WO2014004629A2

WO2014004629A2 - Method for predicting cessation success for addictive substances

Info

Publication number: WO2014004629A2
Application number: PCT/US2013/047828
Authority: WO
Inventors: Jed E. Rose; George R. Uhl
Original assignee: Duke University
Priority date: 2012-06-27
Filing date: 2013-06-26
Publication date: 2014-01-03
Also published as: WO2014004629A3

Abstract

Described herein are methods for treating the abusive or habitual use of an addictive substance in a subject; predicting a subject's success in an addictive substance cessation program; identifying a subject who has an increased risk of becoming dependent on an addictive substance; developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance; and identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial by identifying a quantity of single nucleotide polymorphisms.

Description

METHOD FOR PREDICTING CESSATION SUCCESS FOR ADDICTIVE SUBSTANCES

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This research was conducted with United States government support under grant number P50CA/DA84718 awarded by the National Institutes of Health extramural research program, and funds from the intramural program at the National Institute on Drug Abuse, DHSS. The United States government has certain rights in the inventions described herein.

TECHNICAL FIELD

Described herein are methods for treating the abusive or habitual use of an addictive substance in a subject; predicting a subject's success in an addictive substance cessation program; identifying a subject who has an increased risk of becoming dependent on an addictive substance; developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance; and identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial.

BACKGROUND

Substance dependence, both illegal and controlled, represents one of the most important preventable causes of illness and death in modern society. The path to addiction generally begins with a voluntary use of one or more addictive substances such as tobacco, alcohol, narcotics, or any of a variety of other addictive substances. With extended use of the addictive substance, a voluntary ability to abstain from the addictive substance is compromised in many subjects. As such, substance addiction is generally characterized by compulsive substance craving, habitual substance seeking, and substance use that persists even in the face of negative consequences. Substance addiction is also characterized in many cases by withdrawal symptoms.

Nicotine, as found in tobacco, is one such addictive substance. Worldwide, tobacco use causes nearly 5 million deaths per year, with current trends showing that tobacco use will cause more than 10 million deaths annually by 2020. World Health Organization, The World Health Report 2002: Reducing Risks, Promoting Healthy Life (2002). In the United States, cigarette smoking is a leading preventable cause of death and is responsible for about one in five deaths annually, or about 438,000 deaths per year. Centers for Disease Control and Prevention Morbid. Mortal. Wkly Rep. 54: 625-628 (2005). Nearly 21% of U.S. adults (45.1 million people) are current cigarette smokers. Centers for Disease Control and Prevention, Morbid. Mortal. Wkly Rep. 54: 1121-1124 (2005). Among adult smokers, 70% report that they want to quit completely {id.), and more than 40% try to quit each year. Substance Abuse and Mental Health Services Administration Results from the 2005 National Survey on Drug Use and Health: 5 National Findings, Office of Applied Studies, NSDUH Series H-30, DHHS Publication No. SMA 06-4194 (2006). Quitting smoking even after prolonged use of tobacco has substantial health benefits. Unfortunately, a majority of subjects who report quit attempts report that they failed to abstain permanently. A primary goal of therapy or treatment of substance addiction is to reduce the amount and/or rate of intake of the addictive substance over time, as well as to reduce the rate of relapse. Individuals afflicted with an addictive condition who succeed in obtaining a reduction or complete cessation of intake of the addictive substance remain at a substantial risk to relapse during the course of their lifetimes. To completely eradicate the addictive condition over the subject's lifetime often requires life-long administration of therapy, be it pharmacological, behavioral or both.

Substance cessation programs typically address both pharmacological and psychological factors. Vulnerability to substance dependence, however, is a substantially heritable complex disorder. Karkowski et al., Am. J. Med. Genet. 96: 665-670 (2000); Tsuang et al., Arch. Gen. Psychiatry 55: 967-972 (1998); True et al., Am. J. Med. 20 Genet. 88: 391-397 (1999). Classical genetic studies also indicate that individual differences in an ability to quit successfully using the addictive substance are substantially heritable, but differ from those that influence aspects of dependence. Xian et al., Nicotine Job. Res. 5: 245-254 (2003). Therefore, there remains a need for methods to predict a likelihood of successful cessation of an addictive substance, as well as for methods to predict a potential for substance dependence or addiction.

Twin studies document substantial heritability for smokers' abilities to successfully abstain from smoking; there are thus robust genetic influences likely for individual differences in abilities to quit. Broms et al., Twin Res. Hum. Genet. 9(1): 64-72 (2006); Lessov et al., Psychol. Med. 34(5): 865-79 (2004). Since cigarette smoking remains a significant cause of premature death and disease and since success rates following attempts to quit smoking remain modest, improved understanding of individuals' differences in abilities to quit and careful application of this understanding to clinical settings are significant pharmacogenomic objectives. Adhikari et al., Morbid. Mortal. Wkly Rep. 57: 1226-1228 (2008); Uhl et al., Pharmacogenomics J. 9(2): 111-5 (2009).

A genome wide association studies has been performed for success in quitting smoking in several independent samples of carefully monitored individuals who attempted to quit smoking in clinical trials or who quit in the community. Uhl et al., Arch. Gen. Psychiatry 65(6): 683-93 (2008); Drgon et al., Mol. Med. 15(1-2): 21-27 (2009); Drgon et al., Mol. Med. 15(7-8): 268- 274(2009); Uhl et al., Pharmacogenomics 11(3): 357-367 (2010). Data was used from three initial samples to develop a "vl.O" quit success score that considers allele frequency data from 12,058 quit-success-associated SNPs that were genotyped using Affymetrix 6.0 arrays; see Table 1. Uhl et al., Mol. Med. 16(11-12): 513-526 (2010). Alleles were weighted based on the level of significance identified in these initial samples and extent to which the association was reproducible among these initial samples. Quit success scores were summed from weighted values from presence of abstinence alleles.

A prospective use of this vl.O score in a clinical trial of smoking cessation success in which doses of NRT were randomly assigned to participants has been reported. Rose et al., Mol. Med. 16(7-8): 247-253 (2010). Interactions between the vl.O score and the NRT dose provided a significant predictor of quit success in this sample. There were significant association results in both the larger, European-American and the smaller, African-American portions of this sample.

It appears likely that there should be other behavioral implications of a bona fide genotype score that helps to predict quit success. Twin studies demonstrate shared genetic influences on smoking quantity/frequency and abilities to quit in the community, but provide little direct evidence for which heritable clinical features are likely to be shared with heritable influences on quit success. Morley et al., Psychol. Med. 37(9): 1357-1367 (2007). There is significant overlap between the genes identified in smoking cessation studies and those identified in molecular genetic studies of addiction vulnerability. Uhl et al., Mol. Med. 16(11-12): 513-526 (2010). However, there has been no direct evidence for other behavioral differences between individuals with higher and lower vl.O quit success genotype scores.

Individual differences in the development of involvement with addictive substances have been modeled. Such models often indicate that individuals fall into classes with different temporal trajectories of involvement with addictive drugs. Lynne-Landsman et al., Aggressive Behav. 37(2): 161-176 (2011); Lynne-Landsman, Bradshaw, and lalongo, Devel. Psychopath. 22(4): 933- 48 (2010); Marsiglia et al., Prevention Sci. 12(1): 48-62 (2011). Different individuals can then be characterized as displaying different likelihoods of membership in one of these trajectory-based classes.

Experiments analyzed how genetic influences on quitting might overlap with those that influence earlier manifestations of addiction-related behaviors. It was proposed that individuals with more of the genomic variants that predict ability to quit (after dependence was established) might also display different trajectories whereby addictions were established, typically during adolescence and young adulthood. Failure of early attempts to terminate use might increase the rate of "uptake" of use of common abused substances, for example. Application of the vl.O score to two samples is now reported. In applying the score to data from a new clinical trial in which NRT dose was matched to the intensity of smoking at baseline, evidence was sought for further validation of this score and replication of the encouraging results from the prior clinical trial. Uhl et al., Mol. Med. 16(11-12) : 513-526 (2010). In applying this score to participants who were ascertained as first graders and periodically assessed through later childhood, adolescence, and young adulthood, evidence was sought that genetic determinants for ability to quit might influence the developmental trajectories of involvement with addictive substances. These participants were of especial interest, since the addiction vulnerability data from these individuals identifies many of the same chromosomal regions that were identified by larger research volunteer samples from the same areas of Baltimore (see Table 4). Drgon et al., PloS One 5(1): e8832 (2010). Taken together, these encouraging results show that the vl.O score can reproducibly identify the ability to quit smoking and that it ca n help to predict trajectories of earlier involvement with addictive substances. SUMMARY

One embodiment described herein is a method of treating the abusive or habitual use of an addictive substance in a subject comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SN Ps) listed in Table 1 in the nucleic acid of said subject, wherein said SN Ps are correlated with an increased rate of success in addictive substance cessation; calculating the likelihood of success in addictive substance cessation based on said SN Ps; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

In one aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the quantity of SNPs in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099. In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the quantity of SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; and wherein said SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the method further comprises assessing end-expired CO level of said subject.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the addictive substance is selected from the group consisting of nicotine, alcohol, marijuana, cocaine, heroin, methamphetamine, ketamine, Ecstasy, oxycodone, codeine, morphine, and combinations thereof.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the addictive substance is nicotine.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the subject presently is dependent on an addictive substance.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the subject presently is dependent on nicotine.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the detection of said SNP is carried out by a process selected from the group consisting of allele-specific probe hybridization, allele-specific primer extension, allele-specific amplification, sequencing, nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, single-stranded conformation polymorphism, and combinations thereof.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the replacement therapy is nicotine replacement therapy. In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the nicotine replacement therapy comprises a nicotine patch, nicotine gum, a nicotine inhaler, or a nicotine nasal spray. In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, cessation therapy is provided to said subject.

In another aspect of the method of treating the abusive or habitual use of an addictive substance in a subject described herein, the cessation therapy comprises bupropion or varenicline.

Another embodiment described herein is a method for indentifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in the nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; wherein the quantity of SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased risk of becoming dependent on said addictive substance; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for predicting a subject's success in an addictive substance cessation program comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; wherein the quantity of SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased risk of becoming dependent on said addictive substance; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen comprising replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject. Another embodiment described herein is a method for identifying a subject who has an increased risk of becoming dependent on an addictive substance, comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in a nucleic acid of said subject, wherein the quantity of SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased risk of becoming dependent on said addictive substance; calculating the likelihood of becoming dependent on an addictive substance based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject. Another embodiment described herein is a method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in a nucleic acid of said subject, wherein the quantity of SNPs in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased rate of success in an individualized treatment regimen; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

In one aspect of the method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance, the method further comprises assessing end-expired CO level of said subject.

In one aspect of the method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance, the addictive substance is selected from the group consisting of nicotine, alcohol, marijuana, cocaine, heroin, methamphetamine, ketamine, Ecstasy (MDMA; 3,4-methylenedioxy-N-methylamphetamine), oxycodone, codeine, morphine and combinations thereof.

In one aspect of the method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance, the addictive substance is nicotine.

In one aspect of the method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance, the subject presently is dependent on an addictive substance. In one aspect of the method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance, the detection of said SNPs is carried out by a process selected from the group consisting of allele-specific probe hybridization, allele-specific primer extension, allele-specific amplification, sequencing, nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, single- stranded conformation polymorphism and combinations thereof.

Another embodiment described herein is a method of treating the abusive or habitual use of an addictive substance in a subject comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein aid SNPs are correlated with an increased rate of success in addictive substance cessation; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein said

SNPs are correlated with an increased rate of success in addictive substance cessation; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for predicting a subject's success in an addictive substance cessation program comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen comprising replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for identifying a subject who has an increased risk of becoming dependent on an addictive substance, comprising obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased risk of becoming dependent on said addictive substance; calculating the likelihood of becoming dependent on an addictive substance based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in an individualized treatment regimen; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject. Another embodiment described herein is a method of treating the abusive or habitual use of an addictive substance in a population of subjects comprising: obtaining a nucleic acid from said subjects; identifying a quantity of single nucleotide polymorphisms (SNPs) in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein the quantity of SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; calculating the likelihood of success in addictive substance cessation based on said SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in the nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; calculating the likelihood of success in addictive substance cessation based on said SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject. Another embodiment described herein is a method for identifying a subject (or population of subjects) for inclusion or exclusion in an addictive substance cessation program comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein the quantity of SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; and calculating the likelihood of success in addictive substance cessation based on said SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for predicting a subject's success in an addictive substance cessation program comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein the quantity of SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; and calculating the likelihood of success in addictive substance cessation based on said SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen comprising replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance comprising: obtaining a nucleic acid from said subject; identifying a quantity of single nucleotide polymorphisms (SNPs) in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein the quantity of SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased rate of success in an individualized treatment regimen; calculating the likelihood of success in addictive substance cessation based on said SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject. Another embodiment described herein is a method for doing business by selecting a subject (or population of subjects) for a inclusion or exclusion in a clinical trial, the method comprising: obtaining a nucleic acid from the subjects; identifying a quantity of single nucleotide polymorphisms (SNPs) in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein the quantity of SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased rate of success in addictive substance cessation based on said SNPs; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting subjects for inclusion or exclusion in a clinical trial based on their likelihood of addictive substance cessation; and selecting at least one drug/and or treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and the seeking regulatory approval for the drug and/or treatment regimen. Another embodiment described herein is a method of treating the abusive or habitual use of an addictive substance in a population of subjects comprising: obtaining a nucleic acid from said subject; identifying a quantity of TRAP1 single nucleotide polymorphisms (SNPs) in Table 7 or in linkage disequilibrium with the TRAP1 SNPs listed in Table 7 in the nucleic acid of said subject, wherein said TRAP1 SNPs are correlated with an increased rate of success in addictive substance cessation; and calculating the likelihood of success in addictive substance cessation based on said TRAP1 SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said TRAP1 SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising: obtaining a nucleic acid from said subject; identifying a quantity of TRAP1 single nucleotide polymorphisms (SNPs) in Table 7 or in linkage disequilibrium with the TRAP1 SNPs listed in Table 7 in the nucleic acid of said subject, wherein said TRAPl SNPs are correlated with an increased rate of success in addictive substance cessation; and calculating the likelihood of success in addictive substance cessation based on said TRAPl SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said TRAPl SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for identifying a subject (or population of subjects) for inclusion or exclusion in an addictive substance cessation program comprising: obtaining a nucleic acid from said subject; identifying a quantity of TRAPl single nucleotide polymorphisms (SNPs) in Table 7 or in linkage disequilibrium with the TRAPl SNPs listed in Table 7 in the nucleic acid of said subject, wherein said TRAPl SNPs are correlated with an increased rate of success in addictive substance cessation; and calculating the likelihood of success in addictive substance cessation based on said TRAPl SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said TRAPl SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject. Another embodiment described herein is a method for predicting a subject's success in an addictive substance cessation program comprising: obtaining a nucleic acid from said subject; identifying a quantity of TRAPl single nucleotide polymorphisms (SNPs) in Table 7 or in linkage disequilibrium with the TRAPl SNPs listed in Table 7 in the nucleic acid of said subject, wherein said TRAPl SNPs are correlated with an increased rate of success in addictive substance cessation; and calculating the likelihood of success in addictive substance cessation based on said TRAPl SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said TRAPl SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject. Another embodiment described herein is a method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance comprising: obtaining a nucleic acid from said subject; identifying a quantity of TRAP1 single nucleotide polymorphisms (SN Ps) in Table 7 or in linkage disequilibrium with the TRAP1 SNPs listed in Table 7 in the nucleic acid of said subject, wherein said TRAP1 SNPs are correlated with an increased rate of success in addictive substance cessation; and calculating the likelihood of success in addictive substance cessation based on said TRAP1 SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said TRAP1 SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

