WO2008010082A2 - Diagnostic method for fibromyalgia (fms) or chronic fatigue syndrome (cfs) - Google Patents

Abstract

Methods for determining one or more fibromyalgia syndrome (FMS) or chronic fatigue syndrome (CFS) phenotype and products for use therein.

Description

DIAGNOSTIC METHOD

Related Patent Applications

This application is related to: GB Patent Application No. 0613842.4, filed 12 July 2006 and GB Patent Application No. 0700551.5, filed 11 January 2007, the contents of each of which are incorporated herein by reference in their entirety.

Field of the invention

The invention relates to methods for the diagnosis, prognosis and treatment of Fibromyalgia (FM or FMS) and Chronic fatigue syndrome (CFS) and to products for use therein.

Background of the invention

FMS (code G93.3) and CFS (code M 79.0) are considered as two different diseases based on the International Classification of Diseases (ICD-10) supported by The World Health Organization (WHO).

There is some overlap in symptomology between the diseases: bodily pain, chronic fatigue, unrefreshing sleep, mood disorders, irritable bowel; but the diseases have different clinical presentation profiles, as well as different criteria for case definition, prevalence and prognoses. A detailed clinical history, a full physical exploration and some complementary biochemistry test (routine analysis in blood and urine: a complete blood count with differential, C-reactive protein, alanine aminotransferase, albumin) allow physicians to provide a clinical diagnosis based on: -FMS: Clinical criteria of the American College of Rheumatology (1990)¹⁸ (ACR'90). -CFS: Clinical criteria of the Center for Disease Control and Prevention (¹⁹Fukuda et. al 1994) (CDC94).

Currently the severity of these diseases is principally determined using auto-referenced validation questionnaires, such as the Fibromyalgia Impact Questionnaire (FIQ) for FMS¹² and the CDC Symptom Inventory (CSI) for CFS¹³.

The aetiology of both pathologies still remains unknown and there is no specific treatment for them. Both alterations are considered as chronic diseases.

The number of new cases appearing each year and the prevalence of the diseases seems to be homogenous in different populations as it has been evaluated by different epidemiological studies. It is supposed that between 2.7 and 5% of the general population aged above 16 shows FMS while the figures for CFS varies from 0.2 to 0.5%. For both diseases, patients exhibit considerable variation in the presentation, frequency and intensity of symptoms, as well as different therapeutic responses. There is therefore increased interest in validation markers that allow stratification and definition of subtypes ^9"11.

It is clear that the severity of the disease is not the same in all of the patients. An early knowledge about the prognosis of the disease once the clinical diagnosis has been performed would help to improve the clinical management of the patients - in terms of the treatment and follow-up of the patients in clinical units focused on FMS and CFS, in terms of social and professional aspects since patients with severe symptoms of the disease may have to leave their employment and significantly alter their way of life. Thus those with the most severe forms of the diseases require early diagnosis, more active and integrated therapeutic attention and effective support both socially and in the workplace ¹⁴' ¹⁵.

One of the best therapeutic approaches, based on physical graded exercise (Graded Exercise), has to be appropriate to the severity of the disease. For example, physical exercise can have long- term undesirable effects in the symptomology of a patient with severe CFS.

Antidepressant drugs are the standard first-line pharmacological therapy for FMS as these agents reduce pain, fatigue and sleep dysfunction symptoms. The management of the pain is the primary focus. The hypersensitivity to pain becomes more severe if the pain is not stopped. Therefore, a more aggressive treatment in order to stop pain could have real benefits to those patients who are going to suffer from a severe phenotype.

Therefore, a means of reliably predicting likelihood of developing aggressive FMS or CFS would have clinical benefits.

The quality of the clinical diagnosis has been negatively affected by the high number of new diagnoses of this emergent disease. More than 30% of the clinical diagnoses coming from primary care are rejected by the specialized medical assistance. Depression, anaemia, altered sleep, and stress can produce fatigue and bodily pain but these disorders are completely different from FMS and CFS in treatment efficiency and prognosis. Therefore there is a need for a more accurate and reliable means of diagnosing and differentiating FMS and CFS.

Therefore there remains in the art a need for rapid, cost-effective and reliable means of discriminating and predicting the course of FMS and CFS, and providing a basis for more targeted effective treatments. Summary of the invention

The present inventors have identified positions of single nucleotide polymorphism (SNPs) which can be used for reliably determining FMS and CFS phenotypes. Accordingly the present invention provides a method of diagnosing or prognosing a fibromyalgia syndrome (FMS) or chronic fatigue syndrome (CFS) phenotype in a subject, which comprises:

(i) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs5746847, rs3794808 and rs2020942; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to differentially diagnose between FMS and CFS in the subject; and/or

(ii) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs6713532, rs11246226 and rs7224199; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to prognose FMS disease development in the subject; and/or

(iii) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs1474347 and rs489736; thereby determining outcomes for each of the SNPs; and

(b) using the combination of outcomes determined in step (a) to prognose CFS disease development in the subject; and/or

(iv) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs3794808 and rs11246226; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to diagnose severe FMS phenotype in the subject; and/or (v) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs2020942 and rs1474347; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to diagnose severe CFS phenotype in the subject.

The invention also provides a method of selecting a suitable treatment for treating FMS or CFS in a subject, and a method of treating FMS or CFS in a subject as set out in the present claims. - A -

Also provided is a microarray comprising oligonucleotide probes suitable for determining the allele in a sample nucleic acid at SNPs selected from: the FMS vs CFS discriminating SNPs in Table 3; and/or the FMS prognosis discriminating SNPs in Table 3; and/or the CFS discriminating SNPs in Table 3; and/or the FMS severe diagnosis discriminating SNPs in Table 3; and/or the CFS severe discriminating SNPs in Table 3.

The invention also provides an oligonucleotide probe, probe pair, or 4-probe set listed in Figure 6, Figure 14 or Figure 17A, an oligonucleotide primer or primer pair listed in Figure 7, Figure 13 or Figure 17B, and a kit for diagnosing or prognosing an FM and/or CFS phenotype, as set out in the present claims.

Further provided is a method of deriving a probability function for use in determining a FMS or CFS phenotype in a subject, and a computational method of deriving a probability function for use in determining FMS or CFS phenotype in a subject. The invention further provides a computer system comprising a processor and means for controlling the processor to carry out a computational method of the invention and a computer program comprising computer program code which when run on a computer or computer network causes the computer or computer network to carry out a computational method of the invention, as set out in the present claims.

Further provided is a method of diagnosing or prognosing a FMS or CFS phenotype in a subject comprising deteremining the genotype of the subject at one or more positions of single nucleotide polymorphism selected from the SNPs in Table 2 or 3 and a method of diagnosing or prognosing a FMS or CFS phenotype in a subject comprising determining the haplotype of the subject with respect to one or more of the haplotypes listed in Table 5 or 6 as set out in the present claims.

Brief description of the Figures

Figure 1.

Probability function FMvsCFS (for discrimination between patients suffering from FM and patients suffering from CFS). Probability functions derived in the study in Example 1 are presented as box whisker plots in FM and CFS patients included in the study. Boxes represent the interquartile range and whiskers are lines that extend from the box to the highest and lowest values. A line across the box indicates the median. Outliers and extreme values are represented as small circles.

Figure 2.

Probability function FM (for prognosis of an aggressive FM phenotype).

Probability functions derived in the study in Example 1 are presented as box whisker plots in FM patients included in the study. Boxes represent the interquartile range and whiskers are lines that extend from the box to the highest and lowest values. A line across the box indicates the median. Outliers and extreme values are represented as small circles.

Figure 3. Probability function CFS (for prognosis of an aggressive CFS phenotype).

Probability functions derived in the study in Example 1 are presented as box whisker plots in CFS patients included in the study. Boxes represent the interquartile range and whiskers are lines that extend from the box to the highest and lowest values. A line across the box indicates the median. Outliers and extreme values are represented as small circles.

Figure 4.

Table 1: Clinical characteristics of the FM and CFS patients included in the study in Example 1.

Figure 5. Table 2: Variables included in the probability functions derived in Example 1. FMvsCFS Probability function discriminating FM and CFS. FM Probability function predicting aggressive FM phenotype. CFS Probability function predicting aggressive CFS phenotype.

Figure 6.

Oligonucleotide probe sets used for discrimination between alleles of SNPs in Table 2 in the study in Example 1.

Figure 7. Oligonucleotide primer pairs used for amplification of target DNA fragments comprising the SNPs in Table 2 in the 3 multiplex amplification reactions (A, B and C) in the study in Example 1.

Figure 8A.

Table 3 showing the SNP variables identified by the inventors as useful for determining phenotypes, and which may be included in the probability functions described herein, and their genotype frequency among the patients included in the study in Example 2. "A" is always the first described allele when a search is made under the rs number in the SNP database from the National Center for Biotechnology Information (http://www.ncbi.nlm. nih.qov/entrez/guery.fcgi?CMD=search&DB=snp, as at11^th January 2007). For example, rs6713532 is described as C/T, so that A=C and B=T.

The Chi square p-value for individual SNP allelic association indicates that the SNPs shows a significant individual association with the phenotype in the Chi square test (p<0,05). The individual association is so high that the correction for multiple testing (Bonferroni test, bp value) continues being significant in most of the cases.

Figure 8B. Table 3A shows the nucleotide alleles for each SNP in Table 3 (the identity of "A" and "B" in Table 3).

Figure 8C.

Table 3B shows individual SNP allelic associations for each of the SNPs in Table 3A and each of the phenotypes.

Figure 9.

Table 4 showing the sensitivity, specificity, and positive likelihood ratio (LR+ = sensitivity/(1- specificity)) for the model for differential diagnosis of FMS or CFS₁ the model for FMS prognosis and the model for CFS prognosis established in Example 2. The models were computed by means of Receiver Operating Characteristic curves for both the first study and the validation study, as described in Example 2. The figure also shows sensitivity, specificity, and positive likelihood ratio (LR+ = sensitivity/(1 -specificity)) for the models for diagnosis of severe FMS or severe CFS.

Figure 10.

Probability functions as derived in Example 2 are presented as box whisker plots. Boxes represent the interquartile range and whiskers are lines that extend from the box to the highest and lowest values. A line across the box indicates the median. Outliers and extreme values are represented with ° and * respectively. In A), B) and C)₁ the figures at the left show the box plots for the probability function of the first study, and the ones at the right the box plots for the validation study. A) Probability function for differentiated diagnosis between patients with Fibromyalgia Syndrome and Chronic Fatigue Syndrome B) Probability function for Fibromyalgia Syndrome prognosis. C) Probability function for Chronic Fatigue Syndrome prognosis. D) Probability function for diagnosing severe FMS. E) Probability function for diagnosing severe CFS.

Figure 11.

Table 5 showing haplotype association analysis and individual SNP associations (Example 2).

Figure 12. Table 6 showing major haplotype frequencies (Example 2).

Figure 13.

Oligonucleotide primer pairs for PCR amplification of nucleic acid regions containing the SNPs listed in Table 3 (Example 2) Figure 14^

Oligonucleotide probes for discriminating between possible alleles at the SNPs listed in Table 3.

The Figure lists two probe pairs for each SNP (a 4-probe set) (Example 2).

Figure 15,

Tables showing calculation of probability functions using the minimal discriminating SNPs for determining each phenotype described herein. Regression probability functions are built using the Statistical Package for the Social Sciences (SPSS Inc. Headquarters, Chicago, IL, USA) version 14.0. SPSSv14. B is the coefficient associated to each genotype in the probability function. ET is the error in the calculation of B. WaId is the statistical test. GL freedom degrees. Sig. P value of B for the WaId test. Exp (B) is Relative Risk.

Figure 16 A, B, C.

Tables showing individual SNP allelic associations with each of the given phenotypes. Figure 16 D lists the identities of the alleles 'A' and 'B' for each SNP.

The allelic associations were calculated using HelixTree® software (Golden Helix, Inc., Bozeman, MT, USA) via chi-square tests. Specifically, a chi-square test for independence evaluates statistically significant differences between proportions for two or more groups in a data set.

Figure 17.

(A) Optimised probes for discrimination between alleles of the SNP described herein.

(B) Optimised primers for PCR amplification of nucleic acid regions containing the SNPs described herein.

Detailed description of the invention

The present invention relates to methods for the prognosis and diagnosis of fibromyalgia syndrome (FMS) and chronic fatigue syndrome (CFS). The invention provides methods which allow sensitive and reliable discrimination between FMS and CFS, and which permit accurate prognosis of the development of FMS or CFS in patients. Such reliable differential diagnosis and accurate prediction of likely disease development (in particular the ability to accurately determine the risk of developing severe or aggressive FMS or CFS) in turn allows more targeted and more effective treatment of patients. As described herein, selection of an appropriate therapy at an early stage in disease may in some cases allow alteration of disease course from severe to more mild form.

In addition the invention also provides methods for the accurate diagnosis of severe or aggressive FMS or CFS in a subject. This allows a diagnosis of severe disease, for example in subjects which display some symptoms but who have not been clinically diagnosed. The methods may also be used to confirm a clinical diagnosis. These methods allow severe FMS or severe CFS to be distinguished from other (non-FMS or non-CFS) diseases (which may show similar symptoms) at an early stage and so also permit better treatment. In an initial study (Example 1) the inventors selected 36 single nucleotide polymorphisms (SNPs) (Table 2). 403 FM and CFS women were selected for study (186 FMS patients and 217 CFS patients) as in Example 1. Clinical data and phenotypic characteristics were collected for each patient as in the Example (Table 1).

