front and back of this sheet along with instructions and spaces for responding to each Item.
After ensuring that the demographic information is completely entered at the top of the ADHD SAS Rating Form, the examiner should give the following directions: "Here are statements which will help me to better understand your youngster's behavior. I want you to read each statement and indicate, by marking an "X" in the appropriate column, the amount of time each statement is true. For I S example, consider the statement, "Is 'fidgety' with hands and feet." Does this statement apply to your youngster "None or a little of the time," "Some of the time,"
"A good pan of the time," or "Most or all of the time?"
While presenting these directions aloud, the examiner points to the item and each of the four answer columns. After the youngster's parent has responded, the examiner marks an "X" in the appropriate box and says: "Now finish the rest of the items. If you have questions, be sure to let me know."
Occasionally an individual taking the test will not understand a word or concept. If a parent has difficulty understanding a particular item, the examiner should explain the item as neutrally as possible. For example, if the parent does not understand the word "fidgety" in the item "Is 'fidgety' with hands and feet."
the . examiner might say, "This item is asking how often you think your youngster has difficulty holding his hands and feet still."
The development and maintenance of rapport with the individual completing the scale is very important. From a psychometric perspective, the establishment of WO 98/48785 PCT/US98/08b84 adequate rapport is essential to minimize the amount of intentional distortions of responses, especially denial of difficulties in a parent's child. Any comments the respondent makes may be helpful in evaluating the attitude with which the scale was filled out, and whether or not it is a valid representation of that person's interpretation of his or her child's actions.
Scoring the ADHD SAS. Only the Rating Form is required to score and interpret the ADHD SAS. To score the ADHD SAS, first notice that each item on the ADHD SAS Rating Form has an item number along with a code indicating whether the item measures Attention Deficit or Hyperactivity. For example HOl is the first item measuring Hyperactivity; D04 is the forth item measuring Attention Deficit;
Also note that on approximately 20% of the items, the coded indication is preceded by an "*." The scoring pattern of the items is counterbalanced in order to prevent the respondent from getting into a scoring pattern. Al! items, except those marked with the "*" are scored in the following manner, responses marked: "None or a Little of the 1 S Time" are scored " 1 ", "Some of the Time" are scored "2", "A Good Part of the Time"
are scored "3", "Most or All of the Time" are scored "4". Those items marked with the "*" are scored in the opposite direction; that is, responses marked: "None or a Little of the Time" are scored "4", "Some of the Time" are scored "3", "A Good Part of the Time" are scored "2", "Most or All of the Time" are scored "1 ". Next add all of the items coded "D" to determine the total score for the Attention Deficit Disorder SubScale. Then enter that raw score in the blank after the statement "TOTAL
ATTENTION DEFICIT DISORDER SCALE RAW SCORE."
Add all of the items coded "H" to determine the total score for the Hyperactivity Disorder SubScale. Then enter that raw score in the blank after the statement "TOTAL ATTENTION HYPERACTIVITY DISORDER SCALE RAW
SCORE." Under the profile: LEVEL OF ADHD SYMPTOMS, plot the raw scores which you recorded. By plotting the raw scores there is an automatic conversion of the raw scores to converted scores allowing you to read the level of Attention Deficit Symptoms, Hyperactivity Symptoms, and Attention Deficit - Hyperactivity Symptoms directly. In the COMMENTS AND RECOMMENDATIONS Section you r . i i might wish to record your impressions and directions for actions with this particular patient or family.
Rationale and Theoretical Background of the ADHD SAS. Some of the attention deficit assessment scales which are have been published and are available have merely incorporated the diagnostic criteria of the most recent edition of the Diagnostic and Statistical Manaral of Mental Disorders published by the American Psychiatric Association. However, this approach is of limited applicability in that it is so time bound. As will be noted not only does the interpretation of the primary symptoms change significantly over time, there actually is question if the condition exists, if it exists does it exist as attention deficit alone, or is attention deficit sometimes associated with hyperactivity. As will be noted from the literature review above, the current state of understanding is in somewhat of a flux. The inventors believe in the validity of the construct and believe it is a common, widely spread, somewhat prevalent condition of youngsters. Here is a preferred method of ADHD
diagnosis. , Over the past 30 years there has been considerable discussion relative to the various symptoms and the importance of various symptoms of Attention Deficit -Hyperactivity Disorder. The individual who ties his diagnosis and understanding of Attention Def cit - Hyperactivity Disorder to the Diagnostic and Statistical Manual of Mental Disorders published by the American Psychiatric Association is somewhat vulnerable due to the changeability of the concept. This is particularly true for the test constructor. According to (Newcorn et. al., 1989) assessment scales constructed to meet the diagnostic criteria of any of the Diagnostic and Statistical Manual of Mental Disorders editions or revisions would be of limited applicability. Regarding the various assessment devices which were based upon the diagnostic criteria of the latest Diagnostic and Statistical Manual of Mental Disorders, (Newcorn et. al., 1989), made the point that "structured diagnostic instruments, such as the 'Diagnostic Interview Schedule for Children (DIDC)' (Costello, 1983) and the 'Diagnostic Interview for Children and Adolescents (DICA)' (Herjanic and Campbell, 1977), were based on DSM III criteria and cannot be considered adequate for subject selection in the new diagnostic system."

Item Selection and Validity Considerations of the ADHD SAS. Items on the ADHD SAS were derived from descriptive principles found in the professional literature during the past twenty years. After reviewing the professional literature, all supported and agreed upon principles and theoretical constructs which described or which explained the basis of attention deficit or hyperactivity were converted to behavior statements. For example, the distractibility of these youngsters is universally mentioned as a predominate factor; therefore there are items related to distractibility, such as: "Is quickly and easily distracted," "Finishes what is begun."
Standardization of the ADHD SAS. Traditional standardization of the scale determining the correlation of the ADHD SAS with some other scale, or using teachers', or psychologists', or physicians', or parents' judgments as a comparative criterion by which to measure the ADHD SAS was felt to be unnecessary. The inventors did not find good criterion measures with which to measure the validity of the ADHD SAS. The method of scale construction builds in the validity of the scale in that the scale is measuring the operational definitions of the constructs as put forth in the professional literature over the past twenty plus years.
The ADHD SAS is designed to measure the constructs which have gone into defining Attention Disorder. The scoring is designed to measure the relationship between amount of symptomatology being demonstrated by an individual and the ?0 total amount available to be demonstrated by the upper limits of the scale.
Individuals are evaluated according to the following scale. If an individual has less that 40 percent of the total capacity of Attention Deficit symptoms the Scale measures then the interpretation is "No Symptoms of Attention Deficit Disorder are Indicated." If he demonstrates between 40 to 51 % of the total capacity of Attention Deficit symptoms the Scale measures then the interpretation is "Minimal Level of Attention Deficit Symptoms axe Indicated." If he demonstrates between 52 to 64% of the total capacity of Attention Deficit symptoms the Scale measures then the interpretation is "Mild Level of Attention Deficit Symptoms are Indicated." if he demonstrates between 65 to 77% of the total capacity of Attention Deficit symptoms the Scale measures then the interpretation is "Moderate Level of Attention Deficit r .- , , , Symptoms are Indicated." If he demonstrates between 78 to 90% of the total capacity of Attention Deficit symptoms the Scale measures then the interpretation is "Severe Level of Attention Deficit Symptoms are Indicated." If he demonstrates between to 100% of the total capacity of Attention Deficit symptoms the Scale measures then the interpretation is "Extreme Level of Attention Deficit Symptoms are Indicated."
Individuals are also evaluated according to the following scale. If an - individual has less that 40 percent of the total capacity of Hyperactivity Disorder symptoms the Scale measures then the interpretation is "No Symptoms of Hyperactivity Disorder are Indicated." If he demonstrates between 40 to 51 %
of the total capacity of Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Minimal Symptoms of Hyperactivity Disorder are Indicated."
If he demonstrates between 52 to 64% of the total capacity of Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Mild Symptoms of Hyperactivity Disorder are Indicated." If he demonstrates between 65 to 77% of the total capacity of Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Moderate Symptoms of Hyperactivity Disorder are Indicated."
If he demonstrates between 78 to 90% of the total capacity of Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Severe Symptoms of Hyperactivity Disorder are Indicated." If he demonstrates between 91 to 100%
of the total capacity of Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Extreme Symptoms of Hyperactivity Disorder are Indicated."
Individuals are finally evaluated according to the following scale. If an individual has less that 40 percent of the total capacity of Attention Deficit -Hyperactivity Disorder symptoms the Scale measures then the interpretation is "No Symptoms of Attention Deficit - Hyperactivity Disorder Symptoms are Indicated." If he demonstrates between 40 to 51 % of the total capacity of Attention Deficit -' Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Minimal Symptoms of Hyperactivity Disorder are Indicated." If he demonstrates - between 52 to 64% of the total capacity of Attention Deficit - Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Mild Symptoms of Hyperactivity Disorder are Indicated." If he demonstrates between 65 to 77% of the total capacity of Attention Deficit - Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Moderate Symptoms of Hyperactivity Disorder are Indicated." If he demonstrates between 78 to 90%. of the total capacity of Attention Deficit - Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Severe Symptoms of Hyperactivity Disorder are Indicated."
If he demonstrates between 91 to 100% of the total capacity of Attention Deficit -Hyperactivity Disorder symptoms the Scale measures then the interpretation is "Extreme Symptoms of Hyperactivity Disorder are Indicated."
INTERPRETATION AND CLINICAL USE OF THE
ATTENTION DEFICIT - HYPERACTIVITY DISORDER
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I. ATTENTION DEFICIT DISORDER
PREDOMINATELY INATTENTIVE TYPE

TOTAL SCORE IOF HIGHEST SIX) REQUIRED SCORE FOR DIAGNOSIS (6 X 2.51 15 LEVEL OF RDS: 15 - 20 MODERATE; 21 - 24 SEVERE

Predominately Hyperactive Type TOTALSCORE

REQUIRED SCORE FOR DIAGNOSIS (6 X 2.5) 15 LEVEL OF RDS: 15 - 20 MODERATE; 21 - 24 SEVERE

PREDOMINATELY IMPULSIVE TYPE

TOTALSCORE

REQUIRED SCORE fOR DIAGNOSIS (3 X 2.5) 7 LEVEL OF RDS : 7 - 9 MODERATE; 10 - 12 SEVERE

1 I. TOURETTE'S DISORDER

TOTALSCORE

REQUIRED SCORE FOR DIAGNOSIS (3 X 2.51 7 LEVEL OF RDS : 7 - 9 MODERATE; 10 - 12 SEVERE

I I I. CONDUCT DISORDER

TOTALSCORE

REQUIRED SCORE FOR DIAGNOSIS (3 X 2.5) g LEVEL OF RDS : 9 - 10 MODERATE; 17 - 12 SEVERE

I V. OPPOSITIONAI DEFIANT DISORDER

TOTALSCORE
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REQUIRED SCORE FOR DIAGNOSIS 14 X 2.5) 10 ~i LEVEL OF RDS : 10 -13 MODERATE; 14-16 SEVERE

V. INTERMITTENTENT EXPLOSIVE DISORDER

TOTAL SCORE "~'' REQUIRED SCORE FOR DIAGNOSIS (3 X 2.5) 7 LEVEL OF RDS : 7 - 9 MODERATE; 10 -12 SEVERE

V I. SCH1101DIAUOIDANT PERSONALITY DISORDER

TOTAL SCORE (OF HIGHEST FOUR IN EACH SECTION) REQUIRED SCORE FOR DIAGNOSIS 18 X 2.5) 20 LEVEL OF RDS: (Schizoid + Avoidant) 20-26 MODERATE;

V 1 I. SUBSTANCE USE DISORDER

SUBSTANCE DEPENDENCE

TOTAL SCORE (OF HIGHEST THREE) REQUIRED SCORE FOR DIAGNOSIS (3 X 2.5) 7 LEVEL OF ROS : 7 - 9 MODERATE; 10 - 12 SEVERE

TYPES OF SUBSTANCE DEPENDENCE CHECKED: ALCOHOL
G AMPHETAMINE RELATED G
CARBOHYDRATES G CRACKICOCAINE G HEROIN G MARIJUANA
G NICOTINE G

SUBSTANCE ABUSE

TOTAL SCORE (ONLY HIGHEST OF FOUR) REQUIRED SCORE FOR DIAGNOSIS (1 X 3) 3 LEVEL OF RDS: 3 MODERATE; 4 SEVERE

TYPES OF SUBSTANCE ABUSE CHECKED: ALCOHOL G
AMPHETAMINE RELATED G

G NICOTINE G

V 1 I I. PATHOLOGICAL GAMBLING

TOTAL SCORE (OF HIGHEST FIVE) REQUIRED SCORE FOR DIAGNOSIS (5 X 2.5) 12 LEVEL OF RDS: 12-16 MODERATE; 17-20 SEVERE

I X. POSTTRAUMATIC STRESS DISORDER

TOTAL SCORE (HIGHEST IN 1 ST SECTION, 3 IN THE
2ND, AND 2 IN THE

REQUIRED SCORE FOR DIAGNOSIS (1X3) + (3X2.5) 13 + (2x2.5) LEVEL OF RDS: 13-18 MODERATE; 19-24 SEVERE

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~ ~ m ASSOCIATION OF A TRINUCLEOTIDE (GGC) REPEAT
POLYMORPHISM OF THE ANDROGEN RECEPTOR GENE (AR) WITH
ADHD, CONDUCT AND OPPOSITIONAL DEFIANT DISORDER IN
~ 5 TOURETTE SYNDROME
Many of the behavioral and cognitive disorders including ADHD, CD, ODD, . antisocial personality disorder, dyslexia and other teaming disorders, and autism, are three to five times more common in males than in females {DSM-IV, 1994). While generally assumed to be due to hormonal and environmental factors, genetic factors could be involved. The role of specific genes would be easiest to understand if the gene was X-linked. Thus, depending upon the frequency of the relevant alleles and the percent of the variance attributed to the gene, and assuming a predominantly recessive mode of inheritance, an X-linked gene could account for a portion of the excess in males. If the gene also played an important role in the response to testosterone, it would be unusually well suited to playing a role in the maie/female ratios. The androgen receptor gene {AR) is such a gene. It is located on the X-chromosome at Xql l-12 (Migeon et al., 1981; Brown et al., 1989).
The inventors hypothesize that the shorter of the normal alleles of the AR CAG
and GGC repeats, associated with increased levels of androgen receptor and sensitivity to testosterone, might be associated with one or more of the disruptive behavioral disorders of childhood, especially conduct disorder and oppostional defiant disorder, and might explain a portion of the male predominance of these disorders. To test this the inventors have examined the GGC (Sieddens et al., 1.992,1993), trinucleotide repeat polymorphism in exon 1 of the AR gene in a series of 326 individuals consisting of 267 with Tourette syndrome and 59 controls. Because of the frequent association of dopamine (Valzelli, 1981 ) and serotonin (Brown et al., 1982;
Lindberg et al., 1984; van Praag, 1991; Coccaro, 1989) with aggressive behaviors, the effect of the AR gene on conduct disorder and ODD was compared to that of the dopamine D2 receptor gene (DRD2) and the serotonin transporter gene (HTT~.
This study was conducted to determine if -the shorter alleles of the GGC
trinucleotide repeat polymorphism of the human androgen receptor gene (AR) are associated with the symptoms of conduct disorder (CD), oppositional defiant disorder (ODD} or attention deficit hyperactivity disorder (ADHD).
Methods. The frequency of the alleles was determined in 326 subjects consisting of 267 individuals with Tourette syndrome and 59 controls, 237 males and 89 females, all non-Hispanic Caucasians. Eight quantitative behavioral measures were examined simultaneously by MANOVA for correlation with the presence of hemizygosity for the shorter alleles in males or homozygosity for the shorter alleles in females. Linear regression analysis was used to determine the percent of the variance that was due the AR gene. This was compared to the percent of the variance due to the dopamine D2 receptor gene (DRD2), and the serotonin transporter gene (HTT), individually and in combination.
The subjects tested consisted of 59 controls and 267 individuals with Tourette syndrome. The details concerning the subjects and controls, and the behavioral assessments, are presented herein. In those studies the inventors examined the following quantitative traits: inattention, impulsivity, hyperactivity, CD, ODD, learning disorders (LD), and grade school academic performance (GSAP). In the present study and one of the companion studies the inventors have added a quantitative tic score (Comings et al., 1996).
The GGC repeat polymorphism of the AR gene was amplified by polymerase chain reaction by the technique of Sleddens et al., 1992, 1993. Irvine et al., found the 16 repeat was the most common allele, .57, followed by the 17 repeat (.32}
and the 15 repeat (.08}. The only other allele was the 10 repeat. For analysis of the possible correlation between the AR alleles and individual quantitative behavioral scores, the AR gene was scored as follows: for males > 16 repeats = 0, >_16 repeats =
1; for females: > 16/> 16 repeats = 0; heterozygotes = 0; >_ 16/? 16 repeats =
1.
The TaqI A polymorphism (Grandy et al., 1989) of the dopamine DZ gene (DRD2) was used to evaluate the role of this locus compared to the AR gene, in conduct disorder. Based on evidence of positive heterosis at this polymorphism (Comings and MacMurray, 1998), it was scored as 11, 22 = 0, 12 = 1.

.. ..... ..,.. -.. .