Another embodiment described herein is a method for doing business by selecting a subject (or population of subjects) for a inclusion or exclusion in a clinical trial, the method comprising: obtaining a nucleic acid from said subjects; identifying a quantity of TRAP1 single nucleotide polymorphisms (SN Ps) in Table 7 or in linkage disequilibrium with the TRAP1 SNPs listed in Table 7 in the nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation based on said SNPs; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting subjects for inclusion or exclusion in a clinical trial based on their likelihood of addictive substance cessation; and selecting at least one drug/and or treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and seeking regulatory approval for the drug and/or treatment regimen.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGURE 1: Design of the smoking cessation trial. Each of the 50 European-American smokers who achieved continuous abstinence for 11-weeks were matched to 117 smokers who failed to achieve abstinence for genotyping (from total n = 420). Matching was based on race, gender, nicotine dependence level at screening (assessed by FTN D questionnaire) and arm of the study. Participants were enrolled with consent for genotyping, treated with 21 or 42 mg NRT patches, and stratified for subsequent treatment based on reductions in CO levels in end expired air at the end of the first of two weeks of pre-cessation treatment during which participants were instructed that they could smoke freely until the targeted quit date (two weeks after onset of NRT). Numbers of participants who were genotyped came from the 21 mg NRT, 42 mg NRT, 21 mg NRT + bupropion, 42 mg N RT + bupropion, 21 mg NRT then varenicline and 42 mg NRT then varenicline arms as follows: 37, 49, 21, 24, 19 and 19 (note that the first two values come from individuals falling into both groups Al and A2, below). Mean vl.O scores were 381, 388, 383, 370, 391 and 390, respectively, displaying no significant differences. Ranges of vl.O scores from participants who received NRT, bupropion + N RT and NRT then varenicline treatment were 291-470, 312-456, and 307-450, respectively. N RT doses were gradually reduced beginning 4 or 6 weeks after the quit date for the 42 and 21 mg/24 h groups, respectively. Participants with sleep disturbances removed patches at bedtime and applied new ones upon awakening. Subjects experiencing other symptoms of nicotine toxicity reduced doses until symptoms abated according to the following sequence: reduce morning patch from 21 to 14 to 7 to 0 mg/day, and then discontinue the afternoon patch.

FIGURE 2: Design of prevention intervention trial (Cohort II I) and followup. Seven hundred ninety nine (799) Baltimore first graders in 1993 (Cohort II I; 6.2 ± 0.4 years old when entering) were recruited from 27-classrooms in nine Baltimore-area elementary schools. Kellam et al., Drug Alcohol Depend. 95 (Suppl. 1): S5-S28 (2008); Wang et al., Drug Alcohol Depend. 100(3): 194-203 (2009); lalongo, Poduska, & Werthamer-Larsson, J. Emot. Behav. Disorders 9: 146-160 (2001); Kellam et al., Am. J. Commun. Psychol. 19(4): 563-584 (1991). A majority (i.e., 63%) qualified for free or reduced price lunch. Participants were group-randomized to control or one of two universal, elementary school-based interventions, that targeted improved behavior and school performance as well as subsequent reductions in drug use, antisocial behavior, and school failure. Wang et al., Drug Alcohol Depend. 100(3): 194-203 (2009); lalongo, Poduska, & Werthamer-Larsson, J. Emot. Behav. Disorders 9: 146-160 (2001); Kellam et al., Am. J. Commun. Psychol. 19(4): 563-584 (1991); Uhl, et al., Am. J. Hum. Gen. 69(6): 1290-1300 (2001). Fifty four percent were male, 85% African American and 13% European American. Participants provided data at a median of nine of ten follow-up attempts that asked questions about drug use from 8^th grade until age 24. There was about 20% loss of subjects to follow-up over this period, but there was not any significant difference in the dropout in different arms of the trial. During follow-up visits in 2007-2011, about 80% of participants provided consent for genotyping and blood and/or buccal material for DNA extraction. Data from 555 individuals with both clinical information and genotype scores is presented. FIGURE 3: vl.O scores for non-quitters (NO.) and successful quitters (Q) in this clinical trial. Quitters reported continuous abstinence, confirmed by monitoring of CO in exhaled breath, for at least 11 weeks after the targeted quit date for this trial. Statistical information: p = 0.0005, i-test. SEMs are 2.5 and 4.5, respectively. FIGURE 4: (A) Receiver operating characteristic curve fitted to data for vl.O scores ability to predict continuous abstinence (11-weeks) in the smoking cessation clinical trial described herein. Blue line indicates the area under the fitted curve. Grey lines indicate 95% confidence intervals for this estimate. Area under the curve: 0.67. (B) Receiver operating characteristic curve fitted to data for CO reduction ability to predict continuous abstinence (11-weeks) in the smoking cessation clinical trial described herein. Blue line indicates the area under the fitted curve. Grey lines indicate 95% confidence intervals for this estimate. Area under the curve: 0.67.

FIGURE 5: Receiver operating characteristic curve fitted to data for combined vl.O scores and CO reduction ability to predict continuous abstinence (11-weeks) in the smoking cessation clinical trial described herein. Blue line indicates the area under the fitted curve. Grey lines indicate 95% confidence intervals for this estimate. Area under the curve: 0.73. FIGURE 6: Trajectories of involvement with common abused substances for classes of prevention study subjects as derived using latent class growth analysis implemented in Mplus. Members of Class 1 (—□— ; 80.8% of subjects) used few substances during the followup period. Members of Class 2 (— Δ— ; 8.8%) stably used a number of substances during the followup period. Members of Class 3 (— o— ; 10.6%) escalated use of substances during the followup period. Legend: x-axis: age; y-axis: aggregate score for past year frequency of use of tobacco, alcohol and cannabis derived from self-report data from followup interviews.

FIGURE 7: Probabilities (y-axis) of membership in the two classes most strongly associated with vl.O scores in individuals in prevention study Cohort II I (individuals 1-555 arrayed on x-axis). (A) Class 1. Note that only 3% of participants displayed probabilities between 0.2 and 0.8 of membership in this class. (B) Class 3. Note that only 4.9% of participants displayed probabilities between 0.2 and 0.8 of membership in this class. FIGURE 8: Cartoon suggesting one mechanism by which quit success genetics might influence trajectories of uptake of substance use, dependence, and quitting over time. If initial bouts of use were terminated by processes shared with those involved in quitting after an extended course of substance use and dependence, current results might be explained. Note that the current results may be compatible with other explanatory models.

DETAILED DESCRIPTION

Described herein are SNPs associated with cessation success of an addictive substance or an increased risk of becoming dependent on an addictive substance, nucleic acid molecules containing the SN Ps disclosed herein, methods and reagents for detecting the SNPs disclosed herein, uses of the SN Ps disclosed herein for developing detection reagents, and assays or kits utilizing such reagents. The addictive substance-associated SNPs disclosed herein therefore are useful for diagnosing, screening, triaging, and evaluating cessation success or predisposition to becoming dependent on an addictive substance. The genomes of all organisms undergo spontaneous mutation throughout evolution, generating variant forms of progenitor genetic sequences. Gusella, Ann. Rev. Biochem. 55: 831-854 (1986). A variant form may confer an evolutionary advantage or disadvantage relative to a progenitor form or may be neutral. In some instances, the variant form of the progenitor genetic sequence confers an evolutionary advantage to organisms, is eventually incorporated into the DNA of many or most organisms, and effectively becomes the progenitor form.

In addition, the effects of the variant form may be both beneficial and detrimental, depending on the circumstances. For example, a heterozygous sickle cell mutation confers resistance to malaria, but a homozygous sickle cell mutation is usually lethal. In many cases, both progenitor and variant forms of a genetic sequence survive and co-exist in a species population. The coexistence of multiple forms of a genetic sequence gives rise to genetic polymorphisms, including SNPs. Approximately 90% of all polymorphisms in the human genome are SNPs. SNPs are single base positions in DNA at which different alleles, or alternative nucleotides, exist in a population. SNP position (interchangeably referred to herein as SNP, SNP site, SNP locus, SNP marker or marker) is usually preceded and followed by highly conserved sequences of the allele (e.g., sequences that vary in less than 1/100 or 1/1000 members of the population). A subject may be homozygous or heterozygous for the allele at each SNP position. A SNP can, in some instances, be referred to as a "cSNP," which denotes that the nucleotide sequence containing the SNP is an amino acid coding sequence.

A SNP also may arise from a substitution of one nucleotide for another at the polymorphic site. Substitutions can be transitions or transversions. A transition is the replacement of one purine by another purine, or one pyrimidine by another pyrimidine. A transversion is the replacement of a purine by a pyrimidine or a pyrimidine by a purine. A SNP may also be a single base insertion or deletion variant referred to as an "indel." Weber et al., Am. J. Hum. Genet. 71: 854- 862 (2002). A synonymous codon change, or silent mutation SNP (terms such as "SNP," "polymorphism," "mutation," "mutant," "variation" and "variant" are used herein interchangeably), is one that does not result in a change of amino acid due to the degeneracy of the genetic code. A substitution that changes a codon coding for one amino acid to a codon coding for a different amino acid (i.e., a non-synonymous codon change) is referred to as a missense mutation. A nonsense mutation results in a type of non-synonymous codon change in which a stop codon is formed, thereby leading to premature termination of a polypeptide chain and a truncated protein. A read-through mutation is another type of non-synonymous codon change that causes the destruction of a stop codon, thereby resulting in an extended polypeptide product. While SNPs can be bi-, tri-, or tetra-allelic, the vast majority of the SNPs are bi-allelic, and are thus often referred to as "biallelic markers" or "di-allelic markers."

As used herein, references to SNPs and SNP genotypes include individual SNPs and/or haplotypes, which are groups of SNPs that are generally inherited together. Haplotypes ca n have stronger correlations with increased risk of becoming dependent on an addictive substance compared with individual SNPs, and therefore ca n provide increased diagnostic accuracy in some cases. Stephens et al., Science 293: 489-493 (2001). An association study of a SNP and an increased risk of becoming dependent on an addictive substance involves determining a presence or frequency of the SNP allele(s) in biological samples from test subjects with a dependency of interest, such as nicotine dependency, and comparing the information to that of control subjects (i.e., subjects who are not dependent on the addictive substance) who are usually of similar age and race. A SNP may be screened in any biological sample obtained from a test subject and compared to like samples from control subjects, and selected for its increased occurrence in a specific or general dependency on one or more addictive substances, such as nicotine dependency. Once a statistically significant association is established between one or more SNP(s) and a dependency on an addictive substance of interest, then the region around the SN P can optionally be thoroughly screened to identify the causative genetic locus/sequence(s) (e.g., causative SN P mutation, gene, regulatory region, and the like) that influences the dependency.

Thus, the one aspect described herein pertains to a method for treating the abusive or habitual use of an addictive substance in a subject including identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 or SNPs in linkage disequilibrium with the SNPs listed in Table 1 (e.g., at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or any number in-between) in a nucleic acid of the subject (see, Table 1). The presence of the SNPs is correlated with an increased rate of success in addictive substance cessation. I n addition, the SNPs can be in linkage disequilibrium with the SNPs set forth in Table 1. I n some cases, SN Ps identified by genotyping as described herein may be used to exclude subjects from addictive substance cessation programs, treatment regimens, or clinical trials based on the subjects' low likelihood of success in addictive substance cessation or increased risk of becoming addicted to an addictive substance.

The terms, "quit," "quitting," or "cessation," as used herein, mean the stopping of use, commencing to end the use, or cessation of use of an addictive substance (e.g., quit using an addictive substance, such as nicotine, i.e., stop smoking). An "increased rate" of success in addictive substance cessation, as used herein, means a higher than normal rate of ceasing or stopping use of an addictive substance by a subject, compared to that of the general population. I n a non-limiting example, the addictive substance is nicotine. In a further embodiment, the subject presently is dependent on an addictive substance (e.g., nicotine). The "quit date" is the day on which the subject ceased using the addictive substance. In one aspect, the quit date is the day a subject stops smoking.

An "addictive substance" means substance that causes or is characterized by addiction, that is, strong physiological and/or psychological dependence on the substance. Addictive substances, as used herein, include, but are not limited to, nicotine; alcohol; cannabis (e.g., marijuana); stimulants, such as cocaine and amphetamines (e.g., methamphetamine and Ecstasy); hallucinogens (e.g., LSD, PCP and ketamine); depressants (e.g., diazepam and barbiturates); sleep aids (e.g., eszopiclone, ramelteon and Zolpidem); psychotropic medications, such as anti- psychotics (e.g., haloperidol, loxapine, aripiprazole, and olanzapine); antidepressants (e.g., fluoxetine, nortriptyline, sertraline and bupropion); anti-anxiety agents (e.g., diazepam, alprazolam and sertraline); and narcotics, such as heroin, codeine, morphine and oxycodone. For a review, see, Substance Abuse: A Comprehensive Textbook, Lowinson et al., eds. 2^nd ed. Lippincott Williams & Wilkins, NY, (2005).

The phrase "nucleic acid of a (the, or said) subject" refers to the total nucleic acid content of a subject (e.g., as found in a biological sample, such as a cell, of a subject), and includes a full set of genes (i.e., DNA), their translation products (i.e., RNA), mitochondrial DNA, and non-coding genetic material.

SNP genotyping to treat the abusive or habitual use of an addictive substance, identify a subject (or population of subjects) for inclusion or exclusion in a clinical trial, predict a subject's success in an addictive substance cessation program, identify a subject who has an increased risk of becoming dependent on an addictive substance, or develop an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance using replacement therapy and/or the cessation therapy, and other uses described herein, typically relies on initially establishing a genetic association between one or more (e.g., at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or any number in-between) specific SNPs in Table 1, particularly those with high weighting, and the specific traits, habits, or actions of interest. Several of the values specified above refer to specific numbers of SNPs with a particular weighting. As non limiting examples, the quantity of SNPs listed in Table 1 with a weight greater than about 2.000000 is 97, i.e. about 100; the quantity of SNPs listed in Table 1 with a weight greater than about 0.010000 is 4891, i.e., about 4900; the quantity of SNPs listed in Table 1 with a weight greater than about 0.005000 is 8443, i.e., about 8400; the quantity of SNPs listed in Table 1 with a weight greater than about 0.000100 is 8479, i.e. about 8500; the quantity of SNPs listed in Table 1 with a weight greater than about 0.000001 is 11984, i.e., about 12000, and the quantity of SNPs listed in Table 1 with a weight greater than about 0.00000099 is 12058. See Table 5. In addition, SNPs in linkage disequilibrium with the SNPs listed in Table 1 are also useful for the methods described herein. Individual quit success scores summed from SNP weighted values and the presence of abstinence alleles are also useful for predicting smoking cessation success for particular subjects (data not shown).