Patients were genotyped using microarray technology as in the Example.

Initial statistical analyses of the genotyping data were performed using the Statistical Package for the Social Sciences (SPSS) version 13.0. A χ² test was performed in order to determine that distribution of the genetic polymorphisms under analysis were in Hardy-Weinberg equilibrium. On this basis, the 36 SNPs in Table 2 were in Hardy-Weinberg equilibrium.

Genotype-phenotype associations of the SNPs (Table 2) were then analysed using logistic regression.

In each case the dependent variable was the clinically determined disease phenotype (described in Examples 1.2.1, 1.2.2 and 1.2.3). The differential genetic diagnosis of FMS and CFS was evaluated. The FMS aggressive disease phenotype and the CFS aggressive disease phenotype were investigated.

The independent variables were the SNPs in Table 2.

Using the regression analysis, probability functions were obtained for each phenotype analysed. To evaluate the impact of the SNPs and the clinical variables analysed in the prognosis of the phenotypes, the sensitivity, specificity, and positive likelihood ratio (LR+ = sensitivity/(1 -specificity)) were computed by means of Receiver Operating Characteristic curves. Comparisons of mean probability function values between each of the compared phenotypes were performed using a t- test. The threshold for statistical significance was predefined as a p level of 0.05.

The probability functions calculate the risk or probability of an individual developing the specified phenotype, based on the outcomes for the informative variables in Table 2. The graphs in Figure 1 , 2 and 3 show probability function values for the individuals (of known, clinically determined phenotype) in the clinical validation.

Informative SNPs included in each probability function for each phenotype are shown in Table 2. Thus:

The FMvsCFS variables in Table 2 are those which were found to be informative for discriminating between FMS and CFS. The FIVl variables in Table 2 are those which were found to be informative for predicting aggressive FM phenotype.

The GFS variables in Table 2 are those which were found to be informative for predicting aggressive CFS phenotype.

The inventors realised that by determining the outcomes of the informative variables for a particular phenotype, it was possible to determine the corresponding phenotype in the subject with a new accuracy and reliability, and without the need to analyse a large number of variables.

The inventors then carried out another study of the data from the population, using improved selection criteria for SNPs. Thus the inventors took the same study population of Spanish females, clinically diagnosed as suffering from FMS or CFS according to the strict ACR'90 and CDC94 definition as described in Example 2. The inventors took 107 positions of single nucleotide polymorphism (SNPs) for analysis in the study population. Each individual in the study population was genotyped at each of the SNPs as described in the Examples.

The inventors then used genetic analysis (test of Hardy-Weinberg equilibrium (HWE), and single locus allelic association analysis) as described in Example 2 to select a subset of the most informative SNPs for further modelling.

The subsets of SNPs selected were then used in statistical analysis to establish statistical models (based on combinations of informative SNPs) that would allow diagnosis and prognosis according to the invention, with high specificity, sensitivity and accuracy. Specifically the inventors carried out LR stepwise multivariate logistic regression analysis using SNPs as independent variables and clinically determined disease phenotypes as dependent variables, as described in Example 2. In this way the inventors derived probability functions (based on combinations of informative SNPs) which would discriminate between disease phenotypes in a statistically significant way (Figure 10).

The discriminating SNPs which were selected for inclusion in models for determining phenotypes according to the invention are listed in Table 3 (Figure 8A). Table 3 also lists the distribution of genotypes at each SNP across the study population. Table 3A lists the alleles for each of the SNPs. Figure 8C shows the allelic associations for each of the individual SNPs with each of the phenotypes.

Thus Table 1 lists 15 SNPs which may be used in methods for discriminating between FMS and CFS, 8 SNPs which may be used in methods for prognosing FMS disease development, 6 SNPs which may be used in methods for prognosing CFS disease development, 9 SNPs which may be used in methods for diagnosing severe FMS and 6 SNPs which may be used in methods for diagnosing severe CFS. The use of these SNPs, in such methods is described further herein.

The inventors believe that the models used in the second study result in more accurate results for determining phenotype. Thus, while the original models determined in Example 1 may be used, it is preferred that the models identified in Example 2 and described herein are used to determine phenotype. Nevertheless, the informative SNPs identified in Example 1 are useful in methods for determining phenotype, as described here. Allelic associations for each of these SNPs with each of the phenotypes disclosed herein are presented in Figure 8 and in Figure 16.

The inventors also carried out haplotype regression analysis, as described in Example 2, and identified a number of haplotypes which are useful for differentially diagnosing FMS compared to CFS, for prognosis of FMS (severe (FIQ>76) or milder (FIQ < 76)) or for prognosis of CFS (severe (CDC >84) or milder (CDC ≤ 84)). These are shown in Tables 5 and 6.

Thus the inventors have identified SNPs which are informative for the diagnosis and prognosis of, and the treatment of, FMS and CFS. These are listed in Tables 2 and 3. In one example the invention relates to a method for diagnosing or prognosing FMS and/or CFS in a subject, comprising determining the genotype of the subject for one or more of the informative SNPs in Table 2 and 3. As described herein, the SNPs of the present invention may be used individually, or in combinations, for example, in haplotypes identified by the inventors, or in particularly informative SNP combinations identified by the inventors.

As explained above, FMS and CFS are complex disorders. The course of disease progression is highly variable, with highly heterogeneous disease behaviour. A clinical diagnosis of FMS or CFS may be made based on:

-FMS: Clinical criteria of the American College of Rheumatology (1990)¹⁸ (ACR'90). -CFS: Clinical criteria of the Center for Disease Control and Prevention (¹⁹Fukuda et. Al 1994) (CDC'94).

A subject may be described as meeting the clinical criteria for FMS or CFS according to the ACR'90 or CDC'94 respectively, meaning that the subject meets the clinical diagnostic requirements of the ACR'90 or CDC'94 and would be diagnosed as suffering from FMS or CFS according to these tests. A clinically diagnosed subject as used herein refers to a subject who has been diagnosed as suffering from FMS or CFS according to the ACR'90 or CDC'94 criteria.

The severity of these diseases may be determined using auto-referenced validation questionnaires, such as the Fibromyalgia Impact Questionnaire (FIQ) for FMS¹² and the CDC Symptom Inventory

(CSI) for CFS¹³. It is preferred that the FIQ with the 1997 and 2002 modifications is used to categorise FMS patients ¹²'²⁰. For CFS patients, the CSI may be used ^13lZ1. Its subscale, the Case Definition Score (CDS), reflects the frequency and intensity of symptoms according to the diagnostic criteria.

The FIQ value as used herein refers to the Fibromyalgia Impact Questionnaire value, taking into account the 1997 and 2002 modifications. The CDC value as used herein refers to the CSI Case

Definition Score value. As explained in the Examples, severe or aggressive FMS is defined herein as FMS with a FIQ>76, determined using the FIQ with the 1997 and 2002 modifications as above.

Accordingly, milder FMS is defined as FM with FIQ ≤ 76. Severe or aggressive CFS is defined herein as CFS with a CDC >84, determined using the CSI as above. Accordingly milder CFS is defined as CFS with CDC ≤ 84.

A subject who meets or fulfils the clinical criteria for severe FMS may be a subject who would be determined to have a F1Q>76 using the FIQ with the 1997 and 2002 modifications as above. A subject who meets the clinical criteria for severe CFS may be a subject who would be determined to have CDC>84 using the CS\ as above.

As subject who is symptomatic in general displays one or more symptoms which are typical of or associated with FMS or CFS. Such characteristic symptoms are known to those of skill in the art and are described in, for example, in Wolfe et al, 1990¹⁸ and Fukuda et al, 1994¹⁹. .

The invention is concerned with methods for determining or distinguishing FMS and/or CFS phenotypes. This includes determining a predisposition to or susceptibility to FMS or CFS and/or to the severe form of either of these conditions. This also encompasses the various diagnostic and prognostic methods described herein.

Thus, for example, the invention provides methods for: distinguishing or differentially diagnosing FWIS and CFS; prognosing FMS disease development; prognosing CFS disease development; - diagnosing severe FMS; and/or diagnosing severe CFS.

Distinguishing or differentially diagnosing FMS and CFS typically refers to determining whether a subject has FMS or CFS. In general the subject is symptomatic and meets the clinical criteria for FMS or CFS described herein. The subject may be already clinically diagnosed as described herein. Such a method may therefore be used to confirm a clinical diagnosis.

Prognosing disease development typically refers to determining risk of developing an aggressive phenotype, or the predisposition or susceptibility of a subject to an aggressive phenotype. Thus a prognosis may be for development of a severe (aggressive) phenotype or for development of a milder phenotype. In general the subject is symptomatic for FMS or CFS and meets the clinical criteria for FMS or CFS described herein. The subject may be already clinically diagnosed with FMS or CFS as described herein. The subject may have been provisionally assigned an aggressive FMS or aggressive CFS phenotype, for example, using the FIQ or CSI. Such a method can thus be used to confirm or supplement a clinical diagnosis.

Diagnosing severe FMS or severe CFS typically refers to diagnosing severe or aggressive FMS or CFS in a subject. The method may be used to distinguish severe FMS or severe CFS from other conditions which show similar symptoms, but which would not be considered FMS or CFS according to the ACR'90 or CDC'94 criteria. The subject may be showing symptoms typical of FSM or CFS or may be asymptomatic. Typically the subject is not clinically diagnosed. .

In general the methods are carried out ex vivo, for example, on a sample taken from the subject.

FMS and CFS phenotype as referred to herein may therefore refer to the aggressiveness of the disease on the basis of clinical data. FMS or CFS phenotype may also refer to the presence of FMS compared to CFS. Thus, for example, determining FM or CFS phenotype may refer to making a differential diagnosis between FMS and CFS in a patient. Determining phenotype may also refer to predicting the likelihood of severe or mild FMS or of severe or mild CFS in a patient. Determining phenotype may also refer to diagnosing (risk of) severe FMS or severe CFS in a subject.

Thus the present methods may be useful for (reliably) determining whether a given phenotype already exists in a subject and/or for determining whether a given phenotype is likely to develop in the subject. Typically the method results in a probability of a given phenotype existing or developing in a subject. Thus the present methods may be used to diagnose or prognose the probability of a given FMS and/or CFS phenotype such as disease course or progression in a subject.

In general the subject is a human. The subject may be for example, Chinese, Japanese or a Caucasian. Preferably the subject is a Caucasian, such as a Spanish individual. The subject may be female, e.g. a Spanish female. In one aspect the subject meets the clinical criteria for diagnosis of FMS or CFS according to the ACR'90 or CDC'94 criteria described herein. In one aspect, the subject has already been diagnosed with FMS or CFS according to existing methods described above, for example, according to the strict ACR'90 and CDC'94 definition. The subject may be already diagnosed with FMS or CFS. In another aspect, the subject may not have been diagnosed. Such subjects may be presenting symptoms typical of or associated with FMS and CFS. In one aspect a subject may have been provisionally assigned one or more FMS or CFS aggressive phenotype, for example using the FIQ or CSI methods described herein. Thus the present methods may be used to confirm diagnoses or prognoses, or to make new diagnoses and prognoses.

The present methods involve determining an outcome for each of a number of single nucleotide polymorphism (SNP) variables or predictors. The SNP variables are listed in Tables 1, 2 and 3. RefSNP codes (rs#) for each SNP are taken from the Single Nucleotide Polymorphism Database (dbSNP) curated by the National Center for Biotechnology Information (NCBI) (http://www.ncbi. nlm.nih,qov/entrez/query,fcqi?CMD=search&DB=snp. as at 12 July 2006 (Table 2) and 11^th January 2007(Table 3))

The LTA SNP in Tables 2 and 3 (rs2229094) was incorrectly referred to in the present priority application as rs2857713. The DRD2 SNP in Table 2 (rs6278) was incorrectly referred to in the priority application as rs6277. Accordingly in aspects of the invention and claims which recite rs2229094, it is to be understood that further aspects of the invention and claims relate to the same subject matter where rs2229094 is substituted by rs2857713. Similarly in aspects of the invention and claims which recite rs6278, it is to be understood that further aspects of the invention and claims relate to the same subject matter where rs6278 is substituted by rs6277.

The SCL6A4 SNP with rs3794808 (see Table 3 and Figure 13) was incorrectly referred to in the list of probes in the later priority application as rs2228673. This has been corrected in Figure 14.

An outcome for a given SNP is the identity of the nucleotide at that position in the genomic DNA sequence of a subject, or the genotype of the subject at that SNP. Thus an outcome for a given SNP may be A₁ T, C or G.

Table 3 lists a set of informative or discriminating SNPs for determining each of the phenotypes described herein. Table 3A lists the alleles for each SNP.

Thus the set of informative SNPs (or variables) for differentially diagnosing FMS and CFS lists: rs6713532, rs10194776, rs1549339, rs2168631, rs2229094, rs1800797, rs2770296, rs2020942, rs3794808, rs2297518, rs5746847, rs933271 , rs4680, rs165815 and rs165774.

The set of informative SNPs (or variables) for prognosing FMS disease development lists: rs10194776, rs6713532, rs324029, rs11246226, rs7224199, rs3794808, rs165774 and rs4680.

The set of informative SNPs (or variables) for prognosing CFS disease development lists: rs10488682, rs11246226, rs2020942, rs1474347, rs2284217and rs489736.