WO 98/48785 , PCT/US98/08684 The insertion/deletion polymorphism in the promoter region of the serotonin transporter gene was used (Hells et al., 1995, 1996). The HTT gene was scored as SS
= 0 and SL, LL = 1 (Kauck et al.. 1997; Lesch et al.. 1996).
Possible differences in the frequency of the AR alleles, or groups of alleles, in S TS subjects versus controls, was tested by chi square analysis. To avoid the need for a Bonferroni correction, the eight behavioral scores were simultaneously examined using MANOVA. To examine the additive effect of the three genes, a AR+DRD2+HTT score was formed. Here the individual gene scores were added for each individual. This gave a range from 0 (no relevant genotypes for any of the three genes), to 3 (relevant genotypes for all three genes). Linear regression analysis was utilized determine the percent of the variance of the CD, ODD and ADHD scores that were accounted for by the AR, DRD2, and HTT genes, singly or in combination.
To test the hypothesis that these three genes were additive in their effect, the means for each score, including a total ADHD score, were examined by linear ANOVA.
1 S Results. AR allele frequencies were determined. Among the S9 controls the frequencies of the different repeat alleles were as follows: 10 - .01, 12 -.09, 1 S - .03, I6 - .47, I7 - .36, I8 -.01, 19 -.01, 20 - .01. The frequency of the alleles in the controls compared to the frequencies in the TS subjects was determined. The chi square between the controls and the TS subjects, including all the alleles =
19.69, d.f.
= 1 1, p = .073 1. In the inventors' studies of the potential relationship between the length of repeat alleles and a phenotypic effect, the inventors have found that the most parsimonious approach is to divide the alleles into to a short and a long group, and to make the two groups as equal in size as possible. Following this approach the inventors divided the AR alleles into those > 16 and > 16 repeats. There was a non-2S significant tendency for the TS subjects to have an increase in the frequency of the ?16 alleles (.66) compared to the controls (.60), a2 = 1.06, d.f. = I, p = 30.
Thus, when the comparison was made between the controls vs. the TS subjects, there were no differences at a = .OS.
The results of MANOVA, sorted in decreasing order of significance, are shown in Table I. The CD score was the most significant (p = .OOS), the ODD
score next (p = .022), with the hyperactivity score also being significant (p =
.032). The tic, GSAP and LD scores were the least significant (p > .6). To examine the question of whether this preferential association with CD was only present in males, the inventors compared the results in males (n = 237) and females (n = 89). Because of the loss of power due to the smaller numbers, none of the waits were significant for either sex.
However, in when sorted by p value, CD was the most significant for both sexes.
Because of the potential association between AR variants and sexual behavior, the inventors performed a post hoc analysis of the possible association of the AR gene with a sexual behavior score (Comings, 1994) in a subset of subjects 14 years of age or greater. This was not significant. A post hoc analysis was also performed in females to determine if the effect of the shorter alleles was recessive or dominant. For all the scores the means for heterozygotes were consistently similar to or less than for the >16/>16 homozygotes, indicating the effect of the >_16 repeat alleles were recessive in their effect with only the >_ 16/>_ 16 homozygotes in females showing the I S effect.
The regression analysis results are shown in Table 74. For the CD score, the AR gene accounted for 2.4% of the variance (p = .005). By comparison, the DRD2 gene accounted for 1.3% (p = .041 ) and the HTT gene for 0.5%. The AR and the DRD2 gene combined accounted for 3.3% of the variance (p = .0009). This increased to 3.5% when the HTT gene was added (p = .0007). The AR gene accounted for I
.6%
of the variance of the ODD score (p = .022). Here the AR and DRD2 genes were more comparable with the DRD2 gene accounting for 1.4% of the variance, and the HTT
gene for 0.7%. All three genes accounted for 3.2% of the variance of the ODD
score (p = .001 ). The AR gene accounted for 1. i % of the variance of the ADHD
score (p = .053). All three genes accounted for 2.7% of the variance (p = .0027).
The Linear ANOVA results of an additive AR+DRD2+HTT score was examined by linear ANOVA to determine if there was a progressive increase in the scores in subjects with increasing number of relevant genotypes. The inattention, impuisivity, hyperactivity, ADHD, ODD and CD scores were all significant at a =

~,~

.OS. At a Bonferroni corrected a of .05/6 or .0083, the hyperactivity, impulsivity, ADHD, ODD and CD scores were still significant.
By MANOVA there was a significant association between the presence of the short GGC alleles of the AR gene and symptoms of CD (p = .005), ODD (p =
.022), and hyperactivity (p = .023). The association of these alleles was greatest for CD for both males and females. The AR gene accounted for 2.4% of the variance of the CD
score. The combination of the AR, DRD2 and HTT genes accounted for 3.5% of the variance of the conduct disorder score (p = .0007), 3.2% of the variance of the ODD
score (p = .001 ) and 2.7% of the variance of the ADHD score (p = .0027).

i Table 73 MANOVA for the Behavioral Scores (n = 326) A. All Eight Behavioral Scores Score F-ratio p CD 8.03 .005 ODD 5.32 .022 Hyperactivity 5.20 0.23 Impulsivity 3.18 .075 Inattention 1.76 .181 Tics 0.22 .634 GSAP 0.22 .643 LD 0.95 .923 Total (Wilkes) 1.48 .171 S
B. Males only (n = 237) Score F-ratio p CD 2.57 .110 Hyperactivity 1.50 .221 Tics 1.18 .277 ODD 1.13 .288 GSAP 0.38 .534 Learn 0.34 .559 Impulsivity 0.15 .b95 Inattention 0.11 .736 Total (Wilkes} 1.14 .334 C. Females only (n = 89) Score F-ratio p CD 1.63 .205 r i ' I

ODD 0.61 .433 Inattention 0.54 .463 Impulsivity 0.48 . .489 GSAP 0.41 .523 Hyperactivity 0.15 .703 Tics 0.13 .712 Learn 0.03 .853 Total (Wilkes) 0.38 .929 i o, .-, c~ ~- -" o °.°.~o°o°o °.°.~°0°00 M ~1 ~?' V1 01 M ~~ l~ 1~ ~O ~O N
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~r ~ ~ ~r d o x Q Q d ~3 THE EFFECTS OF CHROMIUM PICOLINATE SUPPLEMENTATION ON
BODY COMPOSITION: A RANDOMIZED DOUBLE-MASKED PLACEBO
CONTROLLED STUDY
Introduction: The inventors also sought to answer several of methodological issues. First, does supplementation with CrP have affects on caloric intake through its impact on appetite and increase caloric expenditure through increased metabolic or daily activity levels? Second, would the same results be achieved if the inventors controlled for, and factored out, differences in caloric intake andlor energy expenditure between the experimental and control groups? Third, would the results be replicated with other measures of body composition, such as Dual Energy X-ray Absorptiometry (DEXA), that are even more precise and less dependent upon the subject's performance in taking the test than underwater testing? Fourth, did the relatively high dropout rates seen in other studies {Anderson, 1995) (29.7%) bias the findings though selective attrition in spite of the similarity of the three study groups on all baseline measures of body composition? Would these same results occur if methods were employed to decrease file dropout rates or if one used "intention to treat" statistical analyses?
To answer this questions the inventors employed measures to control for differences in physical activity and caloric intake, used DEXA testing for body composition and used a method to obtain near-perfect compliance with completion of the ending test.
Materials and Methods. A total of 130 patients were enrolled in the study and 122 (93.8%),I7 males and 105 females with an average age of 42.3, completed alI
ending measurements. Patients were recruited from a variety of fitness and athletic clubs in San Antonio and Houston, Texas by fitness instructors and sales personnel who provided information about the study to club members who either participated or recruited friends or relatives to participate. In most cases, fitness instructors were paid to monitor the subjects as they progressed through the study insuring they reported their physical activity levels and caloric intakes in weekly tracking data and r .

completed the ending testing. All subjects were asked to consult with their personal physician before giving informed consent.
A number of studies have also shown that DEXA can accurately measure fat and lean content in meat samples and animal carcasses (Evans, 1989; Evans and . 5 Meyer. 1992; Evans, 1993; McCarty, 1993) and correlates highly with actual skeletal mass and with total body calcium by neutron activation analysis with a typical _ precision error for total body bone mineral content of less than 1% (Felig, 1975). It has also been shown to be a precise method for assessing body composition in both obese and non-obese patients (Page et al., 1993; Eckel, 1992). The initial studies on the precision of the DEXA were reported in 1990 (Mooney and Cromwell, 1993) and confirmed in three subsequent studies (Page et al., 1993; Hasten et al., 1994;
Evans, 1989) suggesting that while DEXA correlates highly with underwater weighing, deuterium dilution and total body potassium (Page et al., 1992), errors in DEXA
measurements were less than half of those obtained using total body water or underwater testing. Specifically, the coefficients of variations (CV%) for Lunar Corporation's DEXA have been reported as follows: Fat mass = 500 g + -2.Sg;
FFM = 6008 + -1.3g; and Total tissue mass = 4008 + -0.6g. In addition to being used to evaluate a variety of clinical disorders (Lindemann et al., 1993), DEXA's reliability make it possible to monitor the effects of relatively short-term dietary restrictions and/or exercise on regional and total body composition (Page et al., 1993;
Eckel, 1992). A recent review of research on DEXA has led one reviewer to conclude that DEXA is among the most critically analyzed body composition instruments available today (Lindemann et al., 1993).
DEXA provides a three-compartment model of body composition: fat, lean tissue mass, and bone mineral content. Measurements are made using a constant potential energy source at 78 kVp and a K-edge filter (cerium} to achieve a congruent beam of stable, dual-energy beam with effective energies of 40 and 70 keV. The unit performs a series of transverse scans moving from head to toe at I cm intervals; the scan area is approximately 60 em x 200 em. Data are collected for about 120 pixel elements per transverse, with each pixel approximately 5 x 10 min. Total body measurements are completed in 10-20 min with a scan speed of 16 em/sec or 20 min with a scan speed of 8 em/sec. The R-value (ratio of low to high-energy attenuation in soft tissue) ranges from 1.2 m 1.4 (Lindemann et al., 1993}.
In addition to comparing changes in scale weight, % body fat, fat mass and FFM, the inventors also used an index of a body composition improvement (BCI) as described in the inventors' previous study (Glinsmann and Mertz, 1966). The BCI is based on the assumption that losses of body fat and gains in FFM are positive treatment outcomes, while gains in fat mass and losses in FFM are negative treatment outcomes. Therefore, losses of fat mass and/or gains in FFM were scored as positive, gains in fat mass and/or losses in FFM negative, and the BCI was the net result of combining these scores. The superiority of the BCI over scale weight as a measure of change has been demonstrated in study examining changes in body composition during participation in supervised exorcise programs (Liarn et crl.. 1993) as well as with patients undergoing pharmacotherapy with and without a behavior modification program (Hasten et al., 1994). Comparisons were made between the two groups in the placebo group using two-tailed Student t-tests with the assumption of equal variance and, in the experimental group, by using analyses of co-variance to equate the groups on caloric intake and expenditure.
As a method of reducing dropout rates, in conjunction with signing the informed consent form, patients were asked to provide a check for $100 which was not processed unless the patient failed to complete the ending DEXA test and end-of study questionnaire. Patients were advised that return of their deposit check was conditional solely upon completing the ending tests no matter how well or poorly that adhered to the research protocol as long as they reported candidly how much or how Little they complied. After completing an initial DEXA test, patients were provided with a report of their test results and were randomly assigned a number from 1 to 130 which corresponded to a bottle containing capsules with 400 meg of chromium pieolinate or an inactive placebo that was identical in appearance. None of the investigators, research technicians dispensing the product, or patients knew which patient number corresponded to the placebo or active product. An independent local pharmacist acted as trustee for the study and randomly assigned subject numbers to bottles that had been pre-labeled either an "X" or "Y" to correspond with either the W __ ~-.....~ ._.... . fi . ~ ~

active or placebo product. Upon completion of the study and when all data were gathered and computerized, the trustee opened an envelope supplied by the manufacturer indicating which product was active and subsequently notified the senior investigator (GRK). All information was analyzed by the Department of Computing Resource at the University of Texas Health Science Center under the supervision of the second author (KB). At the conclusion of the test period, patients completed ending body composition test, were provided with their test results and deposit checks, and were asked to report how many of the capsules actually consumed each day as a cross-check of the amount of product used. A subsequent analysis of these data revealed the average active subject consumed 357 meg of CrP a day.
Patients were provided with a workbook outlining general procedures for estimating caloric intake, nutritional information for common foods and a log for calculation and recording of daily calorie balances. To monitor and adjust for differences in energy expenditure through physical activity, throughout their waking I S h, all patients wore a pedometer used in previous studies (Evans and Press, 1989;
Kitchalong et al., 1993) that reflected the number of steps they took during each day or the step-equivalents for activities in which it was impractical to wear the unit.
Patients recorded the total number of steps taken each day in the same daily log used to record their caloric intake which was subsequently used to adjust the patients net change in body fat using the formula of + or -3.500 calories for a change of one pound of body fat.
Results Table 75 provides the baseline descriptive statistics for the 122 patients who completed the study. A comparison of those who did not complete the study with those who did revealed that there no significant differences on any of the body composition parameters.

i PATIENTS WHO WERE RANDOMLY SELECTED INTO GROUPS
RECEIVING EITHER A PLACEBO

Body Mass Age (y)* Weight (kg)* % Body Fat* Index (kg/m2)*
Active (n=62) 41.1 + -10.585.5 + 23.042.4% + 8.3% 30.2 +
7.1 Placebo (n=60)43.5 = -7.679.9 = -20.441.8% _ -6.7%28.4 +
5.4 P level (two-tailed)p = 0.24 p = 0.16 p = 0.65 p = 0.13 *Mean + -SD
Table 76 provides a comparison of the changes that occurred during the 90-day test period.

r f ' t Table 77 Comparisons of average changes in body composition parameters between participants receiving a placebo or 400 mcg of chromium picolinate during a 90 day test period. All data are controlled for differences in caloric intake and expenditure.
Weight Fat Free (kg)* % Body Fat Fat Mass* Mass* BCI*
Active -7.g + -g,7 -6.3% = 8.5% -7.7 + -9.5 -0. I + -2.2 +7.6 + -4.5 (N=62) Placebo -1.9 + -4.0 -1.2% + -5.7% -3.4 + -6.8 -0.3 + -2.0 +3.1 + -7.6 (n=60) P level** p = < .001 p = < .001 p = .004 p = .568 p = .004 * Mean + -SD
* * two-tailed Student's t-test Discussion A review of the baseline data in Table 76 reveals that there were no statistically significant differences between the two groups on any of the body composition parameters suggesting the randomization process was successful in providing two equivalent groups of patients. Data in Table 77 reveal that making the groups equivalent with corrections for caloric intake and energy expenditure, supplementation with CrP had a highly significant effect on scale weight, %
body fat and BCI. It is also worth noting that even without correcting for caloric intake and expenditure, changes in the active group were consistent with the changes observed in the inventors' previous study including a significant decrease in body fat (p = .02).
It is worth noting that the major improvement in body composition in this study was the reduction of body fat. DEXA testing is one of the few technologies for measuring body composition that provides a direct physical measurement of adipose tissue. Hydrostatic testing, as well a many other measures of body composition, all _ rely upon estimating the patient's body fat on the assumption that their body density reflects the same percentage of fat as found in a few cadaver studies.
Furthermore, even hydrostatic testing does not actually measure the patient's body volume for calculation of body density--it estimates it from scale weights obtained in and out of water. Thus, even with hydrostatic weighing the patient's body fat is derived from two different estimates, not from a physical measurement of adipose tissue.
And, of course, estimates derived from hydrostatic testing can be affected by the ability of the subject to consistently exhale his/her air while underwater as well as variations in lung volumes over time even when exhalation is consistent. DEXA testing resolves these difficulties since obtaining the measurement requires only that the still on an open testing table for 15-20 min while the body is scanned. It seems to the inventors that when attempting to measure the efficacy of products that produce relatively small changes in body composition. controlling the variability of the testing technology is imperative.
The requirement for patients to provide a conditionally refundable deposit appears to have made a dramatic difference in the number of subjects who completed the final testing negating the need to use statistical controls, such a "intention to treat."
Post study critiques revealed that subjects viewed the requirement to provide a deposit, which was not processed, as a reasonable request. Of the 130 subjects who were recruited for this study, only 8 failed to complete the final test. One subject became pregnant and was asked to withdraw from the study, three others moved from the local area, one was ill during the post-testing and three could not be accounted for.
Thus, for all intent and purposes, there were no dropouts from the study that could bias the results. Although the inventors' data are not definitive, the deposit requirement appears to be something worthy of further study.
Since the requirement for patients to provide a conditionally refundable deposit was based entirely on the subject completing the study and an end-of study questionnaire and had nothing to do with how little or how much the patient complied with the protocol. An equal number of patients failing to take the product in the placebo and active groups does not, of come, balance the effects across the groups.
Placebo patients who fail to take the product will have no effect on the outcome measures since the placebo does not contain the active ingredient. However, failure to take the product in the active group will attenuate the effects of the active product could be having. In fact, a completely noncompliant active patient is actually a placebo patient. Thus, lack of compliance will by its very nature attenuate differences between the two groups stressing the need to obtain accurate data on how much of the product the subject did consume. The use of weekly check-ins and personal monitoring appears to have provided the inventors with more comprehensive data and reduced the mount of bias the lack of compliance could have on the outcome measures.
Conclusion. These data indicate that supplementation with chromium picolinate can lead to significant improvements in body composition when a Body Composition Index is used as the outcome criterion that represents a sum of the net gains in non-fat mass added to the sum of the net losses of body fat.

POLYGENIC INHERITANCE AND MICROIMINISATELLITES
Micro/minisatellite polymorphisms in psychiatric genetics Minisateliites have been defined as repeat sequences of up to 65 base pairs in length (Wright, 1994).
I5 Microsatellites consist of shorter repeats variously defined as 2-5 by in length. For the purposes of this example, unless specifically stated, the inventors will use the term micro/minisateliites to cover both.
Because of the high frequency of micro/minisatellite repeats throughout the genome, the inventors and others have often used these polymorphisms in association studies. The inventors' initial assumption was that like the neutral or silent single base pair polymorphisms, the micro/minisatellite alleles would be in linkage disequilibrium with other 'critical' mutations that affect gene function. However, after working with these polymorphisms for several years the inventors began to suspect that the micro/minisatellites themselves might be the 'critical' mutations. While the inventor has not included the extremely long triplet repeats polygenic inheritance, these studies do introduce the concept that the varying length of repeat alleles, even when they are well into 'normal' range, may affect gene function through a wide variety of mechanisms. There are two aspects of these micro/minisatellite poiymorphisms that are relevant - their mutation rate and their size.
-S l l -WO 98/48785 PCT/US98108b84 The mutation rate of micro/minisatellite alleles is higher than for non-repeat sequences (3effreys et al., 1987). Lack of exchange of flanking markers suggests the new mutations are due to replication slippage or complex conversion-like events (Wolff et al., 1989). A high mutation rate could make these polymorphisms less valuable for association studies since over many generations there would be too much noise introduced into linkage disequilibrium relationships which require that two different polymorphisms remain in phase far many generations. However, if the micro/minisatellite mutations were themselves the 'critical' alleles, they would actually be more powerful for association studies, despite the higher mutation rate.
If linkage disequilibrium was involved in the association of microlminisatellite alleles with specific phenotypes, each time a new mutation in the satellite occurred there would be a specified chance it was occurring on the same chromosome as the 'critical' alleles. However, on average there should be no trend for the longer vs the shorter mutant alleles to preferentially be in linkage disequilibrium with these 'critical' alleles. However, if the size of the repeats played a role in gene regulation (see below) there should be a trend for an association of quantitative traits with groups of different sized alleles rather than with specific individual alleles or allele sequences.
If there is a major peak of repeat gene frequency, both the shorter and the longer alleles may be associated with phenotypic effects.
There is the possibility that both length and sequence can be involved, and that the different sized micro/minisatellite alleles might have an effect on gene function.
This hypothesis would be much more plausible if different micro/minisatellite alleles could be shown to have an effect on the rate of transcription or translation of the genes. There are, in fact, many examples of this.
A general hypothesis of polygenic inheritance. The various aspects of a micro/minisatellite hypothesis of polygenic inheritance are as follows: many micro/minisatellites have an effect on the expression of the genes with which they are associated; many genes are associated with one or more micro/minisatellite polymorphisms; as a result, many genes will present with a range of functional alleleomorphic variants. Those genes associated with several micro/minisatellite ...,.... ,..... .r .