Different study designs may be used for genetic association studies. See, e.g., Modern Epidemiology pp. 609-622, Lippincott Williams & Wilkins (1998). One such study design is an observational study. Observational studies are most frequently carried out in which a response of subjects is not interfered with. One type of observational study is a case-control or retrospective study. In typical case-control studies, samples are collected from subjects with the habit or action of interest (cases), such as dependency on one or more addictive substances, and from individuals in whom dependency is absent (controls) in a population (target population) that conclusions are to be drawn from. Then, the possible causes of the traits, habits or actions, e.g., dependency on an addictive substance, such as nicotine, are investigated retrospectively. There may be potential confounding factors that should be taken into consideration. Confounding factors are those that are associated with both the real cause(s) of the dependency and the dependency itself, and they may include demographic information such as age, gender and ethnicity, as well as environmental factors. When confounding factors are not matched in cases and controls in a study, and are not controlled properly, spurious association results can arise. If potential confounding factors are identified, they can be controlled for by analysis methods well known to those of ordinary skill in the art.

Another study design is a genetic association study. In a genetic association study, a cause of interest to be tested is a certain allele or a SNP, or a combination of alleles or a haplotype from several SNPs. Thus, tissue specimens (e.g., blood) from a subject can be collected and genomic DNA genotyped for the SNP(s) of interest. In addition to the trait or habit of interest, other information such as demographic (e.g., age, gender and ethnicity), clinical and environmental information that may influence the outcome of the trait or habit can be collected to further characterize and define the sample set. In many cases, this information is known to be associated with dependency and/or SNP allele frequencies. There are likely gene-environment and/or gene-gene interactions as well.

After all the relevant trait, habit and/or action information and genotypic information is obtained, statistical analyses can be carried out to determine if there is any significant correlation between the presence of an allele or a genotype with the substance dependency of the subject. Data inspection and cleaning can be first performed before carrying out statistical tests for genetic association. Epidemiological and clinical data of the samples can be summarized by descriptive statistics with tables and graphs. Data validation can be performed to check for data completion, inconsistent entries, and outliers. Chi-squared tests and i-tests (Wilcoxon rank-sum tests if distributions are not normal) then can be used to check for significant differences between cases and controls for discrete and continuous variables, respectively. To ensure genotyping quality, Hardy-Weinberg disequilibrium tests can be performed on cases and controls separately. Significant deviation from Hardy-Weinberg equilibrium in both cases and controls for individual markers can be indicative of genotyping errors.

To test whether an allele of a SNP is associated with the case or control status of a trait or habit, one of ordinary skill in the art can compare allele frequencies in cases and controls. Standard chi-squared tests and Fisher exact tests can be carried out on a 2 ^χ 2 table (2 SNP alleles by 2 outcomes in the categorical trait of interest). To test whether genotypes of a SNP are associated, chi-squared tests can be carried out on a 3 ^χ 2 table (3 genotypes by 2 outcomes). Score tests can also carried out for genotypic association to contrast the three genotypic frequencies (major homozygotes, heterozygotes and minor homozygotes) in cases and controls, and to look for trends using three different modes of inheritance, namely dominant (with contrast coefficients 2, -1, -1), additive (with contrast coefficients 1, 0, -1) and recessive (with contrast coefficients 1, 1, 2). Odds ratios for minor versus major alleles, and odds ratios for heterozygote and homozygote variants versus the wild-type genotypes are calculated with the desired confidence limits, usually 95%. For samples genotyped in DNA pools, ί-tests assess the relationship between relative allelic frequencies in cases versus controls. To control for confounders and to test for interaction and effect modifiers, stratified analyses can be performed using stratified factors that are likely to be confounding, including demographic information such as age, ethnicity and gender, or an interacting element or effect modifier such as known major genes (e.g., nicotine metabolizing enzymes for nicotine dependency) or environmental factors such as polysubstance abuse.

In addition to performing association tests one marker at a time, haplotype association analysis can also be performed to study a number of markers that are closely linked together. Haplotype association tests may have better power than genotypic or allelic association tests when the tested markers are not the mutations causing the predisposition to dependency themselves, but are in linkage disequilibrium with such mutations. In order to perform haplotype association effectively, marker-marker linkage disequilibrium measures, both D and R2, are typically calculated for the markers within a gene to elucidate the haplotype structure. Studies in linkage disequilibrium suggest that SNPs within a given gene are organized in block pattern, and a high degree of linkage disequilibrium exists within blocks and very little linkage disequilibrium exists between blocks. Daly et al., Nat. Gen. 29: 232-235 (2001). Haplotype association with predisposition to dependency on an addictive substance can be performed using such blocks once they have been elucidated. Haplotype association tests can be carried out in a similar fashion as the allelic and genotypic association tests. Each haplotype in a gene is analogous to an allele in a multi-allelic marker. One of ordinary skill in the art can compare the haplotype frequencies in cases and controls or can test genetic association with different pairs of haplotypes. An important decision in performing genetic association tests is determining a significance level at which significant association can be declared when a p-value of the tests reaches that level. In an exploratory analysis where positive hits will be followed up in subsequent confirmatory testing, an unadjusted p-value < 0.1 can be used for generating hypotheses for significant association of a SNP with certain traits or habits associated with substance dependency. Generally, a p-value < 0.05 is required for a SNP for an association with a predisposition to dependency on an addictive substance, and a p-value < 0.01 is required for an association to be declared. When hits are followed up in confirmatory analyses in more samples of the same source or in different samples from different sources, adjustment for multiple testing can be performed so as to avoid excess number of hits while maintaining the experimental error rates at 0.05. While there are different methods known to one of ordinary skill in the art to adjust for multiple testing to control for different kinds of error rates, a commonly used method is Bonferroni correction to control the experimental or family-wise error rate. Westfall et al., Multiple Comparisons and Multiple Tests, SAS Institute (1999). Permutation tests to control for false discovery rates also can be used. Benjamini & Hochberg, J. Royal Stat. Soc. B 57: 1289- 1300 (1995). Monte Carlo simulation studies are especially useful in correcting for false positive results, since these tests can take into account many of the features of the true datasets without reliance on underlying statistical models. Since both genotyping and addiction status classification can involve errors, sensitivity analyses may be performed to see how odds ratios and p-values would change upon various estimates on genotyping and addiction status classification error rates.

Determining which specific nucleotide (i.e., allele) is present at each of one or more SNP positions, such as the SNPs disclosed in Table 1, is referred to as SNP genotyping. Some aspects described herein are methods for SNP genotyping, such as predicting success in addictive substance cessation in a subject, predicting success in nicotine cessation in a subject using a nicotine replacement source and/or a smoking cessation aids such as bupropion or varenicline, identifying a subject with an increased risk of becoming dependent on an addictive substance, or other uses as described herein. In some cases, SNP genotyping may be used to exclude subjects from addictive substance cessation programs, treatment regimens, or clinical trials based on the subjects' low likelihood of success in addictive substance cessation.

Some SNPs are in linkage disequilibrium with other SNPs. Linkage disequilibrium is the non- random association of alleles, at two or more loci, that are not necessarily on the same chromosome. The amount of linkage disequilibrium depends on the difference between the observed allelic frequency and that expected by random distribution. Linkage disequilibrium is due to genetic linkage, selection, recombination rate, mutation rate, genetic drift, non-random mating, and population structure. As used herein, the phrase "SN Ps in linkage disequilibrium with SN Ps" refers to SNPs that are non-randomly associated with one anther. For example, SNPs in linkage disequilibrium with the SNPs in Table 1, as used herein, are SNPs that are associated with the SN Ps listed in Table 1 through linkage selection, recombination rate, mutation rate, genetic drift, non-random mating, and/or population structure. SNPs in linkage disequilibrium with the SN Ps in Table 1 are also useful for the methods described herein.

Nucleic acid samples can be genotyped to determine which alleles are present at any given genetic region (e.g., SNP position) of interest by methods well known in the art. Neighboring sequences can be used to design SN P detection reagents such as oligonucleotide probes, which may optionally be implemented in a kit format. Exemplary SNP genotyping methods are known in the art. Chen et al., Pharmacogenomics J. 3: 77-96 (2003); Kwok et al., Curr. Issues Mol. Biol. 5: 43-60 (2003); Shi, Am. J. Pharmacogenomics 2: 197-205 (2002); and Kwok, Annu. Rev. Genomics Hum. Genet. 2: 235-258 (2001). Exemplary techniques for high-throughput SNP genotyping are described by Marnellos, Curr. Opin. Drug Discov. Devel. 6: 317-321 (2003). Common SNP genotyping methods include, but are not limited to, TaqMan^® Gene Expression Assays (Applied Biosystems, Inc.; Foster City, CA), molecular beacon assays, nucleic acid arrays, allele-specific primer extension, allele-specific polymerase chain reaction (PCR), arrayed primer extension, homogeneous primer extension assays, primer extension with detection by mass spectrometry, pyrosequencing, multiplex primer extension sorted on genetic arrays, ligation with rolling circle amplification, homogeneous ligation, multiplex ligation reaction sorted on genetic arrays, restriction-fragment length polymorphism (RFLP) and single base extension-tag assays. Such methods can be used in combination with detection mechanisms such as, e g., luminescence or chemiluminescence detection, fluorescence detection, time-resolved fluorescence detection, fluorescence resonance energy transfer, fluorescence polarization, mass spectrometry, and electrical detection.

Various methods for detecting polymorphisms include, but are not limited to, methods in which protection from cleavage agents is used to detect mismatched bases in RNA/RNA or RNA/DNA duplexes by comparison of the electrophoretic mobility of variant and wild type nucleic acid molecules. Myers et al., Science 230: 1242-1246 (1985); Cotton et al., Proc. Natl. Acad. Sci. USA 85: 4397-4401 (1988); Saleeba et al., Meth. Enzymol. 217: 286-295 (1992); Orita et al., Proc. Natl. Acad. Sci. USA 86: 2766-2770 (1989); Cotton et al., Mutat. Res. 285: 125-144 (1992); Hayashi et al., Genet. Anal. Tech. Appl. 9: 73-79 (1992). Assaying the movement of polymorphic or wild-type fragments in polyacrylamide gels containing a gradient of denaturant using denaturing gradient gel electrophoresis are described by Myers et al., Nature 313: 495-498 (1985). Sequence variations at specific locations also can be assessed by nuclease protection assays such as RNase and SI protection assays or chemical cleavage methods.

In one specific embodiment, SNP genotyping is performed using the TaqMan^® Assay, which also is known as a 5'-nuclease assay. See, e.g., U.S. Patent Nos. 5,210,015 and 5,538,848. The TaqMan^® Assay detects accumulation of a specific amplified product during PCR. It utilizes an oligonucleotide probe labeled with a fluorescent reporter and quencher dye. When the reporter dye is excited by irradiation at an appropriate wavelength, it transfers energy to the quencher dye in the same probe via a process called fluorescence resonance energy transfer (FRET). As such, when attached to the probe, the excited reporter dye does not emit a signal. The proximity of the quencher dye to the reporter dye in the intact probe maintains a reduced fluorescence for the reporter dye. The reporter and quencher dyes can be at the 5'-most and the 3'-most ends of the probe, respectively, or vice versa. Alternatively, the reporter dye can be at the 5'- or 3'-most end of the probe, while the quencher dye is attached to an internal nucleotide, or vice versa. Alternatively, both the reporter and quencher dyes can be attached to internal nucleotides of the probe at a distance from each other, such that fluorescence of the reporter dye is reduced. During PCR, the 5'-nuclease activity of DNA polymerase cleaves the probe, thereby separating the reporter dye and the quencher dye and resulting in increased fluorescence of the reporter. Accumulation of PCR product is detected directly by monitoring the increase in fluorescence of the reporter dye. The DNA polymerase cleaves the probe between the reporter dye and the quencher dye only if the probe hybridizes to the target SNP-containing template, which is amplified during PCR, and the probe is designed to hybridize to the target SNP site only if a particular SNP allele is present. Preferred TaqMan^® primer and probe sequences can readily be determined using the SNP and associated nucleic acid sequence information provided herein. A number of computer programs, such as Primer Express (Applied Biosystems, Foster City, CA), can be used to rapidly obtain optimal primer/probe sets. It will be apparent to one of skill in the art that such primers and probes for detecting the SNPs described herein are useful in diagnostic assays for identifying a subject who has an increased risk of becoming dependent on an addictive substance, predicting success in addictive substance cessation in a subject and predicting success in nicotine cessation in a subject using a nicotine replacement source and/or bupropion or varenicline, and can be readily incorporated into a kit format. Also described herein are modifications of the TaqMan^® Assay, such as the use of molecular beacon probes. See, e.g., U.S. Patent Nos. 5,118,801; 5,312,728; 5,866,336; and 6,117,635.

Another method for SNP genotyping is based on mass spectrometry, and takes advantage of the unique mass of each of the four nucleotides of DNA. Single nucleotide 10 polymorphisms can be unambiguously genotyped by mass spectrometry by measuring the differences in the mass of nucleic acids having alternative SNP alleles. Matrix Assisted Laser Desorption lonization-Time of Flight (MALDI-TOF) mass spectrometry technology can be used for extremely precise determinations of molecular mass such as SNPs. Wise et al., Rapid Commun. Mass Spectrom. 17: 1195-1202 (2003). Numerous approaches to SNP analysis have been developed based on mass spectrometry. Some mass spectrometry-based methods of SNP genotyping include primer extension assays, which can also be utilized in combination with other approaches, such as traditional gel-based formats and microarrays. SNPs also can be scored by direct DNA or RNA sequencing. A variety of automated sequencing procedures can be utilized, including sequencing by mass spectrometry (see, e.g., WO 94/16101; Cohen et al., Adv. Chromatogr. 36: 127-162 (1996); Griffin et al., Appl. Biochem. Biotechnol. 38: 147-159 (1993). The nucleic acid sequences described herein enable one of ordinary skill in the art to design sequencing primers for such automated sequencing procedures. Commercial instrumentation, such as the analyzers supplied by Applied Biosystems, is commonly used in the art for automated sequencing.

Sequence-specific ribozymes also can be used to score SNPs based on the development or loss of a ribozyme cleavage site. See, e.g., U.S. Patent No. 5,498,531. Perfectly matched sequences can be distinguished from mismatched sequences by nuclease cleavage digestion assays or by differences in melting temperature. If the SNP affects a restriction enzyme cleavage site, the SNP can be identified by alterations in restriction enzyme digestion patterns, and the corresponding changes in nucleic acid fragment lengths determined by gel electrophoresis. In some assays, the size of the amplification product is detected and compared to the length of a control sample. For example, deletions and insertions can be detected by a change in size of the amplified product compared to a control genotype.