The set of informative SNPs (or variables) for diagnosing severe FMS lists: rs10194776, rs6713532, rs11246226, rs2770296, rs7224199, rs3794808, rs165774, rs4680 and rs2428721. The set of informative SNPs (or variables) for diagnosing severe CFS lists: rs2168631 , rs1474347, rs2284217, rs2069827, rs11246226 and rs2020942.

The inventors found that by determining outcomes for these informative variables (i.e. nucleotide identities at the SNPs), or particular combinations thereof, it is possible to determine the corresponding phenotype in a subject with a new accuracy and reliability, and without the need to analyse a large number of SNPs or variables.

Once the outcomes for a combination of informative variables are known (for a test individual), a probability function value can be calculated for the test individual (using a suitable probability function). Outcomes are used in or inserted in a suitable probability function (for prediction of that phenotype), as described herein and a probability function value is calculated. Outcomes may be codified for use in the probability function and calculation of the probability function value. The probability function value can then be compared to probability function values obtained from a population of individuals of known, clinically determined phenotype. Typically this may be done by comparison with a graph showing the distribution of values in the population, such as those in Figure 3. It can thus be determined whether a test individual is at high or low risk based on the phenotypic group to which the test probability function value belongs.

Accordingly the invention in one aspect provides a method for determining FMS or CFS phenotype as described herein for a subject, comprising the step of determining, for that subject, outcomes for one or more SNP variables listed in Table 2 or 3.

The method may be used to differentially diagnose FMS and CFS using one or more SNPs selected from the 15 FMSvsCFS discriminating SNPs listed in Table 3.

The method may be used to prognose development of aggressive disease behaviour in FMS, using one or more SNPs selected from the 8 FMS prognosis discriminating SNPs in Table 3.

The method may be used to prognose development of aggressive disease behaviour in CFS, using one or more SNPs selected from the 6 CFS prognosis discriminating SNPs in Table 3.

The method may be used to diagnose aggressive disease behaviour in FMS, using one or more SNPs selected from the 9 severe FMS diagnosis discriminating SNPs in Table 3.

The method may be used to diagnose aggressive disease behaviour in CFS₁ using one or more SNPs selected from the 6 severe CFS diagnosis discriminating SNPs in Table 3. Preferably any of the above methods comprises determining outcomes for all of the SNPs listed as discriminating for the particular phenotype in Table 3. For example, for differential diagnosis of FMS or CFS, preferably all of the 15 FMSvsCSF discriminating SNPs are tested. It is believed that this will produce the most accurate prediction of disease phenotype.

For example, as described in Example 2, use of all 15 FMSvsCSF SNPs allows discrimination between FMS and CSF in a Spanish female population with an LR+ (positive likelihood ratio = sensitivity/1 -specificity) of 11.5. Use of all 8 FMS prognosis SNPs allows prognosis of aggressive FMS in a Spanish female population with an LR+ of 12.4. Use of all 6 CFS prognosis SNPs allows prognosis of aggressive CFS in a Spanish female population with an LR+ of 12.4. Probability functions constructed using the full sets of discriminating SNPs for each of these phenotypes are shown in Figure 10 (see Examples).

Similarly, use of all 9 severe FMS diagnosis SNPs allows diagnosis of severe FMS in a Spanish female population with an LR+ of 17.9 (Table 4). Use of all 6 severe CFS diagnosis SNPs allows diagnosis of severe CFS in a Spanish population with an LR+ of 12.2 (Table 4). Probability functions constructed using the full sets of discriminating SNPs for each of these phenotypes are shown in Figure 10 (see Examples).

However, the diagnostic method may also be carried out using fewer than the total number of discriminating SNP variables listed for any given phenotype. Using fewer SNPs typically results in a lower LR value, but will still provide useful diagnosis or prognosis.

For example, if x is the number of discriminating SNPs listed for a given phenotype in Table 3, the method may comprise determining the outcomes of (at least) (x-n) of the SNPs where n is any number from 1 to 14 (1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13 or 14), and using the outcomes of these

SNPs to predict the given phenotype. Preferably the minimum number of SNPs used is the number that allow a discrimination power significantly greater than the discrimination power provided by chance (Press's Q test)(Hair J, Black B, Babin B, Anderson R, Tatham R. Multivariate Data Analysis. 6/E. Prentice Hall 2006).

Preferably the number and combination of SNPs used to construct a model for predicting a given phenotype according to the invention, is such that the model allows prediction to be made with an LR+ value of at least 5, such as at least 6, 7, 8, 9, or 10. Calculation of LR+ values is described herein.

In a method for differentially diagnosing FMS and CFS, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or all 15 of the FMSvsCFS discriminating SNPs in Table 3 may be tested for outcomes and used in the prediction. Preferably the SNPs are selected from: rs5746847, rs3794808, rs2020942, rs1800797, rs2297518, rs2229094, rs2168631 , rs933271 , rs2770296, rs165774, rs10194776 and rs6713532. For example, the SNPs may be selected from rs5746847, rs3794808 and rs2020942.

Preferably the method for differential diagnosis comprises determining outcomes for (at least) the 5 SNPs: rs5746847, rs3794808 and rs2020942 (the minimal SNPs). Details for calculation of a probability function from these minimal SNPs are given in Figure 15. The inventors have shown that use of these minimal SNPs alone allows differential diagnosis of FMS and CFS in a Spanish female population with an LR+ of 5.66, specificity of 96% and sensitivity of 25% (see Example 2). 0 The differential diagnosis may be based on the minimal SNPs alone, or on these SNPs and additionally, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or al! 12 of the remaining discriminating SNPs listed in Table 3 for this phenotype. Preferably these additional SNPs are selected from: rs1800797, rs2297518, rs2229094, rs2168631, rs933271, rs2770296, rs165774, rs10194776 and rs6713532. Thus in one aspect the method comprises determining outcomes for the 3 minimal SNPs and 1, 2,5 3, 4, 5, 6, 7, 8, or all 9 of the following discriminating SNPs: rs1800797, rs2297518, rs2229094, rs2168631 , rs933271, rs2770296, rs165774, rs10194776 and rs6713532.

For example, the method may comprise determining outcomes for the 3 minimal SNPs and all 9 of rs1800797, rs2297518, rs2229094, rs2168631 , rs933271 , rs2770296, rs165774, rs10194776 and0 rs6713532 (the 12 preferred SNPs).

In a method for FMS prognosis, at least 2, 3, 4, 5, 6, 7, or all 8 of the FMS prognosis discriminating SNPs in Table 3 may be tested for outcomes and used in the prediction. Preferably the SNPs are selected from: rs6713532, rs11246226, rs7224199, rs324029 and rs3794808. For example, the5 SNPs may be selected from rs6713532, rs11246226 and rs7224199.

Preferably the method for prognosis comprises determining outcomes for (at least) the SNPs: rs6713532, rs11246226 and rs7224199 (the minimal SNPs). Details for calculation of a probability function from these minimal SNPs are given in Figure 15. The inventors have shown that use of0 these minimal SNPs alone allows prognosis of FMS in a Spanish female population with an LR+ of 5.55, specificity of 92% and sensitivity of 47% (see Example 2).

The prognosis may be based on the minimal SNPs alone, or on these SNPs and additionally, 1, 2, 3, 4 or all 5 of the remaining discriminating SNPs listed in Table 3 for this phenotype. Preferably5 these additional SNPs are selected from: rs324029 and rs3794808. Thus in one aspect the method comprises determining outcomes for the 3 minimal SNPs and 1or both of the following discriminating SNPs: rs324029 and rs3794808.

For example, the method may comprise determining outcomes for the 3 minimal SNPs and o rs324029 and rs3794808 (the 5 preferred SNPs). In a method for CFS prognosis, at least 2, 3, 4, 5 or all 6 of the CFS prognosis discriminating SNPs in Table 3 may be tested for outcomes and used in the prediction. Preferably the SNPs are selected from: rs1474347 and rs489736.

Preferably the method for prognosis comprises determining outcomes for (at least) the SNPs: rs1474347 and rs489736 (the minimal SNPs). Details for calculation of a probability function from these minimal SNPs are given in Figure 15. The inventors have shown that use of these minimal SNPs alone allows prognosis of CFS in a Spanish female population with an LR+ of 5.25, specificity of 96% and sensitivity of 22.6% (see Example 2).

The prognosis may be based on the minimal SNPs alone, or on these SNPs and additionally, 1, 2, 3 or all 4 of the remaining discriminating SNPs listed in Table 3 for this phenotype.

In a method for diagnosing severe FMS, at least 2, 3, 4, 5, 6, 7, 8 or all 9 of the severe FMS diagnosis discriminating SNPs in Table 3 may be tested for outcomes and used in the prediction. For example, the SNPs may be selected from: rs3794808 and rs11246226.

Preferably the method for diagnosis comprises determining outcomes for (at least) the SNPs: rs3794808 and rs11246226 (the minimal SNPs). Details for calculation of a probability function from these minimal SNPs are given in Figure 15. The inventors have shown that use of these minimal SNPs alone allows diagnosis of severe FMS in a Spanish female population with an LR+ of 6.14, specificity of 94% and sensitivity of 39.7% (see Example 2).

The diagnosis may be based on the minimal SNPs alone, or on these SNPs and additionally, 1, 2, 3, 4, 5, 6, or all 7 of the remaining discriminating SNPs listed in Table 3 for this phenotype.

In a method for diagnosing severe CFS, at least 2, 3, 4, 5, or all 6 of the severe CFS diagnosis discriminating SNPs in Table 3 may be tested for outcomes and used in the prediction. For example, the SNPs may be selected from: rs2020942 and rs1474347.

Preferably the method for diagnosis comprises determining outcomes for (at least) the SNPs: rs2020942 and rs1474347 (the minimal SNPs). Details for calculation of a probability function from these minimal SNPs are given in Figure 15. The inventors have shown that use of these minimal SNPs alone allows diagnosis of severe CFS in a Spanish female population with an LR+ of 7.06, specificity of 93% and sensitivity of 48.3% (see Example 2).

The diagnosis may be based on the minimal SNPs alone, or on these SNPs and additionally, 1, 2, 3 or all 4 of the remaining discriminating SNPs listed in Table 3 for this phenotype. For each of the methods, any combination of the above may be used to define the combination of SNPs tested in the method. Thus for example, differential diagnosis of FMS and CFS may comprise determining outcomes for at least 8 of the FMSvsCFS discriminating SNPs including the minimal SNPs, and so as to give an LR+ of at least 7.

In one instance, a method of the invention comprises diagnosing or prognosing more than one phenotype. For example, the method may comprise determining outcomes for FMS vs CFS, FMS prognosis, CFS prognosis, severe FMS diagnosis and/or severe CFS diagnosis SNPs selected in number and type as described above or any combination thereof. The method may comprise determining an outcome for all of the SNPs in Table 3. Thus, for example, it may be possible to simultaneously differentially diagnose FMS or CFS in a subject with a clinical diagnosis of FM or CFS and at the same time determine the likelihood of development of an aggressive disease course.

For example, it may be desirable to differentially diagnose FMS and CFS in a subject, and simultaneously determine the FMS or CFS prognosis. In that case the method may comprise genotyping the patient at FMSvs CFS, FMS prognosis and/or CFS prognosis discriminating SNPs, selected as described herein. For example, the method may comprise determining the genotype at the minimal SNPs for each phenotype, or at all of the SNPs for each phenotype.

Similarly, in a subject showing symptoms typical of FMS and/or CFS, but not clinically diagnosed, or an asymptomatic subject, it may be desirable to determine the genotype of the subject at both severe FMS discriminating SNPs and severe CFS discriminating SNPs, each selected as described herein. For example, such a method may comprise determining the genotype at the minimal SNPs for each phenotype, or at all of the SNPs for each phenotype.

In some aspects the present methods may include determining other factors for a subject. For example, the subject may be genotyped for one or more other genetic variations (such as other SNPs not listed in Table 3). These may be mutations associated with FMS, CFS or another condition. Other markers (e.g. SNPs) associated with other diseases may also be determined.

The present methods may also be used in conjunction with or in addition to standard clinical tests for FMS and CFS as described herein. For example, the present methods for differentially diagnosing FMS and CFS, for prognosing FMS disease development and for prognosing CFS disease development may be carried out for subjects who meet the ACR'90 or CDC'94 criteria for clinically diagnosing FMS or CFS. The subjects may have been or be being clinically diagnosed with FMS or CFS according to the ACR'90 or CDC'94 clinical criteria, as described herein. The present methods may therefore complement, and confirm or supplement the clinical diagnosis. Similarly the present methods for prognosing severe FMS or prognosing severe CFS may in some cases be applied in addition to a clinical diagnosis of severe FMS or severe CFS using the FIQ or CSI as described herein.

As described above, the present methods allow accurate prediction of FMS and CFS phenotypes based on a relatively small number of informative SNPs. This can be advantageous in that it allows use of genotyping techniques that would not necessarily be suitable for large scale SNP screening, as well as larger scale genotyping methods.

In general, even if a larger number of SNPs or genetic variations or factors are tested in the present methods, prediction of FMS or CFS phenotype can be made based only on outcomes of informative SNPs listed in Table 3 and selected as described above. The SNPs in Table 3, selected in type and number as described above, are sufficient for the prediction. Therefore in one example, the present methods allow differential genetic diagnosis of FMS and CFS, prognosis of

FMS, prognosis of CFS, diagnosis of severe FMS and/or diagnosis of severe CFS based on (at a maximum) the outcomes for Table 3 SNPs selected in number and type as described above.