polymorphisms, each with multiple alleles, will present with an especially large number of functional haplotypes. While most genes will be associated with t10-15%
of the average IeveI of gene activity, the range may be as high as ~40%, and if multiple micro/minisatellite polymorphisms are involved their effects can be additive.
Additionally, significant functional variants are common in the general population, with prevalences ranging from 1 to 100%. A 100% prevalence can occur if there is a bimodal distribution of alleles consisting entirely of hyperfunctional alleles (>10% of the average) and hypofunctional variants (<10% of the average).
Individually these functional variants have only a modest effect on the phenotype (0.5-8% of the variance), and rarely cause disease, i.e. they are generally not responsible for the classic one gene-one disease autosomal dominant or recessive disorders. Since most of the micro/minisatellite poiymorphisms are not in exons, the mutations in poiygenic disorders are usually outside the exons and often outside the transcribed sequences. Log score linkage studies lose power when more than six I ~ genes are involved in a given disorder (Risch and Merikangas, 1996; Weeks and Lathrop, 1995). Linkage studies are based on the assumption that the presence of a given allele is associated with the presence of the disorder and the absence of the allele is associated with the absence of the disorder. By contrast, in poiygenic inheritance the disorder is associated with the presence of a threshold number of alleles of several different genes. As such, many members of a pedigree may carry a given mutant gene but not have the disorder, because of the absence of the necessary threshold number of other mutant genes. Thus, many members of a family may carry a specific gene and not have the disorder, and other members of the family with the disorder may not carry the mutant allele (Comings, 1996f, l,m).
Both hyper- and hypo-functional alleles can have an effect on the phenotype.
For example, in psychiatric genetics, both an increase in the expression of a given receptor gene leading to receptor supersensitivity, or a decrease in the expression of a receptor gene leading to receptor hyposensitivity, could be associated with an altered phenotype. A polygenic disorder occurs when an individual inherits a threshold number of hypo- or hyper-functional genes affecting the same phenotype. Thus, if 20 different polygenes play a role in a given quantitative variable, anyone with 10 or WO 98/48785 PCTlUS98/08684 more of these hypo- or hyperfunctional variants would present with a significantly altered phenotype. The threshold number would vary according to the degree of hypo- or hyper-functionality of the alleles, and the severity of the altered phenotype would depend on the extent to which the number of polygenes exceeded a critical threshold number.
Because micro/minisatellites are so common, the probability of a polygenic disorder occurring is relatively high. Thus, polygenic disorders are much more common than single gene disorders. With the general population frequency of many polygenes ranging from 25% to over SO% the chance of inheriting a threshold number is much higher than for the single gene disorders. For a given micro/minisatellite the alleles can have a negative or positive effect on the phenotype depending upon the genetic background of other genes and on the nature of the phenotype. A
practical example is the observation by Gelernter et al. (1994) of an association between cocaine-induced paranoia and the 9 allele of the dopamine transporter gene (DA
TI) gene. By contrast, Cook et al. (1995) and Comings et al. (1996j) observed an association between the 10 allele of the DA TI gene and attention deficit hyperactivity disorder. One might conclude from such divergent reports that one or the other observation must be incorrect. However, there is a reasonable probability that both are correct, and that different alleles of a given micro/minisatellite may be associated with different phenotypes. A reasonable approach to the study of polygenic disorders is to choose candidate genes and examine the effect of alleles of the nearest micro/minisatellite polymorphisms on relevant quantitative traits.
The above proposed characteristics of polygenic inheritance carry a number of additional implications. If many micro/minisatellite polymorphisms have the potential of altering the rates of transcription or translation of the gene they are closest to, and if many genes are associated with at least one micro/minisatellite, then many and possibly most genes have the potential to contribute in some degree to the polygenic inheritance of the phenotypes they control.
Many studies of the potential role of specific genes in psychiatric or other disorders start with a search for mutations in the exons of a candidate gene.
When .. , . , . , such studies are negative it is often concluded that the gene has nothing to do with the disorder being examined. If the majority of the mutations involved in complex, polygenic traits are in non-exons, such conclusions would be invalid. The present hypothesis does not exclude a role for exon mutations that have only a minor effect on the function of the gene, it only emphasizes that these studies can be negative despite the presence of functionally important variants of that gene.
If on average, each candidate gene contributes to only 0.5-8% of the variance of a given quantitative trait, depending upon the phenotype and the size of the study, the results may be of modest, borderline or negative significance. However, the examination of the additive or epistatic effect of two or more candidate genes may show a much more significant effect.
Many association studies simply examine the frequency of specif c alleles in subjects compared to controls. However, when a diagnosis is based on a complex set of symptoms, a given allele may show a significant association with several of the quantitative traits involved in the diagnosis, but not the diagnosis itself.
For example, if schizophrenia is a polygenic disorder, the alleles of a specific candidate gene may show a significant association with negative symptoms (e.g., affective flattening), which are present in some but not all cases, but no association with positive symptoms (e.g., delusions or hallucinations), or with the diagnosis of schizophrenia itself.
Limiting the analysis to simple dichotomous diagnoses, rather than the examination of sub-syndromal quantitative traits, may miss genes that make an important contribution to a polygenic disorder. Heterosis may also allow a gene to have a significant effect on the phenotype in the absence of difference in the frequency of individual alleles in controls vs patients (Comings and MacMurray, 1977).
A hypothesis is most useful if it has immediate heuristic value. One of the disadvantages of the use of highly polymorphic micro/minisatellite polymorphisms in association studies is that when each of the large number of alleles, and even larger number of potential genotypes is examined, the power of the study may be so seriously compromised as to render it useless. However, if the size of the repeats is the critical variable, even the most complex of polymorphisms can be reduced to three genotypes consisting of homozygosity for the shorter alleles, homozygosity for the longer alleles, and heterozygotes. The inventors have found this approach of value for studies of the role of a number of genes (OB, MA OA, MAOB, CNRI. GABRA3, GABRB3, DBH, FRAXA, and NOSI) in behavioral traits.
While many of the potentially interesting candidate genes have been cloned and sequenced, for most, no polymorphisms have been reported in the Genome Data Base, making association studies impossible. Based on the present hypothesis, if the sequence is known, one method of identifying useful micro/minisatellites is to obtain large genomic clones carrying the gene of interest from commercial sources.
These can then be screened for repeat sequences which may be highly informative in association studies.
The present model suggests that the repeat sequences capable of forming Z-DNA will prove to be the most valuable polymorphisms in studies of polygenic inheritance. This suggests that screening known sequences using the Z-hunt-II
program (Schroth et al., 1992), could provide important clues about the location of the most informative polymorphic regions. To test this, the inventors utilized this program to examine the sequence of NOSI (Hall et al., 1994). Based on the results of studies showing aggression in knock-out mice, without the NOSI gene (Nelson et al., 1995), the inventors had examined various behavioral traits in humans using a repeat polymorphism the inventors identified by visual examination of the sequence of the NOSI gene (Hall et al., 1994). Since some associations were observed, the inventors wondered if: the polymorphism the inventors were using would be identified by the Z-hunt II program; and if there were other regions in the gene that might contain informative polymorphisms. Both predictions were true. Based on the assumption that the various lengths of the NOSI (CA)n alleles would play a role in the function of the NOSI gene, the inventors divided the alleles into two groups consisting of the shortest 50% of the alleles (<199 bp) and the longest 50% of the alleles (>_199 bp).
Preliminary studies suggest some associations with behavioral phenotypes. The inventors then screened the published sequence of the NOSI gene for Z-DNA
regions using the Z-hunt-II algorithm (Wang et al., 1979). This showed that the polymorphism the inventors were using was one of three regions of high Z-DNA

.. . r .. , . , content. A second region with an even higher Z-score identified a new previously undetected polymorphic region.
These three examples illustrate in practical terms how the predictions of the model may lead to the acceleration of the identification of the genes involved in polygenic disorders. The use of this mode! in conjunction with the sequencing data soon to be available from the Human Genome Project may contribute greatly to the inventors' knowledge about polygenic disorders through an increased ability to identify the presence of micro/minisatellites in proximity to the known candidate genes.
Monoamine oxidase A gene (MA OA) and VNTR polymorphism alleles divided into four groups. There was a significant association with a number of quantitative variables relating to specific symptoms. The results are shown for a manic symptoms score in 351 Tourette syndrome probands, their relatives and controls. There was a significant increase in the score in subjects carrying the longest alleles (Gade et al., 1997). (b) Use of the same polymorphism in a group of substance abusers and controls. There was again a significant association with the longest alleles and a drug abuse score (Gade et al., 1997). (c) Association between a quantitative score for anxiety, based on the SCL-90 test, and the obesity gene (OB) microsatellite polymorphism alleles divided into subjects homozygous for the <208-by alleles (left) and individuals homozygous or heterozygous for the >_208-by alleles (right). (d) Association between the amplitude of the event-related potential (ERP) P300 wave (in uvolts) and the CNRI cannabinoid receptor gene microsatellite polymorphism alleles divided into subjects homozygous for the >_S repeat alleles (right) and individuals homozygous or heterozygous for the <5 repeat alleles (left) (Johnson et al., 1997). (e) Association between the Brown Adult ADD score and the GABAA, B3 (GABR83) receptor gene microsatellite polymorphism alleles divided into subjects homozygous for >_185-by alleles (left) and those homozygous or heterozygous for the <185-by alleles. (f) Association between performance IQ
and the normal alleles of the FRAXA locus in random adults assessed for IQ
(Comings et al., 1997a, b or c?).

WO 98/48785 PCTIUS98108b84 Additive Effect of Three Adrenergic Genes (ADRA2A, ADRA2C, DBH) on ADHD in Subjects With and Without Learning Disabilities The inventors have tested for associations or additive effects between three adrenergic genes, adrenergic a2A receptor (ADRA2A), adrenergic a.2C receptor (ADRA2C), and dopamine (~-hydroxylase (DBI~, and ADHD with and without learning disabilities (LD).
These two types of ADHD and the potential candidate genes the inventors have assigned to them, are summarized in Table 78.

ADHD With and Without Cognitive Disabilities (CD) ADHD without CD ADHD with CD
Cognitive disordersabsent present Verbal IQ normal low Brain region involvedprefrontal lobes parietal/temporal lobes Brain nucleus involvedventral tegmental locus coeruleus area Primary neurotransmitterdopamine noradrenaline Secondary NA, GABA, serotonin,dopamine, GABA, neurotransmitter and other serotonin, and other Function analyzes data and orients to and engages initiates a responsenew stimuli Type of attention general selective deficit Executive dysfunctionpresent usually not present Candidate genes DRD2, DRD4, DRD~, ADRAIA-ID, ADRA2A, DATl, DBH ADRA2C DBH, NT, PNMT

The correlations reported by Halperin et al. (1997) between plasma MHPG
and verbal but not performance IQ were remarkably similar to the findings of lower verbal IQ scores in juvenile delinquents arrested for violent or other major crimes r i,~

(Hirschi and Hindelang, 1977; Miller, 1988; Shulman, 1951; Moffitt and Silva, 1988).
The negative correlation between plasma MHPG levels and low verbal IQ suggest that NA genes might also be involved in antisocial behaviors.
Since adrenergic a2 receptors are the site of action of clonidine. are enriched in the prefrontal and parietal attentional centers, and play a role in the regulation of synaptic NA turnover, the inventors have sought to determine if different genotypes of the adrenergic a2A (ADRA2A), or the adrenergic a2C (ADRA2C) receptor, or the dopamine ~3-hydroxylase genes are associated with ADHD per se, and if there is a preferential association with the ADI-ID + cognitive disorders subtype. The inventors utilized the MspI polymorphism in the promoter region of the ADRA2A gene (Lario et al., 1997), the dinculeotide repeat polymorphism of the ADRA2C gene (Riess et al., 1992). and the TagI B 1/12 polymorphism of the DBH gene (d'Amato et al., 1989;
Wu et al., 1997).
Methods. The genotype frequencies of single base pair polymorphisms at the ADRA?A and DBH genes and a dinucleotide repeat polymorphism at the ADRA2C
gene were determined in 325 subjects, 267 individuals with Tourette syndrome and 58 normal controls. The behavioral variables for ADHD and LD were assessed by parental or self=report questionnaire and personal interview. To assess a possible relationship to antisocial behaviors the symptoms of conduct and oppositional defiant disorder were also assessed.
The study group consisted of 325 unrelated subjects. Of these 267 fulfilled the DSM-III-R and DSM-IV criteria for Tourette syndrome and all were personally interviewed by D.E.C. The remaining 58 were controls. All were non-Hispanic Caucasians. The TS subjects came from the Tourette syndrome Clinic at the City of Hope Medical Center. The inventors have previously divided the inventors' TS
subjects into those with mild (grade l, chronic tics too mild to treat), moderate (grade 2, severe enough to require treatment), and severe (grade 3, very significant effect on some aspect of their Life) (Comings, 1990; Comings and Comings, 1987b; Comings and Comings, 1984). Among the TS subjects 25% were grade 3, 12% were grade 1, and the remaining 71 % were grade 2. TS and ADHD are similar disorders and the majority of TS subjects that come to clinics have comorbid ADHD (Comings, 1990;
Comings and Comings, 1987b; Comings and Comings, 1984). The presence of controls, TS subjects without ADHD and TS subjects with ADHD make this group particularly well suited to examining the association between the alleles of different genes and ADHD as a continuous trait variable. Both the TS subjects and the controls have been described in detail elsewhere {Comings et al., 1996j; Comings, 1994a;
Comings 1994b; Comings 1995a, Comings, 199Sb}. The age of the TS subjects averaged 18.0 years (S.D. 13.2). While the majority were older children and adolescents, 29% were 21 years of age or older. The mean age of the controls was 46.3 years {S.D. 15.38).
Each control and TS proband was required to fill out a questionnaire which included assessment of all of the DSM-III, DSM-III-R, and DSM-IV criteria for ADHD. For the older subjects, the questions referred to when they were children.
The ADHD score was based on the DSM-IV (1994) criteria for ADHD. The l5 questions asked if, during childhood and adolescence, these symptoms were never or rarely present (score = 0), occasionally present (score = 1 ) or always present (score = 3). All three responses were used to provide a full discrimination between those with all degrees of severity. The ADHD score was the sum of the DSM-IV
inattention. impulsivity, and hyperactivity symptoms.
Parents filled out the questionnaires when the children were less than 14 years old, while subjects 14 years of age or older filled out the questionnaires themselves or with the assistance of their parents. The questionnaires were reviewed in person with subjects and family members to ensure their accuracy.
To assess the presence of problems with teaming disorders, subjects were 2S asked three questions. 1 ) Have you ever been placed in an educationally handicapped (EH), learning handicapped (LH) or learning disorder (LD) special class? 2) Have you ever been placed in a resource class? 3) Have you ever been told you had a learning disorder? Each question was scored no = 0 or yes = 1 and added to form the LD score. In California, placement in any of the above special classes requires a .._..~.........~._ .~W.._.,.. . ~

thorough evaluation by one or more educational psychologists, and the assessment that the student is two years or more behind his peers.
. To assess the academic performance in grade school the subjects were asked "For grades 1 to 6 was your school performance on the whole average, below average, or above average in the following? a) math, b) reading, c) writing. The answers for a - c were scored as above average = 0, average = I , and below average = 2, and summed to give the GSAP score.
A similar 0, 1, and 2 grading of the DSM-IV criteria for conduct disorder and oppositional defiant disorder was used.
For dichotomous classifications, subjects were considered to have significant ADHD problems if they met DSM-IV criteria for Attention-Deficit/Hyperactivity Disorder, Combined type; Predominately Inattentive type; or Predominately Hyperactive-impulsive type. Subjects were considered to have a learning disorder if they had been placed in one of the above special classes and told they had a learning disorder. Subjects that meet criteria for ADHD and a learning disorder were classified as a A+LD+ group. Subjects that did not meet criteria for ADHD but did meet criteria for a learning disorder, were classified as a A-LD+ group. Subjects that met criteria for ADHD but not for LD were classified in the A+LD- group. Finally, subjects that meet neither criteria were classified in the A-LD- group. A similar grouping was used based on the GSAP scores where those with a score of 0 - 4 were scored as GS-, and those with a GSAP score of 5 - 6 were scored as GS+.
The questionnaire (Comings, 1990) and is meant to provide a highly structured method of providing quantitative variables relating to ADHD, the subscores of the ADHD scale, problems with learning, grade school academic performance, conduct disorder and oppositional behavior. The accuracy, utility and sensitivity of a questionnaire based approach to symptom evaluation has been demonstrated by others (Gadow and Sprafkin, 1994, Grayson and Carlson, 1991 ) by comparing the use of - such an instrument to an interviewer administration of the same structured instrument The inventors' review of the questionnaires with each subject has indicated they accurately reflect the information obtained by personal interview.

The ADRA2A was genotyped using a single base pair MspI polymorphism of the promoter region (Lario et al, 1997). Their PCRTM conditions and primers were used. The ADRA2C was genotyped using the dinucleotide repeat polymorphism and PCRT~" procedure (Riess et al., 1992). The polymerise chain reaction (PCRT'~) was used to amplify the target DNA using 0. 1 pM of each fluorescent labeled primers.
The PCRT~' product was diluted with 100 pl of deionized water and 0.5 p.l was added to 2.5 pl of a mixture of 75 ~1 formamide + 9.5 pl of ROX standard + 9.5 pl of Blue dextrin dye. This was denatured for 2 sec at 92°C and the sample loaded on 6%
PAGE of Applied Biosystems 373 DNA sequencer (Applied Biosystems, Inc., (ABI) Foster City, CA) and gel was run for 4 h at 1200 volts and constant 30 W. The gel was preprocessed and analyzed using the internal standard, ROX 500. The peaks were recognized by genotyper (version 1.1 ) (Applied Biosystems, Inc.) based on the color and the size of the fragments. This produced major alleles at 183 and 185 bp, a less frequent 181 by allele, in addition to several very minor alleles . These were placed into three genotypes: 5183/<_183 by = 1, heterozygotes = 2, and <_183/_<183 by = 3.
The DBH gene was genotyped for the TaqI B 1/B2 polymorphism (d'Amato et ul., 1989). While this originally required southern blotting with a labeled DBH clone, the inventors have adopted it to a PCRT"" based test (Wu et al., 1997).
The genotyping of the dopamine genes (DRD2 and DA TI ) has been described previously (Comings et al., 1996). For the DRD~ gene the inventors used the microsatellite polymorphism and technique {Sherrington et al., 1994).
Three types of statistical analysis were performed. First, to evaluate the effect of the ADRA2A and ADRA2C genotypes, ANOVA was performed on the total ADHD
score with each gene scored as 11 = l, 12 = 2, and 22 = 3. Once the genotype grouping was chosen each gene was assigned a dichotomous variable to indicate the presence or absence of the relevant genotype. An A2A+A2C score was made to evaluate the additive effect of the two genes. Here those with an ADRA2A score of 0 and a ADRA2C score of 0 = 0. Those with an ADRA2A score of l and an ADRA2C

.__.. .._ . .~._. r . i ~

score of 1 = 2. Those with a score of 1 for either gene were scored as 1. The NA
genes score was formed by adding the individual scores for all three NA genes.
- To eliminate the need for Bonferroni correction, all six behavioral scores were examined as a group using MANOVA. This was done for each gene separately and for the genes together. To obtain an estimate of the percent of the variance of the scores that were accounted for by each gene individually and together, a linear . regression analysis was performed for each of the individual gene scores, the A2A+A2C genes score, and the NA genes score, against the behavior scores.
Based on the a priori hypothesis that the effect of the NA genes would be additive, to statistically evaluate the magnitude of this effect a linear ANOVA (polynomial subcommand = 1 ) was performed for the results of adding the ADRA2A and ADRA2C
genes, or the ADRA2A, ADRA2C and DBH genes. A post hoc tukey analysis for means that were significantly different at a = .05 was performed. All statistical tests were performed using the SPSS statistical package (SPSS, Inc, Chicago, IL).
Results. ANOVA was performed to examine the effect of the ADRA2A
genotypes on the ADHD score. The mean score for those carrying the I 1 genotype was 17.4 (n = I 80, s.d. = 11.1 }, for the 12 genotype was 19.05 (n =125, S.D.
= 10.52) and the 20 genotype was 22.05 (n =20, S.D. = 11.67), F-ratio = 2.05, p =.13.
For MANOA the ADRA2A gene was scored as 11 =1, 12 =2 and 22 =3. For regression analysis it was scored as 11, 12 = 0 and 22 =1.
MANOVA was performed for the six scores (Table 80-A). The significant associations were with LD, GSAP, impulsivity, and the total MANOVA. Linear regression analysis was performed for the ADHD, LD, GSAP, CD and ODD scores (Table 3). There was a significant association with the LD and GSAP scores. If a Bonferroni corrected a of .OS/5 or .O1 was the LD score remained significant.