Further embodiments described herein are methods for treating the abusive or habitual use of an addictive substance, predicting a subject's success in an addictive substance cessation program, identifying a subject who has an increased risk of becoming dependent on an addictive substance, or developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance using replacement therapy and/or the cessation therapy. A non-limiting example of an addictive substance is nicotine. The methods include identifying a quantity of SNPs in the nucleic acid of the subject (e.g., at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or any number in-between) SN Ps (see, Table 1) and calculating the likelihood of success in addictive substance cessation based on the SNPs. I n a non-limiting example, the nucleotide sequences can be at least 100 or more of the SNPs with high weighting as set forth in Table 1. See also Table 5. In addition, the nucleotide sequences can be in linkage disequilibrium with the SNPs set forth in Table 1. The presence of some SNPs as set forth in Table 1 are correlated with an increased rate of success in nicotine cessation in a subject using behavioral modification and/or a nicotine replacement source and/or the smoking cessation aids bupropion or varenicline, i.e., pharmacological therapy. In other cases, the presence of SN Ps with high weighting as set forth in Table 1 (or some SNPs in linkage disequilibrium with the SN Ps with high weighting as set forth in Table 1) can be used to select or include subjects in addictive substance cessation programs and treatment regimens based on the subjects' likelihood of success in addictive substance cessation. In some cases, the presence of some SNPs or the absence of SNPs listed in Table 1 (or some SNPs in linkage disequilibrium with the SN Ps listed in Table 1) may be used to exclude subjects from addictive substance cessation programs and treatment regimens based on the subjects' low likelihood of success in addictive substance cessation. In some instances, no treatment (i.e., behavioral modification and/or pharmacological therapy) may be provided to some subjects if the subjects have few or no SNPs set forth in Table 1 or in linkage disequilibrium with the SN Ps set forth in Table 1 as described herein. I n other cases, the absence of SN Ps listed in Table 1 are correlated with an decreased rate of success in nicotine cessation in a subject using behavioral modification and/or a nicotine replacement source and/or the smoking cessation aids bupropion or varenicline.

"Replacement therapy" as used herein refers to the treatment of (i.e., facilitating cessation of use) the addictive or habitual use of a substance with the same substance through a different route or with a different substance (i.e., a less addictive or pernicious substance). A "nicotine replacement source" as used in "nicotine replacement therapy (NRT)" is intended a source of nicotine separate or apart from tobacco (e.g., an isolated and/or purified source of nicotine). An exemplary nicotine replacement source is a nicotine patch (e.g., Habitrol™, Nicoderm^® CQ^® and Nicotrol^®) which releases a constant amount of nicotine into the body. Unlike nicotine in tobacco smoke, which passes rapidly into the blood through the lining of the lungs, nicotine in a nicotine patch takes about an hour to pass through the layers of skin and into the subject's blood. An additional nicotine replacement source is nicotine gum (e.g., Nicorette^® gum), which delivers nicotine to the brain more quickly than a patch. However, unlike the nicotine in tobacco smoke, the nicotine in the gum takes several minutes to reach the brain, making the nicotine "hit" less intense with the gum than with a cigarette. Yet another nicotine replacement source is a nicotine lozenge (e.g., Commit^® or Nicorette^® lozenges), which comes in the form of a hard tablet or lozenge and releases nicotine as it slowly dissolves in the mouth of a subject.

Electronic cigarettes (E-cigarettes), also known as personal vaporisers, are electronic devices that vaporize a liquid solution containing nicotine into an aerosol mist that is inhaled by a user. The E-cigarette simulates the act of smoking, but is believed to reduce the health risks associated with tobacco smoke. The benefits and risks of electronic cigarettes are not yet fully understood. Electronic cigarettes may be useful as nicotine replacement sources. A nicotine nasal spray (e.g., Nicotrol^® nasal spray) is another example of a nicotine replacement source. Nicotine nasal spray, dispensed from a pump bottle similar to over-the-counter decongestant sprays, relieves cravings for a cigarette, as the nicotine is rapidly absorbed through the nasal membranes and reaches the bloodstream faster than any other nicotine replacement therapy (NRT) product. Yet another example of a nicotine replacement source is a nicotine inhaler (e.g., Nicotrol^® inhaler), which generally consists of a plastic cylinder containing a cartridge that delivers nicotine when a subject puffs on it. Although similar in appearance to a cigarette, a nicotine inhaler delivers nicotine into the mouth, not the lungs, and the nicotine enters the body much more slowly than the nicotine in tobacco smoke. Cessation therapy, as used herein, is medication administered to subject desiring to cease the use of an addictive substance in order to reduce withdrawal symptoms and/or the urge to continue usage of the addictive substance. "Smoking cessation medications," "smoking cessation drugs," or "smoking cessation aids," as used herein, refers to drugs administered to subjects desiring to quit smoking in order to reduce withdrawal symptoms and/or the urge to smoke. Two common smoking cessation medications are bupropion hydrochloride, e.g., Zyban^® (GSK) and varenicline tartrate, e.g., Chantix^® (Pfizer).

The term "bupropion," as used herein, includes bupropion hydrochloride, an antidepressant sold under various trade names, e.g., Zyban^®, Wellbutrin^®, Wellbutrin SR^®, Wellbutrin XL^®, Budeprion^®, Aplenzin^®, Forfivo and Voxra. Bupropion is a relatively weak inhibitor of the neuronal uptake of norepinephrine and dopamine, and does not inhibit monoamine oxidase or the re-uptake of serotonin. The mechanism by which bupropion enhances the ability of patients to abstain from smoking is unknown. However, it is presumed that this action is mediated by noradrenergic and/or dopaminergic mechanisms.

The term "varenicline" as used herein, includes varenicline tartrate, e.g., Chantix^® or Champix^® (Pfizer), which is a partial agonist selective for α₄β₂ nicotinic acetylcholine receptor subtypes. The efficacy of varenicline in smoking cessation is believed to be the result of varenicline's activity at α₄β₂ sub-type of the nicotinic receptor where its binding produces agonist activity, while simultaneously preventing nicotine binding to these receptors.

I n addition, one aspect described herein pertains to a method for identifying a subject with an increased risk of becoming dependent on an addictive substance, including identifying a quantity (e.g., at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or any number in-between) of SN Ps in a nucleic acid of the subject (see, Table 1) and calculating the likelihood of addiction based on the SNPs. In a non-limiting example, the nucleotide sequences can be at least 100 or more of the SNPs with high weighting as set forth in Table 1. See also Table 5. In addition, the nucleotide sequences can be in linkage disequilibrium with the SNPs set forth in Table 1. The presence of some SNPs set forth in Table 1 or in linkage disequilibrium with the SNPs set forth in Table 1 is correlated with an increased risk of becoming dependent on an addictive substance. In some cases, SNPs set forth in Table 1 as described herein may be used to exclude subjects from addictive substance cessation programs, treatment regimens, or clinical trials based on the subjects' low likelihood of success in addictive substance cessation or increased risk of becoming addicted to an addictive substance. By an "increased risk" of becoming dependent on an addictive substance is intended a subject that is identified as having a higher than normal chance of developing a dependency to an addictive substance, compared to the general population. The term "becoming dependent" (i.e., "dependent on" or "addicted to" an addictive substance) refers to exhibiting dependence or dependency, a state in which there is a compulsive or chronic need for the addictive substance. Thus, a subject dependent on an addictive substance exhibits compulsive use of the substance despite experiencing significant problems or adverse effects resulting from such use.

Hallmarks of dependency include, but are not limited to, taking a substance longer or in larger amounts than planned, stockpiling the substance for anticipated use, repeatedly expressing a desire or attempting unsuccessfully to cut down or regulate use of a substance, continuing use in the face of acknowledged substance-induced physical or mental problems, tolerance, and withdrawal.

Furthermore, one aspect described herein provides methods for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance, including identifying a quantity (e.g., at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or any number in-between) of SNPs in a nucleic acid of the subject (see, Table 1) and calculating the likelihood of success in addictive substance cessation based on said SNPs. I n a non-limiting example, the nucleotide sequences ca n be at least 100 or more of the SNPs with weighting set forth in Table 1. I n addition, the nucleotide sequences can be in linkage disequilibrium with the SNPs set forth in Table 1. The presence of one or more SNPs is correlated with an individualized treatment regimen by establishing a genetic association between specific SN Ps, the particular addictive substance the subject is dependent on and rates of success in addictive substance cessation in individuals utilizing behavioral modification and/or pharmacological therapy. In a non-limiting example, the addictive substance is nicotine and the behavioral modification and/or pharmacological therapy includes i.e., nicotine replacement therapy and/or smoking cessation therapy such as the smoking cessation aids bupropion or varenicline. In a further embodiment, the subject presently is dependent on an addictive substance (e.g., nicotine). The presence of some SNPs are correlated with an increased rate of success in nicotine cessation in a subject using a nicotine replacement source and/or the smoking cessation aids bupropion or varenicline. In some cases, the absence of some SN Ps set forth in Table 1 or in linkage disequilibrium with the SN Ps set forth in Table 1 can be used to exclude subjects from individualized treatment regimens, including behavioral modification and/or pharmacological therapy as described herein. In some instances, no treatment (i.e., behavioral modification and/or replacement or pharmacological therapy) may be provided to some subjects if the subjects have few or no SNPs set forth in Table 1 or in linkage disequilibrium with the SNPs set forth in Table 1 as described herein. In other cases, the presence of some SN Ps set forth in Table 1 or in linkage disequilibrium with the SNPs set forth in Table 1 can be used to exclude subjects from individualized treatment regimens, including behavioral modification and/or pharmacological therapy as described herein. An additional embodiment described herein is a method for identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, where addictives substances may be administered or dependence on an addictive substance may affect the clinical trial. Such method includes identifying a quantity (e.g., at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or any number in-between) of SNPs in a nucleic acid of the subject(s) (see, Table 1), wherein the presence of said SNP is correlated with an increased risk of becoming dependent on an addictive substance and calculating the likelihood of becoming dependent on an addictive substance based on the SNPs. I n addition, the nucleotide sequences ca n be in linkage disequilibrium with the SNPs set forth in Table 1. In some instances, the absence of SNPs listed in Table 1 or in linkage disequilibrium with the SNPs set forth in Table 1, as described herein, may be used to exclude individuals from clinical trials based on the increased risk of becoming addicted to an addictive substance or being addicted to an addictive substance. I n some instances, no treatment (i.e., including behavioral modification and/or pharmacological therapy) may be provided to some subjects if the subjects have few or no SN Ps set forth in Table 1 or in linkage disequilibrium with the SNPs set forth in Table 1 as described herein. In a non-limiting example, the addictive substance is nicotine or alcohol. In other examples, the addictive substance may be prescription medication (e.g., pain medication). I n further examples, the addictive substance may be illicit drugs. For example, the addictive substance may be one or more of nicotine, alcohol, marijuana, cocaine, heroin, methamphetamine, ketamine, Ecstasy (M DMA; 3,4-methylenedioxy-N-methylamphetamine), oxycodone, codeine, morphine and/or combinations thereof. In other instances, the addictive substance may be a combination of addictive substances, as described herein.

A further embodiment described herein is isolated nucleic acid molecules that contain one or more SN Ps useful for predicting success in nicotine cessation in a subject (e.g., at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or any number in-between), as disclosed Table 1. I n addition, the nucleotide molecules can contain SNPs in linkage disequilibrium with the SNPs set forth in Table 1. Nucleic acid molecules containing one or more SNPs disclosed herein may be interchangeably referred to as "SNP-containing nucleic acid molecules." Isolated nucleic acid molecules described herein also include probes and primers, which can be used for assaying the disclosed SNPs. As used herein, an "isolated nucleic acid molecule" is one that contains a SNP described herein, or one that hybridizes to such molecule such as a nucleic acid with a complementary sequence, and is separated from most other nucleic acids present in the natural source of the nucleic acid molecule. Moreover, an "isolated" nucleic acid molecule, such as a cDNA molecule containing a SNP described herein, may be substantially free of other cellular material, or culture medium when produced by recombinant techniques, or chemical precursors or other chemicals when chemically synthesized. A nucleic acid molecule can be fused to other coding or regulatory sequences and still be considered "isolated."

Isolated nucleic acid molecules may be in the form of cDNA, RNA, such as mRNA, and include in vivo or in vitro RNA transcripts of the isolated SNP-containing DNA molecules described herein. Isolated nucleic acid molecules described herein further include such molecules produced by molecular cloning or chemical synthetic techniques or by a combination thereof. See, e.g., Sambrook & Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, NY (2000). Generally, an isolated SNP-containing nucleic acid molecule includes one or more SNP positions described herein with flanking nucleotide sequences on either side of the SNP positions. A flanking sequence can include nucleotide residues that are naturally associated with the SNP site and/or heterologous nucleotide sequences. Generally, the flanking sequence is up to about 5000, 1000, 500, 250, 200, 100, 80, 60, 50, 40, 30, 25, 20, 15, 10, 8, 6, or 4 nucleotides (or any other length in-between) on either side of a SNP position.

An isolated nucleic acid molecule described herein further encompasses a SNP-containing polynucleotide that is the product of any one of a variety of nucleic acid amplification methods, which are used to increase the copy numbers of a polynucleotide of interest in a nucleic acid sample. Such amplification methods are well known in the art and include, but are not limited to, PCR (U.S. Patent Nos. 4,683,195 and 4,683,202), ligase chain reaction, Wu & Wallace, Genomics 4: 560-569 (1989); Landegren et al., Science 241: 1077-1080 (1988); strand displacement amplification, U.S. Patent Nos. 5,270,184 and 5,422,252; transcription-mediated amplification, U.S. Patent No. 5,399,491; linked linear amplification, U.S. Patent No. 6,027,923; and isothermal amplification methods such as nucleic acid sequence based amplification and self-sustained sequence replication, Guatelli et al., Proc. Natl. Acad. Sci. USA 87: 1874-1878 (1990). Based on such methodologies, one of ordinary skill in the art readily can design primers in any suitable 5'- and/or 3'-regions adjacent to a SNP disclosed herein. Such primers can be used to amplify DNA of any length so long as it contains the SNP of interest in its sequence.

Furthermore, isolated nucleic acid molecules, particularly SN P detection reagents such as probes and primers, also can be partially or completely in the form of one or more types of nucleic acid analogs, such as peptide nucleic acid, PNA; see U.S. Patent Nos 5,539,082; 5,527,675; 5,623,049; and 5,714,331. N ucleic acids, especially DNA, can be double-stranded or single-stranded. Single-stranded nucleic acid can be the coding strand (sense strand) or the complementary non-coding strand (anti-sense strand). DNA, RNA, or PNA segments can be assembled, e.g., from fragments of the human genome (in the case of DNA or RNA) or single nucleotides, short oligonucleotide linkers, or from a series of oligonucleotides, to provide a synthetic nucleic acid molecule. Nucleic acid molecules ca n be readily synthesized using the sequences provided herein as a reference. Furthermore, large-scale automated oligonucleotide/PNA synthesis (including synthesis on an array, or bead surface or other solid support) can be readily accomplished using commercially available nucleic acid synthesizers, such as the Applied Biosystems 3900 High-Throughput DNA Synthesizer (Foster City, CA), and the sequence information provided herein.

The nucleic acid molecules described herein have a variety of uses, such as predicting success in addictive substance cessation in a subject and predicting success in nicotine cessation in a subject using a nicotine replacement source and/or bupropion or varenicline or identifying a subject who has an increased risk of becoming dependent on an addictive substance. Additionally, the nucleic acid molecules are useful as hybridization probes, such as for genotyping SNPs in messenger RNA, cDNA, genomic DNA, amplified DNA or other nucleic acid molecules, and for isolating full-length cDNA and genomic clones as well as their orthologs.