In some instances though, it may be that some additional variables such as SNPs or other factors are used in the prediction. For example, in the present methods, prognosis or diagnosis may be made based on the outcomes of a maximum of 100, 90, 80, 70, 60, 50, 45, 40, 35, 30, 25, 20, 19,18,17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, or 2 variables such as SNPs or FMS- or CFS- associated SNPs. The SNPs may comprise (or consist of), or be selected from the Table 3 SNP variables selected as described above.

For example, the minimal SNPs listed for each phenotype constitute 8 different SNPs in total. Diagnosis or prognosis may be made on the outcomes of at least these 8 or a maximum of (no more than) these 8 SNPs. Prognosis or diagnosis may be made based on the outcomes of at least all of, or a maximum of (no more than) all of, the SNPs listed in Table 3.

Diagnosis or prognosis may be made based on the outcomes of the 12 preferred FMS vs CFS SNPs₁ the 5 preferred FMS prognosis SNPs, all 6 CFS prognosis SNPs, all 9 severe FMS diagnosis SNPs, and/or all 6 severe CFS diagnosis SNPs, or any combination thereof.

In one aspect the method may involve genotyping a maximum of 100, 90, 80, 70, 60, 50, 40, 30, 25, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, or 2 SNPs or FMS- or CFS- associated SNPs. The method may involve genotyping a maximum of (no more than) all the SNPs in Table 3. In some instances, the method comprises genotyping at a maximum, the FMSvsCFS, FMS prognosis, CFS prognosis, severe FMS diagnosis, and/or severe CFS diagnosis SNPs selected as described above. Thus for example, the method may involve genotyping at a maximum a combination of FMS vs CFS discriminating SNPs selected in number and type as described above. The method may comprise genotyping, at a maximum, the total of 8 minimal SNPs described above. The method may comprise genotyping, at a maximum, the 12 preferred FMS vs CFS SNPs, the 5 preferred FMS prognosis SNPs, all 6 CFS prognosis SNPs, all 9 severe FMS diagnosis SNPs, and/or all 6 severe CFS diagnosis SNPs, or any combination thereof.

In some instances, the only FMS and/or CFS associated SNPs which are genotyped in the method are those selected in number and type from Table 3 as described above.

Once an outcome is determined for each of the selected SNP variables for prediction of a given phenotype, these outcomes are then used to predict phenotype. The outcomes are inserted in a suitable probability function (for prediction of that phenotype) and a probability function value is calculated. The probability function value is then compared with probability function values obtained for a population of individuals of known (clinically determined) phenotype. The risk of the subject having or developing the particular phenotype is thereby determined.

A suitable probability function for determining a given phenotype may be derived by methods as set out in the present Examples and described herein. Typically a study population of individuals is provided. These individuals are of known (clinically determined) phenotype with respect to the phenotype that the probability function will be used to determine. Clinical diagnosis and phenotype determination of FMS can be made following Clinical criteria of the American College of Rheumatology (1990) (ACR'90) as described herein. Clinical diagnosis and phenotype determination of CFS can be made following Clinical criteria of the Center for Disease Control and Prevention (1994) (CDC'94) as described herein. Preferably the individuals in the study population meet the strict ACR'90 and CDC'94 definitions. Severity of FMS and CFS can be determined using the FIQ and CSI as described herein.

In general at least n individuals are included in the study population. Typically n is 200-1000, for example 300, 400, 500 or 600. Where a probability function is for determining between alternative phenotypes, preferably there are approximately equal numbers of individuals with each of the alternative phenotypes in the population. Thus where there are two alternative phenotypes, A and

B, the population is preferably approximately 50% phenotype A and 50% phenotype B. However, the ratios may be for example, 60%/40%, 70%/30%, 80%/20%, 90%/10% or any statistically acceptable distribution. For example, where the probability function is for differential diagnosis of

FMS vs CFS, preferably about 50% of the population are of clinically determined FMS phenotype and about 50% of the population are of clinically determined CFS phenotype. Where the probability function is for prognosis of FMS, preferably about 50% of the population are of clinically determined severe FMS phenotype (FIQ >76) and about 50% of the population are of clinically determined milder FMS phenotype (FIQ ≤ 76). Where the probability function is for diagnosis of severe FMS, preferably about 50% of the population is of clinically determined severe FMS phenotype (FIQ> 76) and about 50% of the population is not clinically diagnosed as suffering from FMS or CFS, e.g. does not meet the clinical criteria.

The population may be for example, a Chinese, Japanese or a Caucasian population, such as Spanish population. The population may be female, e.g. Spanish female. Preferably the population used for deriving a probability function comprises a representative sample of the population in which the probability function will be applied.

Each individual in the study population is then tested to determine the identity of the nucleotide in the individual's genomic DNA (or the individual's genotype) at discriminating SNPs for the particular phenotype (the SNPs may be selected in number and type from the list in Table 3 as described above for the diagnostic methods). This provides a number of outcomes for each individual. Testing, e.g. genotyping, may be carried out by any of the methods described herein, e.g. by microarray analysis as described herein. Testing is typically ex vivo, carried out on a suitable sample obtained from an individual.

Multiple genotype-phenotype associations may then be analysed using stepwise multivariate logistic regression analysis, using as the dependent variable the clinically determined disease phenotype and as independent variables the outcomes of the informative SNPs, e.g. as recommended by Balding DJ. (2006³⁵). The goodness of fit of the models obtained may be evaluated using Hosmer-Lemeshow statistics and their accuracy assessed by calculating the area under the curve (AUC) of the Receiver Operating Characteristic curve (ROC) with 95% confidence intervals (see, e.g. (Janssens ACJW et al., 2006³⁶. Suitable methods are described in Example 2.

The sensitivity, specificity, and positive likelihood ratio (LR+ = sensitivity/(1 -specificity)) may be computed by means of ROC curves. Preferably the model has an LR+ value of at least 5, for example, at least 5, 6, 7, 8, 9 or 10.

Mean probability function values for each of the alternative phenotypes in the population can be compared using a t test. In general the probability functions are able to distinguish between the different phenotypes in the study population in a statistically significant way, for example, at p ≤ 0.05 in a t-test. Thus the probability functions produce a statistically significant separation between individuals of different phenotype in the population.

Statistical analyses may be performed, for example, using the Statistical Package for the Social Sciences (SPSS Inc. Headquarters, Chicago, IL, USA) version 14.0.

Probability function values can be calculated for each individual of known phenotype in the study population and plotted in a suitable graph. For example, suitable graphs are shown in Figure 10. In order to carry out the present methods of diagnosis and prognosis, a probability function value is calculated for the test individual, and this is compared with the probability function values for the individuals of known phenotype in the study population in order to determine the risk of a given phenotype in that individual. The comparison may be done by comparison with a graph such as that in Figure 10 or by any other suitable means known to those skilled in the art.

Thus for example, in deriving a probability function for use in differentially diagnosing FMS vs CFS, a study population of individuals clinically diagnosed as FMS and individuals clinically diagnosed as CFS is provided. Each individual may then be tested to determine an outcome for each of the 15 FMS vs CFS discriminating SNPs in Table 3. Stepwise multiple logistic regression is performed on the "outcomes" and "phenotype" data and a probability function is derived which is able to distinguish between the two phenotypic groups in the study population in a statistically significant way.

Thus in one aspect the invention further provides a method of deriving a probability function for use in determining a FMS or CFS phenotype as described herein, comprising:

(i) providing a study population of individuals, wherein each individual is of known clinically determined phenotype with respect to the FMS or CFS phenotype;

(ii) determining the for each individual the identity of the nucleotide in the individual's genomic DNA at each SNP in a set of SNPs, thereby obtaining a set of outcomes for each individual;

(iii) applying stepwise multiple logistic regression analysis to the outcomes obtained in (ii) and the known phenotypes referred to in (i); and

(iv) thereby deriving a probability function which produces a statistically significant separation between individuals of different phenotype in the population; wherein:

(a) the probability function is for distinguishing or differentially diagnosing FMS and CFS according to the invention, and the set of SNPs is selected from the set of FMSvsCFS discriminating SNPs in Table 3;

(b) the probability function is for prognosing FMS disease development according to the invention and the set of SNPs is selected from the set of FMS prognosis discriminating SNPs in Table 3;

(c) the probability function is for prognosing CFS disease development according to the invention and the set of SNPs is selected from the set of CFS prognosis discriminating SNPs in Table 3;

(d) the probability function is for diagnosing severe FMS according to the invention and the set of SNPs is selected from the set of severe FMS diagnosis discriminating SNPs in Table 3; and/or (e) the probability function is for diagnosing severe CFS according to the invention and the set of SNPs is selected from the set of severe CFS diagnosis discriminating SNPs in Table 3.

Derivation of the probability functions may be carried out by a computer. Therefore in one aspect, the invention also relates to a computational method of deriving a probability function for use in determining FMS or CFS phenotype which method comprises applying stepwise multiple logistic regression analysis to outcomes data and phenotype data obtained from a suitable study population of individuals, wherein each individual is of known clinically determined phenotype with respect to the FMS or CFS phenotype, thereby deriving a probability function which produces a statistically significant separation between individuals of different phenotype in the population; wherein:

(i) the phenotype data comprises the known clinically determined phenotype of each individual;

(ii) the outcomes data for each individual comprises the genotype of the individual at each SNP in a set of SNPs; and wherein: (a) the probability function is for distinguishing or differentially diagnosing FMS and CFS according to the invention, and the set of SNPs is selected from the set of FMSvsCFS discriminating SNPs in

Table 3;

(b) the probability function is for prognosing FMS disease development according to the invention and the set of SNPs is selected from the set of FMS prognosis discriminating SNPs in Table 3; (c) the probability function is for prognosing CFS disease development according to the invention and the set of SNPs is selected from the set of CFS prognosis discriminating SNPs in Table 3;

(d) the probability function is for diagnosing severe FMS according to the invention and the set of SNPs is selected from the set of severe FMS diagnosis discriminating SNPs in Table 3; and/or

(e) the probability function is for diagnosing severe CFS according to the invention and the set of SNPs is selected from the set of severe CFS diagnosis discriminating SNPs in Table 3.

In the derivation methods for a probability function for determining a particular phenotype, the SNPs to be tested may be selected from the discriminating SNPs listed in Table 3 for that phenotype as already described herein in relation to the diagnostic methods. Preferably all of the SNPs listed as discriminating for the phenotype, or all of the minimal discriminating SNPs described herein for that phenotype are tested.

Suitable study populations and statistical analysis methods are described above. Reference may also be made to the present Examples.

Details for calculation of a probability function from the minimal SNPs listed for each phenotype are given in Figure 15. Statistical analyses may be performed, for example, using the Statistical Package for the Social Sciences (SPSS Inc. Headquarters, Chicago, IL, USA) version 14.0. These may be used for calculation of probability function values for use in the methods herein. The probability functions, together with the information in Tables 3 and 3A may be used to determine a diagnosis or prognosis according to the invention.

In one aspect the invention relates to probability functions constructed or derived using the data in any of Tables in Figure 15, and to their use in a method, e.g. a computational method, determining a FMS or CFS phenotype. The invention further relates to associated computer programs and computer systems as described herein. The invention also relates to the probability functions derived according to the present methods and to their use in the methods described herein.

The process of calculating a probability function value for a test subject and comparing the value to values obtained from a study population of individuals of known phenotypes in order to evaluate the risk of developing a phenotype in the test subject may also be carried out using appropriate software.

Therefore in one aspect the invention relates to a computational method for determining a FMS or CFS phenotype using the outcomes of discriminating SNPs ("outcomes data") for that phenotype obtained according to the methods described herein. In the computational method, outcomes data for the discriminating SNPs for a particular phenotype obtained from a test subject (test outcomes data) is inputted in a suitable probability function to produce a probability function value for the test subject. The test probability function value is then compared with probability function values for individuals of known phenotype in order to diagnose or prognose the phenotype of the test individual. The comparison may be made using the methods described herein.

The invention further relates to a computer system comprising a processor and means for controlling the processor to carry out a computational method described herein, and to a computer program comprising computer program code which when run on a computer or computer network causes the computer or computer network to carry out the computational method. In one aspect, the computer program is stored on a computer readable medium.

As described above and in the Examples, the present inventors have identified a number of single nucleotide polymorphisms (SNPs) which show single locus allelic association with FMS and/or CFS and/or one or more FMS or CFS phenotypes. These SNPs, e.g those for which the association has P≤0.05 are useful for determining whether subject is at high or low risk of developing the particular phenotype. For example, a person carrying one or two copies of a high-risk SNP (a polymorphic allele which is significantly associated with a given disease or trait) is at increased risk of developing the associated disease or having the associated trait.

Table 3 (Figure 8A) shows the genotype distribution at each of the SNPs included in the SNP models in Example 2 in populations of individuals having different disease phenotypes. Figure 8C shows the Chi square p-value for individual SNP allelic association for each of these SNPs with each of the given phenotypes. The Bonferroni test bP value is shown in Table 3.

Genotype distributions, chi-square P values and bP values for individual SNP associations with particular phenotypes for the SNPs identified as informative in Example 1, but not included in the Example 2 models are shown in Figures 16A to C. Figure 16D identifies the nucleotide alleles "A" and "B" for each SNP. In Table 3, "A" is the first described allele given under the rs number in the SNP database from the National Center for Biotechnology Information. For example, rs6713532 is described as CfT, so that A=C and B=T. The identities of A and B for each SNP in Table 3 are also reproduced in Table 3A.