I I I

MANOVA for the ADRA2A, ADRA2C and DBH Genes (N = 325) Scores F-ratio p A. ADRA2A gene Inattention 1.45 .236 Impulsivity. 3.67 .027 Hyperactivity 1.23 .295 GSAP 3.42 .034 LD 3.80 .023 conduct 0.27 .763 ODD I .21 .299 Total (Wilkes) 1.8i .034 B. ADRA2C gene Inattention 5.18 .006 Impulsivity 4.11 .Oi 7 Hyperactivity 2.82 .061 GSAP 1.91 .149 LD 1.78 .169 conduct 1.44 .239 ODD 1.64 .194 Total (Wilkes) 1.09 .360 C. DBH gene Inattention 2.02 .134 Impulsivity. 1.28 .278 Hyperactivity 1.12 .325 GSAP 2.48 .086 LD 1.40 .506 conduct 1.40 .247 ODD 1.71 .182 _.._ eu ... _ . . r . i ~

Scores F-ratio p Total (Wilkes) 0.53 .9I5 D. ADRA2A + ADRA2C genes Inattention 5.20 .006 Impulsivity 5.51 .004 Hyperactivity 3.73 .025 GSAP 5.26 .006 LD 5.40 .005 conduct 0.35 ,709 ODD 3.06 .050 Total (Wilkes) 1.62 .070 E. ADRA2A + ADRA2C + DBH genes Inattention 5.25 .002 Impulsivity 4.21 .006 Hyperactivity 3.76 .011 GSAP 5.01 .002 LD 3.62 .013 conduct 0.85 .465 ODD 3.38 .018 Total (Wilkes) 1.49 .070 F. ADRA2A + ADRA2C + DBH genes + DRD2 + DA TI + DRDS

Inattention 4.81 <.001 Impulsivity 5.31 <,0p l Hyperactivity 4.44 <,p v GSAP 4.41 <.001 LD 2.91 .009 - conduct 2.80 .011 ODD 4.73 <.001 Total (Wilkes) 1.58 .01 I I I

Table 81 Linear Regression Analysis for the ADRA2A, ADRA2C and DBH Genes (N = 325) r rZ T p ADHD score ADRA2A gene .086 .007 1.57 .117 ADRA2C gene .161 .026 2.94 .0035 DBH gene .107 .011 1.94 .054 A2A + AZC .181 .033 3.32 .0010 All 3 NA genes .200 .040 3.67 .0003 All 3 DA genes .157 .025 2.85 .0047 NA + DA genes .247 .061 4.56 <.0001 LD score ADRA2A gene .152 .023 2.78 .0056 ADRA2C gene .077 .006 1.40 . I 62 DBH gene .057 .003 1.04 .291 A2A + A2C .136 .019 2.49 .013 All 3 NA genes .136 .018 2.48 .0135 All 3 DA genes .028 .001 0.51 .61 NA + DA genes .I 1 .012 1.97 .049 I

GSAP score ADRA2A gene .128 .016 2.32 .020 ADRA2C gene .102 .010 1.86 .065 DBH gene .122 .015 2.22 .027 A2A + A2C .148 .022 2.70 .007 All 3 NA genes .185 .034 3.40 .0008 All 3 DA genes .097 .009 1.73 .085 r r2 T p NA + DA genes .203 .042 3.66 .0003 conduct score ADRA2A gene .0008 .0000 0.01 .989 ADRA2C gene .045 .002 0.83 .409 DBH gene .088 .008 1.59 .113 A2A + A2C .041 .002 0.74 .462 All 3 NA genes .085 .007 1.54 .125 All 3 DA genes .071 .005 1.25 .211 NA+DA genes .109 .012 1.92 .054 ODD score ADRA2A gene .085 .007 1.55 .122 ADRA2C' gene .093 .009 1.70 .091 DBH gene .100 .010 1.81 .070 A2A + A2C .I21 .015 2.20 .028 All 3 NA genes .151 .023 2.77 .006 All 3 DA genes .162 .026 2.89 .004 NA + DA genes .221 .049 4.00 .0001 ANOVA was performed to examine the effect of the ADRA2C genotypes on the ADHD score. The mean score for those carrying the 11 genotype was 18.9 {n = 122, S.D. = 10.41 ), for the 12 genotype was 15.90 (n = 112, S.D. =
10.99), and for the 22 genotype was 20.54 (n = 91, S.D. = 11.1 ), F-ratio = 4.90, p =
.0080. By the post hoc Tukey test the mean for the 12 heterozygote was significantly lower than for the 1 I or 22 homozygotes indicating the presence of negative heterosis (Comings and MacMurray, 1998). Thus, for the linear regression analysis the ADRA2C gene was scored as 12 = 0 and 11 or 22 = 1.
MANOVA for the ADRA2C gene score was performed for the six behavioral scores (Table 80-B). The significant associations were with the inattention and impulsivity. By linear regression analysis (Table 81 ) the correlations were significant for the ADHD score. A t a = .01, the ADHD score was still significant.
The inventors have previously studied the dopamine B-hydroxylase gene in TS - ADHD subjects (Comings et al., 1996j) and found the presence of the Tag allele (d'Amato et al., 1989) was associated with ADHD. Thus, the inventors have scored the DBH gene as 22 = 0 and 11 or 12 = 1. By MANOVA (Table 80-C) none of the scores were significant. By linear regression analysis, the correlation with the GSAP scores were significant. At a .01, none were significant.
To examine the possible additive effect of both adrenergic a2 receptor genes the individual gene scores were added to form a A2A+A2C score. Of 325 subjects, 106 (32.6%) had a score of 0, 205 (63.1%) had a score of l, and 14 (4.3%) a score of 2. The results of MANOVA for A2A+A2C score and the six behavioral scores are shown in Table 80-D. The results were significant for the inattention, impulsivity, hyperactivity, GSAP, LD and ODD scores. By linear regression analysis (Table 81 ) the correlations were significant for the ADHD, LD, GSAP, and ODD scores. At a = .O1 the ADHD, GSAP scores remained significant. Linear ANOVA analysis for all except the CD score, showed a progressive increase in all scores progressing from subjects with 0, 1 or 2 variant genes. The ADHD, inattention, impulsivity, hyperacidity, and GSAP scores were all significant at p < .Ol .
To examine the possible additive effects of three adrenergic genes, the ADRA2A, ADRA2C and DBH gene scores were added to form the NA genes score.
Of the 325 subjects, 36 (11.1% had a score of 0, 132 (40.6%) a score of 1, 145 (44.6%) a score of 2, and 12 (3.7%) a score of 3. The results of MANOVA for NA
genes score and the six behavioral scores are shown in Table 80-E. The scores for inattention, impulsivity, hyperactivity, GSAP, LD, and ODD were all significant. By linear regression analysis (Table 8 I ) the scores for ADHD, LD, GSAP, and ODD
were significant. At a -- .O1 the ADHD, GSAP and ODD scores remained significant.
Linear ANOVA analysis of the ADHD subscores, LD, GSAP, and ODD
scores for NA genes score showed there was a progressive increase in all scores across ..._.._-.___....~, ..~~_.~._ ... r , ~ , ~

subjects with 0, 1, 2, or 3 variant genes. The ADHD, hyperactivity, inattention, impulsivity, GSAP, and ODD scores were all significant at p < .01.
. The inventors performed a chi square analysis of the NA gene score versus the four groups of ADHD-LD-, ADHD+LD-, ADHD-LD+ and ADHD~LD+ using linear _ 5 ANOVA. These results are shown in Table 82. This showed that the frequency of subjects carrying all three variant NA genes increased across these groups from .6%
for the ADHD-LD- group, to 5.4% for the ADHD+LD- group, to 7.7% for the ADHD-LH+ group, to 11.4% for the ADHD+LD+ group. The frequency of subjects carrying no variant NA genes decreased across these four groups from 14.4% to 8.9%
to 7.7% to 2.9%. When the entire 4 x 4 tables was tested by linear trend chi square analysis (Cochran, 1954) p = .0005. If the A-LD+ and A+LD+ groups were combined, Pearson chi square = 17.6, d.f. = 6, p = .007, and linear trend chi square = 12.9, d.f.: 1., p = .0003.

Chi square Analysis of the NA Gene Score versus the ADHD(A)-LD-, ADHD+LD-, ADHD-LD+ and ADHD+LD+ Groups [Number of cases (%)]
NA score A-LD- A+LD- A-LD+ A+LD+ T

0 24 (14.5) 10 (8.9) 1 (7.7) 1 (2.9) 36 (11.1) 1 73 (44.2) 41 (36.6) 4 (30.8) 13 (37.1 131 (40.3) ) 2 67 (40.6) 55 (49.1 7 (53.8) 17 (48.6) 14b (44.9) ) 3 1 (.6) 6 (5.4) 1 (7.7) 4 (11.4) 12 (3.7) T 165 {51.1)112 (34.3) 13 (4.0) 35 (10.7) 325 ( 100.0) Pearson chi square 18.3, d.f. = 9, p -.03 Linear trend Chi square 12.0, d.f. = 1, p = .0005.
Since the association between the NA genes and the GSAP score was often greater than with the LD score, the inventors also examined a cross tabulation between the GSAP score and the NA genes score. This showed that frequency of subjects with a NA genes score of 3 ranged from 1.6% for those with a GSAP
score of 0 to 2, to 0.8% for those with a score of 3 to 4, to 11.8% for those with a score of 5 to 6. For the whole range of both scores, the linear chi square was significant at p = .004. Thus, independent of the presence or absence of ADHD, those children who performed below average in 2 or 3 academic subjects in grade school, were most likely to carry variant NA genes.
To examine the effect of the presence or absence of ADHD. the subjects were again divided into four groups. Here GS+ = those with a GSAP score of 5 or 6 and GS- = those with a GSAP score of 0 to 4, and the four groups were A-GS-, A+GS-, A-GS+ and A+GS+. The results of a chi square analysis versus the NA score is given in Table 83. The frequency of those with a NA genes score of 3 increased from .6%
to 2.4% to 4.8%, to 12.5% across the four groups. The linear chi square for the 4 x 4 table was signif cant at p = .0003.

Chi square Analysis of the NA Gene Score versus the ADHD(A)-GS-, ADHD+GS-, ADHD-GS+ and ADHD+GS+ Groups [Number of cases (%)1 NA score A-GS-- A+GS- A-GS+ A+GS+ T

0 23 ( 14.7) 7 (8.4) 2 (9.5) 4(6.3 ) 36 ( 1 I .1 ) 1 71 (45.2) 31 (37.3) 6 (28.6) 23 (35.9) 131 (40.3) 2 62 (39.5) 43 (51.8) 12 (57.1) 29 (45.3) 146 (44.9) 3 1 (.6) 2 (2.4) 1 (4.8) 8 (12.5) 12 (3.7) T 157 (48.3) 83 (25.5) 21 (6.5) 64 (19.7) 325 ( 100.0) Pearson chi square 25.89, d.f. =-9, p = .002 Linear trend Chi square 12.97, d.f. = 1, p = .0003.
The inventors examined a below average grade school performance in math, reading and writing separately. This was done by a chi square analysis of the NA
genes score versus scoring the math, reading and writing performance as average or above average = 0, and below average = I . For math the Pearson x2 = 9.17, p =
.027, linear x2 = 4.18, p = .04. For reading the Pearson x2 = 15.14, p = .0017, linear r2=

13.14, p = .0003. For writing the Pearson X2 = 13.18, p = .004, linear x2 =
10.54, p = .001. Thus, while below average performance in all three areas was significantly associated with the NA genes score, the effect was greatest for reading, then writing, and least for math.
For comparison the inventors also examined the dopamine genes score against each academic subject. For reading Pearson x2 = 12.08, p = .007, linear x 2 =
8.24, p = .004. For math Pearson X2 = 2.90, p = .41, linear x 2 = 2.21, p = .137.
For writing Pearson r 2 = 1 1.1 l, p = .011, linear ~. 2 = .02. Thus, the dopamine genes also played a role in reading performance, but a much less of a role in reading and math performance.
To examine the relative importance of NA versus dopamine (DA) genes the inventors included the DA genes score in the MANOVA (Table 80-F). With all six genes the inattention, impulsivity, hyperactivity, GSAP, LD, conduct, and ODD
scores were all significant. By linear regression analyses for all six genes (Table 81 ) the correlations were significant for the ADHD, LD, GSAP, and ODD scores. As shown previously (Comings et ul., 1996) the dopamine genes play a significant role in ADHD. This was confirmed with the three DA genes used here. They accounted for 2.5% of the variance of the ADHD score and when added to the NA genes all six accounted for 6.1 % of the variance (p < .0001 ). By contrast, the DA genes played little role in the LD and GSAP scores. However, for the GSAP scores, the percent of the variance increased from 3.4% for the three NA genes alone to 4.2% when the DA
genes were added. While the DA genes played little role in the conduct score, they played a significant role in the ODD score, accounting for 2.6% of the variance (p = .004). All six genes accounted for 4.9% of the variance of the ODD score (p = .0001 ).
When the frequency of the three DA genes were the inventors examined in the four ADHD:LD groups, all three genes were present in 10.0% of the ADHD-LD-group, 19.8% of the ADHD+LD- group, I 5.4% of the ADHD-LD+ group, and 17.1 of the ADHD+LD+ group. Thus, in contrast to the NA genes, there was no progressive increase across the three groups. For the entire 4 x 4 table, the Pearson chi square = 10.48, d.~ = 9, p = .31, and the linear chi square = 0.90, d.~ =
I, p = .34.
MANOVA showed a significant association between quantitative scores for inattention. impulsivity, hyperactivity, learning disorders, grade school academic performance and oppositional defiant behavior with the NA genes examined. The greatest association (p = .0003) was for the additive effect of ail three genes. There was a significant increase in the number of variant NA genes progressing from subjects without ADHD (A-) or learning disorders (LD-), to A+LD, to A-LD+, to A+LD+ (p = .0005), but no comparable effect for dopamine genes.
Discussion. The identification of two subtypes of ADHD, one with and one without cognitive defects, that involve distinct regions of the brain, distinct neurotransmitters, and distinct sets of genes (Table 80), could have considerable diagnostic and treatment value in the care of ADHD children. It must be kept in mind, however. that ADHD is a polygenic disorder (Comings, 1996a) with multiple variant genes being inherited from both parents (Comings, 1996b). As such it is likely that there would be considerable overlap with most children and adults having components of both types.
However, as shown in Table 82 and Table 83, the defects in NA metabolism and variant NA genes are more likely to be involved in ADHD children with cognitive defects than ADHD children without cognitive defects.
Looking at additive scores also has the advantage that if a gene happens not to play a role in a given subject, or group of subjects, but a different gene with a similar function is present, the effect of both can be included. This is well illustrated in the present study. For example, when the phenotypic effect of each of three NA
genes was examined separately, the significance levels of the MANOVAs were modest (Table 80-A, Table 80-B and Table 80-C} and the correlation coefficients frequently failed to withstand a Bonferroni correction for the five scores examined by linear regression analysis (Table 81 ). However, when either the ADRA2A and ADRA2C or the ADRA2A and ADRA2C and DBH genes were added, the results were robust. This was also the case when the progressive additive effect was tested by linear ANOVA.