A probe can hybridize to any nucleotide sequence along the entire length of a nucleic acid molecule provided herein. Generally, a probe described herein hybridizes to a region of a target sequence that encompasses a SNP position indicated in Table 1. In some instances, the probe hybridizes to a SNP-containing target sequence in a sequence-specific manner, such that it distinguishes a target sequence from other nucleotide sequences that vary from the target sequence only by the nucleotide present at the SNP site. Such a probe is particularly useful for detecting a SNP-containing nucleic acid in a test sample, or for determining which nucleotide (allele) is present at a particular SNP site (i.e., genotyping the SNP site). In some cases, the probe can hybridize to a region of a target sequence that encompasses a SNP s in linkage disequilibrium with the SNPs set forth in Table 1.

A nucleic acid hybridization probe can be used for determining the presence, level, form, and/or distribution of nucleic acid expression. The nucleic acid whose level is determined can be DNA or RNA. Accordingly, probes specific for the SNPs described herein can be used to assess the presence, expression and/or gene copy number in a given cell, tissue or organism. In vitro techniques for detection of mRNA include, e.g., Northern blot hybridizations and in situ hybridizations. In vitro techniques for detecting DNA include Southern blot hybridizations and in situ hybridizations. Probes can be used as part of a diagnostic test kit for identifying cells or tissues in which a SNP is present, such as by determining if a polynucleotide contains a SNP of interest.

One of ordinary skill in the art will recognize that, based on the SNP and associated sequence information disclosed herein, detection reagents can be developed and used to assay any SNP described herein individually or in combination, and such detection reagents can be readily incorporated into one of the established kit or system formats which are well known in the art. The terms "kits" and "systems," as used herein in the context of SNP detection reagents, are intended to refer to such things as combinations of multiple SNP detection reagents, or one or more SNP detection reagents in combination with one or more other types of elements or components (e.g., other types of biochemical reagents, containers, packages, such as packaging intended for commercial sale, substrates to which SNP detection reagents are attached, electronic hardware components, and the like). Accordingly, some aspect described herein are SNP detection kits and systems, including but not limited to, packaged probe and primer sets (e.g., TaqMan^® Probe Primer Sets), arrays/microarrays of nucleic acid molecules, and beads that contain one or more probes, primers, or other detection reagents for detecting one or more SNPs described herein. The kits/systems optionally can include various electronic hardware components. For example, arrays (e.g., DNA chips) and microfluidic systems (e.g., lab-on-a-chip systems) provided by various manufacturers typically include hardware components. Other kits/systems (e.g., probe/primer sets) may not include electronic hardware components, but can include, e.g., one or more SNP detection reagents along with other biochemical reagents packaged in one or more containers.

A SNP detection kit typically also can contain one or more detection reagents and other components (e.g., a buffer, enzymes, such as DNA polymerases or ligases, chain extension nucleotides, such as deoxynucleotide triphosphates, positive control sequences, negative control sequences, and the like) necessary to carry out an assay or reaction, such as amplification and/or detection of a SNP-containing nucleic acid molecule. A kit can further contain means for determining the amount of a target nucleic acid, and means for comparing the amount with a standard, and can include instructions for using the kit to detect the SNP- containing nucleic acid molecule of interest. In one embodiment described herein, kits are provided that contain the necessary reagents to carry out one or more assays to detect one or more SNPs disclosed herein. In a non-limiting example, SNP detection kits/systems are in the form of nucleic acid arrays or compartmentalized kits, including microfluidic/lab-on-a-chip systems. SNP detection kits/systems may contain, e.g., one or more probes, or pairs of probes, that hybridize to a nucleic acid molecule at or near each target SNP position. Multiple pairs of allele- specific probes can be included in the kit/system to simultaneously assay large numbers of SNPs, at least one of which is a SNP described herein. I n some kits/systems, the allele-specific probes are immobilized to a substrate, such as an array or bead. For example, the same substrate can comprise allele-specific probes for detecting at least about 2, at least about 10, at least about 100, at least about 250, at least about 500, at least about 750, at least about 1000, at least about 2500, at least about 4900, at least about 8400, at least about 8500, at least about 12000, at least about 12058, or a greater number of SNPs. The terms "arrays," "microarrays" and "DNA chips" are used herein interchangeably to refer to an array of distinct polynucleotides affixed to a substrate such as glass, plastic, paper, nylon, or other type of membrane, filter, chip, or any other suitable solid support. The polynucleotides can be synthesized directly on a surface of the substrate, or synthesized separate from the substrate and then affixed to the substrate's surface.

The scope of the compositions or methods described herein includes all combinations of aspects, embodiments, examples, and preferences herein described. EXAMPLES

Example 1

Smoking Cessation Subjects

Adult smokers who expressed the desire to stop smoking were recruited and screened at one of four North Carolina clinical research sites. Participants provided written informed consent, reported smoking an average of > 10 cigarettes/day that each yield > 0.5 mg nicotine, displayed end-expired air CO > 10 ppm, failed to display any exclusionary features on history, physical exam or laboratory evaluations, and were compensated up to $140 (Clinicaltrials.gov NCT00894166). Smokers were subdivided into low- and high- dependence subgroups, based on expired air CO levels of≤ 30 ppm or > 30 ppm, respectively. Those with higher and lower levels of CO were assigned to 42 mg/24 h or 21 mg/24 h Nicoderm^® (GlaxoSmithKline) nicotine patch doses, respectively. During seven study sessions, brief supportive counseling was provided, clinical trials materials were dispensed and dependent measures assessed. Dependent measures included measured end-expired air CO and reports of smoking, withdrawal symptoms, and adverse effects including nausea and/or emesis.

Each participant wore N RT skin patches daily and was provided with his usual brand of cigarettes to smoke during the 2-week pre-cessation period. I ndividuals who reduced baseline end expired air CO levels by more than 50% were maintained on NRT. Those who did not were randomized to one of the following three regimens: (a) maintenance on NRT, (b) maintenance on NRT with the addition of bupropion (Zyban^®), 150 mg bid or (c) discontinuation of NRT and treatment with an ascending then stabilized dose regimen of varenicline (Chantix^®), beginning from 0.5 mg/d and escalating to 1 mg bid for 12 weeks. NRT doses were gradually reduced beginning 4- or 6-weeks after the quit date (i.e., the date of smoking cessation) for the 42 and 21 mg/24 h groups, respectively. Participants with sleep disturbances removed patches at bedtime and applied new ones upon awakening. Subjects experiencing other symptoms of nicotine toxicity reduced doses until symptoms abated according to the following sequence: reduce morning patch from 21 to 14 to 7 to 0 mg/day, and then discontinue the afternoon patch.

The primary outcome for this report, continuous abstinence for the 11-weeks following the quit date, was assessed based on self reports of continuous abstinence that were confirmed by end- expired CO levels≤ 10 ppm. DNAs selected for genotyping included those from all abstinent subjects from European American racial/ethnic backgrounds (n = 50), and more than twice the number of gender- and ethnically-matched comparison individuals who were in the same arm of the study but reported failure to achieve and/or sustain abstinence through the 11-week followup period (n = 117). Prevention Study Samples

Baltimore, MD first graders in 1993 (Cohort III) were group-randomized to control (an ineffective mastery learning strategy or no intervention) or preventive (good behavior game) interventions intended to improve behavior and school performance that were applied in first grade classrooms. Kellam et al., Drug Alcohol Depend. 95 (Suppl. 1): S5-S28 (2008); Wang et al., Drug Alcohol Depend. 100(3): 194-203 (2009); lalongo, Poduska, and Werthamer-Larsson, J. Emot. Behav. Disord. 9: 146-160 (2001); Kellam et al., Am. J. Commun. Psychol. 19(4): 563-584 (1991). Participants provided data at a median of nine of ten follow-up attempts that asked questions about drug use from 8^th grade until age 24. There was ca 20% loss of subjects to follow-up over this period. During follow-up visits in 2007-2011, almost 80% of participants provided consent for genotyping and blood and/or buccal material for DNA extraction. Comparison of data from substance dependent and control individuals from this and other cohorts from this prevention study provides confidence that this sample displays genetic features of other larger African American research volunteer samples ascertained in Baltimore (Table 1). The analyses described herein focus on data from 555 individuals for whom both genotype and detailed clinical information during development is available.

Genotyping and Assignment of vl.O Quit Success Genotype Scores

DNA was extracted from blood or buccal material, quantitated and genotyped using Affymetrix 6.0 microarrays according to manufacturer's instructions. Uhl et al., Am. J. Hum. Genet. 69(6): 1290-300 (2001); Smith et al., Arch. Gen. Psychiatry 49(9): 723-727 (1992); Persico et al., Biol. Psychiatry 40(8): 776-784 (1996). Genotypes and vl.O scores were assigned for each participant by investigators blinded to clinical phenotype. Genotypes for each individual passed Affymetrix quality control metrics with contrast quality control threshold > 0.4 and provided calls for > 98% of SNP genotypes. Alleles were assessed for the 12,058 SNPs in which at least one of three initial smoking cessation success clinical trial samples had identified nominally- highly-significant (p < 0.01) differences between successful vs unsuccessful quitters, based on strength and replicability of the associations in these studies. Missing genotypes were assigned based on mean allele frequencies for the appropriate sample group. These SNPs and the vl.O score have been previously discussed. Uhl et al., Pharmacogenomics 11(3): 357-67 (2010); Rose et al., Mol. Med. 16(7-8): 247-253 (2010). The SNPs that comprise the score and their weights are shown in Table 1. Linkage disequilibrium values among pairs of SNPs (r² values > 0.2) were used to generate vl.O quit success scores. These values come from PLIN K, vl.07 tests of SN Ps that are not more than 10 SN Ps apart within a 1 M b sliding window, and provides r² correlations based on genotypic allele counts between variables, coded 0, 1, or 2 to represent the number of non-reference alleles at each. See Table 2.

Smoking Cessation Success

The significance of the differences between vl.O scores from successful quitters vs. unsuccessful quitters was assessed using one-tailed ί-tests of the directional hypothesis that higher quit success scores would be associated with greater likelihood of quitting. The significance of the difference in quit success between the genotyped subjects with the highest vs. lowest tercile vl.O scores was assessed with Fisher's exact test.

ROC Analysis

Receiver operating characteristic analyses of the vl.O quit success score data were performed using JROCFIT. Eng J., ROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University (2007). See Figures 4 and 5.

Power for Quit Success Comparisons

To assess the power for vl.O score application in smoking cessation success, current sample sizes and standard deviations, the program PS v2.1.31 and a = 0.05 were utilized. Dupont and Plummer, Control Clin. Trials 11(2): 116-28 (1990); Dupont and Plummer, Control Clin. Trials 19(6): 589-601 (1998). Smoking Prevention Study Samples with Longitudinal Followup

Beginning in eighth grade, subjects provided data about their past year use of addictive substances. For latent class growth analyses, data from the most commonly used addictive substances, i.e., tobacco, alcohol, and cannabis, were used. At each time point, past year use of each of these substances was assessed on a scale as follows: 0 = none, 1 = once, 2 = twice, 3 = 3-4 times, 4 = 5-9 times, 5 = 10-19 times, 6 = 20-39 times, 7 = 40 or more. To create the drug frequency for the three combined drugs, a summed score ranging from 0 to 21 for each interview/time point was created. Latent class growth analysis (LCGA) was performed for this data using Mplus (M uthen & M uthen, Los Angeles, CA). This analysis explores substantively meaningful groups under the assumption that there are unobserved subpopulations whose members display different patterns of development. To allow the latent class variables to capture the heterogeneity in the growth factors (e.g., the temporal profile of change in substance use), LCGA sets the variance of the intercept and slope factors to be zero within each class and sets covariance between the growth factors at zero.

LCGA model estimation began with class enumeration. A class was added with each subsequent run, testing goodness-of-fit as well as Bayesian Information Criterion (BIC), Vuong- Lo-Mendell-Rubin likelihood ratio test and bootstrap likelihood ratio test parameters (Table 3). Once the three-class model was selected as one of the models with the highest entropy and biological plausibility, covariates gender and race/ethnicity were entered. Each subject's probabilities of membership in each of the three classes were thus calculated (Figure 1).

Table 3: Characteristics of latent class growth analyses for developmental profiles of use of

common addictive substances from ages 14-23

Number Log Number free , _ Smallest class

, BIC VLMRLRT Entropy

classes likelihood parameters frequency

1 -17323 12 34726 n/a n/a N/A

2 -16656 15 33412 0.003 0.918 0.15

3 -16519 18 33158 0.175 0.915 0.07

4 -16382 21 32904 0.009 0.894 0.04

5 -16309 24 32779 0.021 0.839 0.04 Primary preplanned analyses of these data were selected to parallel primary analyses of the quit success data. Class membership probabilities from subjects displaying vl.O quit success scores in the top tercile were compared to those of subjects displaying vl.O scores in the lowest tercile by i-test. Other analyses sought the significance of the vl.O score when it was added to subsequent latent class growth analysis, as described in the Mplus latent growth class modeling software.

Unsuccessful vs. Successful Smoking Quitters

There were 50 successful European-American trial participants who maintained continuous abstinence for the 11-week duration of the quit success study. These individuals were matched to 117 individuals (based on ethnicity, arm of the study and gender) who did not abstain for 11- weeks; 77% of these unsuccessful individuals also reported smoking by the 4^th week after the targeted quit date. These sample sizes displayed substantial, 0.95 power to detect the differences in vl.O scores and standard deviations that were actually observed.

Mean vl.O scores for these successful vs. unsuccessful quitters displayed highly significant differences (p = 0.0005, one tailed i-test) (Figure 3). The quit success scores were significantly higher in the successful quitters than in the matched comparison group of non-quitters.

Genotyped trial participants were also ranked based on their quit success scores. Quit success was compared for individuals in the upper tercile of vl.O scores to success of individuals in the lower tercile of vl.O scores. About 43% of individuals in the upper tercile achieved continuous abstinence for at least 11-weeks, compared with 13% of individuals in the lower tercile (p = 0.0006; Fisher exact test).

Receiver operating characteristic (ROC) curves evaluate the likely distributions of true and false positive results based on experimental data. A genotype score that predicted quit success at chance levels would provide, on average, 0.5 area under the ROC curve. Analyses of the present data provides an area under the ROC curve of 0.67 (Figure 4). The 95% confidence limits for the present data lies above 0.5 in most areas of the curve.

Latent class growth analysis: development of a three-class model from prevention study subjects: There were sizable individual differences in the developmental profiles of frequency of use of the common addictive substances alcohol, tobacco and cannabis, among subgroups of the 555 individuals available for these analyses, as anticipated from prior analyses of other similarly-treated cohorts that used different trajectory modeling approaches. Kellam et al., Drug Alcohol Depend. 95 Suppl. 1: S5-S28 (2008). The genome wide data for development of substance dependence for these and other prevention study subjects fit remarkably well with data from research volunteers that were previously obtained (Table 4), supporting the validity of this sample. Latent class growth analyses (LCGA) of two and three-class models provided similar estimates of entropy and Bayesian information content (Table 3). The richer three-class model was chosen for subsequent analyses (prior to testing any association of class membership probabilities with vl.O scores). After deciding on a three-class model, covariates of gender and race were added to generate the estimated trajectories shown in Figure 6.