Those skilled in the art can determine which allele is associated with which phenotype (i.e. the susceptibility allele for a given phenotype) from the frequencies in Table 3/3A and in Figure 16. For example, for rs6713532 "AA" (CC) is more frequent in CFS than in FM. Therefore, C is associated with CFS and T with FM.

By identifying the nucleotide in the genomic DNA of a subject at one (or more) of these SNPs, it is possible to determine the risk or susceptibility of that individual to the phenotype with which the SNP is associated.

In one aspect the invention relates to the use of one or more of the SNPs in Table 2 or 3 in a method for diagnosing or prognosing FMS and/or CFS, such as one or more of the methods described herein. Thus the invention relates to a method for determining FMS and/or CFS phenotype (as described herein) comprising determining the genotype of an individual at one or more of the SNPs in Table 2 or 3. In one aspect the one or more SNPs does not comprise COMT rs4680.

In one example, the invention provides a method for determining the susceptibility of an individual to an FMS or CFS phenotype comprising determining the identity of a nucleotide present at one or more positions of single nucleotide polymorphism (SNP) within a genomic DNA sequence obtained from the individual, said one or more SNPs being selected from the group consisting of the discriminatory SNPs listed in Table 2 or 3.

A method may be for determining an FMS or CFS phenotype and may comprise use of one or more SNPs included in a model for determining that phenotype, as in Table 2 or 3.

In one example, the method is for differentially diagnosing between FMS and CFS in a subject, as described herein and the one or more SNPs is selected from the FMSvsCFS discriminating SNPs in Table 3 and/or the FMvCFS SNPs in Table 2.

In one example, the method is for prognosing FMS disease development and the one or more SNPs is selected from the FMS prognosis discriminating SNPs in Table 3 and/or the FM SNPs in Table 2. In one example, the method is for prognosing CFS disease development and the one or more SNPs is selected from the CFS prognosis discriminating SNPs in Table 3 and/or the CFS SNPs in Table 2.

In one example, the method is for diagnosing severe FMS and the one or more SNPs is selected from the severe FMS diagnosis discriminating SNPs in Table 3.

In one example, the method is for diagnosing severe CFS and the one or more SNPs is selected from the severe CFS diagnosis discriminating SNPs in Table 3.

A method for determining a given phenotype may comprise the use of one or more SNPs selected from the SNPs having a P-value for allelic association with that phenotype of ≤0.05, in Figure 8C or Figure 16.

Still further, a method for determining a given phenotype may comprise the use of one or more SNPs selected from SNPs having a bP+ value for allelic association with that phenotype of ≤0.05 in Figure 8 (Table 3) or Figure 16.

In such methods, diagnosis or prognosis may be made based on the particular allele at the SNP tested. For example, diagnosis or prognosis may in some cases be made based on one SNP.

The identity of the nucleotide at the SNP determines the susceptibility of the individual to disease.

The identity of the nucleotide at the SNP may be determined using the methods described herein. For example, the nucleotide may be determined by binding of an oligonucleotide probe to a genomic DNA sample,- the probe comprising a nucleotide sequence which binds specifically to a particular allele of the one or more SNPs and does not bind specifically to other alleles of the one or more SNPs. Suitable probes are described herein.

Once the allele is determined, those skilled in the art may determine susceptibility to or the risk of a given phenotype using the frequency and probability information in Figure 8, Figure 11 and Figure 16, using known statistical methods.

The inventors also identified particular haplotypes, consisting of groups of the above SNPs, which show an association with particular phenotypes. Table 5 shows the haplotypes and the Chi-square P and bP values as a measure of association of the haplotypes with the particular phenotypes. Table 6 shows the haplotype frequencies in the different phenotypes across a study population, as described in Example 2. As shown in Tables 5 and 6, haplotypes 1, 2, and 3 (chromosomes 2, 17 and 22 respectively) may be used to differentially diagnose FWlS vs CFS. Haplotypes 4, 5 and 6 (chromosomes 2, 17 and 22 respectively) may be used to prognose FMS₁ in particular the likelihood of development of severe FMS as opposed to milder FMS. Haplotypes 7 and 8 (chromosomes 7 and 11 respectively) may be used to prognose CFS₁ in particular the likelihood of development of severe CFS as opposed to milder CFS.

Thus the invention also relates to the use of one or more of these haplotypes in a method for diagnosing or prognosing FMS and/or CFS, such as one or more of the methods described herein. Thus the invention relates to a method for diagnosing or prognosing FMS and/or CFS comprising determining the haplotype of an individual for any one or more of the haplotypes listed in Tables 5 and 6.

For example, a method may be for differentially diagnosing between FMS and CFS in a subject as described herein and may comprise determining the haplotype of the subject with respect to hap 1 and/or hap2 and/or hap 3 listed in Tables 5 and 6.

The method may be for prognosing FMS disease development in a subject as described herein and may comprise determining the haplotype of the subject with respect to hap 4 and/or hap5 and/or hap 6 listed in Tables 5 and 6.

The method may be for prognosing CFS disease development in a subject as described herein and may comprise determining the haplotype of the subject with respect to hap 7 and/or hap 8 listed in Tables 5 and 6.

A method may comprise determining a combination of haplotypes in a subject. For example, haplotypes 1 and 4 (both on chromosome 2), and/or hapiotypes 2 and 5 (both on chromosome 17), and/or haplotypes 3 and 6 (both on chromosome 22).

The haplotype of an individual may be determined using the methods described herein and known in the art. For example, the SNPs in each haplotype may be genotyped and the haplotype may be estimated by probabilistic analysis, e.g. using the Helix Tree software as described in Example 2.

Once haplotype has been determined, the risk or susceptibility of the individual to a particular phenotype may be determined from the probabilities in Table 5 and the frequencies in Table 6, using methods known in the art.

In general the present methods are carried out ex vivo or in vitro, e.g. using a sample obtained from the individual. Various methods are known in the art for determining the presence or absence in a test sample of a particular nucleic acid sequence, for example a nucleic acid sequence which has a particular nucleotide at a position of single nucleotide polymorphism as shown in Table 2 or 3. For example, genotype may be determined by microarray analysis, sequencing, primer extension, ligation of allele specific oligonucleotides, mass determination of primer extension products, restriction length polymorphism analysis, single strand conformational polymorphism analysis, pyrosequencing, dHPLC or denaturing gradient gel electrophoresis (DGGE). Furthermore, having sequenced nucleic acid of an individual or sample, the sequence information can be retained and subsequently searched without recourse to the original nucleic acid itself. Thus, for example, a sequence alteration or mutation may be identified by scanning a database of sequence information using a computer or other electronic means.

Typically a genotype is determined from nucleic acid obtained from the subject. The nucleic acid (DNA or RNA) may be obtained from any appropriate biological sample which contains nucleic acid. The sample may be taken from a fluid or tissue, secretion, cell or cell line derived from the human body.

For example, samples may be taken from blood, including serum, lymphocytes, lymphoblastoid cells, fibroblasts, platelets, mononuclear cells or other blood cells, from saliva, liver, kidney, pancreas or heart, urine or from any other tissue, fluid, cell or cell line derived from the human body. For example, a suitable sample may be a sample of cells from the buccal cavity. Preferably nucleic acid is obtained from a blood sample.

Methods according to the invention may include obtaining a genomic sample.

In general, nucleic acid regions which contain the SNPs to be identified (target regions) are subjected to an amplification reaction. Any suitable technique or method may be used for amplification. In general, where multiple SNPs are to be analysed, it is preferable to simultaneously amplify all of the corresponding target regions (comprising the variations).

Methods according to some aspects of the present invention may comprise determining the binding of a oligonucleotide probe to a genomic sample. The probe may comprise a nucleotide sequence which binds specifically to a particular allele of an SNP and does not bind specifically to other alleles of the SNP.

The oligonucleotide probe may comprise a label and binding of the probe may be determined by detecting the presence of the label.

A method may include hybridisation of one or more (e.g. two) oligonucleotide probes or primers to target nucleic acid. Where the nucleic acid is double-stranded DNA, hybridisation will generally be preceded by denaturation to produce single-stranded DNA. The hybridisation may be as part of an amplification, e.g. PCR procedure, or as part of a probing procedure not involving amplification, e.g. PCR. An example procedure would be a combination of PCR and low stringency hybridisation. A screening procedure, chosen from the many available to those skilled in the art, is 5 used to identify successful hybridisation events and isolated hybridised nucleic acid.

Binding of a probe to target nucleic acid (e.g. DNA) may be measured using any of a variety of techniques at the disposal of those skilled in the art. For instance, probes may be radioactively, fluorescently or enzymatically labelled. Other methods not employing labelling of probe include0 examination of restriction fragment length polymorphisms, amplification using PCR, RN'ase cleavage and allele specific oligonucleotide probing. Probing may employ the standard Southern blotting technique. For instance DNA may be extracted from cells and digested with different restriction enzymes. Restriction fragments may then be separated by electrophoresis on an agarose gel, before denaturation and transfer to a nitrocellulose filter. Labelled probe may be5 hybridised to the DNA fragments on the filter and binding determined. DNA for probing may be prepared from RNA preparations from cells.

Those skilled in the art are well able to employ suitable conditions of the desired stringency for selective hybridisation, taking into account factors such as oligonucleotide length and base o composition, temperature and so on.

Suitable selective hybridisation conditions for oligonucleotides of 17 to 30 bases include hybridization overnight at 42°C in 6X SSC and washing in 6X SSC at a series of increasing temperatures from 42⁰C to 65⁰C. 5

Other suitable conditions and protocols are described in Molecular Cloning: a Laboratory Manual: 2nd edition, Sambrook et al., 1989, Cold Spring Harbor Laboratory Press and Current Protocols in Molecular Biology, Ausubel et al. eds., John Wiley & Sons, 1992. 0 An oligonucleotide for use in nucleic acid amplification may be about 30 or fewer nucleotides in length (e.g. 18, 20, 22, 24 or 26). Generally specific primers are upwards of 14 nucleotides in length. Those skilled in the art are well versed in the design of primers for use in processes such as PCR. Various techniques for synthesizing oligonucleotide primers are well known in the art, including phosphotriester and phosphodiester synthesis methods. Primers and primer pairs5 suitable for amplification of nucleic acid regions comprising the SNPs in Table 2 are listed in Figure 7. Primers and primer pairs suitable for amplification of nucleic acid regions comprising the SNPs in Table 3 are listed in Figure 13. Optimised primers and primer pairs for amplification of target regions comprising the SNPs in Tables 2 and 3 are listed in Figure 17B. Nucleic acid may also be screened using a variant- or allele-specific probe. Such a probe may correspond in sequence to a region of genomic nucleic acid, or its complement, which contains one or more of the SNPs described herein. Under suitably stringent conditions, specific hybridisation of such a probe to test nucleic acid is indicative of the presence of the sequence alteration in the test nucleic acid. For efficient screening purposes, more than one probe may be used on the same test sample.

Nucleic acid in a test sample, which may be a genomic sample or an amplified region thereof, may be sequenced to identify or determine the identity of a polymorphic allele. The allele of the SNP in the test nucleic acid can therefore be compared with the susceptibility alleles of the SNP as described herein (see Tables 3 and 3A and Figure 16) to determine whether the test nucleic acid contains one or more alleles which are associated with disease.

Since it will not generally be time- or labour-efficient to sequence all nucleic acid in a test sample, a specific amplification reaction such as PCR using one or more pairs of primers may be employed to amplify the region of interest in the nucleic acid, for instance the particular region in which the

SNPs of interest occur. The amplified nucleic acid may then be sequenced as above, and/or tested in any other way to determine the presence or absence of a particular feature. Nucleic acid for testing may be prepared from nucleic acid removed from cells or in a library using a variety of other techniques such as restriction enzyme digest and electrophoresis.

Sequencing of an amplified product may involve precipitation with isopropanol, resuspension and sequencing using a TaqFS+ Dye terminator sequencing kit. Extension products may be electrophoresed on an ABI 377 DNA sequencer and data analysed using Sequence Navigator software.

Nucleic acid in a test sample may be probed under conditions for selective hybridisation and/or subjected to a specific nucleic acid amplification reaction such as the polymerase chain reaction (PCR) (reviewed for instance in "PCR protocols; A Guide to Methods and Applications", Eds. lnnis et al, 1990, Academic Press, New York, Mullis et al, Cold Spring Harbor Symp. Quant. Biol., 51 :263, (1987), Ehrlich (ed), PCR technology, Stockton Press, NY, 1989, and Ehrlich et al, Science, 252:1643-1650, (1991)). PCR comprises steps of denaturation of template nucleic acid (if double-stranded), annealing of primer to target, and polymerisation. The nucleic acid probed or used as template in the amplification reaction may be genomic DNA, cDNA or RNA.

Other specific nucleic acid amplification techniques include strand displacement activation, the QB replicase system, the repair chain reaction, the ligase chain reaction, rolling circle amplification and ligation activated transcription. Methods of the present invention may therefore comprise amplifying the region in said genomic sample containing the one or more positions of single nucleotide polymorphism of interest.

Allele- -specific oligonucleotides may be used in PCR to specifically amplify particular sequences if present in a test sample. Assessment of whether a PCR band contains a gene variant may be carried out in a number of ways familiar to those skilled in the art. The PCR product may for instance be treated in a way that enables one to display the polymorphism on a denaturing polyacrylamide DNA sequencing gel, with specific bands that are linked to the gene variants being selected.