Using the ADHD score as an example, univariate regression analysis showed that each gene individually accounted for only 0.7 to 2.6% of the variance of the quantitative score (Table 82). However, when the ADRA2A and ADRA2C genes were added they accounted for 3.3% of the variance (p = .001 ) and when all three NA
genes were added they accounted for 4.0% of the variance (p = .0003). The three dopamine genes accounted for 2.5% of the variance of the ADHD score, and when all six genes were combined, they accounted for 6. I % of the variance (p < .0001 }. This indicates that NA and DA genes play an additive role in ADHD.
Twin studies suggest that additive genetic effects account for 70%, or more of ADI-ID (Sherman et al., 1997a; Sherman et al., 1997b; Gillis et al., 1992).
This indicates that the three NA genes accounted for approximately 6% of the genetic component of ADHD, and the NA plus the DA genes accounted for approximately 10% of the genetic component. While this may seem small, as more and more genes are added, the percent continues to rise (Comings et al., 1998). It should also be noted that even though the six genes account for only 6. I % of the variance of the ADHD
score, based on the correlation coefficient of .25, depending on other variables, these six genes have up to 25% predictability for ADHD.
The additive effect of the NA genes was also impressive for the GSAP score with the percent of the variance increasing from 1.0 to 1.6% for the individual genes, to 3.4% for all three.
The results shown in Table 83, showing a progressive increase in the frequency of subjects carrying 2 or more variant NA genes across the A-LD-, A+LD-, A-LD+, and A+LD+ groups. However, an alternative hypothesis is that the ADHD +
LD group, or ADHD + any other comorbid disorder group, simply represents a group with greater genetic loading for all the ADHD genes. The inventors were able to test this alternative by determining if a score for three different dopaminergic genes gave the same progressive increase across the three groups. In the subjects, the inventors have observed that of six different dopamine genes (DRDI, DRD2, DRD3, DRD~, DRDS, and DATl ) the DRD2, DA TI and DRDS had the most significant additive effect on the ADHD score. Individually they accounted for .5 to 1.0% of the variance while together they accounted for 2.5% of the variance. Thus, the inventors made a dopamine genes score that also ranged from 0 to 3. The percent of the subjects that carried all three variant genes ranged from 10.0% for the A-LD- group, to 19.8% for the A+LD- group, to 15.4% for the A-LD+ group, to 17. I % for the A+LD+ group.
Thus, contrary to the alternative hypothesis, there was not an increased genetic loading for these three dopamine genes for the A+LD+ group compared to the A+LD-group. In addition, for the whole range of dopamine gene scores, neither the Pearson nor the linear trend chi square results were significant. This stands in marked contrast to the linear chi square p = .0005 for the NA genes score. These results further support the I-Ialperin-Pliszka hypothesis b supporting the concept that NA
genes are preferentially involved in ADHD+LD individuals, while dopamine genes are equally involved in ADHD whether cognitive defects are present or not.
An additional feature of note was that simply inquiring about an individual's academic performance in grade school for math, reading and writing gave as good a separation by the number of variant NA genes as whether they had been placed in a special LD class. When examined separately the effect was greatest for reading, almost comparable for writing, and least for math performance. These results suggest that grade school children with ADHD who are doing below average in reading, writing and math are the children most likely to be carrying variant NA genes.
This also suggests a need for long term double blind studies of the efficacy of clonidine in the treatment of the academic problems of such children. In the personal clinical experience of one of the inventors {D.E.C.), over a period of several months, the treatment of ADHD children with poor grades with transdermal clonidine alone can result in a significant increase in academic performance.
The association between low verbal IQ and aggressive juvenile antisocial behavior has been replicated in many studies (Hirschi and Hindelang, 1977;
Miller, 1988; Shulman, 1951; Moffitt and Silva, 1988; Moffitt, 1990). This association between iow verbal IQ and delinquency holds up even after controlling for factors of socioeconomic status, race, academic achievement and motivation in taking the test (Moffitt, 1993). Moffitt and Silva (Moffitt and Silva, 1988) also showed that the association of low IQ and delinquency is not an artifact of slow-witted delinquents being more easily caught by the police since undetected delinquents identified by interview were also found to have low IQs. The inventors tested NA genes for a possible association with conduct disorder or oppositional defiant disorder.
While there was no significant association between any of the three genes and conduct disorder, there was a modest association with oppositional defiant behavior.
The use of subjects with TS to study ADI-iD potentially has both advantages and disadvantages. A practical advantage is that the inventors have a large collection of over 1,500 DNA samples on TS patients, all of whom have completed an extensive, highly structured questionnaire based on the Diagnostic Interview Schedule (Robins et ul., 1981 ) and DSM-IIIR criteria. This allows the inventors to select for study subjects with relatively severe symptoms. As such they are more likely to have high genetic loading than those with minimal symptoms. The use of more extreme phenotypes has been widely recommended in genetic studies {Risch and Zhang, 1996;
Plomin et al., 1994). Since the majority, but not all (Comings and Comings, 1984;
1987b; Knell and Comings, 1993) TS patients referred to the clinic also have ADHD, this provides a set of subjects both with and without ADHD. This is particularly useful for the inventors' approach of examining the entire range of a quantitative ADHD trait, since it provides a wider range of scores than if only ADHD
subjects were studied (Comings et al.. 1996j). A TS population is also useful because of the high frequency of comorbid disorders such as ADHD, conduct disorder, oppositional defiant disorder, and learning disorders in both the TS probands and their relatives (Knell and Comings, 1993; Comings and Comings, 1987a; Comings, 1995a, Comings, 1995b). Biederman and colleagues have reported the same high degree of comorbidity for ADHD probands and their relatives (Biederman et al., 1990a,b;
Biederman et al., 1993). A potential disadvantage of using a TS sample is that despite the considerable overlap in clinical and genetic aspects (Comings and Comings, 1993), ADHD subjects with tics might have a slightly different set of variant genes than ADHD subjects without tics. Thus, it would be important to replicate these findings in ADHD subjects without tics. A further limitation of this study was the reliance on parental and self report questionnaires. Studies using WISC-R, WRAT-R
and other direct testing are desirable. However, in the inventors' experience, based on actual personal interviews with all the subjects studied, these questionnaires accurately reflect the subject's respective behaviors. In addition, since the inventors have never observed a case in which a child has been placed in an EH, LD, LD
or special education class who did not in fact have significant learning problems, the inventors believe that the assessments for the LD score in this manner represents a robust test for the presence of cognitive disabilities. Ironically, the assessment of whether a child performed below average in two or more academic subjects (math, reading, writing) in grade school, provided a better separation of the NA
genes score than having been in an LD class. This indicates that actual classroom performance can provide a reliable estimate of cognitive abilities, and that many children who are doing poorly academically, are never placed into special classes.
Since there were so few 22 homozygotes at the ADRA2A gene, and thus a small number of subjects scored as carrying both adrenergic a,2 genes or all three NA
genes, one could argue that the present results might be driven by the chance presence of high scores in these few homozygotes. To test for this, the inventors also scored the ADRA2A gene as 11 = 0 and 12 or 2 = 1. When the two a2 genes were combined using this scoring, the MANOVA was still significant for inattention (p =
.OIO), for impulsivity (p < .OOI ) and for hyperactivity (p = .02 1 ) and by linear regression this combination still accounted for 3.1% of the variance (p = .0014). When the gene scored in this fashion was added to the ADRA2C and DBH genes there were now 66 subjects with a NA genes score of 3. The MANOVA was significant for the inattention (p = .00I ), the impulsivity (p = .001 ) and hyperactivity (p =
.004) scores.
By linear regression analysis this NA score accounted for 4.1 % of the variance (p = .0002). These results indicate that the present findings are not simply due the chance occurrence of high scores in the few ADRA2A 22 subjects.
The inventors contemplate that the present findings could be strengthened by adding several additional NA genes were not included in the study. These include the genes for the noradrenaline transporter (NT), the adrenergic a 1 receptors (ADRAIA to ADRAID), and the PNMT gene, all of which play a role in the regulation of synaptic NA levels.

..~ .~.._~ _ _. _T . , , ASSOCIATION OF THE NEURONAL NICOTINIC ACETYLCHOLINE
RECEPTOR a4 (CHR1VA4) GENE WITH ATTENTION DEFICIT
HYPERACTIVITY DISORDER AND TOURETTE SYNDROME
Introduction. Nicotine is an addicting drug which stimulates dopamine reward pathways, enhances attention, arousal, learning and memory and has been effectively used transdermally in the treatment of Tourette syndrome and ADHD.
The - most prominent neuronal form of the nicotinic acetylcholine receptor is a4~32. The inventors hypothesized that genetic variants at the neuronal nicotinic acteylcholine a4 receptor CHRNA4 gene might be associated with ADHD or Tourette syndrome.
The neuronal nicotinic acetylcholine receptors are composed of a (a2 - a9) and ~3 (~32 - (34) subunits arranged around a central channel (Unwin, 1993).
The a subunits possess a pair of cysteines similar to the a l subunit of the muscle type of nicotinic acetylcholine receptor. The ~3 subunits do not have this pair of cysteines.
I S The major neuronal nicotinic acetycholine receptor subtype is composed of a4 and (32 subunits {Whiting et al., 1991; Schoepfer et al., 1988). Because the a4 and X32 were the most widely expressed subunits in the brain (Wada et al., 1989; Whiting and Lindstrom, 1988; Whiting et al., 1991), the inventors sought to examine the association between alleles of the CHRNA=l gene and CHRNB2 genes in Tourette syndrome and ADHD. Since suitable polymorphisms were available only for the CHRNA=l gene (Weiland and Steinlein, 1996), this is the gene the inventors examined.
Methods. The inventors examined the association of the alleles of a complex VNTR polymorphism of the CHRNA4 gene in 282 unrelated, non-Hispanic Caucasian Tourette syndrome subjects and 63 controls. The study group consisted of 345 unrelated subjects. Of these 282 fulfilled the DSM-IV criteria for TS and all were personally interviewed by D.E.C. The remaining 63 were controls. The TS
subjects came from the Tourette Syndrome Clinic at the City of Hope Medical Center.
These are from the same group of subjects described in Example 27 dealing with the role of norepinephrine genes in ADHD and learning disorders. The behavioral scores and the separation by presence or absence of ADHD and learning disorders, are as described in that article. The ADHD score was based on the DSM-IV (Diagnostic, 1994) WO 98148785 PCTlUS98/08684 criteria for ADHD. The questions asked if, during childhood and adolescence, these symptoms were never or rarely present (score =_0), occasionally present (score = 1 ) or always present {score = 3). All three responses were used to provide a full discrimination between those with all degrees of severity. The ADHD score was the sum of the DSM-IV inattention, impulsivity, and hyperactivity symptoms.
The polymorphism in the first intron of the CHRNA~ gene described by Welland and Steinlein (Weiland and Steinlein, 1996) was utilized. The PCRTM
primers and conditions reported by them was used. In a study of 88 unrelated Caucasians, they identified 14 alleles ranging from 196 to 264 by in length (see Table 84).
Polymerase chain reaction (PCRT~ was used to amplify the target DNA using 0.2 ~M of each fluorescent labeled primers. The PCRTM product was diluted 10 fold with deionized water and 0.5 pi of the diIuent was added to 2.5 p.l of mixture made of 75 pl formamide + 9.5 ~1 of ROX standard + 9.5 pl of Blue dextrin dye. This was denatured for 2 min at 92 °C and the sample loaded on 6% PAGE of Applied Biosystems 373 DNA sequencer (Applied Biosystems, Inc., (ABI) Foster City, CA) and gel was run for S h at 1200 volts and constant 30 W. The gel was preprocessed and analyzed using the internal standard, ROX 500. The peaks were recognized by genotyper (version 1.1 ) (Applied Biosystems, Inc.) based on the color and the size of the fragments.
Results. The inventors have genotyped 345 individuals in the present study and several hundred in other studies. Not surprisingly, in addition to those described by Wetland and Steinlein (Weiland and Steinlein, 1996) the inventors found a number of additional alleles - 21 in all. Of these 17 were observed in the subjects in the present study. Since Wetland and Steinlein (Weiland and Steinlein, 1996) utilized silver staining and began counting alleles from the top to the bottom of the gel, their low numbered alleles were of the highest by and the highest numbered alleles were of the lowest bp. Since the ABI sequencer scans bands as they reach the bottom of the gel, in the inventors' procedure the low number alleles represented the lowest by alleles while the highest number alleles are the largest alleles. While the inventors r. .. , , ~

initially made every attempt to use the same numbering system as that of Welland and Steinlein (Weiland and Steinlein, 1996) since the sizes of the alleles in bp, based on sequencing, were different from theirs, especially at the extremes, this was difficult.
Thus, the inventors have used the ABI numbering system and for comparative purposes, the inventors have listed the two nomenclatures in Table 84.
Table 85 shows the results with sequencing four of the PCRT"' products for different alleles. This illustrates the complexity, of this VNTR polymorphism.
Of the major alleles, the frequency of the 9, 12, 14, and 16 alleles were higher in TS subjects while the frequency of the 13, 15 and 18 alleles were higher in the controls. By chi square analysis of the 17 alleles x2 = 33.65, d.f. = 16, p =
.0061.
When the analysis was restricted to the alleles present in either the controls or TS
subjects at a frequency of .OS or more, x2 = 71.6, d.f: = 6, p < .0000001. The greatest difference in allele frequencies was for allele #9.
To keep the analysis straight-forward and to allow the examination of I S quantitative variables and the effects of homozygosity versus heterozygosity, and to produce a variable for regression analysis, the CNRNA=1 gene score assigned those with an x/x genotype (non-9/non-9) = 1, 9/x = 2 and 9!9 = 3.
In addition to the set of behavioral variables examined Example 28 on norepinephrine genes (Comings et al.. 1998), the inventors also examined a total tic score (Comings et crl., 1996). The association between the CNRNA~ gene score and these variables were simultaneously examined using MANOVA. The results (Table 86) showed that there was a significant association between the CNRNA4 gene and the grade school academic performance (GSAP), learning disorder (LD), oppositional defiant (ODD) and hyperactivity scores.
To evaluate the role of the different genotypes on these scores, a post hoc ANOVA analysis of the total ADHD score and each variable significant by MANOVA at p < .1 was performed (Table 87). This showed that every score was highest for 9/9 homozygotes.

WO 98148785 . PCT/US98/08684 Since an additional question was whether there was any association between the CNRNA~t gene and smoking, the inventors examined two variables, 'ever smoked?' and 'packs smoked per day 7' in subjects who were over 17 years of age.
Neither was significant, the mean number of packs smoked per day was 0.5 for the 9/9 homozygotes versus .10 to .20 for the other genotypes. Since there were only 9 subjects who were 9/9 homozygotes, a larger sample might give significant results.
When a chi square analysis of the three CNRNA=I genotypes versus the four categories of ADHD-LD-, ADHD+LD-, ADHD-LD+ and ADHD+LD+ were examined, the frequency of the 9/9 genotype increased from 5.6% in the ADHD-LD-group, to 6.0% for ADHD+LD- group, to 7.1 % for the ADHD-LD+ group and 13.5%
for the ADHD+LD+. The total correlation was not significant by Pearson chi square (p = .385), and of borderline significance by linear chi square (p = .051).
Thus, unlike the norepinephrine genes examined (Comings et al., 1998), there was little evidence that the CNRNA;t gene played a role in the type of ADHD-LD associated with the parietal lobe.
Univariate regression analysis using the genotype variable versus the total ADHD score gave the following results: r = .123, r2 = .015, T = 2.30, p =
.022. There was an increase in the frequency of the 9, 12, 14 and 16 alleles, greatest for the 9 allele, in TS subjects, and an increase in the 13, 15 and 18 alleles, greatest in the 13 allele, in controls. When all alleles were compared by chi square they were significantly different at p = .002. When only the alleles with a frequency of greater than .05 were compared the groups were significantly different at x2 = 71.6, d.f. = 6, p < .0000001. Eight quantitative scores relating to ADHD, tics, conduct and learning versus genotype groupings based on the 9 allele (9/9, 9/x, x/x) were examined by MANOVA. The association was significant for the grade school academic performance, learning disabilities, oppositional defiant, and hyperactivity scores.
ANOVA showed that the magnitude of all scores was greatest for the 9/9 homozygotes. Linear regression analysis of the genotype scoring showed the CHRNA4 gene accounted for up to 1.5% of the variance of the ADHD score {p =
.022).

.. .. . .. . ~. , Conclusion. These results suggest the CHRNA=t locus is one of a polygenic set of genes that contribute to the risk for ADHD and TS.
. Sequencing of the PCRT"' amplified segment used in these studies indicates this is a very complex polymorphism involving both differences in size and differences in sequence. The repeat polymorphisms themselves may play a role in the regulation of the genes with which they are associated. This is based on the tendency for these repeats to form Z-DNA. Both changes in length and sequence of the repeats play a role in determining the amount of Z-DNA formed which plays a role in gene regulation in a number of ways (Comings, 1997). The complex polymorphism examined here showed significant changes in both size and sequence. As such it may be uniquely suited for association studies.
To avoid the loss of power involved in the independent assessment of many of the other scores, the inventors restricted their analysis to the same behavioral scores examined in Example 2'7 plus a total tic score. Since nicotine has been used in the treatment of tics, this was added to determine if this gene was associated with tics per a~e. MANOVA was used to assess the scores as a group, obviating the need for a Bonferroni correction. This showed a significant association with four of the eight scores (Table 86). When a post hoc ANOVA study of each variable was undertaken, the scores were highest for the 9/9 homozygotes. The combined results suggest the CHRNA~ is one of the genes that plays a role as a genetic risk factor in ADHD
and TS, and especially TS subjects with ADHD. Regression analysis suggests it may contribute to up to i .5% of the variance of the ADHD score.
Although the CHRNA4 showed somewhat greater association with learning disorders and grade school academic performance than the ADHD subscores, unlike the norepinephrine genes examined in the companion article, there was little evidence for a progressive increase in the frequency of the 9/9 genotype across the four groups of those with or without ADHD and with or without learning disorders.
. While the inventors do not yet know whether the genotype groupings described are associated with an increase or decrease in expression of the gene, the present studies support the clinical effectiveness of nicotine on ADHD and TS symptoms. It is also likely that in other populations the differences between controls and TS or ADHD may involve some of the alleles other than the 9 alleie.
Over multiple samples, the most reproducible result may be the comparison by chi square analysis of the frequencies of the common alleles in controls versus TS
or ADHD subjects.

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bP bP bP bP

I 4 196 .04 13 198 .O1 1 203 .008 2 207 .000 12 208 .01 3 215 .024 4 217 .063 221 .000 11 208 .O1 6 223 .008 10 220 .04 7 225 .238 9 222 .Oi 8 227 .024 9 231 .015 8 228 .06 233 .008 11 235 .032 12 237 .063 13 239 .302 7 236 .45 14 241 .024 243 .238 6 240 .19 _ 16 245 .008 5 242 .O1 17 253 .000 4 248 .Ol 18 255 .063 3 250 .11 19 257 .024 2 252 .03 259 .000 1 264 .0l 21 272 .008 WO 98/48?85 PCT/US98I08684 H

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Table 86 MANOVA for the Eight Behavior Scores (N = 345) CHRNA4 9/9=3, 9/x=2, xlx=1 F-ratio p GSAP 3.60 .028 LD 3.60 .028 ODD 3.46 .033 Hyperactivity 3.10 .046 Inattention 2.92 .055 Tics 1.63 .198 Impulsivity 0.92 .401 CD 0.55 .573 Total (Wilks) 1.22 .246 Table ANOVAs for he ividual t Ind Scores for the ADHD
Factor CHRNA4 (n =
345) Score Genotype N Mean S.D. F p F~ p~

ADHD xlx 204 17.28 11.12 13/x 113 18.94 11.05 9/9 23 22.61 9.09 2.83 .060 5.27 .022 *

Hyperactivityxlx 209 5.75 4.43 91x 113 6.18 4.18 9/9 23 8.08* 3.86 3.1I .045 4.85 .028 Inattention x/x 209 8.72 5.53 9/x 112 9.73 5.5 5 9/9 23 11.08 4.88 2.69 .069 5.34 .021 ODD x/x 209 3.45 3.21 9/x 113 3.83 3.19 9/9 23 5.26* 2.87 3.46 .032 5.60 .0185 LD ~ xlx 209 0.61 0.93 91x 113 0.84 1.03 score Genotype N Mean S.D. F p F~ p~

9/9 23 1.04 1.10 3.61 .0287.21 .0076 GSAP x/x 209 2.78 1.99 9/x 113 3.35* 1.90 9/9 23 3.40 1.92 3.61 .0286.49 .011 Other Ever smoked x/x 91 1.39 0.49 (age> I 7 yrs) 9/x 49 I .31 0.46 9/9 6 1.66 0.52 1.66 .1920.000 .995 Packs/day xlx 91 0.12 0.44 9/x 49 0.10 0.37 9/9 6 0.50 0.84 2.24 .1101.02 .314 * significantly different .OS
from x/x at a = by Tukey test.