Validation of the prevention study samples: Genomic regions and genes identified by prevention samples overlap with those identified by previously reported NI DA-MN B research volunteer samples, based on clustered, nominally significant SNPs. Monte Carlo simulations support p < 10-5 for overall overlap of these dependence vs. control datasets from these prevention samples and larger, research volunteer samples. These data compare African Americans from prevention and research volunteer samples and display this remarkable overlap of results despite modest differences in the ages (mid 20s vs. mid 30s, respectively), gender distribution (more males in the research volunteer group) and dependence (dependence on any addictive substance vs. heavy use of and dependence on at least one illegal substance, respectively. Prevention study samples and interventions: Baltimore first graders in 1985, 1986, and 1993 (Cohorts l-l ll, respectively) were group-randomized to control or preventive interventions intended to improve behavior and school performance that were applied through grades 1 (Cohort II I) or 1 and 2 (Cohorts I and I I), as noted above. Participants provided data at a median of six of the eight (Cohorts I and I I) and nine of ten (Cohort I II) follow-up attempts, including extensive data about substance use, abuse and dependence. Opportunities to use addictive substances of specific classes were assessed contemporaneously at each assessment (Cohort I II) or retrospectively, seeking lifetime data on opportunities to use, through questions asked at age ca. 18-19 (Cohorts I and II). DSM diagnoses of substance dependence came from the Diagnostic Interview Schedule (DIS). Robins et al., Arch. Gen. Psychiatry 38(4): 381-389 (1981). Nicotine dependence was diagnosed using the Fagerstrom Test for Nicotine Dependence (FTN D), with a cut-off of > 6. Fagerstrom, Addict. Behav. 3(3-4): 235-241 (1978); Heatherton et al., Br. J. Addict. 86(9): 1119-1127 (1991); Fagerstrom and Schneider, J. Behav. Med. 12(2): 159- 182 (1989). There was ca 20% loss of subjects to follow-up over this period.

Probabilities of membership in the two classes most strongly associated with vl.O scores in individuals in prevention study Cohort I II were assessed (i.e., Classes 1 and 3). Only 3% of participants in Class 1 displayed probabilities between 0.2 and 0.8 of membership in this class (Figure 7A). In Class 3, only 4.9% of participants displayed probabilities between 0.2 and 0.8 of membership in this class (Figure 7B).

Substance dependence was diagnosed using DSM and/or FTND criteria in 81 of these African- American study participants. These individuals were matched for gender, age, and ethnicity to the 175 African-American controls who reported the most opportunities to use addictive substances but displayed neither dependence, abuse, nor extensive use of any addictive substance. For each control individual in Cohort I II, the number of times that the subject responded "yes" to questions about opportunities to use substances of each class during their follow-up assessment were summed. For each individual in Cohorts I and I I, the number of times that the subject responded "yes" to questions about retrospective opportunities to use substances in each class during the age 18/19 assessment were summed.

Comparison research volunteer sample for assessments of substance dependence: Results from these prevention study subjects were compared to data from ethnically matched M NB research volunteers who provided informed consents, ethnicity data, drug use histories and DSMI II-R or IV diagnoses, or control histories. DNA from 35 and 12 pools sampled 700 "abusers" with DSMI II-R/IV dependence on at least one illegal abused substance and 240 "controls" who reported no significant lifetime use of any addictive substance, respectively.

Analyses: SNPs that displayed χ² statistics with p < 0.05 for differences between prevention study substance dependent vs. control individuals often clustered so that at least four of these nominally-significant SNPs lay within 10 kb of each other (average distances between nominally-significant SNP was about 3 kb). The preplanned analyses were focused on the chromosomal regions and genes identified by these clusters of nominally significant SN Ps in these samples that overlap with those identified, using the same criteria, in 1M SNP studies of DNA pools from N IDA M NB African American research volunteers. Drgon et al., PLoS One 5(1): e8832 (2010). The chromosomal regions that were identified by clustered, nominally positive SNPs from both samples to those identified by chance were compared in random Monte Carlo trials. Each Monte Carlo trial began with random sets of SNPs of the same sizes as those identified by the true data in each sample. For each trial, the random set of SN Ps was assessed to see if it provided results equal to or greater than the results that were observed. The number of trials for which the randomly selected SNPs displayed (at least) the same features displayed by the observed results was then tallied to generate an empirical p value for each region. These simulations, not corrected for multiple comparisons, test the null hypothesis that the results from the prevention and research volunteer samples identify the same chromosomal regions at the rates expected by chance. Table 4: Genomic regions and genes identified by prevention samples overlap with those identified by previously-reported NIDA-MNB research volunteer samples, based on clustered, nominally-significant SNPs

# p < 5 x 10^'2 SNPs

ch bp start bp: stop Prevention MNB gene(s) p region

1 160191373 160202745 5 4 ATF6 0.0177

1 161203260 161213817 6 5 0.0119

1 175138260 175154142 4 4 ASTN1 0.0248

1 175396660 175414782 6 5 ASTN1, FAM5B 0.0117

1 180378729 180451983 17 4 0.0025

1 202827613 202856667 10 5 L N2 0.0062

1 202942264 202979528 4 8 0.0274

1 232160317 232175256 6 4 SLC35F3 0.0142

1 239112219 239119745 7 6 RGS7 0.0072

2 18512168 18552016 5 11 0.0072

2 19809895 19832145 9 4 0.0095

2 23512565 23535429 4 6 KLHL29 0.0243

2 40203135 40231274 4 9 SLC8A1 0.0227

2 41927142 41958171 4 9 0.0240

2 52116162 52119167 5 4 0.0169

2 59959186 59981948 4 4 0.0282

2 60009197 60034167 4 4 0.0282

2 60056121 60074321 5 5 0.0142

2 65611155 65637534 4 4 0.0282

2 102465003 102470720 7 6 SLC9A4 0.0072

2 102531561 102549119 4 6 0.0227

2 153589361 153606718 5 4 0.0183

2 180157402 180173092 7 6 ZNF385B 0.0084

2 205024284 205030863 5 4 0.0169

2 205043875 205056347 4 5 0.0223

2 205764143 205772717 4 4 PARD3B 0.0229

2 213230739 213253635 4 7 0.0236

3 342757 362699 5 4 CHL1 0.0183

3 3314720 3330967 10 9 0.0034

3 4703008 4726802 4 6 ITPR1 0.0243

3 8311105 8341118 7 6 0.0098

3 25352331 25371869 6 4 0.0144

3 45346691 45365948 5 4 0.0183

3 59953952 60014051 4 21 FHIT 0.0050

3 125407985 125429920 5 7 KALRN 0.0100

3 133258006 133274794 4 4 0.0248

3 141263730 141276065 5 7 CLSTN2 0.0086

3 144384755 144408513 7 11 PBX2P1 0.0040 147137838 147147943 4 4 0.0248

188867480 188880377 6 5 SST 0.0119

62981022 63014022 11 8 0.0039

96335801 96348982 7 6 UNC5C 0.0082

183366064 183388602 5 5 0.0151

3125862 3150123 6 6 0.0142

7591237 7618466 8 7 ADCY2 0.0064

31125851 31142561 7 4 0.0126

62256957 62281661 6 4 0.0158

65207338 65234468 12 6 0.0038

91916876 91937276 5 6 0.0121

92251888 92266636 4 4 0.0248

122557150 122592530 5 7 0.0116

123654964 123666386 4 4 0.0248

141840281 141860047 4 5 0.0236

146670562 146709155 8 5 STK32A 0.0100

146736071 146771949 5 6 DPYSL3 0.0140

157948815 157985228 7 4 0.0130

160128669 160151601 11 6 ATP10B 0.0049

687311 700033 4 4 0.0248

804115 831884 4 6 0.0245

2734370 2755888 4 4 W NIP1 0.0282

4663878 4676575 5 6 CDYL 0.0106

23883209 23906003 8 5 0.0088

37616607 37635955 4 5 FU45825 0.0236

39200945 39214254 7 5 0.0100

117353122 117403717 4 19 RFXDC1 0.0030

119898516 119914369 4 7 0.0222

162655871 162660096 4 6 PARK2 0.0201

9177036 9190246 4 7 0.0205

13284537 13295146 5 4 0.0177

15445209 15469838 9 4 TMEM195 0.0095

19339741 19366356 4 10 0.0223

28655263 28671580 4 5 CREB5 0.0236

32173701 32195183 4 4 0.0282

35470724 35503030 5 6 0.0134

38064580 38078323 4 4 0.0248

42467012 42491390 4 8 0.0232

46759527 46782062 8 5 0.0088

46905831 46937852 10 6 0.0054

54893891 54918376 5 5 0.0151

77135477 77152754 4 7 0.0222

111888889 111896959 4 5 IFRD1 0.0214

140653479 140663829 5 5 0.0136

3056359 3094761 11 12 CSM D1 0.0019 4786983 4811413 5 5 CSM D1 0.0151

6238342 6274083 4 7 MCPH1 0.0281

15283611 15322656 9 8 0.0050

15692048 15713201 4 5 0.0251

18393182 18423793 4 6 0.0262

21493077 21505860 4 7 0.0205

32199213 32226575 9 4 NRG1 0.0098

54269163 54299512 5 4 0.0216

56198701 56221035 5 4 XKR4 0.0200

115903803 115922272 6 5 0.0117

129235452 129267085 5 5 0.0165

131870682 131915474 11 6 ADCY8 0.0055

132476142 132507111 8 5 0.0108

135691115 135715753 9 6 ZFAT1 0.0059

4010629 4025710 5 5 GLIS3 0.0142

8164345 8188199 8 4 0.0111

16733441 16743455 7 5 BNC2 0.0087

82292729 82324088 6 6 0.0111

98548498 98565308 10 5 ZNF510 0.0057

105667384 105678132 5 5 0.0136

109401821 109408916 5 6 0.0104

117343285 117355509 4 4 0.0248

121554837 121570015 4 4 0.0248

2326924 2339357 4 4 0.0248

4335886 4351265 6 5 0.0117

17037713 17086575 7 11 CUBN 0.0050

24820930 24847103 4 6 KIAA1217 0.0245

25794787 25824558 6 5 GPR158 0.0128

36514828 36526506 4 4 0.0248

54320949 54350408 4 4 0.0282

72016066 72025812 4 4 PRF1 0.0229

80586387 80607803 5 4 0.0200

89418974 89472813 16 7 PAPSS2 0.0040

97313525 97362549 10 11 ALDH18A1, SORBSl 0.0030

100128973 100149126 7 5 C10orf33 0.0097

106028196 106081085 4 13 KIAA1754 0.0065

118416353 118480052 16 10 C10orf82, HSPA12A 0.0020

1576943 1607115 8 5 KRTAP5-3, KRTAP5-5 0.0108

11122049 11147986 5 7 0.0100

11346019 11359653 4 6 GALNTL4 0.0211

35319654 35343994 5 8 SLC1A2 0.0093

36686399 36707703 7 6 0.0079

57549133 57568460 8 5 OR6Q1, VN2R9P 0.0087

78889654 78910751 5 4 0.0200

87925679 87929030 6 4 GRM5 0.0127 88095026 88111682 4 5 GRM5 0.0236

88183404 88207021 4 7 GRM5 0.0236

90003835 90024933 5 4 0.0200

117507582 117535296 9 4 SCN2B, SCN4B 0.0098

120201507 120227667 6 4 GRIK4 0.0163

5954452 5975760 4 5 VWF 0.0251

28063014 28074553 12 5 0.0033

53644958 53704649 26 7 KIAA0748 0.0005

80315592 80341556 6 4 PPFIA2 0.0163

102769996 102815351 22 6 0.0006

114214545 114265105 6 7 0.0075

114523726 114549440 11 5 0.0056

127467828 127500575 4 9 0.0240

128793865 128819117 13 4 TMEM132D 0.0048

21194721 21221683 8 4 0.0123

25981865 25995235 4 4 0.0248

27864067 27892655 10 4 FLT1 0.0080

30208972 30225032 9 4 AL0X5AP 0.0089

60305634 60313485 5 5 0.0131

64463933 64482043 6 6 0.0093

84440888 84464746 11 4 0.0069

92898354 92910211 4 4 GPC6 0.0248

TRAV12-1JRAV13-

21389400 21423943 13 5 1,TRAV8-3,TRA@ 0.0040

33309047 33322858 5 4 NPAS3 0.0177

57540518 57563968 6 5 C14orf37 0.0149

60991294 61031660 10 13 PRKCH 0.0028

68068686 68082222 5 4 RAD51L1 0.0177

96487821 96499623 5 4 0.0177

28063745 28078024 6 4 0.0142

52142632 52164106 8 4 UNC13C 0.0111

77684874 77701881 5 4 0.0183

90348062 90370961 8 6 SLC03A1 0.0070

6285925 6343776 15 4 A2BP1 0.0070

6604055 6622204 5 4 A2BP1 0.0183

6695415 6722737 4 6 A2BP1 0.0245

7640614 7673810 6 6 A2BP1 0.0111

56783625 56821913 9 8 0.0050

63678539 63699968 4 4 CDH11 0.0282

70869327 70887011 4 5 0.0236

76412206 76453522 5 8 KIAA1576 0.0108

77411027 77444363 16 4 WWOX 0.0031

78144242 78168710 4 7 0.0236

79127742 79144221 8 5 DYNLRB2 0.0087

79953788 79990571 4 7 0.0281 81258834 81284585 8 7 CDH13 0.0064

81364154 81394958 13 5 CDH13 0.0040

81820098 81868770 4 16 CDH13 0.0050

84693857 84711479 4 4 0.0248

84943714 84996559 11 10 0.0030

3154169 3161719 10 4 OR3A4 0.0062

9910218 9948656 4 5 GAS7 0.0307

11199762 11216770 5 6 FU45455 0.0114

14264098 14290428 4 5 0.0254

36844906 36884548 7 16 KRT32, KRT38 0.0025

652370 674766 8 4 ENOSFl, TYMS 0.0111

3209215 3219023 7 4 MYOM1 0.0105

5950673 5966903 4 5 L3M BTL4 0.0236

31933579 31955587 8 4 ELP2, SLC39A6 0.0111

51199883 51218182 8 4 TCF4 0.0104

72289304 72313005 4 4 ZNF516 0.0282

74013910 74023976 5 4 0.0177

6926998 6933336 4 5 EMR4 0.0214

8063957 8098408 6 4 PLCB1 0.0174

9573787 9597288 10 4 PAK7 0.0075

12924151 12938482 8 4 SPTLC3 0.0101

15409635 15424726 7 4 MACROD2 0.0126

16679408 16695738 4 5 OTOR 0.0236

17421671 17446930 10 5 BFSPl, RPS27AP2 0.0062

40034283 40045102 5 4 0.0177

52138031 52170007 8 4 0.0131

24988736 25031874 9 5 0.0117

24957142 25011209 7 13 SEZ6L 0.0045

25094638 25131527 7 4 0.0130

31884373 31896587 4 5 0.0223

32912694 32943347 9 5 0.0083

43494161 43512004 6 4 PRR5 0.0144

48459258 48481837 4 4 0.0282

Class 1 consists of individuals (about 80% of the total) who use common addictive substances at low levels if at all, both in eighth grade and beyond. Class 2 contains individuals (about 6% of the total) who already report substantial frequencies of use of common addictive substances by eighth grade, and maintain that use through adolescence and early adulthood. Class 3 consists of individuals (about 10% of the total) who report only modest frequencies of addictive substance use in eighth grade, but who escalate their drug use through much of the period of observation. Many individuals have very high probabilities of membership in each of these three classes, though some individual display moderate probabilities of falling into two or more classes, as is common in these analyses (Figure 1).