In some embodiments, the region of genomic sample comprising a polymorphism may be amplified using a pair of oligonucleotide primers, of which the first member of the pair comprises a nucleotide sequence which hybridises to a complementary sequence which is proximal to and 5' of the position of single nucleotide polymorphism, and the second member of the primer pair comprises a nucleotide sequence which hybridises to a complementary sequence which is proximal to and 3' of the position of single nucleotide polymorphism.

In other embodiments, the first member of the pair of oligonucleotide primers may comprise a nucleotide sequence which hybridises to a complementary sequence which is proximal to and 5' or 3¹ of the polymorphism, and the second member of the pair may comprise a nucleotide sequence which hybridises under stringent conditions to a particular allele of the polymorphism and not to other alleles, such that amplification only occurs in the presence of the particular allele.

A further aspect of the present invention provides a pair of oligonucleotide amplification primers suitable for use in the methods described herein.

A suitable pair of amplification primers according to this aspect may have a first member comprising a nucleotide sequence which hybridises to a complementary sequence which is proximal to and 5¹ of a single nucleotide polymorphism shown in Table 2 or 3 and a second member comprising a nucleotide sequence which hybridises to a complementary sequence which is proximal to and 3' of the single nucleotide polymorphism.

The allele of the at least one polymorphism (i.e. the identity of the nucleotide at the position of single nucleotide polymorphism) may then be determined by determining the binding of an oligonucleotide probe to the amplified region of the genomic sample. A suitable oligonucleotide probe comprises a nucleotide sequence which binds specifically to a particular allele of the at least one polymorphism and does not bind specifically to other alleles of the at least one polymorphism.

Other suitable pairs of amplification primers may have a first member comprising a nucleotide sequence which hybridises to a complementary sequence which is proximal to and 5' or 3' of a single nucleotide polymorphism shown in Table 2 or 3 and a second member of the pair comprising a nucleotide sequence which hybridises under stringent conditions to a particular allele of the polymorphism and not to other alleles, such that amplification only occurs in the presence of the particular allele.

PCR primers suitable for amplification of target DNA regions comprising the SNPs in Table 2 are listed in Figure 7. PCR primers suitable for amplification of target DNA regions comprising the SNPs in Table 3 are listed in Figure 13. Optimised primers for amplification of target regions comprising SNPs in Tables 2 and 3 are listed in Figure 17B. The present methods may comprise the use of one or more of these primers or one or more of the listed primer pairs. In one aspect the method comprises use of all of the primers listed in Figure 7 and/or all of those in Figure 13 (optionally with one or more of the optimised primer pairs in Figure 17B substituted for the relevant SNP(s)). Suitable reaction conditions may be determined using the knowledge in the art. In one aspect the invention relates to one or more of the listed primer or primer pairs.

A further aspect of the present invention provides an oligonucleotide which hybridises specifically to a nucleic acid sequence which comprises a particular allele of a polymorphism selected from the group consisting of the single nucleotide polymorphisms shown in Tables 2 or 3, and does not bind specifically to other alleles of the SNP. Hybridisation may be determined under suitable selective hybridisation conditions as described herein.

Such oligonucleotides may be used in a method of screening nucleic acid.

In some preferred embodiments, oligonucleotides according to the present invention are at least about 10 nucleotides in length, more preferably at least about 15 nucleotides in length, more preferably at least about 20 nucleotides in length. Oligonucleotides may be up to about 100 nucleotides in length, more preferably up to about 50 nucleotides in length, more preferably up to about 30 nucleotides in length. The boundary value 'about X nucleotides' as used above includes the boundary value 'X nucleotides'. Oligonucleotides which specifically hybridise to particular alleles (can discriminate between alternative alleles) of the SNPs listed in Table 3 are listed in Figure 14 and are described herein. Oligonucleotides which specifically hybridize to particular alleles of the SNPs listed in Table 2 are listed in Figure 6. Optimised oligonucleotides which specifically hybridize to particular alleles of the SNPs in Tables 2 and 3 are listed in Figure 17A.

Approaches which rely on hybridisation between a probe and test nucleic acid and subsequent detection of a mismatch may be employed. Under appropriate conditions (temperature, pH etc.), an oligonucleotide probe will hybridise with a sequence which is not entirely complementary. The degree of base-pairing between the two molecules will be sufficient for them to anneal despite a mis-match. Various approaches are well known in the art for detecting the presence of a mis- match between two annealing nucleic acid molecules. For instance, RN'ase A cleaves at the site of a mis-match. Cleavage can be detected by electrophoresis test nucleic acid to which the relevant probe or probe has annealed and looking for smaller molecules (i.e. molecules with higher electrophoretic mobility) than the full length probe/test hybrid.

Genotype analysis may be carried out by microarray analysis. Any suitable microarray technology may be used. Preferably the methodology reported in Tejedor et al 2005⁴¹, and in International Patent Application No. PCT/IB2006/00796 filed 12 January 2006 (the contents of which are hereby incorporated by reference) is used. This technology uses a low-density DNA array and hybridisation to allele-specific oligonucleotide probes to screen for SNPs.

Typically in this technology, nucleic acid regions which contain the SNPs of interest (target regions) may be subjected to an amplification reaction. Any suitable technique or method may be used for amplification. In general, where multiple SNPs are to be analysed, it is preferable to simultaneously amplify all of the corresponding target regions (comprising the variations).

For example, multiplex PCR may be carried out, using appropriate pairs of oligonucleotide PCR primers. Any suitable pair of primers which allow specific amplification of a target region may be used. In one aspect, the primers allow amplification in the fewest possible number of PCR reactions. PCR primers suitable for amplification of target DNA regions comprising the SNPs in Tables 2 and 3 are listed in Figures 7 and 13 respectively. Optimised primers for the SNPs are in Figure 17B.

Following amplification, the amplified nucleic acid may undergo fragmentation, e.g. by digestion with a suitable nuclease such as DNAse I. Typically the amplified (optionally fragmented) DNA is then labelled. Suitable labels are known in the art.

A microarray typically comprises a plurality of probes deposited on a solid support. In general the solid support comprises oligonucleotide probes suitable for discrimination between possible nucleotides at each SNP variable (and optionally other genetic variations) to be determined in the method. The microarray typically also comprises additional positive and/or negative controls.

Typically, for a SNP with the possible alleles A and B, there will be at least one probe which is capable of hybridising specifically to allele A (probe 1) and one probe which is capable of hybridising specifically to allele B (probe 2) under the selected hybridisation conditions. These probes form a probe pair. Typically the probes can be used to discriminate between A and B (e.g. the wildtype and mutant alleles). The probes may examine either the sense or the antisense strand. Typically, probes 1 and 2 examine the same nucleic acid strand (e.g. the sense strand or antisense strand) although in some cases the probes may examine different strands. In one aspect probes 1 and 2 have the same sequence except for the site of the genetic variation.

In one instance, the probes in a probe pair have the same length. In some aspects, where two or more pairs of probes are provided for analysis of a genetic variation, the probes may all have the same length.

Preferably more than one probe pair is provided for detection of each genetic variation. Thus, at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more probe pairs may be provided per genetic variation. In one aspect, (at least) 2 probe pairs are provided. The aim is to reduce the rate of false positives and negatives in the present methods.

For example, for a given genetic variation there may be:

Probe 1 which is capable of hybridising to genetic variation A (e.g. a normal allele)

Probe 2 which is capable of hybridising to genetic variation B (e.g. a mutant allele)

Probe 3 which is capable of hybridising to genetic variation A (e.g. a normal allele) Probe 4 which is capable of hybridising to genetic variation B (e.g. a mutant allele).

The probes may examine the same or different strands. Thus in one embodiment, probes 3 and 4 are the complementary probes of probes 1 and 2 respectively and are designed to examine the complementary strand. In one aspect it is preferred that the probes provided for detection of each genetic variation examine both strands.

More than 2 pairs of probes may be provided for analysis of a genetic variation as above. For example, where a genetic variation exists as any one of 4 bases in the same strand (e.g. there are three mutant possibilities), at least one pair of probes may be provided to detect each possibility. Preferably, at least 2 pairs of probes are provided for each possibility.

A number of methods are known in the art for designing oligonucleotide probes suitable for use in DNA-chips. These include "standard tiling", "alternative tiling" "block tiling" and "alternative block tiling". Any one or more of these strategies may be used to design probes for the present invention. Preferably standard tiling is used, in particular with 2 pairs of probes e.g. 2 pairs of complementary probes as above. Thus it is preferable that the oligonucleotide sequence is complementary to the target DNA or sequence in the regions flanking the variable nucleotide(s). However, in some cases, one or more mismatches may be introduced. 60

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The oligonucleotide probes for use in the present invention typically present the base to be examined (the site of the genetic variation) at the centre of the oligonucleotide.

In general the probes for use in the present invention comprise or in some embodiments consist (essentially) of 17 to 27 nucleotides, for example, 19, 21, 23, or 25 nucleotides or 18, 20, 22, 24 or 26 nucleotides.

The probes provided for detection of each genetic variation (as described above) are typically capable of discriminating between genetic variants A and B (e.g. the normal and mutant alleles) under the selected hybridisation conditions. Preferably the discrimination capacity of the probes is substantially 100%. If the discrimination capacity is not 100%, the probes are preferably redesigned. Preferably the melting temperature of the probe/target complexes is in the range of 75-85 ° C.

Oligonucleotide probes suitable for genotyping of each of the SNP variables listed in Table 3 are provided in Figure 14 herein. Oligonucleotide probes suitable for genotyping of each of the SNP variables listed in Table 2 are provided in Figure 6 herein. Optimised oligonucleotides for discriminating between alleles of the SNPs are listed in Figure 17A. In one aspect the invention relates to any one or more of the oligonucleotide probes, pairs of probes or 4-probe sets listed in Figure 6 and/or Figure 14 and/or Figure 17A, and to their use in the methods of the invention. A probe according to the invention typically comprises a nucleotide sequence which binds specifically to a particular allele of one or more of the SNPs and does not bind specifically to other alleles of the one or more SNPs, under suitable selective hybridisation conditions.

Preferably a microarray for use in the invention comprises at least one probe pair or one 4-probe set listed in Figure 6 and/or Figure 14 and/or Figure 17A. In one aspect a microarray comprises at least 5, 10, 15, 20, or all 25 of the probe sets in Figure 14. A microarray may comprise at least 5, 10, 15, 20, 25, 30, 35 or all of the probe sets in Figure 6. One or more of the probe sets in Figure 17A may be included or substituted as appropriate. A microarray may comprise at least 5, 10, 15, 20, 25, 30, 35, 40 or all 43 of the probes from Figure 17A.

In general probes are provided on the support in replicate. Typically, at least 4, 6, 8, 10, 12, 14, 16, 18 or 20 replicates are provided of each probe, in particular, 6, 8 or 10 replicates. Thus for example, the support (or DNA-chip) may comprise or include 10 replicates for each of (at least) 4 probes used to detect each genetic variation (i.e. 40 probes). Alternatively the support (or DNA- chip) may comprise or include 8 replicates for each of (at least) 4 probes used to detect each genetic variation (i.e. 32 probes). Still further the support (or DNA-chip) may comprise or include 6 replicates for each of (at least) 4 probes used to detect each genetic variation (i.e. 24 probes). In general the support also comprises one or more control oligonucleotide probes which are useful as positive and/or negative controls of the hybridisation reactions. These are also provided in replicate as above.

Typically the chip or array will include positive control probes, e.g., probes known to be complementary and hybridisable to sequences in the target polynucleotide molecules, probes known to hybridise to an external control DNA, and negative control probes, e.g., probes known to not be complementary and hybridizable to sequences in the target polynucleotide molecules. The chip may have one or more controls specific for each target, for example, 2, 3, or more controls. There may also be at least one control for the array.

In one instance, the nucleotide sequence of an external control DNA is the following (5'->3'):

CEH: GTCGTCAAGATGCTACCGTTCAGGAGTCGTCAAGATGCTACCGTTCAGGA and the sequences of the oligonucleotides for its detection are the following: ON1 : CTTGACGACTCCTGAACGG

ON2: CTTGACGACACCTGAACGG

Positive control probes are generally designed to hybridise equally to all target DNA samples and provide a reference signal intensity against which hybridisation of the target DNA (sample) to the test probes can be compared. Negative controls comprise either "blanks" where only solvent (DMSO) has been applied to the support or control oligonucleotides that have been selected to show no, or only minimal, hybridisation to the target, e.g. human, DNA (the test DNA). The intensity of any signal detected at either blank or negative control oligonucleotide features is an indication of non-specific interactions between the sample DNA and the array and is thus a measure of the background signal against which the signal from real probe-sample interactions must be discriminated.

Desirably, the number of sequences in the array will be such that where the number of nucleic acids suitable for detection of genetic variations is n, the number of positive and negative control nucleic acids is n', where n' is typically from 0.01 to 0.4n.

A microarray for use in the present methods may include probes for determination of genetic variations such as SNPs which are not listed in Table 3. These may be FMS or CFS associated SNPs or other genetic variations.

One example of a DNA chip/microarray which may be used is Fibrochip.