Fi - linear ANOVA, p, = p for linear ANOVA

.f. .. ~ , t WO 98/48785 PCTlUS98108684 CORRELATION OF LENGTH OF VNTR ALLELES AT THE X-LINKED
MAOA GENE AND PHENOTYPIC EFFECT IN TOURETTE SYNDROME
AND DRUG ABUSE
Introduction. Abnormalities in monoamine oxidase (MAO) levels have been implicated in a wide range of psychiatric disorders. The inventors have examined a VNTR polymorphism at the X-linked MA OA gene to test two hypotheses: ( 1 ) Do variants of the MA OA gene play a role in any of the behavioral disorders associated with Tourette syndrome or drug abuse? (2) If so, is there any correlation between the length of the alleles and the phenotypic effect? The inventors examined two independent groups: 375 TS patients, relatives and controls, and 280 substance abusers and controls. The alleles were divided into four groups of increasing size.
There was a significant association between the MA OA gene and behavioral phenotypes in both groups, and in both the longest alleles were associated with the greatest phenotypic effect. The strongest effect was for the diagnosis of drug dependence (P = 0.00003). The VNTR allele groups were in significant link-age disequilibrium with the Fnu4H1 polymorphism previously shown to be associated with MAO-A activity. These results are consistent with the possibility that different-sized alleles of the short-repeat polymorphisms themselves may play a role in gene regulation.
Because of the potential effect of age (Devor et al., 1994), alcohol, anti-depressants, drugs, gender, laboratory technique, diet, and other variables, on platelet enzyme levels (Fowler et al., 1982), the use of genetic polymorphisms at the MAO genes may give more reproducible results than enzyme levels. The cloning and sequencing of the MA OA and -B genes and identification of associated polymorphisms now allow such genetic studies.
To determine if genetic variants at the MAOA gene were associated with TS or ADHD, the inventors have examined the MA DA VNTR polymorphism (Hinds et al., 1992) in a series of controls, TS probands and their relatives, using the techniques reported in the DRD2, D~H and DA TI genes in TS (Comings et al., 1996a). Since a simple comparison of the frequency of the different alleles in controls vs TS
probands might miss the possibility that the MAO genes were only associated with a few specific behaviors not present in all cases, the inventors tested for the possible role of these genes in 27 different behavioral variables.
The inventors have become interested in the hypothesis that the length of the alleles per se might be related to phenotypic effect. The rationale for this is that the sequence of most simple repeats result in the formation of Z-DNA with the amount being dependent upon the length of the repeat (Schroth et al., 1992). The Z-DNA
conformation opens the DNA helix and exposes the individual bases, making it uniquely capable of interacting with nuclear proteins (Rich et al., 1984). For these and other reasons Z-DNA has been implicated in gene regulation (Comings, 1996a;
1996b). If the mini- and microsatellite polymorphisms do play a role in the variations in gene function involved in polygenic inheritance, their effect must be subtle, since if the effects were major, they would result in single gene rather than polygenic I S disorders.
X-linked genes form a unique vehicle to examine this hypothesis and search for subtle effects since, at least in males, each allele is hemizygously present thus eliminating the confounding factor of heterozygosity, which can be extensive when multiple alleles of different size are present. To test the hypothesis that repeat length might be related to phenotypic effect the inventors divided the VNTR alleles into four groups of increasing length. This allowed the inventors to determine if the shorter or longer alleles were preferentially associated with a greater phenotypic effect.
Methods. The subjects included 57 controls, 229 TS probands most of whom were severely affected with multiple associated behavioral disorders (Comings, 1990), and 90 affected and unaffected relatives of TS probands. All subjects were non-Hispanic Caucasians and over 90% were of Western European origin. The controls for the TS group consisted of adopting and step parents of TS
probands, subjects with non-psychiatric disorders from other clinics at the City of Hope, and professional and non-professional hospital staff from the City of Hope Medical . .._. ~.._.. . m~.-.-.~.....~..... _ _.... ~ . , .

Center. Both the TS subjects and the controls have been described in detail elsewhere (Comings, 1995b; Comings et al., 1996a; 1997b).
Each TS control and TS proband or relative was required to fill out a questionnaire based on the Diagnostic Interview Schedule(Robins et al., 1981 ) or DSM-III-R(Diagnostic and Statistical Manual of Mental Disorders, 1987) criteria.
This provided a structured review of a wide range of psychiatric symptoms.
These symptoms were grouped into 27 different behaviors including ADHD, substance abuse, mood, anxiety, school performance, stuttering, tics and others. The questions used for these behavioral scores have been described in detail elsewhere (Comings, 1995a; 1994a; 1994b; 1995b; Comings et al., 1996a; Robins et al., 1981;
Comings, 1995c). Two behavioral scores were used to assess ADHD. The first, called ADHD, was based on the presence of at least half of a series of 22 ADHD variables from DSM-III and DSM-III-R criteria. The second, ADHD-R was based on the DSM-III-R
diagnostic criteria. Three QTVs not used previously were inattention, impulsivity and hyperactivity. These were the three subscores that cumulatively produced the ADHD
score. QTV abbreviations include CD for conduct disorder, ODD for oppositional defiant disorder (Comings, 1995a), and MDE for major depressive episode (Comings, 1995c) symptoms.
The rationale for examining comorbid behaviors is the prior observation that certain genes may be more strongly associated with specific comorbid behaviors present in TS than with the diagnosis per se (Comings et al., 1996a). This questionnaire is not meant to provide DSM-III-R or DSM-IV diagnoses but rather to provide a highly structured method of producing QTVs for different areas of behavior.
The advantage of continuous traits is that they provide a greater range of severity than - 25 dichotomous diagnoses. The accuracy, utility and sensitivity of a questionnaire-based approach to symptom evaluation has been demonstrated by others (Gadow and - Sprafkin, 1994; Grayson and Carlson, 1991) by comparing the use of such an instrument to an interviewer administration of the same structured instrument.
The . inventors' review of the questionnaires with many hundreds of subjects has indicated that they accurately reflect the information obtained by personal interview.

A second group of subjects consisted of 120 non-Hispanic Caucasian males from an inpatient Addiction Treatment Unit (ATU) of the Jerry L Pettis Veterans Administration Hospital in Loma Linda, California, Since October 1994, all new admissions to the ATU who give informed consent, were entered into a National Institute of Drug Abuse sponsored study of genetic factors in drug abuse/dependence.
All ATU subjects were assessed with the Michigan Alcoholism Severity Test (Davis et al., 1987}, a 24-item self administered questionnaire revised to include drug abuse {MAST-R), the clinician-administered Diagnostic lnrerv;P.N c~-hP.~l7~P
(DSM-III-R version) (Robins et al., I981), to diagnose the presence of substance dependence disorders, and the clinician-administered Addiction Severity Index Fifth Edition (ASI) (Hodgins and Guebaly, 1992), to evaluate a range of alcohol and drug use variables.
The inventors utilized the Drug/Alcohol use and the legal status sections of the ASI. The areas covered were the following:
a) Specific substances used To assess the use of specific substances, questions were asked about the lifetime use (in years) of alcohol use to intoxication, heroin, other opiates/analgesics, barbiturates, other sedatives/hypnotics/tranquilizers, cocaine, amphetamines, cannabis, hallucinogens, and inhalants.
b) Route o~~ administration. For each of the above, where relevant, the subjects were asked about the route of administration. The options were oral, nasal, smoking, and IV injection. The continuous variable #IV drugs used was calculated by adding up the total number of different drugs injected IV. The variable IY
drug use was a dichotomous variable of 0 for no IV drug use and <_1 for use of one or more drugs IV.
c) Problems. 'How many times have you had alcohol DTs? Overdosed on drugs?' 'How many days in the past 30 days have you experienced alcohol problems? Drug problems?' d) Money spent. 'How much would you say you spent during the past 30 days on alcohol? On drugs?' Ww~.... _ ___.~_....a.._. ...... ..... ~ . , , e) Severity. An interviewer-based severity assessment for the need for treatment ranged from 0 (no treatment necessary} to 9 (treatment needed to intervene in a life-threatening situation}. Alcohol abuse? Drug abuse?
Legal status. Questions were also asked about various legal aspects of drug and alcohol abuse. 'How many times in your lifetime were you charged with driving while intoxicated?' 'How many times in your lifetime were you arrested and charged with drug charges? How many of these charges resulted in convictions?' g Summary scores. When the responses could range from 0 to any number, they were scored as a '0' for a 0 and a ' 1' for any other number.
Those questions relevant to alcohol use were summed for a total alcohol score and those relevant to drug use were summed for the drug score.
The controls for the substance abuse group were independent of the controls for the TS patients. They consisted of two sets. The first were 45 older male, non-Hispanic Caucasian students from the California State University at San Bernardino (mean age of 30.1 years). Those with significant problems with substance abuse were excluded on the basis of the MAST-R test. The second set consisted of the male parents of twins from the Minnesota Twin Family study. Since these are ascertained from the entire state simply on the basis of having had twins 1 1 or 17 years of age, they represent a more random set of all socioeconomic and educational groups than the college students. Although all the controls were scored as negative on the substance abuse variables, since the results of substance abuse assessments were not yet available on the twin controls, some may have been positive. However, since this is a random cross-section of a predominately rural state the inventors assume the number of false negatives in this group is small.
The rationale for choosing the VNTR polymorphism at the MA OA gene is as follows. A short tandem repeat polymorphism was chosen to specifically examine the hypothesis that the length of the repeat might be associated with a phenotypic effect.
An X-linked gene was chosen since, at least when males are studied, the complication of how to interpret heterozygotes is avoided. An MAO gene was chosen because it is X-linked. The MAOA gene was chosen because two different repeat polymorphisms WO 98/48785 . PCT/US98/08684 have been reported to be associated with it. The inventors chose the VNTR
polymorphism (Hinds et al., 1992) because it gave a wider spread in allele size (40+bp) than the (CA)n repeat (16 bp).
This complex polymorphism consists of a GT microsatellite directly adjacent to an imperfectly duplicate novel 23-by VNTR motif, with alleles differing in both the number of dinucleotide repeats and VNTR repeats. The VNTR polymorphism was present in a 2.9-kb SaII-EcoRI fragment from phage 6.12 which contained the first exon of the MA OA gene (Hinds et al., 1992). DNA was extracted from whole blood by standard procedures. Target DNA was amplified by PCRT"' (Mullis et al., 1986).
To label the PCRT"' products, 0.1 ~,M of each primer labeled with fluorescent HEX or FAM Amidite (Applied Biosystems, Foster City, CA, USA) primers were used in the reactions (Table 88). Two microliters of the 10-fold diluted PCRTM product were added to 2.5 p.I deionized formamide and 0.5 p,l of ROX 500 standard (Applied Biosystems) and denatured for 2 min at 92°C and loaded on 6%
polyacrylamide gel in an Applied Biosystems 373 DNA sequencer. The gel was electrophoresed for 5 h at 1100 V and constant 30 W. The gel was laser scanned and analyzed using the internal ROX 500 standards. The peaks were recognized by Genotyper (version 1.1 ) {Applied Biosystems) based on the color fragments sized by base pair length. Complete information for each sample was printed from every gel file and the compiled data were submitted for analysis.

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C_4 To examine the hypothesis that the length of the MA OA alleles might correlate with a phenotypic effect, the alleles were divided into four groups (see Results).
These were labeled 1 to 4, shortest to longest to form the MAOA genotype variable.
Females were utilized only in the TS group. Only those that were homozygous for a given allele group were included in the analysis.
The inventors used two rules for bining: a) there must be enough groups to examine a range of lengths; and b) to maximize statistical power the number of subjects should be similar in each group. Thus, if there was a single teak of allele frequencies the division would have been into the shortest 1/3, the middle 1/3 and the longest 1I3. However, the distribution of allele sizes for the MA OA VNTR was into two peaks. For males only, the smaller peak contained 32% of the alleles and ranged in size from 299 to 314 by in length. The larger peak contained 68% of the alleles ranging in size from 323 to 338 by in length. Since the majority of the alleles were in this peak the inventors divided it as if there was a single peak (i.e.
shorter, middle, and I S longer group of alleles). The center of this peak contained a single 334 by group consisting of 31 % of the alleles that could not be subdivided. The application of these rules resulted in four bins <320 bp, 320-333 bp, 334 by and >_335 by consisting of 32, 23, 31 and 14% of the alleles.
It was not possible to use the binning described by Hinds et al. (1992) because three of their five groups had a very iow allele frequency. In fact, the inventors had no subjects in any of these three minor groups.
Fnu=lHl polymorphism The test for this polymorphism was based on the procedure of Hotamisligil and Breakefield (1994). The inventors have termed their'-' as the inventors' 1' allele and their '+' as the inventors' '2' allele. In their study the +
allele had the higher MAOA activity.
For the Tourette syndrome group, ANOVA was used to examine the relative magnitude of each QTV for the four different allele groups. Linear ANOVA was used to test for a significant progressive increase in means across the four allele groups.
The SPSS (SPSS, Inc, Chicago, IL, USA) statistical package was used. For linear ANOVA the subcommand polynomial was set to I . MANOVA was used to determine r . , , r WO 98/48785 PCTlUS98/08684 if any of the QTVs were significant when all the variables were examined simultaneously. Multivariate linear regression analysis was used as a second approach to determine if any of the QTVs was significant when all the variables were examined simultaneously. The MA OA genotype was set as the dependent variable and the 27 QTVs were entered stepwise as the independent variables.
Chi Squared analysis indicated that the group with the longest alleles had the highest means for the majority of the QTVs. The potential progressive decrease in frequency of the >_335 by allele group was compared across four groups with progressively fewer TS symptoms: TS probands with ADHD, TS probands without ADHD, relatives with TS and relatives without TS.
For the Substance Abuse Group, MANOVA was used to determine if there was a significant association between the four MAOA allele groups and the two summary variables, the alcohol and the drug score. ANOVA was used to examine the means of the alcohol and drug scores for the four allele groups.
Linear chi square was used to examine the potential progressive increase in the frequency of the __>335 by group across three groups: controls, the substance abusers without the behavior (ATU without), and the substance abusers with the behavior (ATU with). The ATU without group was included to rule out the possibility that this allele group might be increased in frequency in the substance abusers because of comorbidity for a different behavior. To help exclude this, the frequency of the allele group had to be at least 20% higher in the substance abusers with the behavior than without the behavior. Since the hypothesis was that the frequency of these alleles would progressively increase across these three groups, the linear chi square statistic was used.
To determine the maximum percent of the variance of drug-related variables accounted for by the MA OA gene, regression analysis was performed in which subjects carrying the <335 by alleles were scored as 1, and those carrying the >_335 alleles scored as 2. This was performed for the drug dependence variable (controls: 1, ATU without scored 2, and ATU with scored 3) since this was the chi square variable most highly associated with the MA OA gene.

Results. Distribution of the alleles of the MAOA VNTR polymorphism (total number of was alleles = 768). Since this was a complex VNTR the alleles did not fall into a clear-cut pattern of even or odd numbers of base pairs. The results are shown exactly as they were generated by the Genotyper program. There were no alleles between 316 by and 323 bp, thus producing two clear major groups of <320 and >320 bp. However, to allow an examination of the hypothesis that phenotypic effects might be related to size, the alleles of the larger 323-339 by group were divided into three sub-groups consisting of alleles shorter than the main peak 320-333 bp, the main peak of 334 bp, and alleles longer than the main peak of >_335 bp. There were males and 156 females for a total of 375 subjects in the TS group. Of the females, 88 were heterozygotes. When these were removed it left 287 subjects in the study of whom 36 were controls. In this final group, there were no significant differences in the frequency distribution of the four allele groups in males vs females.
The ANOVA results for each of the QTVs vs the four allele groups for the TS
group are shown in Table 88. The results for regular ANOVA are shown under F-ratio and P value. The F-ratio for linear ANOVA is shown under the FZ
column, with a superscript of a for those that were significant at <0.05. The QTVs are ordered by the decreasing magnitude of the F-ratio in the FZ column. Those allele groups where the means were significantly less than for the >_335 by group, as determined by the Tukey test with a set at <_0.05, are shown by an asterisk. With the exception of stuttering, shopping and panic (which gave the lowest F-ratio), for the remaining 24 QTVs the means were highest for those subjects carrying the >_35 alleles.
The results of MANOVA for all 27 QTVs were significant for sexual {P = 0.012), learning problems {P = 0.023), gambling (P = 0.025), and mania (P = 0.025).
When all 27 QTVs were examined simultaneously in a stepwise multivariate regression analysis, the variable grade school problems (P = 0.012) and gambling (P = 0.038) were signif cant. Based on the r' values, the MA OA gene accounted for only 3.9% of the variance of these QTVs.

.. ., .... ...._...... j,.. ,.. .r Using Chi Square analysis, there was a significant progressive decrease in the percent of subjects that carried the >_335 alleles, progressing from TS
probands with ADHD (24%, n = 129), to TS probands without ADHD (20.0%, n = 50), to relatives with TS (12.5%, n = 16) to non-TS relatives (5.6%, n = 56) (P = 0.003).
S Controls vs ATU subjects in the Substance Abuse Group were compared. For the 160 combined controls, the distribution of the four allele groups was as follows:
<320 34.4%, 320-333 38.1%, 334-335 21.3%, ?335 6.3%. For the 120 ATU subjects, the frequencies were as follows: <320 39.2%, 320-333 i 8.3%, 334 20.8%, >_335 21.7%. These were significantly different, X~ = 22.17, P = 0.00006. The frequency of the >_235 by group was comparable in the two control groups, 8.9% for the San Bernardino group and 5.2% for the parents of the twins (X' = 0.744, P = 0.38).
MANOVA for the alcohol and drug score indicated that while both showed a significant association with the MA DA gene VNTR alleles, this was more significant for the drug score (P = 0.001 ) than for the alcohol score (P = 0.012) (Table 89). The result for the combined MANOVA was also significant (P = 0.007). The n of 257 is smaller than the total of 160 controls + 120 ATU or 280, because only 97 ATU
subjects had completed the ASI. By contrast, all 120 completed the DIS for verification of the DSM diagnosis of alcohol and/or drug dependence.

MANOVA for Alcohol and Drug Scores for the Substance Abuse Group vs the MA OA Allele Groups (n = 257 Males Only) Variable F ratio p Alcohol score 3.72 0.012 Drug score 5.85 0.001 Total (Wilks) 2.99 0.007 ANOVA for the two scores showing the means for each allele group, are shown in Table 90. As for the TS group, the highest means were present in the >_335 by allele group. For the drug score, the three other allele groups were significantly lower than for the >_335 by group by the Tukey test.

ANOVA for Alcohol and Drug Scores of the Substance Abuse Group vs MA DA
Allele Groups Allele group n Mean s.d F ratio P
Alcohol score <320 94 2.27 3.1 320-333 81 1.49a 3.0 334 56 1.94 2.7 >_335 26 3.73 3.52 3.72 0.012 Drug score <320 94 3.59a 5.2 320-333 81 1.94a 3.8 334 56 3.34a 4.9 >_335 26 6.42 6.2 5.85 0.0007 aSignificantly lower than the mean for <335 allele group at ce = 0.05 by the Tukey test.
To determine if the MAO gene was preferentially associated with certain types of substance abuse, 14 of the variables relevant to the type of substance used were examined using Chi square analysis. The frequency of the >_335 by allele group in the controls vs ATU subjects without the behavior (ATU without) vs ATU subjects with the behavior (ATU with), is shown in Table 91. Since 14 types of substance use variables were examined only those with a P of less than 0.0036 (0.05/14) are considered significant with a Bonferroni correction. Only those with a P of <0.01 are shown. The exception is alcohol dependence only. This is shown to illustrate the fact that there was little increase in frequency of the >_335 by alleles in subjects with alcohol dependence only compared to those with drug dependence, or drug and alcohol dependence. By contrast. the drug dependence only variable gave the highest value (X~ = 17.4, P = 0.00003).