The probabilities that each individual would fall into each of these three classes were used, after correction for covariates, as the preplanned, primary test of association with vl.O scores. I ndividuals were identified who displayed upper- or lower-tercile vl.O scores, and assessed differences in the probabilities for membership in each of the three classes. There were significant differences in the probabilities of membership in each of the classes in subjects in the upper vs lower tercile of vl.O scores that were especially marked for Classes 1 and 3. P values were 0.00036, 0.046, and 0.00098, respectively. These p-values support the ideas that high vl.O scores increased likelihood of membership in Class 1 that displays slow/low level uptake of addictive substances. Conversely, lower vl.O scores are associated with increased likelihood of membership in Class 3 that displays increasing use of addictive substances during development. There was a more modest negative association with membership in Class 2 that represented stable levels of significant use of these substances through the developmental period examined here. I n additional analyses, the vl.O score provided a highly significant covariate when it was added to the LCGA model, or as a covariate for longitudinal latent class analyses (data not shown).

Results

The current results provide additional and independent support for validity of a vl.O smoking cessation genotype score in predicting ability to quit smoking in the setting of a randomized controlled clinical trial that provided pharmacological and modest behavioral support. They also support shared molecular genetic determinants between quit success and the rate at which/degree to which addictive substances are taken up during adolescence (Figure 8). Classical genetic evidence suggests that about half of individual differences in abilities to quit smoking are heritable, and that half are mediated by environmental influences. Morley et al., Psychological Med. 37(9): 1357-1367 (2007); Broms et al., Twin Res. Hum. Genet. 9(1): 64-72 (2006). The model would thus anticipate that even a perfect genetic score would be able to predict quit success with less than perfect accuracy. The robust predictive ability of the vl.O score described here is thus remarkable. The area under the ROC curve for this vl.O score is of the same magnitude as those provided by complex genotype scores for other disorders in which there are also strong genetic and environmental components of roughly similar magnitudes, including diabetes, heart disease and inflammatory bowel disease (Figures 4 and 5). Zheng et al., Prostate 72(5): 577-583 (2011); Wang et al., Nephrol. Dial. Transplant. 27(1): 190-196 (2011); Kang et al., Hum. Mol. Genet. 20(12): 2435-2442 (2011); Renstrom et al., Diabetes 60(1): 345-354 (2011); Xu et al., BMC Med. Genet. 12: 90 (2010); Mealiffe et al., J. Natl. Cancer Inst. 102(21): 1618-1627 (2010); Lu et al., Atherosclerosis 213(1) : 200-205 (2010); Lluis-Ganella et al., Rev. Esp. Cardiol. 63(8): 925-33 (2010). Genotyping is highly desirable in clinical trials, in which there are high costs when false-negative results emanate from trials in which stochastic mechanisms provide unfavorable distributions of quit success genotypes in placebo vs active treatment arms. Uhl et al., Pharmacogenomics J. 9(2): 111-115 (2009). As genotyping costs drop and as the abilities to develop efficacious strategies to match genetic predispositions with more effective prevention and treatment strategies increases, such scores are likely to provide increasingly valuable tools for more routine clinical use.

The latent class growth analyses used in this work have differences from, and potential advantages in comparison to, the latent growth mixture modeling that has been applied more frequently to developmental datasets. M uthen and Muthen, Alcoholism Clin. Exp. Res. 24(6): 882-891 (2000). Like growth mixture models, latent class growth analyses explore substantively meaningful groups under the assumption that there are unobserved subpopulations which display different patterns of development. Unlike growth mixture models, however, latent class growth analyses allow the latent class variable to capture all of the heterogeneity in the growth factors, based on key postulates that the variance of the intercept and slope factors within classes are zero and that there is no covariance between the growth factors.

The results of this latent class growth modeling differentiate three classes with strikingly different patterns of substance involvement during adolescence and early adulthood (Figure 6). While the majority of subjects display little involvement with common addictive substances and are thus represented in Class 1, two other latent classes provide different overall profiles. The Class 2 trajectory is nearly flat, since these individuals report significant use of common addictive substances when they were first queried about such use in eighth grade and maintain this substantial level of use throughout adolescence. By contrast, members of Class 3 report little use of common addictive substances in the year before interviews performed in eighth grade, but subsequently report use that escalates during much of adolescence, to average levels that, by the end of adolescence, even exceed those displayed by members of Group 2.

There was low correlation between vl.O scores and a measure of smoking, the heaviness of smoking index assessed at baseline for smoking cessation clinical trial participants (-0.114; Pearson correlation coefficient). Further, there was a low correlation between vl.O scores and another measure that can predict quit success, reduction in exhaled CO during the first week of pre-cessation N RT for smoking cessation clinical trial participants (0.072 Pearson correlation coefficient between z transformed CO reduction and Fl scores). The area under Receiver Operating Characteristic curve for CO reduction during the first week of pre-cessation NRT in predicting 11-week continuous abstinence was 0.67, similar to the predictive ability of the genotypic vl.O scores (0.67), even though there was little correlation between these two predictors of quit success (Figure 4B). Power for quitter vs. non-quitter (n = 50 vs. 117) with differences of 0.1, 0.05, 0.025 and 0.01 using observed mean standard deviation (0.07) from prior studies of smoking cessation success would be 1, 0.99, 0.55 and 0.13, respectively. There were no significant relationships between vl.O scores and treatment arm (ANOVA F = 2.27, p = 0.1, power = 0.4), patch dose (ANOVA F = 0.007, p = 0.93, power = 0.05), or whether a participant achieved > 50% CO reduction during pre-cessation NRT (ANOVA F = 1.25, p = 0.27, power = 0.19). The vl.O score predicted abstinence using logistic regression with and without treatment in the model with similar p- values (0.029 and 0.03, respectively). End-expired air CO measurements from trial participants during screening correlates with screening values on the Fagerstrom Test for Nicotine Dependence (ANOVA F = 12.1, p = 0.006) and Heaviness of Smoking index screening values (ANOVA F = 32.5, p < 0.001). While there was significant overlap at the level of some genes (e.g., CDH13, see Table 2), there was only chance overlap of SNPs from the vl.O score and those identified by tests of several phenotypes in smoker samples using different genotyping platforms. Liu et al., Nat Genet. 42(5): 436-440 (2010); Thorgeirsson et al., Nat. Genet. 42(5): 448-453 (2010); Furberg et al., Nat. Genet. 42(5): 441-447 (2010). ROC curves for an unweighted score comprised of the sum of risk alleles provided results that were slightly less significant than those for the vl.O score in which risk alleles were differentially weighted (AUC = 0.67). In secondary analyses of the association of vl.O scores with developmental trajectories of substance use, the vl.O score provided a highly significant covariate when it was added to two additional analyses: (i) a latent class growth LCGA model, or (ii) a longitudinal latent class analyses, using Mplus.

Evidence for influences of vl.O score genotypes on these developmental trajectories comes from both primary preplanned and secondary analyses of these data. This data provides some of the first direct molecular genetic evidence for overlaps between genetic influences on the extent of use of common addictive substance use during adolescence and the ability to quit use of at least one of these substances, tobacco, later in life. These results ca n also be discussed in light of several other strengths, including: (1) the parallels between recruitment of subjects who were ultimately successful and unsuccessful in smoking cessation with methods used to recruit subjects for some of the prior studies used to formulate and to previously test the vl.O score; (2) the careful clinical and biochemical monitoring of the smoking cessation participants, supporting accuracy of smoking cessation assessments; (3) the matching of successful quitters with twice the number of unsuccessful quitters in the same arm of the study, adding assurance that stratification should be modest at worst; (4) the reassuring quality control and genotype call rates for both samples; (5) the relatively good long term followup rate for the prevention study subjects, and good degree of consent for genetic studies among participants; 6) the strong overlaps between the addiction genetics documented in these modest sized samples and those from larger research volunteer samples; (7) the clearcut differences between the trajectories established by the 3 class latent class growth analyses; (8) the good distribution of probabilities for membership in distinct classes from most prevention study participants; (9) the fit between clinical characteristics during development and at the end of addictive trajectories, which provides a set of plausible arenas in which some of the same genetic variation might exert influences. While it is plausible that increasing use of addictive substances in adolescence might represent, in part, failure of early attempts to quit use (Figure 8), such influences have been scantily documented, if at all, in previous work. There are also limitations of these datasets and to these analyses. (1) While the current samples are relatively large from the perspective of clinical trial and long-term follow up samples, their modest sample size provides power less than 1. The 0.95 power of the quit success sample is likely to be greater than the power of the prevention sample, limited by the modest sizes of the two smaller classes (n = 120 and 56). This modest power precludes effective searches for specific influences on developmental patterns of use of specific substances, such as tobacco, for example. (2) Clinical trial results were pooled across individuals who received differing treatments. While the quitters and non-quitters were matched based on the arm of the trial in which they participated, the variance associated with the different treatments remains unknown. (3) Since the successful and unsuccessful smoking cessation subjects were recruited in parallel (and in ways that parallel those used to recruit some of the subjects of prior studies used to formulate and to previously test the vl.O score), replication noted here does not guarantee generalization of these results to individuals recruited in ways that might provide different genetic backgrounds. (4) There is no consensus about any single approach that characterizes individual differences in trajectories of involvement with addictive substances. While data is reported from a single, preplanned approach to the prevention study cohort in this paper, other approaches might have been used with differing results. Failure to identify the third class studied here, for example, would have been likely to make the effect of the vl.O score smaller, since the association with membership in class 2 was less robust despite the larger number of members of class 2. (5) In interim analyses based on the first part of the quit success sample, there was poorer performance of subsets of SNPs selected based on: (i) Bayesian network analyses of data from a previous smoking cessation trial and (ii) a trimmed vl.O score in which the SNPs with the lowest weights were removed. Thus, the full score for the current preplanned analysis of the complete dataset is retained, despite the fact that this score mandates more extensive genotyping than several of the other complex genotype scores cited above.

The current data add appreciably to the increasingly robust sets of studies that document molecular genetic contributions to the ability to quit smoking and the ability of the vl.O score to predict ability to quit. They also add to increasing conviction that complex genetic scores, in the appropriate clinical contexts, can significantly reduce noise in clinical trial settings in highly cost effective ways. As genotyping costs are reduced, and as strategies that allow differing therapeutic approaches to groups of individuals with high vs low genetic propensity for quitting smoking are tailored, these data suggest that the results should be useful for more generalized use in clinical practice and general health promotion strategies as well. The influences of "quit success" variants on trajectories of involvement with common addictive substances during adolescence also suggests that parallel efforts to define the best type and/or intensity of prevention strategies to maximize their benefits might aid in preventing development of dependence in otherwise vulnerable individuals. Focus on preventing substance use in individuals with low vl.O scores, who appear to be genetically predisposed to display both the escalating pattern of substance use and the most difficulty in quitting once they are dependent, may provide an attractive strategy for effective targeting of resources.

Table 1: vl.O Quit Success Associated SNPs

Lengthy table referenced here.

Table 2: Linkage disequilibrium (r² values > 0.2) among pairs of SNPs used to generate vl.O quit success scores

Lengthy table referenced here.

LENGTHY TABLES

This patent application contains a lengthy table section. A copy of the tables is available in electronic form from the USPTO web site (http://seqdata.uspto.gov/ ). An electronic copy of the tables will also be available from the USPTO upon request and payment of the fee set forth in 37 CFR § 1.19(b)(3).