A Fibro-chip comprises oligonucleotide probes suitable for detection of some or all of the genetic variations (SNPs) in Table 2 and/or Table 3. Suitable probes are listed in Figure 6, Figure 14, and Figure 17A in probe sets (25 sets in total in Figure 14, 36 in Figure 6, 43 in Figure 17A)₁ each set being for detection or determination of the identity of the nucleotide at a given genetic variation (SNP). At least two pairs of probes are listed in each set. A Fibro-chip may comprise at least one probe pair or at least one probe set, or a selection of the probe sets, for example, at least 5, 10, 15, 20, or all 25 sets in Figure 14 (optionally with optimised probes from Figure 17A included or substituted), or at least 5, 10, 15, 20, 25, 30, 35 or all of the sets in Figure 6 (optionally with optimised probes from Figure 17A included or substituted), or at least 5, 10, 15, 20, 25 , 30, 35, 40 or all 43 sets in Figure 17A, according to the genetic variations being tested. A Fibro- chip for use in the present invention will comprise probes for detection of each of the Table 2 and/or Table 3 SNP variables which are to be genotyped in the method.

A Fibrochip may comprise probes for determining (in a sample nucleic acid) the identity of the nucleotide at each of the Table 2 and/or Table 3 SNP variables selected in type and number as described in relation to the diagnostic/prognostic methods herein. Preferably the probes for detection of a given SNP comprise the probes listed for detection of that SNP in Figure 6 or Figure 14 or Figure 17A.

A Fibro-chip may additionally comprise oligonucleotide probes for detection of genetic variations not currently known to be associated with FMS and/or CFS. For example, the chips may comprise probes for detection of genetic variations such as SNPs associated with another (related) condition or other (related) antigen(s). Typically, in a Fibro-chip or microarray according to the invention the number of nucleic acids suitable for detection of genetic variations associated with FMS and/or CFS represent at least 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or more of the nucleic acids in the array.

In one aspect, in a microarray for use in the present invention, the probes for detection of SNPs selected from Table 2 and/or Table 3 make up at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 100% of the test probes (i.e. excluding control probes) in the array or of the test probes for detection of FMS and/or CFS associated SNPs in the array.

In general the support or chip has from 300 to 40000 nucleic acids (probes), for example, from 400 to 30000 or 400 to 20000. The chip may have from 1000 to 20000 probes, such as 1000 to 15000 or 1000 to 10000, or 1000 to 5000. A suitable chip may have from 2000 to 20000, 2000 to 10000 or 2000 to 5000 probes. For example, a chip may have 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 12000, 14000, 16000, 18000 or 20000 probes. Smaller chips 400 to 1000 probes, such as 400, 500, 600, 700, 800, 900 or 950 probes are also envisaged.

In general an array comprises a support or surface with an ordered array of binding (e.g. hybridisation) sites or probes. Each probe (i.e. each probe replicate) is located at a known predetermined position on the solid support such that the identity (i.e. the sequence) of each probe can be determined from its position in the array. Preferably, the probes deposited on the support, although they maintain a predetermined arrangement, are not grouped by genetic variation but have a random distribution. Typically they are also not grouped within the same genetic variation. If desired, this random distribution can be always the same. Probes may be arranged on the support in subarrays.

The support, on which the plurality of probes is deposited, can be any solid support to which oligonucleotides can be attached. For example, the said support can be of a non-porous material, for example, glass, silicon, plastic, or a porous material such as a membrane or filter (for example, nylon, nitrocelullose) or a gel. In one embodiment, the said support is a glass support, such as a glass slide.

Probes may be attached to the support using conventional techniques for immobilization of oligonucleotides on the surface of the supports.

In one embodiment, the support is a glass slide and in this case, the probes, in the number of established replicates (for example, 6, 8 or 10) are printed on pre-treated glass slides, for example coated with aminosilanes, using equipment for automated production of DNA-chips by deposition of the oligonucleotides on the glass slides ("micro-arrayer"). Deposition is carried out under appropriate conditions, for example, by means of crosslinking with ultraviolet radiation and heating (8O⁰C), maintaining the humidity and controlling the temperature during the process of deposition, typically at a relative humidity of between 40-50% and typically at a temperature of 2O⁰C.

The replicate probes are distributed uniformly amongst the areas or sectors (sub-arrays), which typically constitute a DNA-chip. The number of replicas and their uniform distribution across the DNA-chip minimizes the variability arising from the printing process that can affect experimental results. Likewise, positive and negative hybridisation controls (as described herein) may be printed.

To control the quality of the manufacturing process of the DNA-chip, in terms of hybridization signal, background noise, specificity, sensitivity and reproducibility of each replica as well as differences caused by variations in the morphology of the spotted probe features after printing, a commercial DNA can be used. For example, as a quality control of the printing of the DNA-chips, hybridization may be carried out with a commercial DNA (e.g. k562 DNA High Molecular Weight, Promega)

In general, methods for using microarrays for genotyping are known in the art.

The microarray technology described in Tejedor et al 2005⁴¹ may be used. In one aspect the data from the present microarrays may be analysed and used to determine genotype according to the methods in International Patent Application No. PCT/IB2006/00796 filed 12 January 2006, the contents of which are hereby incorporated by reference. Typically, following amplification of the target DNA₁ and optional fragmentation (e.g. by digestion with DNase I), the target DNA is labelled as described herein and/or as illustrated in the Examples.

The labelled DNA may then be hybridised with a microarray under suitable hybridisation conditions which may be determined by the skilled person. For example, an automatic hybridisation station may be used as in the present Examples.

In general the microarray is then scanned and the label intensities at the specific probe positions determined in order to determine which allele is present in the target DNA hybridised to the array.

In one aspect, following hybridisation, the signal intensity of the label is detected at each probe position on the microarray to determine extent of hybridisation at each position. This may be done by any means suitable for detecting and quantifying the given label. For example, fluorescent labels may be quantified using a confocal fluorescent scanner, e.g. as in the present Examples.

This signal intensity value is typically corrected to eliminate background noise by means of controls on the array. Where a microarray includes probe pairs and probe replicates as described herein, a hybridisation signal mean can then be calculated for each probe (based on the signals from the probes replicates). The ratio of the hybridisation signal mean of the A allele to the sum of the hybridisation signal means of the A and B alleles can then be defined for each probe pair used for genotyping of each SNP (ratios 1 and 2).

The 2 ratio values corresponding to each of the 3 possible genotypes (AA, AB and BB) may be calculated using target DNA from control individuals of each genotype identified previously by, e.g. sequence analysis(at least 10 per genotype) as in the present Examples.

By comparison of test DNA results with the control ratios, a genotype may be assigned to a test individual. As in the present Examples, this may be done using the MG 1.0 software (Tejedor et al 2005⁴¹).

In one aspect the present invention relates to a microarray adapted for use in the present methods as described herein.

As described herein, genotyping may also be carried out using sequencing methods. Typically, nucleic acid comprising the SNPs of interest is isolated and amplified as described herein. Primers complementary to the target sequence are designed so that they are a suitable distance (e.g. 50-

400 nucleotides) from the polymorphism. Sequencing is then carried out using conventional techniques. For example, primers may be designed using software that aims to select sequence(s) within an appropriate window which have suitable Tm values and do not possess secondary structure or that will hybridise to non-target sequence. The invention further relates to the use of one or more oligonucleotide probe(s) and/or one or more primer(s) or primer pair(s) of the invention in a method for diagnosing or prognosing FMS or CFS, such as a method described herein. Such probes and/or primers may be used for example in methods for determining susceptibility to disease.

Once a subject has received a differential diagnosis of FMS or CFS or an FMS or CFS prognosis or a diagnosis of aggressive FMS or CFS phenotypes, the most appropriate treatment for that subject can be selected. In this way, the invention allows better targeting of therapies to patients. Selection of an appropriate therapy may, particularly if used at an early stage of the disease, allows alteration of disease course from severe to more mild form. Treatment as used herein may also refer to the provision of a statement of laboral incapacity - thus the methods of the invention allow a more accurate determination as to when provision of such a statement is appropriate.

Thus in a further aspect, the invention provides a method of selecting a suitable treatment for FM and/or CFS in a subject, the method comprising:

(a) determining a FMS or CFS phenotype in the subject by a method described herein; and

(b) selecting a treatment which is suitable for the determined phenotype(s).

The selected treatment may then be administered to the subject. Thus the invention also relates to a method of treating FMS or CFS in a subject comprising:

(a) determining a FMS or CFS phenotype in the subject by a method described herein; and

(b) treating the subject with a treatment suitable for the determined phenotype.

For example the present methods may be used to reliably distinguish FMS and CFS. A patient who is diagnosed as FMS or CFS according to the invention can be given a treatment appropriate to this. The present methods may be used to predict an aggressive (severe) FMS phenotype or an aggressive CFS phenotype. For example, a patient may be given a prognosis of severe FMS or severe CFS according to the invention, or may be diagnosed with severe FMS or severe CFS.

Thus, patients may be assigned to a "severe phenotype" subgroup according to the invention. Such subjects can be given a treatment which is most suitable for those who have or will develop a severe condition.

For example, physical exercise can have long-term undesirable effects in the symptomology of a patient with severe CFS₁ although it is recommended in patients with severe FMS.

Antidepressant drugs are the standard first-line pharmacological therapy for FMS as these agents reduce pain, fatigue and sleep dysfunction symptoms. The management of the pain is the primary focus. The hypersensitivity to pain becomes more and more severe if the pain is not stopped. Therefore, a more aggressive treatment in order to stop pain could have real benefits to patients who are going to suffer from a severe phenotype. Thus if an individual is predicted to be at significant risk of developing an aggressive phenotype, the individual may be selected for a more aggressive treatment.

Means for carrying out the present methods may be provided in kit form e.g. in a suitable container such as a vial in which the contents are protected from the external environment. Therefore in one aspect the invention further relates to diagnostic kits suitable for use in the methods described herein. Typically a kit comprises:

(i) means for determining outcomes for the selected (SNP) variable(s); and (ii) instructions for determining FMS and/or CFS phenotype and probable disease course based on the outcomes.

The means (i) may comprise one or more oligonucleotide probes suitable for detection of one or more SNP variables to be determined. For example, the means (i) may comprise one or more probe pairs or probe sets listed in Figure 6, Figure 14 or Figure 17A. In one instance the kit may comprise all of the probe sets in Figure 6 (optionally with some or all of the probes from Figure 17A) or Figure 14 (optionally with some or all of the probes from Figure 17A), or Figure 17A.

The means (i) may comprise a suitable microarray, as described herein. The means (i) may comprise one or more pairs of sequencing primers suitable for sequencing one or more of the SNP variables to be determined.

The instructions (ii) typically comprise instructions to use the outcomes determined using the means (i) for the prediction of FMS and/or CFS phenotype. The instructions may comprise a chart showing risks of particular disease course occurring. The kit may include details of probability functions which may be used in diagnosis or prognosis, such as those described herein.

A kit may in some cases include a computer program as described herein.

A kit may include other components suitable for use in the present methods. For example, a kit may include primers suitable for amplification of target DNA regions containing the SNPs to be determined, such as those described herein. For example, a kit may contain one or more primer pairs listed in Figure 7 and/or Figure 13 and/or Figure 17B. A kit may also include suitable labelling and detection means, controls and/or other reagents such as buffers, nucleotides or enzymes e.g. polymerase, nuclease, transferase.

Nucleic acid according to the present invention, such as an oligonucleotide probe and/or pair of amplification primers, may be provided as part of a kit. The kit may include instructions for use of the nucleic acid, e.g. in PCR and/or a method for determining the presence of nucleic acid of interest in a test sample. A kit wherein the nucleic acid is intended for use in PCR may include one or more other reagents required for the reaction, such as polymerase, nucleosides, buffer solution etc. The nucleic acid may be labelled.

A kit for use in determining the presence or absence of nucleic acid of interest may include one or more articles and/or reagents for performance of the method, such as means for providing the test sample itself, e.g. a swab for removing cells from the buccal cavity or a syringe for removing a blood sample (such components generally being sterile).

Further aspects of the invention will now be illustrated with reference to the accompanying Figures and experimental exemplification, by way of example and not limitation. Further aspects and embodiments will be apparent to those of ordinary skill in the art. All documents mentioned in this specification are hereby incorporated herein by reference.

EXAMPLES

Example 1 Methods Selection of Patients

A cohort of 186 Spanish Caucasian women with FM and 217 Spanish Caucasian women with CFS were selected from the "Register of patients suffering from Fibromyalgia and Chronic Fatigue Syndrome" supported by Fibromyalgia and Chronic Fatigue Syndrome Foundation (www.fundacionfatiqa.org). These patients fulfilled the American College of Rheumatology clinical criteria for FM (1990) and the USA CDC Clinics clinical criteria for CFS (1994) (CDC'94) (ACR'90). CFS diagnosis was considered as exclusion criteria for suffering FM. None of the patients showed depression or other exclusion criteria. All of the patients involved in the study filled the Fibromyalgia Impact Questionnarie (FIQ) and the CDC CFS Sympton Inventory (CDC-CFS or CSI).

The starting point of the disease was in all cases at least 5 years ago and the clinical diagnosis was made at least 3 years ago.

Clinical characteristics of the FMS and CFS patients included in the present study are shown in Table 1 (Figure 4).

Since the inventors were aware that the SNP rs4680-COMT was associated with aggressive FMS phenotype (Spanish patent application number P200500249, Filed 8 February 2005, Applicant: INSTITUT FERRAN DE REUMATOLOGfA₁ SL NIF-EU: ES-B25492398) this marker was used for determine those FMS patients with an aggressive phenotype. A value of FIQ=76 showed statistically significant differences in the genotypical distribution of SNP rs4680-COMT between FMS patients above FIQ=76 and below FIQ=76. Therefore, this cutoff was selected for the clinical diagnosis of aggressive phenotype. This cut off is in concordance with the worldwide accepted clinical criteria of FIQ>70 as indicator of FM aggressiveness. By these means, 68 patients showed an aggressive FM phenotype.