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~ O C7 D Q O U ~ ~ O d The results of regression analysis of the allele group (<335 vs >_335) vs the diagnosis of drug dependence gave the following results: r = 0.25, r' =
0.0625, T = 4.305, and P = 0.0001.
To examine the potential linkage disequilibrium between the VNTR and Fnu4H I alleles, the inventors genotyped 273 males that were also genotyped at the VNTR polymorphism. The inventors restricted the analysis to males since the results were clearer than in females. There was a highly significant non-random association of the alleles at the two polymorphisms (X2 = 132.91, P < 0.000001). The <320 VNTR allele group was associated with the less common Fnu4H 1 2 allele, while the remaining three VNTR groups were associated with the Fnu4H 1 1 allele.
Based on the comparison of the data, it would be expected that if the VNTR
alleles were divided into <320 by and >320 by it should give results similar to the Fnu4I-I1 polymorphism with the Fnu4H1 2 ~ <320 and Fnu4H1 1 allele ~ >320. For the TS group there were 71 subjects genotyped at both polymorphisms. Since mania gave the most significant results (Table 88) this variable was used for the comparison.
The mean for the 53 subjects carrying the Fnu4H1 I allele was 2.01 (s.d. 2.17) and for the 2 allele was 1.55 (s.d.). The comparable figures for the VNTR were 1.94 (s.d.
2.12) for the >320 bp, and 1.75 (s.d. 1.98) for the <320 by group. (The mean for the 16 >_335 subjects was 2.43 (s.d. 2.42).) Because of the relatively small numbers neither grouping was significant. Since the Hotamisligil and Breakefield study (1994) showed the Fnu4H 1 allele {the inventors' I allele) was associated with lower MAOA
activity, the inventors assume the >_335 VNTR allele group was associated with the lowest MAOA activity.
Tourette syndrome is uniquely suited for such studies because it is highly heritable and is often associated with a wide range of impulsive, aggressive, affective, hypersexual and other behaviors. The present results suggest that the MA OA
gene is one of the genes playing a modest role in the etiology of a number of the associated behaviors in TS.
While MANOVA showed a significant association between the MA OA alleles and both the alcohol and drug scores, there is a great deal of comorbidity of these two _.-.~......~...._-.~.._.... ~ , . r forms of substance abuse. As shown in Tabie 91, when drug dependence and alcohol dependence were examined separately the association was much greater with drug than with alcohol dependence.
While the predominance in males is probably due in part to hormonal and - 5 environmental factors, X-linked genes could also be a factor. For the TS
group, determination of r' using a regression coefficient. indicated that for the different QTVs the MA OA gene accounted for at most 2.5% or less of the variance of any QTV
suggesting that the X-linked MA OA gene does not account for the male predominance of TS, ADI-1D or related disorders. By contrast, the r 2 for the absence or presence of the >_335 by alleles vs the diagnosis of drug dependence, suggested that up to 6.2% of the variance could be due to the MfIOA gene. This could play a modest role in the male predominance of drug dependence.
The inventors have begun to suspect that the different length alleles of micro-and minisatellite polymorphisms might play a role in the regulation of the genes with which they are associated. While the association of the longer minisatellite alleles with specific QTVs in the Tourette syndrome group was modest, as shown in Table 89, there was a remarkable degree of uniformity in the trends across ali the QTVs.
Since this could have been a chance, random association, the inventors sought to determine if they could replicate these results in a totally separate group of subjects and controls. This group (the substance abuse group) showed an even stronger association between the longer alleles of the MA OA VNTR, especially the >_335 by alleles, than was observed in the TS group. The pattern for the two groups is remarkably similar, with the highest scores for >_335 by alleles, modestly higher scores for the lowest size alleles (<320), and intermediate scores for the 334-335 by alleles.
To gain some insight into whether the >_33~ by alleles might be associated with a higher or lower MAO-A activity the inventors also genotyped 273 of the inventors' males for the Fnu4H1 polymorphism. The linkage disequilibrium with the VNTR allele groups was highly significant (P < 0.000001 ). The less common Fnu4H 1 2 allele was associated with the <320 VNTR group while the more common I

WO 98/48785 PCT/~3598/08684 allele was associated with the 320-333, 334 and >335 VNTR groups. Since others have shown that a range of behavioral disorders are associated with low MAD-A
activity, and since the inventors observed that the greatest phenotypic effect of the VNTR polymorphism was associated with the >_335 by group, this suggests that this group is also associated with the lowest MAO-A activity. These results indicate that when the subjects carrying the Fnu4H 1 1 allele are placed into subgroups on the basis of the VNTR polymorphism, it is the subjects carrying the >_335 by alleles that are driving the Fnu4H 1 results. While the ultimate proof of these suggestions will require studies of the VNTR allele in subjects tested for serum or fibroblast MAO-A
activity, the findings are consistent with the possibility that the reason the Fnu4Hl polymorphism is associated with differences in MAO-A activity is that the 1 allele is in linkage disequilibrium with the >_335 VNTR allele, and the large difference in number of repeats between the <320 alleles (associated with high MAO-A
activity) vs the >_335 by alleles (presumably associated with lowest MAO-A activity) plays a role 1 ~ in the regulation of the MAO-A gene.
This correlation with the size of the repeat alleles is consistent with the possibility that the minisatellites themselves might play a role in the regulation of the MAO genes. However, it is clear that this does not prove the hypothesis since linkage disequilibrium with another as yet unidentified site could still be occurring.
Studies with expression vectors, and the possible interaction of the longer alleles with transcription factors, is needed to prove the case for the MA OA gene.
The results for the TS group indicated a relatively low magnitude of the effect of the MA DA gene on a range of behaviors. Although four variables were significant by MANOVA, two were significant by multivariate regression analysis, and 12 of the 27 were significant by linear ANOVA, one could object that when a complete Bonferroni correction is applied to the ANOVA results none are significant at 0.05/27 or 0.0018. However, this is exactly the point, i. e. that despite the large literature implicating MAO in different behaviors, when examined at the level of a specific gene polymorphism, the ~1~IAOA gene appears to make only a modest contribution to a wide range of behavioral variables.- While the effect was much stronger in drug abuse, even here the percent of the variance accounted for by the MAOA alleles was ..~_._. .... -..~~.-.~...~... .. _., r , , , still modest. Replication is an important aspect of association studies, and these results were found in two completely different sets of subjects. These findings are consistent with the concept of polygenic inheritance in which a number of genes are involved in various behaviors, each with a small effect; and with the hypothesis that the minisatellite polymorphisms themselves may play a role in providing the functional allelomorphic variants fundamental to polygenic inheritance.

A PROPHETIC EXAMPLE RELATES TO AMINO-ACID THERAPY AND
PREMENSTRUAL DYSPHORIC DISORDER (PMDD) Premenstrual dysphoric disorder (PMDD) is a premenstrual mood disorder that cyclically recurs during the majority of menstrual cycles. It is included under the category of "depressive disorders not otherwise specified" in the DSM-IV.
However, a number of factors (biological and cognitive studies, treatment responses) differentiate PMDD from other mood disorders (Yonkers, 1997).
I S Despite the predictability of luteal phase symptom expression, the etiology of this disorder has not been established. Theories regarding hormonal ad vitamin deficiencies have been associated with PMS and may or may not be relevant to PMDD. Nonetheless, neither absolute nor relative deficits of progesterone, estrogen, prostaglandin, insulin, vitamin B~, or thyroid hormone (Severino, and Moline, 1989;
Rickels et al., 1990; Bancroft and Retmie, 1993) have been established in patient groups with either PMS or PMDD. Similarly, functional hormonal tests such as the thyroid-releasing hormone response to thyroid-stimulating hormone and the results of glucose tolerance testing are not abnormal in patients with PMDD (Casper et al., 1989; Girdler et al., 1995; Haskett et al., 1984; Roy-Byrne et al., 1987).
Invoking a hypothesis that premenstrual symptoms are induced by withdrawal of endogenous opiates, several groups have evaluated (3endorphin levels in symptomatic women and controls. In a study that included women who retrospectively reported premenstrual symptoms, Giannini and colleagues found a decline in ~3endorphin during the luteal phase of the cycle (Giannini et al., 1984);
however, there was no control group in this study. Nonetheless, four other studies have found lower luteal phase (3endorphin levels in symptomatic patients compared with controls (Tulenheimo et al.. 1987; Facchinetti et al., 1987; Chuong et al., I985;
and. Giannini et al., 1990). One of the aforementioned studies found lower levels in follicular phase as well as luteal phase (Tulenheimo et al., 1987), and, in an additional study, (3endorphin levels were lower in symptomatic women during the peri-ovulatory phase (Chuong et al., 1994). Notably, only two of the investigations previously mentioned included a population in which symptoms were prospectively determined that may or may not have met severity criteria for a diagnosis of PMDD.
Differences in patient populations, a small sample size or a combination of the two may be the I O basis for different conclusions in a recent study that failed to find differences between PMDD patients and controls during either phase of the cycle {Bloch et al., 1996). In this study, however, ~3endorphin levels decreased during the premenstrual period in both groups. Changes in portal blood levels of ~3endorphin during the menstrual cycle have also been found in primates, although the difference was most notable during the 15 peri-ovulatory phase (Wehrenberg, et al., 1982).
Fluctuations of ~3endorphin that decline precipitously during the menstrual cycle can increase adrenergic activity in women with PMDD and may explain the results of an investigation into adrenergic receptor binding. Halbreich and colleagues (Holbreich et al., 1993) found increased imidazoline receptor binding in 20 premenstrually symptomatic women during the luteal phase of the cycle. As reviewed by Grunhaus and colleagues ( 1990), alterations in adrenergic receptor binding are also associated with MDD and panic disorder, although the direction of the change (increased vs. decreased affinity) is dependent on the platelet preparation and the ligand used in the assay.
25 Moreover, endorphin and estrogen levels have been shown to vary. During the postpartum and premenstrual period, levels of both change rapidly and substantially (Halbreich and Endicott, 1981 ) and others have shown that the narcotic antagonists reduce PMS symptoms.
Halbreich and colleagues found decreases in plasma gamma-aminobutyric acid 30 (GABA) levels during the luteal phase in women with dysphoric premenstrual .. ~.~,~.. _. ~ ~ . .. .... . _ u.._ ~ .. , , symptoms (Holbreich et al., 1996). Low plasma GABA levels have also been found in patients with MDD (Petty et al., 1992), although how this may be related to the above findings is not known.
Theories on the etiology of PMS have focused almost exclusively on estrogen, ' S progesterone, or prolactin secretion. In contrast, Labrum (1983) in the mid-80's first proposed that symptoms occurring in PMS had a common etiological base involving abnormal fluctuations in brain levels or serotonin, GABA and interrelated neuroendocrine processes. Estrogen feedback may be a factor in the excessive fluctuations, particularly of serotonin.
With regard to serotonin, a number of different approaches have been used to evaluate this system in women with both PMS and PMDD, including measurements of serotonin in whole blood, platelet 5-HT uptake, and neuroendocrine challenge. On the basis of primate and other evidence that low serotonin is associated with changes in sleep, appetite, and irritability, Rankin ( 1992) investigated whole-blood serotonin I S in women with severe premenstrual dysphoria and found that compared with asymptomatic controls, symptomatic women have lower levels of serotonin. Some investigators (Ashby et al., 1988; Taylor et al., 1984; Ashby et al., 1990) but not all groups (Mahngren et al., 1987; Rojansky et al., 1991) find that luteal phase platelet S-HT uptake is decreased in women with PMS or PMDD compared with controls.
lmipramine binding sites have also been shown to be reduced in women specifically evaluated for PMDD compared with controls during either the early luteal phase (Rojansky et al., 1991 ) or both phases of the cycle (Steege et crl., 1992).
In the later study, statistical significance was attained only during the follicular phase.
Administration of tryptophan to women with PMDD produces a blunted growth hormone and cortisol response during both phases of the menstrual cycle (Bancroft et al.. 1991 ), suggesting trait differences between PMDD patients and controls. However, in the same two groups, the prolactin response to tryptophan is . blunted only during the premenstrual phase of the cycle (Bancroft et al., 1991 ). On the other hand, when the 5-HT,A partial agonist buspirone is administered to PMDD
patients and healthy controls during the follicular phase, it produces a blunted prolactin response (Yotham, 1993). Data regarding blunted prolactin response to fenfluraminc administration are mixed with one group finding a blunted response in well characterized PMDD subjects versus controls (FitzGerald et al., 1996) and another group finding no differences (Bancroft and Cook, 1995). Finally, depleting the serotonin precursor tryptophan is significantly more likely to provoke premenstrual symptoms during both luteal and follicular phases in PMDD
patients compared with asymptomatic women (Menkas e1 al., 1994).
A number of recent investigations have been conducted to evaluate certain psychoactive drugs for the relief of PMDD and includes antidepressants, including clonipramine (Sunblad et crl., 1993) fluoxetine (Stone et al., 1991; Wood et al., 1992;
Steiner et al., 1995; Brandenberg et al.,; Pearlstein and Stone, 1994), bupropion (Pearlstein et al., 1995), paroxetine (Eriksson et al., 1995; Yorkers et al., 1996a), maprotiline (Erikssor. et al., 1995), sertraline (Yorkers et al.. 1996b), nefaxodone (Girdler et al., 1995), and fenfluramine (Brzezinski et al., 1990).
It is expected by the inventors that one-single drug with only limited effects on one single or possibly even two individual neurotransmitters is insufficient to overcome the abnormal state of the "reward" system which occurs by hormonal shifts in the female pre-, during, and post-menstrual phase. Moreover, while the above biological evidence does not definitively implicate any single, neurobiological system, changes in the adrenergic receptor binding, GABA levels, and various assays of the 5-HT system suggest neurobiological abnormalities associated with the expression of PMDD. Changes in these markers also are found for unipolar MDD. To date no studies reported on the positive effects of enkephalinase inhibitors on PMDD.
In fact the use of Narcotic antagonists like TrexanR (Dupont, Delaware) have been shown to reduce rather than enhance PMDD, opposite to what the inventors are doing in this invention.
With this in mind coupled with the most recent findings by Figuerola and associates (deLourdes-Figuerola et al., 1997) showing an increase in plasma ME
and a decrease in plasma norepinephrine (NE) levels on day 22 in the menstrual migraine group and an increase in plasma ME, NE during pain. The authors conclude that ._ .... .. ~ . , . fi changes occur in plasma ME and in the sympathoadrenal function, not only during pain but also in the mid-luteal phase.
The inventors contemplate that the use of amino-acid therapy would be of benefit to sufferers of PMDD. Work accomplished in a number of alcoholic patients S utilizing the composition as proposed by Table 11 specifically for this disorder reduces significantly typical PMS symptoms including irritability, tension, painful breasts. headaches, and depression. The specific example to be utilized is specified in the PMXTM formula outlined in Table 11.
A double blind placebo controlled study will be pursued in an outpatient PMDD clinic in Dallas, Texas. A total of 100 patients will be studied. A PMDD
Scale will be developed by the inventors and provided to all one hundred participants.
The scale will be scored prior to receiving any medications (either placebo or PMXTM). For this study at least three cycles will be the minimum for inclusion into the study. The patients must by in child bearing age and not pregnant. A
comparison 1 S will be obtained between the two groups and statistical analysis will be performed by the University of Texas Health Sciences Center Department of Computing Resource under the direction of Robert Wood. Only Morbid PMDD candidates assessed via the DSM-IV criteria will be studied. Following the three month phase, each patient will be crossed with either the placebo or PMXTM. Additionally, each patient will be genotyped utilizing the MAA technique as proposed in this invention. Therefore a total of at least 29 genes will be evaluated.
It is expected that carriers of the DRD2 A1 allele, and the other RDS related alleles disclosed herein, will respond well to PMXT"' and will have the most difficult time under the placebo. It is further expected that genotyping for individuals will provide additional information to predict specific targeted treatment outcomes.

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Claims (93)

WHAT IS CLAIMED IS:

1. A composition for the treatment of RDS behaviors in a subject consisting essentially of a) an opiate destruction-inhibiting amount of at least one substance which inhibits the enzymatic destruction of a neuropeptidyl opiate, said substance being selected from the group consisting of amino acids, peptides, and structural analogues or derivatives thereof:
b) a neurotransmitter synthesis-promoting amount of at least one neurotransmitter precursor selected from the group consisting of dopamine precursors L-Tyr, L-Phe and L-dopa, serotonin precursors L-Trp and 5-hydroxytryptophan, and gamma amino butyric acid (GABA) precursors L-glutamine, L-glutamic acid, and L-glutamate: and c) a tryptophan concentration enhancing amount of chromium picolinate or chromium nicotinate, the amount of said substance and said neurotransmitter precursor and said chromium compound being effective in reducing the subject's RDS behaviors.

2. A composition for the prevention or treatment of unwanted weight gain consisting essentially of a) an opiate destruction-inhibiting amount of at least one substance which inhibits the enzymatic destruction of a neuropeptidyl opiate. said substance being selected from the group consisting of amino acids, peptides, and structural analogues or derivatives thereof;
b) a neurotransmitter synthesis-promoting amount of at least one neurotransmitter precursor selected from the group consisting of dopamine precursors L-Tyr, L-Phe and L-dopa, serotonin precursors L-Trp and 5-hydroxytryptophan, and gamma amino butyric acid (GABA) precursors L-glutamine, L-glutamic acid, and L-glutamate; and c) a trypophan concentration enhancing amount of chromium picolinate or chromium nicotinate.

the amount of said substance and said neurotransmitter precursor and said chromium compound being effective in preventing or reducing a subject's unwanted weight.

3. A composition for the treatment of Attention Deficits disorder consisting essentially of a) an opiate destruction-inhibiting amount of at least one substance which inhibits the enzymatic destruction of a neuropeptidyl opiate, said substance being selected from the group consisting of amino acids, peptides, and analogues or derivatives of amino acids or peptides;
b) a neurotransmitter synthesis-promoting amount of at least one neurotransmitter precursor selected from the group consisting of the dopamine precursors L-Tyr, L-Phe and L-dopa, the serotonin precursors L-Trp and 5-hydroxytryptophan, and the gamma amino butyric acid (GABA) precursors L-glutamine, L-glutamic acid, and L-glutamate;
c) a tryptophan concentration enhancing amount of a mineral compound selected from the group consisting of chromium picolinate and chromium nicotinate;
and d) a neurotransmitter synthesis-promoting amount of at least one neurotransmitter synthesis promoting substance selected from the group Rhodila or hubazine.
the amount of said substance and said neurotransmitter precursor and said mineral compound and said neurotransmitter synthesis-promoting substance being chosen so that the composition is effective in reducing the Attention Deficits disorder, attentional processing, or memory.

4. A method of treating a subject for RDS behavior selected from the group consisting essentially of SUD, Obesity, Smoking, Tourettes Syndrome, ADHD, Schizoid/Avoidant Behavior, Aggression, Posttraumatic stress syndrome, PMS or tobacco use by administering to a subject a composition comprising:
a) an opiate destruction-inhibiting amount of at least one substance which inhibits the enzymatic destruction of a neuropeptidyl opiate, said substance being selected from the group consisting of amino acids, peptides, and structural analogues or derivatives thereof;
b) a neurotransmitter synthesis-promoting amount of at least one neurotransmitter precursor selected from the group consisting of the dopamine precursors L-Tyr, L-Phe and L-dopa, the serotonin precursors L-Trp and 5-hydroxytryptophan, and the gamma amino butyric acid (GABA) precursors L-glutamine, L-glutamic acid, and L-glutamate; and c) a tryptophan concentration enhancing amount of chromium picolinate or chromium nicotinate.