Table 5: Summary Data for Smoking Cessation Success SNPs Showing

Weighting Level Subset Cutoffs

Datum Quit

Subset Rs chr bp weight No. Allele

1 first rs2036867 1 63679468 T 19.696000

97 first rs6971845 7 67395195 G 2.055000

98 second rs979336 1 34514843 A 0.016670

4891 second rsl980576 20 56479073 A 0.010000

4892 third rsl2410640 1 34464326 G 0.009990

8443 third rs8138344 22 32150256 C 0.005000

8444 fourth rs4854187 2 3363203 G 0.004990

8479 fourth rsll221067 11 127198904 T 0.004960

8480 fifth rs9872857 3 187690256 T 0.004950

11984 fifth rs708812 12 114724217 G 0.000100

11985 sixth rsl474908 1 28085800 C 0.000090

12058 sixth rs7151749 14 99615311 T 0.000000

Table 6: Highest-Weighted SNPs for Smoking Cessation Success

Datum quit

rs chr bp weight Rank allele

1 rs2036867 1 63679468 T 19.6960

2 rs7484640 12 5859898 c 19.2480

3 rsl7093112 10 117214465 A 18.9560

4 rs6482809 10 132206587 G 18.8280

5 rs453151 15 67884729 G 18.2190

6 rs4759462 12 129476345 G 18.2090

7 rs2122776 11 102897552 A 18.0780

8 rsl7344070 2 14405331 T 17.8340

9 rs 11648790 16 7567099 G 17.7540

10 rsl0503896 8 32050583 A 17.2380

11 rs498831 1 54916688 G 17.0680

12 rsl476029 22 34847878 A 16.8270

13 rsl7547685 9 17622364 C 16.6240

14 rsl7502069 18 6318558 A 16.3710

15 rs2945839 8 8238782 A 16.2630

16 rs6897944 5 78306219 A 16.1480

17 rs 17046446 2 24735170 A 14.6600

18 rs9484058 6 137888129 T 14.4850

19 rs885003 9 86099255 G 13.0070

20 rs4981250 14 34324224 C 12.8860

21 rs4751217 10 132205806 T 12.8470

22 rs983273 8 35534272 G 12.5850

23 rs7794021 7 11364881 G 12.2000

24 rsl2369491 12 11781775 A 11.9920

25 rs4722438 7 3122339 G 11.6710

26 rs4513200 18 65399549 G 11.5010

27 rsl59094 20 53068550 T 10.7870

28 rs7132043 12 79492531 G 10.7860

29 rsl2975753 19 34193380 T 10.4550

30 rsl7602278 11 55605691 c 10.4100

31 rs6091633 20 51139407 A 10.3670

32 rsl3134194 4 173922367 C 10.3510

33 rsl7760425 2 184032166 A 10.3300

34 rs7520640 1 228295603 G 10.2370

35 rs 1486582 11 32680449 G 9.9110

36 rsll851223 14 85335323 G 9.8620

37 rsl360985 13 101721943 A 9.7580

38 rs5753966 22 31045559 T 9.6870

39 rsl863943 5 5450165 T 9.6660

40 rsl6934692 12 29702738 A 9.6550

41 rs28483233 8 138673785 G 9.3270

42 rs9920823 15 58461141 T 9.1240

43 rs4916669 5 88428880 G 9.0350

44 rs6452806 5 88355663 A 8.9580

45 rs6759994 2 58517880 A 8.8620

46 rs3784334 15 66397438 C 8.8140

47 rs753727 2 121767700 A 8.7380 rs2272619 8 116666214 A 8.7160 rs8131150 21 39599185 G 8.6020 rs520536 7 83802374 C 8.2660 rs241772 17 23631847 T 8.2550 rsl2082523 1 15878379 G 8.2060 rsl0521437 9 71865359 C 8.1630 rsl595338 12 102792249 A 7.9830 rs7047921 9 6245319 A 7.7790 rs4355254 3 113525909 G 7.6710 rsl023671 5 54300768 T 7.6510 rs4916663 5 88338086 T 7.5940 rsl427967 5 88364322 C 7.3920 rs6449781 5 64643239 A 7.2920 rsl433622 4 66500848 A 7.1460 rs6429753 1 15773124 A 7.0960 rs6930270 6 2678774 C 6.9200 rs6742888 2 23469562 T 6.9050 rs998422 20 44792815 A 6.8450 rs2060715 7 127686365 A 6.7430 rsl2544168 8 35523949 G 6.6810 rsl977137 20 56636409 G 6.5500 rsl0894126 11 129118377 C 6.3600 rs7155203 14 58817716 C 6.2460 rs 10465043 9 12950607 G 6.1540 rs9392436 6 2816084 G 6.1310 rs9291837 5 64743875 C 6.0660 rs4681024 3 25450886 C 6.0390 rs2708437 12 38901046 A 5.9150 rs 12698688 7 67406049 C 5.7940 rs2021033 6 167444229 C 5.7460 rsl6970513 16 69349418 T 5.4760 rs2404150 5 78735782 A 5.1860 rsl2507746 4 170916769 G 4.5470 rs7167951 15 69466017 A 4.3940 rs36858 7 14931688 G 4.3810 rsl7155051 10 128300416 A 4.2250 rs9815819 3 113847817 A 4.2180 rs6778627 3 145203559 T 4.1180 rs4634972 1 216189142 C 3.9590 rs7187758 16 64129516 A 3.6860 rsl7447926 4 42304221 A 3.4680 rsl68558 5 63426154 C 3.0600 rsl582839 2 214391459 T 2.9710 rs 10918683 1 165568622 T 2.8470 rsl0179027 2 58505942 T 2.6380 rsl7653177 5 97290848 C 2.5740 rs7791504 7 51697174 T 2.4200 rs9925238 16 8544856 G 2.3640 rs2429752 11 22875997 T 2.1920 rs6971845 7 67395195 G 2.0550 Example 2

Menthol Preference Haplotypes

Among smokers, there are substantial individual differences in preference for mentholated brands of cigarettes. Kreslake, Wayne, & Connolly, Nicotine Job. Res. 10: 705-715 (2008). There are also differences in the fraction of menthol-containing cigarettes that are sold to and smoked by individuals with different racial/ethnic backgrounds.

Sociologic explanations have been offered for menthol preference. Attention has been focused on the ways in which advertisement and promotions have been aimed at communities rich in individuals of African or Asian decent. Landrine et al., Ethnicity and Disease 15: 63-67 (2005); Cummings, Giovino & Mendicino, Public Health Rep. 102: 698-701 (1987). A priori, biological contributions to individual differences in menthol preference are also plausible. Menthol acts at transient receptor potential (TRP) channels that include the TRPA1 channel that is extensively expressed by nociceptive primary afferent nerve fibers in the airways and elsewhere. Karashima et al., J. Nenrosci. 27: 9874-9884 (2007); Lee, J. Physiol. 588: 747-748 (2010); Simon and Liedtke, J. Clin. Invest. 118: 2383-2386 (2008). By cross-desensitizing and/or otherwise altering activities of noxious smoke constituents at these TRP channels, menthol might alter smoking. Individuals with menthol preferences modified by TRPA1 channel gene variants might smoke more cigarettes, smoke cigarettes with higher nicotine yields, extract more nicotine from each cigarette, and/or display greater difficulties in quitting smoking. Bover et al., Int. J. Clin. Pract. 62: 182-190 (2008); Harris et al., Prev. Med. 38: 498-502 (2004); Pletcher et al., Arch. Intern. Med. 166: 1915-1922 (2006); Foulds et al., Nicotine Job. Res. 12 (Suppl. 2): S102-109 (2010). Menthol effects might thus be sought separately in individuals who smoke more heavily vs. those who smoke less. Despite the possible modes through which menthol might modify effects of smoking higher numbers of cigarettes and despite the wide availability of menthol containing cigarettes, however, only a minority of smokers of European ancestry prefer mentholated cigarettes. Giovino et al., Nicotine Job. Res. 6 (Suppl. 1): S67-81 (2004). It was thought that allelic variants at the TRPAl channel might contribute to the individual differences in preference for mentholated cigarettes among individuals of European ancestry. A number of TRPAl gene variants display minor allele frequency differences in HapMap samples that parallel the racial/ethnic differences in fraction of mentholated cigarettes consumed, providing suggestive evidence for possible roles for allelic variants in this gene in menthol preference. The preference for mentholated vs. nonmentholated cigarettes was assessed in samples of European-American smokers who volunteered for participation in randomized controlled trials of smoking cessation (Raleigh-Durham, NC) and non therapeutic research in addiction genetics (Baltimore, MD). Individuals with high vs. low levels of smoking, based on available self-reporting data for smokers of >15 vs. <15 cigarettes/day were studied. The allele frequencies at TRPAl SNPs in smokers with menthol preference were compared to those who preferred nonmentholated cigarettes. Sixty-eight SNPs distributed through TRPAl were genotyped. Data from the 51 SNPs that displayed minor allele frequencies > 0.05 were analyzed using χ² tests with the program, PLINK, and a threshold for nominal significance of p < 0.05.

The association between menthol preference and haplotypes (i.e., groups of variants) in a transient receptor potential (TRPAl) gene in each of two independent samples of heavy European-American smokers has recently been conducted and reported. See Uhl et al., Nicotine Job. Res. 13: 1311-1315 (2011). This "menthol preference" haplotype is associated with higher levels of TRPAl expression in postmortem dorsal medulla brain samples. This missense SNP haplotype adds an additional cysteine to the TRPAl N-terminal intracellular domain, the site where acrolein and other cell-permeant electrophilic cigarette smoke irritants bind and activate TRPAl. Uhl et al., Nicotine Job. Res. 13: 1311-1315 (2011). The haplotype and menthol preference are both more frequent in smokers of African descent. Initial analyses also support association of a related "quit success" haplotype with smoking cessation success (i.e., quitting smoking) in previous clinical trial samples. See Table7. Table 7: TRAPl Haplotypes Associated with Smoking Cessation Success

SNP Allele

rsl0091803 A rs28546865 A rs920829 G

rs3735945 G

Four TRPAl haplotypes associated with smoking cessation success displaying ca. 0.1 minor allele frequencies (0.01 < p < 0.03).

Claims

CLAIMS What is claimed is:

1. A method of treating the abusive or habitual use of an addictive substance in a subject comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in the nucleic acid of said subject, wherein the presence of said SNPs is correlated with an increased rate of success in addictive substance cessation;

calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

administering the treatment regimen to said subject.

2. The method of claim 1, wherein the quantity of SNPs in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058.

3. The method of claim 1, wherein the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099.

4. The method of claim 1, wherein the quantity of SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; and

wherein said SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099.

5. The method of any one of claims 1-4, further comprising assessing end-expired CO level of said subject.

6. The method of any one of claims 1-5, wherein said addictive substance is selected from the group consisting of nicotine, alcohol, marijuana, cocaine, heroin, methamphetamine, ketamine, Ecstasy, oxycodone, codeine, morphine and combinations thereof.

7. The method of claim 5, wherein said addictive substance is nicotine.

8. The method of any one of claims 1-7, wherein said subject presently is dependent on an addictive substance.

9. The method of claim 8, wherein said subject presently is dependent on nicotine.

10. The method of any one of claims 1-9, in which detection of said SNP is carried out by a process selected from the group consisting of allele-specific probe hybridization, allele- specific primer extension, allele-specific amplification, sequencing, nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, single-stranded conformation polymorphism, and combinations thereof.

11. The method of any one of claims 1-10, wherein the replacement therapy is nicotine replacement therapy.

12. The method of claim 11, wherein the nicotine replacement therapy comprises a nicotine patch, nicotine gum, a nicotine inhaler, or a nicotine nasal spray.

13. The method of any one of claims 1-10, wherein cessation therapy is provided to said subject.

14. The method of claim 13, wherein said cessation therapy comprises bupropion or varenicline.

15. A method for indentifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in the nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation;

wherein the quantity of SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058;

wherein the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased risk of becoming dependent on said addictive substance;

calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

administering the treatment regimen to said subject.

16. A method for predicting a subject's success in an addictive substance cessation program comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation;

calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen comprising replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

administering the treatment regimen to said subject.

17. A method for identifying a subject who has an increased risk of becoming dependent on an addictive substance, comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) listed in Table 1 in a nucleic acid of said subject,

wherein the quantity of SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058; wherein the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased risk of becoming dependent on said addictive substance;

calculating the likelihood of becoming dependent on an addictive substance based on said SNPs;

selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

administering the treatment regimen to said subject.

A method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance comprising:

obtaining a nucleic acid from said subject;

wherein the quantity of SNPs in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058;

wherein the SNPs in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and

wherein said SNPs are correlated with an increased rate of success in an individualized treatment regimen; calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

administering the treatment regimen to said subject.

19. The method of claim 21 further comprising assessing end-expired CO level of said subject.

20. The method of claim 22, wherein said addictive substance is selected from the group consisting of nicotine, alcohol, marijuana, cocaine, heroin, methamphetamine, ketamine, Ecstasy, oxycodone, codeine, morphine and combinations thereof.

21. The method of claim 23, wherein said addictive substance is nicotine.

22. The method of claim 22, wherein said subject presently is dependent on an addictive substance.

23. The method of claim 22, in which detection of said SNPs is carried out by a process selected from the group consisting of allele-specific probe hybridization, allele-specific primer extension, allele-specific amplification, sequencing, nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, single-stranded conformation polymorphism and combinations thereof.

24. A method of treating the abusive or habitual use of an addictive substance in a subject comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein aid SNPs are correlated with an increased rate of success in addictive substance cessation;

administering the treatment regimen to said subject.

25. A method for identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation;

administering the treatment regimen to said subject.

26. A method for predicting a subject's success in an addictive substance cessation program comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in addictive substance cessation;

administering the treatment regimen to said subject.

27. A method for identifying a subject who has an increased risk of becoming dependent on an addictive substance, comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased risk of becoming dependent on said addictive substance;

administering the treatment regimen to said subject.

28. A method for developing an individualized treatment regimen for addictive substance cessation in a subject dependent on an addictive substance comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein said SNPs are correlated with an increased rate of success in an individualized treatment regimen;

administering the treatment regimen to said subject.

A method of treating the abusive or habitual use of an addictive substance in a population of subjects comprising:

obtaining a nucleic acid from said subjects;

identifying a quantity of single nucleotide polymorphisms (SNPs) in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 in the nucleic acid of said subject, wherein the quantity of SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058;

wherein the SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and

wherein said SNPs are correlated with an increased rate of success in addictive substance cessation;

calculating the likelihood of success in addictive substance cessation based on said SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said SNPs;

administering the treatment regimen to said subject.

30. A method for identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising:

obtaining a nucleic acid from said subject;

selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

administering the treatment regimen to said subject.

31. A method for identifying a subject (or population of subjects) for inclusion or exclusion in an addictive substance cessation program comprising:

obtaining a nucleic acid from said subject;

wherein the SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 have a weight greater than about 2.000000, a weight greater than about 0.010000, a weight greater than about 0.005000, a weight greater than about 0.000100, a weight greater than about 0.000001, or a weight greater than about 0.00000099; and wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; and

administering the treatment regimen to said subject.

A method for predicting a subject's success in an addictive substance cessation program comprising:

obtaining a nucleic acid from said subject;

wherein said SNPs are correlated with an increased rate of success in addictive substance cessation; and

calculating the likelihood of success in addictive substance cessation based on said SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said SNPs; selecting at least one treatment regimen comprising replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

administering the treatment regimen to said subject.

obtaining a nucleic acid from said subject;

identifying a quantity of single nucleotide polymorphisms (SNPs) in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 in a nucleic acid of said subject, wherein the quantity of SNPs in Table 1 or in linkage disequilibrium with the SNPs listed in Table 1 is at least about 100, about 250, about 500, about 750, about 1000, about 2500, about 4900, about 8400, about 8500, about 12000, or about 12058;

wherein said SNPs are correlated with an increased rate of success in an individualized treatment regimen;

administering the treatment regimen to said subject.

34. A method for doing business by selecting a subject (or population of subjects) for inclusion or exclusion in a clinical trial, the method comprising:

obtaining a nucleic acid from said subjects;

wherein said SNPs are correlated with an increased rate of success in addictive substance cessation based on said SNPs;

calculating the likelihood of success in addictive substance cessation based on said SNPs; selecting subjects for inclusion or exclusion in a clinical trial based on their likelihood of addictive substance cessation; and

selecting at least one drug/and or treatment regimen selected from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and

seeking regulatory approval for the drug and/or treatment regimen.

35. A method of treating the abusive or habitual use of an addictive substance in a population of subjects comprising:

obtaining a nucleic acid from said subject; identifying a quantity of TRAP1 single nucleotide polymorphisms (SNPs) in Table 7 or in linkage disequilibrium with the TRAP1 SNPs listed in Table 7 in the nucleic acid of said subject,

wherein said TRAP1 SNPs are correlated with an increased rate of success in addictive substance cessation; and

calculating the likelihood of success in addictive substance cessation based on said TRAP1 SNPs;

excluding subjects who have a low likelihood of success in addictive substance cessation based on said TRAP1 SNPs;

administering the treatment regimen to said subject.

A method for identifying a subject (or population of subjects) for inclusion or exclusion in a clinical trial, comprising:

obtaining a nucleic acid from said subject;

identifying a quantity of TRAP1 single nucleotide polymorphisms (SNPs) in Table 7 or in linkage disequilibrium with the TRAP1 SNPs listed in Table 7 in the nucleic acid of said subject,

selecting at least one treatment regimen from the group of replacement therapy or cessation therapy based upon the likelihood of success in addictive substance cessation; and administering the treatment regimen to said subject.

37. A method for identifying a subject (or population of subjects) for inclusion or exclusion in an addictive substance cessation program comprising:

obtaining a nucleic acid from said subject;

administering the treatment regimen to said subject.

38. A method for predicting a subject's success in an addictive substance cessation program comprising:

obtaining a nucleic acid from said subject;

calculating the likelihood of success in addictive substance cessation based on said TRAP1 SNPs; excluding subjects who have a low likelihood of success in addictive substance cessation based on said TRAP1 SNPs;

administering the treatment regimen to said subject.

obtaining a nucleic acid from said subject;

administering the treatment regimen to said subject.

A method for doing business by selecting a subject (or population of subjects) for inclusion or exclusion in a clinical trial, the method comprising:

obtaining a nucleic acid from said subjects; identifying a quantity of TRAP1 single nucleotide polymorphisms (SNPs) in Table 7 or in linkage disequilibrium with the TRAP1 SNPs listed in Table 7 in the nucleic acid of said subject,

seeking regulatory approval for the drug and/or treatment regimen.