5. The method of claim 4, wherein said administration is a daily dietary consumption comprising 32 to 10,000 mg DL-phenylalanine, 5 to 5,000 mg L-tryptophan, 3 to 30,000 mg L-glutamine, and the composition further comprising 1-300 mg pyridoxal-5'-phosphate.

6. The method of claim 4, wherein said RDS behavior is Obesity.

7. The method of claim 6, wherein said administration is a daily dietary consumption of about 460 mg DL-phenylalanine, 25 mg L-tryptophan, 25 mg L-glutamine, and the mixture further comprises 5 mg pyridoxal-5'-phosphate.

8. The method of claim 6, wherein the subject has a family history of chemical dependency, wherein said family history indicates an improved likelihood for success.

9. The method of claim 6, wherein said administration inhibits binge eating.

10. The method of claim 6, wherein said administration inhibits craving.

11. The method of claim 6, wherein the presence in a subject of at least one of the following alleles: D2 TaqI A1, B1, C1 or exon 6-7 haplotype HTR2A - C allele homozygous OB - homozygosity for <208 BP alleles of 1875 dinucleotide repeat polymorphism human chromosome 2 microsatellite polymorphism, APO-D - TaqI
2.2 or 2.7 BP, or OB gene D7S1875 alleles indicates an improved likelihood for a successful response.

12. The method of claim 6, wherein said administration comprises an effective amount of chromium nicotinate, and wherein the presence in the subject of the A1 allele indicates an improved likelihood of response.

13. The method of claim 11, wherein said administration comprises an effective amount of chromium picolinate, and wherein the presence of a DRD2 A2 allele in the subject indicates an improved likelihood of response.

14. The method of claim 4, wherein said RDS behavior is tobacco usage.

15. The method of claim 14, wherein the presence in the subject of at least one of the following alleles: D1 (homozygosity of Dde A1) D2(TaqI A1) D4 (VNTR 2) D5 (dinucleotide 13 alleles range 135-159 BP) DAT1 VNTR (10/10) D.beta.H (TaqI B1 allele) indicates an improved likelihood for a successful response.

16. The method of claim 4, wherein said RDS behavior further includes Autism, Tourette's Syndrome or ADHD, and wherein said subject has at least one of the following alleles: D (homozygosity of Dde A) D (Taq) D (VNTR) D (dinucleotide alleles range - BP) DAT VNTR (/) D.beta.H (Taq B allele) MAOA(X) and the presence of at least one alleles indicates an improved likelihood for a successful response.

17. The method of claim 16, wherein said administration further comprises an effective amount of Rhodila or hubazine.

18. The method of claim 4, wherein said RDS behavior is Pathological gambling and wherein the presence in a subject of at least one of the following alleles: D
(homozygosity of Dde A) D (Taq A, B, C) indicates an improved likelihood for a successful response.

19. The method of claim 4, wherein said RDS behavior further comprises pathological violence, Schizoid/Avoidant (SAB), Aggression, Anger, Hostility, or Posttraumatic Stress Disorders, wherein the presence in the subject of at least one of the following alleles D (Taq A, B, C, exon -) DAT (VNTR /) mNOSIa -homozygosity for ~ BP allele indicates an improved likelihood for a successful response.

20. The method of claim 4, wherein said RDS behavior is PMS, wherein the presence of at least one of the following alleles DAT1 VNTR (10/10) D2 TagI
A1, B1, C1, exon 6-7 haplotype, or alleles from the DRD1, DRD2, DRD4, HTT, HTRIA, TDO2, D.beta.H, MAO, COMT, GABRAB, GABRB3, PENk, ADRA2A or ADRA2C
genes indicates an improved likelihood for a successful response.

21. The method of claim 4, wherein said RDS behavior further comprises substance abuse disorder.

22. The method of claim 21, wherein said RDS behavior is substance use disorder.

23. A method of determining a genetic predisposition of a subject to at least one RDS behavior, comprising detecting at least one allele from the group comprising the DRD1, DRD2, DRD3, DRD4, DRDS, DAT1, HTT, HTR1A, TDO2, DBH, ADRA2A, ADRA2C, NET, MAOA, COMT, GABRA3, GABRB3, CNR1, CNRA4, NMDAR1, PENK, AR, CRF, HTR1D.beta., HTR2A, HTR2C, interferon-.gamma., CD8A, or PS1 genes.

24. The method of claim 23, wherein said RDS behavior is selected from the group comprising a disorder involving mania, OCD, sexual, sleep, grade school behavior, gambling, learning, inattention, ADHD, ADDR, impulsivity, MDE, CD, hyperactivity, phobia, schizoid behavior, general anxiety, somatization, drugs, IV
drugs, read, ODD, tics, alcohol, or tobacco use.

25. The method of claim 24, wherein said allele is a VNTR polymorphism of a MAOA gene.

26. The method of claim 23, wherein said RDS behavior is schizoid, or Avoidant.

27. The method of claim 26, wherein said allele is selected from the group comprising the DRD2 gene A1 allele, the DAT1 gene, VNTR 10/10 allele, or the D.beta.H
gene B1 allele.

28. The method of claim 23, wherein said RDS behavior is Drug Use.

29. The method of claim 28, wherein said allele is an increased number of (AAT)n triplet repeats in the CNR1 gene.

30. The method of claim 23, wherein said RDS behavior is selected from the group comprising obesity, anxiety, depression, psychoses, hostility, paranoid ideation, obsessive-compulsive, symptom total, general symptom index, novelty seeking, overall total, neuroticism and conscientiousness.

31. The method of claim 30, wherein said allele is selected from the group comprising an increased number of the D7S1873, D7S1875, D7S514 or D7S680 dinucleotide repeats in the OB gene.

32. The method of claim 31, wherein the number of said D7S1875 dinucleotide repeats is greater than 225 by in length in both copies of the CNR1 gene.

33. The method of claim 32, wherein said allele is the D2A1 allele of the DRD2 gene.

34. The method of claim 30, wherein said detecting is by detecting the D2A1 allele of the DRD2 gene and an allele selected from the group comprising the an increased number of the D7S1873, D7S1875, D7S514 or D7S680 dinucleotide repeats in the OB gene.

35. The method of claim 34, wherein said determining is for obesity.

36. The method of claim 23, wherein said RDS behavior is selected from the group comprising Tourette's Syndrome, manic symptoms, oppositional defiant, sexual, ADHD-R, schizoid, ADHD, tics, major depression, conduct, stuttering, obsessive-compulsive, somatization, alcohol abuse, learning, and sleep problems.

37. The method of claim 36, wherein said allele is the D2A1 allele of the DRD2 gene.

38. The method of claim 23, wherein said RDS behavior is selected from the group comprising Tourette's Syndrome, ADHD, smoking, learn, grade school, ADHD-R, oppositional defiant, tics, mania, alcohol, reading, drug abuse, sleep, stuttering, obsessive compulsive, somatization and major depression.

39. The method of claim 38, wherein said allele is the Taq A1 allele of the D.beta.H
gene.

40. The method of claim 38, wherein said RDS behavior is Tourettes Syndrome, and wherein said determining is by detection is by an increased number of the Taq B1 allele and the Taq A1 allele of the D.beta.H gene.

41. The method of claim 23, wherein said RDS behavior is selected from the group comprising Tourette's syndrome, autism, somatization, alcohol, ADHD-R, major depression, panic, obsessive compulsive, general anxiety, mania, oppositional defiant, sexual, read, and ADHD.

42. The method of claim 41, wherein said Tourettes Syndrome is determined by detecting an increased number of at least one 10 allele of the DAT1 gene.

43. The method of claim 23, wherein said RDS behavior is selected from the group comprising ADHD, stuttering, ADHD-R, oppositional, defiant, tics, conduct, obsessive compulsive, mania, alcohol, general anxiety, panic schizoid, sleep, sexual, drugs, and major depression.

44. The method of claim 43, wherein said RDS behavior is determined by detecting an increased number of at least one allele selected from the group comprising the 10 allele of the DATA gene, the Taq A1 allele of the D.beta.H
gene, or the D2A1 allele of the DRD2 gene.

45. The method of claim 23, wherein said RDS behavior is selected from the group relating to alcohol, smoking, compulsive eating, tics, gambling, drugs, reading, shopping, oppositional defiant, major depressive episode, schizoid, ADHD, conduct disorder, obsessive compulsive, and mania.

46. The method of claim 45, wherein said determining is by detecting homozygosity for the Ddel allele of the DRDI gene.

47. The method of claim 23, wherein said RDS behavior is selected from the group comprising oppositional defiant, conduct disorder. eating, smoking, gambling, ADHD, obsessive compulsive, mania, and alcohol.

48. The method of claim 47, comprising detecting the TagI A1 and the TagI A2 alleles of the DRD2 gene.

49. The method of claim 23, wherein said RDS behavior is selected from the group comprising Tourettes syndrome, smoking, and gambling.

50. The method of claim 49, wherein said determining is by detecting the 11 or the 22 genotype of the DRD1 gene.

51. The method of claim 50, wherein said determining is by detecting two copies per genome of the Dde1 allele of the DRD1 gene.

52. The method of claim 23, wherein said RDS behavior is selected from the group comprising oppositional defiant behavior, conduct disorder, compulsive eating, smoking, gambling, ADHD mania, stuttering, obsessive-compulsive, and schizoid behaviors.

53. The method of claim 52, comprising said determining is by detecting an increased number of the 11 genotype of the DRD1 gene.

54. The method of claim 23, wherein said determining is by detection of the DRD2 A1 allele, and said RDS behavior is selected from the group comprising gambling, smoking, compulsive eating, oppositional defiant, major depressive episode, ADHD, conduct disorder, schizoid, obsessive-compulsive, mania, and alcohol.

55. The method of claim 54, wherein said determining is by detecting two copies per genome of the Dde 1 allele of the DRD1 gene.

56. The method of claim 23, wherein said RDS behavior is selected from the group comprising alcohol, smoking, compulsive eating, tics, gambling, drugs, reading, shopping, gambling, and grade school problems.

57. The method of claim 56, wherein said determining is by detecting the 11 or the 22 genotype of the DRD1 gene.

58. The method of claim 23, wherein said RDS behavior is Tourettes Syndrome.

59. The method of claim 58, wherein said determining is by detecting the intron 6 G~A polymorphism of the Tryptophan 2,3 dioxygenase gene.

60. The method of claim 23, wherein said RDS behavior is selected from the group comprising ADHD, alcohol dependence, drug dependence, pathological gambling.

61. The method of claim 60, wherein said determining is by detecting the intron 6 G~T polymorphism of the Tryptophan 2,3 dioxygenase gene.

62. The method of claim 23, wherein said RDS behavior is selected from the group comprising ADHD, alcohol dependence, drug dependence, pathological gambling, and depression.

63. The method of claim 62, wherein said determining is by detecting the intron 6 DGGE polymorphism of the Tryptophan 2,3 dioxygenase gene.

64. The method of claim 23, wherein said RDS behavior is drug use.

65. The method of claim 64, wherein said determining is by detecting the low base pair alleles (~181 bp) polymorphism of the ADRA2C dinucleotide repeat polymorphism.

66. The method of claim 23, wherein said RDS behavior is alcohol use.

67. The method of claim 66, wherein said determining is by detecting two high base pair alleles for the ~183 by of the ADRA2C dinucleotide repeat polymorphism.

68. The method of claim 23, wherein said RDS behavior is selected from the group comprising alcohol and tobacco use.

69. A method of claim 68, wherein said determining is by detecting two homologous alleles for the presenilin-1 (PS1) polymorphism.

70. A method of claim 68, wherein said determining is by detecting two homologous alleles of greater than 80 by of the CA dinucleotide repeat polymorphism of the PENK gene.

71. The method of claim 23, wherein said RDS behavior is selected from the group comprising CD, ODD, or hyperactivity.

72. A method of claim 71, wherein said determining is by detecting the presence of short GGC alleles of the AR gene.

73. The method of claim 23, said determining is for Type B behavior in alcoholics, cocaine addicts, or RDS probands, comprising detecting the presence of the DRD2 allele associated with said predisposition in said patients.

74. A method for determining a genetic predisposition to a polygenic trait comprising detecting at least one allele associated from the group comprising the DRD1, DRD2, DRD5, DAT1, HTT, HTR1A, TDO2, DBH, ADRA2A, ADRA2C, NET, MAOA, COMT, GABRA3, GABRB3, CNR1, CNRA4, NMDAR1, PENK, AR, CRF, DRD3, DRD4, HTR1D.beta., HTR2A, HTR2C, interferon-.gamma., CD8A, or PS1 genes.
75. The method of claim 74, wherein said genetic predisposition is to ADHD, and said determining comprises detecting at least one allele associated with the DRD1, DRD2, DRD5, DAT1, HTT, HTR1A, TDO2, DBH, ADRA2A, ADRA2C, NET, MAOA, COMT, GABRA3, GABRB3, CNR1, CNRA4, NMDAR1, PENK, AR, or CRF genes.

75. The method of claim 74, wherein said genetic predisposition is to a lack of susceptibility to ADHD, said determining comprises detecting at least one allele associated with the DRD3, DRD4, HTR1D.beta., HTR2A, HTR2C, interferon-.gamma., CD8A, or PS1 genes.

76. The method of claim 74, wherein said genetic predisposition is to OOD, said determining comprises detecting at least one allele associated with the DRD1, DRD2, DRD3, DAT1, HTT, HTR1A, HTR2A, HTR2C, DBH, ADRA2A, ADRA2C, MAOA, GABRA3, GABRB3, CNR1, CHRNA4, NMDAR1, PENK, AR or CD8A genes.

77. The method of claim 74, wherein said genetic predisposition is to tics, said determining comprises detecting at least one allele associated with the DRD1, DRD5, HTR1A, HTR1D.beta., HTR2C, TDO2, DBH, ADR2C, COMT, GABRA3, CNR1 or CHRNA4 genes.

78. The method of claim 74, wherein said genetic predisposition is to LD, said determining comprises detecting at least one allele associated with the DRD1, HTR2C, TDO2. DBH, ADR2A, ADR2C, MAOA, CNR1 or CNRA4 genes.

79. The method of claim 74, wherein said genetic predisposition is to elevated LDL levels, said determining comprises detecting at least one allele associated with the HTT, OXYR, DRD2 or PS1 genes.

80. The method of claim 74, wherein said genetic predisposition is to elevated cholesterol levels, said determining comprises detecting at least one allele associated with the HTT, OXYR, DRD2 or PS1 genes.

81. The method of claim 74, wherein said genetic predisposition is to longevity, said determining comprises detecting at least one allele associated with the PS1, OXYR or APOE genes.

82. A method for developing a polygenic assay that is diagnostic, comprising the steps of:
a). identifying the trait that is to be studied;
b). creating a scale measuring the severity of the trait to be studied;
c). selecting at least one candidate gene that may contribute to said trait;
d). identify at least one polymorphism associated with said candidate gene;
e). correlating allelic patterns of said polymorphism with said scale;
f). comparing the association of said allelic pattern to the correlation of said candidate gene to said trait; and g). wherein the allelic patterns that are positively associated with said trait are added, to form a polygenic assay that is diagnostic.

83. The method of claim 82, wherein the candidate genes comprise the DRD1, DRD2, DRD5, DAT1, HTT, HTR1A, TDO2, DBH, ADRA2A, ADRA2C, NET, MAOA, COMT, GABRA3, GABRB3, CNR1, CNRA4, NMDAR1, PENK, AR, CRF, DRD3, DRD4, NTR1D.beta., HTR2A, HTR2C, interferon-.gamma., CD8A, or PS1 genes.

84. The method of claim 82, wherein the polygenic traits comprise ADHD, lack of ADHD, ODD, CD, LD, Tics, Drug Abuse/Dependence, Smoking, osteoarthritis, elevated cholesterol levels, elevated LDL levels, or longevity

85. The method of claim 84, wherein said polygenic assay to said ADHD
comprises detecting at least one allele associated with the DRDl, DRD2, DRDS, DA TI, HTT, HTRlA, TD02, DBH, ADRA2A, ADRA2C, NET, MAOA, COMT, GABRA3, GABRB3, CNRI, CNRA4, NMDARI, PENK, AR, or CRF genes.

86. The method of claim 84, wherein said polygenic assay to said lack of ADHD
comprises detecting at least one allele associated with the DRD3, DRD4, HTRID~i, HTR2A, HTR2C, interferon-y, CDBA, or PS1 genes.

87. The method of claim 84, wherein said polygenic assay to said OOD comprises detecting at least one allele associated with the DRDI, DRD2, DRD3, DA TI, HTT, HTRIA, HTR2A, HTR2C, DBH, ADRA2A, ADRA2C, MAOA, GABRA3, GABRB3, CNRl, CHRNA=~, NMDARl, PENK, AR or CDBA genes.

88. The method of claim 84, wherein said polygenic assay to said tics comprises detecting at least one alieie associated with the DRDl, DRDS, HTRIA, HTRID(3, HTR2C, TDO2, DBH, ADR2C, COMT, GABRA3, CNRI or CHRNA4 genes.

89. The method of claim 84, wherein said polygenic assay to said LD comprises detecting at least one allele associated with the DRDl, HTR2C, TD02, DBH, ADR2A, ADR2C. MA OA, CNRI or CNRA4 genes.

90. The method of claim 84, wherein said polygenic assay to said elevated LDL
levels comprises detecting at least one allele associated with the HTT, OXYR, DRD2, or PS1 genes.

91. The method of claim 84, wherein said polygenic assay to said longevity comprises detecting at least one allele associated with the PS1, OXYR or APOE
genes.

92. The method of claim 84, wherein said polygenic assay to said osteoarthritis further comprises detecting at least one allele associated with the COL2A1, COL2A1, COL2A1, COL9A1, COL9A1, AGC1, IGF1, IGF1, IGF1R, IGF1R, IGF2, IGF2R, TGFB1, TGFB2, IL1A, IL1B, ILIR1, IL1RN, MMP9, TIMP1 or Vitamin D3 genes.

93. A composition for the treatment of RDS behaviors in a subject consisting essentially of a) an opiate destruction-inhibiting amount of at least one substance which inhibits the enzymatic destruction of a neuropeptidyl opiate, said substance being selected from the group consisting of amino acids, peptides, and structural analogues or derivatives thereof;
b) an enkephalinase releasing substance which releases neuronal endorphins or nkephalins, said substance being selected from the group consisting of polypeptides or amino-acid; and c) an opiate antagonist amount of at least one compound which blocks the effects of an opiate at either the delta, mu, kappa, sigma, or epsilon receptors, said substance being selected from the group consisting of narcotic antagonists such as nalopotone or ICI154129, the amount of said substance and said neurotransmitter precursor and said chromium compound and said opiate antagonist being effective in reducing the subject's RDS
behaviors.