No general CDC_CFS-based criteria have been accepted to design aggressive CFS phenotype. Therefore, percentile 85 (CDC__CFS=84) of the normal distribution of CDC-CFS score was considered as the cut off for clinical diagnosis of aggressive CFS phenotype. By these means, 31 patients showed an aggressive CFS phenotype.

The medical-ethics committee of Clfnica CIMA approved the study, and the participants gave written informed consent. The study has followed the ethical issues of the The World Medical Association's Declaration of Helsinki. Genotvpinα bv the Fibro DNA array

Venous blood samples were collected into tubes containing anticoagulant to obtain genomic DNA. DNA was isolated from peripheral blood cells using the salting out method (Miller et al., 1988)

The methodology for genotyping based on DNA arrays has been previously reported (Tejedor et al., 2005). Briefly, two pairs of oligonucleotides were designed for the detection of each SNP to ensure the accuracy of SNP detection. Each probe pair consisted of a probe specific for the A allele and a probe specific for the B allele. Several replicates of each oligonucleotide probe were spotted on aminosilane-coated glass slides (UltraGAPs, Corning) using a Microgrid Il robotic spotter (BioRobotics). The length of the oligonucleotides ranges from 17 to 25 nucleotides with the target polymorphic nucleotide located in the central position of the oligonucleotide in order to maximize hybridization specificity (Figure 6)

Target DNA for hybridisation was prepared in 3 independent multiplex amplification reactions, each of which contained 15, 10 and 11 separate primer pairs respectively (Figure 7). The multiplex amplification reactions were performed simultaneously using the same thermocycling program, allowing amplification of 36 DNA fragments. Each multiplex amplification reaction was performed using genomic DNA as template, and the appropriate primer pairs. The amplification reaction was performed using an initial denaturation at 95⁰C for 15 min, followed by 45 cycles of denaturation at 95⁰C for 30 s, primer annealing at 62⁰C for 90 s, and primer extension at 72⁰C for 90 s, after the final amplification cycle primer extension was extended to 10 min at 72⁰C. The sizes of the fragments amplified (amplicons) ranged from 100 to 400 bp.

Following amplification the resulting products were fragmentated by digestion with DNAase I, and terminal transferase (Roche) was used to catalyse the addition of a biotinylated nucleotide (Perkin Elmer) to the 3'-hydroxyl termini of the double stranded DNA fragments.

Hybridization was carried out automatically at 45⁰C for 1 h in a Ventana Discovery station using ChipMap hybridization buffers and the protocol for the Microarray 9.0 Europe station (Ventana Medical Systems). Following labelling the biotinylated DNA fragments were allowed to hybridise to the array in the automated hybridization station and stained with Cy3-conjugated streptavidin (Amersham Biosciences). Prior to scanning the DNA arrays were washed in order to remove non- specifically bound Cy3 molecules.

DNA array images were captured by use of a GenePix Pro 4100 confocal fluorescent scanner (Axon), equipped with a green laser (543 nm for Cy3 excitation). Absolute values of the Cy3 hybridization signal from each oligonucleotide probe were obtained by use of Gene Pix Pro Acuity 4.0 software (Axon). After scanning and quantifying the hybridization signals from the array, the export file from the scanner was processed with the genotyping software MG v1.0 (Tejedor D et al 2005). The ratio of the hybridization signal mean of the A allele to the sum of hybridization signal means of the A and B alleles was then defined for the 2 pairs of oligonucleotides used for genotyping each SNP (ratios 1 and 2). The specificity and sensitivity of the DNA array were assessed by use of at least 10 control DNA samples for each genotype group, identified previously by nucleotide sequence analysis in at least one of the ten cases for each genotype. These DNA control samples were used to determine the 2 ratio values corresponding to the 3 clusters (AA, AB and BB). In the present study, MG 1.0 software was used to determine to which of the previously defined clusters each of the 403 samples belonged.

Statistical Analysis

Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 13.0. A χ² test was performed in order to determine that distribution of the genetic polymorphisms under analysis were in Hardy-Weinberg equilibrium. Genotype-phenotype associations were analysed by means of multivariate logistic regression with clinically determined disease phenotypes as dependent variables and the SNPs as independent variables. To evaluate the impact of the SNPs included in the DNA array in the prognosis of the analysed phenotypes, the sensitivity, specificity, and positive likelihood ratio (LR+ = sensitivity/(1 -specificity)) were computed by means of Receiver Operating Characteristic curves. Comparisons of mean probability function values between each of the compared phenotypes were performed using a t-test. The threshold for statistical significance was predefined as a p level of ≤0.05.

Results

1.1. Specificity and sensitivity of Fibro DNA array as a qenotvpinq tool The specificity and sensitivity of this genotyping DNA array technology are 99,7% and 99,9%, respectively, as has been described previously (Tejedor et al, 2005). Three independent genotyping clusters were obtained for each SNP analyzed.

.1.2. Genotype-Phenotvpe Associations All genotype distributions of FMS and CFS patients were in Hardy-Weinberg equilibrium.

Genotype-phenotype associations were analysed by means of logistic regression including as the dependent variable the clinically determined disease phenotype (described in 1.2.1, 1.2.2 and 1.2.3) and as independent variables the SNPs shown in Table 2 (Figure 5). Probability functions were obtained for each phenotype analysed. Informative SNPs included in each probability function are shown in Table 2 (Figure 5). 1.2.1. Genetic discrimination between FM and CFS

To identify predictors for genetic discrimination of FM and CFS, a logistic regression analysis was performed with the presence of both diseases as dependent variable.

The probability function FMvsCFS compares patients suffering from FM (1) against patients suffering from CFS (0). The variables included in the FMvsCFS function are indicated in Table 2.

The sensitivity and specificity values of FMvsCFS were 71% and 95%, respectively, with a positive likelihood ratio (LR+) of 15.4. The positive predictive value (PPV) was 93% and the negative predictive value (NPV) was 77%. The 1 and 2 patients are also represented in the box plot Figure 1. Comparing mean probability function values FMvsCFS, statistically significant differences were found between both subgroups: 1 vs 0, P<0.05.

1.2.2. Genotvpe-Phenotype Associations in FM To identify predictors for phenotype variability in FM, a logistic regression analysis was performed with aggressive FMS phenotype as clinical dependent variable.

The statistical analysis showed that the SNPs included in the probability function FWI (Table 2) were able to predict a severe FM phenotype with a sensitivity of 62% at a specificity of 95% (LR+=12). The PPV was 93% and the NPV was 71%. Comparing the mean probability function values of FM from patients with and without aggressive phenotype, a statistically significant difference was found (P<Q.O5) (Figure 2).

1.2.3. Genotype-Phenotype Associations in CFS To identify predictors for phenotype variability in CFS, a logistic regression analysis was performed with aggressive CFS phenotype as clinical dependent variable.

The statistical analysis showed that the SNPs included in the probability function CFS (Table 2) were able to predict a severe FM (CFS) phenotype with a sensitivity of 90% at a specificity of 95% (LR+=18,7). The PPV was 95% and the NPV was 90%. Comparing the mean probability function values of CFS from patients with and without aggressive phenotype, a statistically significant difference was found (P<0.05) (Figure 3).

Discussion Currently diagnosis and phenotypic classification of patients with FM and CFS is based on clinical criteria (American College of Rheumatology and USA CDC clinics). Although the diseases are completely different, many patients receive both clinical diagnoses. The Fibro-chip described in the present study is the first low-density DNA array for diagnosis and prognosis of FM and CFS based - Al -

on hybridisation to allele-specific oligonucleotide probes that is able to screen for such a large number of SNPs.

The present methods using Fibrochip allow a powerful discrimination between the diseases, thus allowing accurate disease diagnosis. The present methods using Fibro-Chip also provides the first tool for selecting those patients with more aggressive phenotype.

Although the diagnostic and prognostic methods of the invention have been described above in relation to models based on the Table 3 variables, the invention also describes predictive and diagnostic models based on the Table 2 SNPs. In general therefore the present invention may also provide a method for diagnosing or prognosing a FM or CFS phenotype in a subject comprising the step of determining outcomes for variables listed in Table 2 for that subject.

The method may be used to differentially diagnose FM and CFS using variables selected from the FMvsCFS variables in Table 2, and/or to diagnose or prognose development of aggressive disease behaviour in FM, using variables selected from the FM variables in Table 2, and /or to diagnose or prognose development of aggressive disease behaviour in CFS, using variables selected from the CFS variables in Table 2.

The methods may comprise determining outcomes for all of the FMvsCSF, FM or CFS variables listed as informative in Table 2. It is believed that this will produce the most accurate prediction of disease phenotype.

However as described herein in relation to the methods based on the SNPs in Table 3, it is envisaged that the diagnostic method may be carried out (to a lower degree of accuracy) using fewer than the listed variables or SNP variables.

For example, Table 2 lists 18 FMvsCFS SNP variables which are informative for discriminating FM from CFS. Preferably the present method comprises determining outcomes for all 18 FMvsCFS SNP variables, and predicting the phenotype on the basis of these outcomes. However, in some cases, the method may comprise determining outcomes for as few as 8 or 10 of these variable - the minimum number of variables being the number that allow a discrimination power significantly greater than the discrimination power provided by chance (Press's Q test)(as above).

At least one of the FMvsCFS, FM and/or CFS variables is tested in the present methods.

In the case of the FMvsCFS variables, it is preferred that at least 2, for example at least 3, 4, 5, 6,

7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or all 18 variables are tested for outcomes and used in the prediction. In the case of the FM variables, it is preferred that at least 2, for example at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or all 13 of the variables are tested for outcomes and used in the prediction. In the case of the CFS variables, it is preferred that at least 2, for example, at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all 14 variables are tested for outcomes and used in the prediction.

In one instance, the method comprises diagnosing or prognosing more than one FM or CFS phenotype. For example, the method may comprise determining outcomes for FMvsCFS, FM and/or CFS variables selected as above or any combination thereof. The method may comprise determining an outcome for each of the variables in Table 2. Thus, for example, it may be possible to simulataneously test a subject for FM or CFS and at the same time determine the likelihood of development of an aggressive disease course.

In general the methods involve genotyping at least one SNP.

Aspects described herein for the methods using the Table 3 variables and models also relate to the Table 2 models and their use. For example, as described herein in relation to the methods based on the Table 3 variables, other factors may be determined and the methods may be used in conjunction with clinical tests. However, the variables in Table 2, selected in type and number as above, may be sufficient for the prediction.

Similarly the invention also envisages methods of deriving a probability function for use in diagnosing or prognosing FM or CFS phenotype, comprising:

(i) providing a population of individuals each of known clinically determined phenotype;

(ii) determining ex vivo the outcomes of a set of variables for each individual in the population;

(iii) applying multiple logistic regression analysis to the outcomes obtained in (ii) and the known phenotypes obtained in (i); and (iv) thereby deriving a probability function which produces a statistically significant separation of individuals of different phenotype in the population; wherein:

(a) the phenotype is suffering FM vs suffering CFS and the set of variables is selected from the set of FMvsCFS variables in Table 2; (b) the phenotype is aggressive disease behaviour in FM and the set of variables is selected from the set of FM variables in Table 2; and/or

(c) the phenotype is aggressive disease behaviour in CFS and the set of variables is selected from the set of CFS variables in Table 2.

The invention may also relate to the associated computational methods, computer programs and computer systems described herein, and to the probability functions derived and their use. Example 2 Methods

Study design

The individuals included in the current analysis were chosen randomly among the individuals register in the Spanish "Fibromyalgia and/or Chronic Fatigue Syndrome patients Record" (www.fundacionfatiga.orq/reαistro pacientes.htm). In a first stage (study 1) 2000 subjects from all around Spain diagnosed with FMS, CFS or both were invited to participate in the study, of whom 1371 gave written consent to take part and filled in a questionnaire which included details about their diagnosis, phenotypic characteristics, inherited diseases and presence of mental disorders. To ensure proper diagnosis, they had to fulfil the American College of Rheumatology (ACR) classification for FM¹⁸ or the US Centers for Disease Control criteria for CFS developed by Fukuda et a/.¹⁹. They also answered the Fibromyalgia Impact Questionnaire¹² [Bennett, 2005²⁰] and the CDC 2005 Symptom Inventory [Wagner, 2005¹³] for CFS and were ask to provide a blood sample for DNA extraction.

Taking into account that there is a recognized gender bias in FIQ²⁰, only women were included in the study. Further more stringent inclusion criteria - only including women who fulfilled the strict ACR'90 and CDC'94 definition and excluding patients with abnormal psychiatric profiles - reduced the recruited subject number to 403 (186 FMS patients, age: 45-54 years and 217 CFS patients, age 30-39 years). Later, a second recruitment using comparable inclusion/exclusion criteria was carried out (n=282; 126 FMS patients and 156 CSF patients) to obtain samples to validate the results obtained in the first study. The first set of patients (study 1) was used to explore the data and develop the models; the second set (validation study) was used to test them.

In addition, 240 women from the recruitment carried out for study 1 that suffered pains and/or fatigue but did not fit into the FMS and/or CFS criteria were used as non-FMS and non-CFS controls.

Both studies adhered to the Helsinki Declaration (World Medical Association) and the EMEA (European Medicines Agency) recommendations, and were approved by the "Clinica CIMA" (Barcelona, Spain) and the "National DNA Bank" (Salamanca, Spain) Ethical Committees. All participants gave written informed consent.

Stratification of disease severity Two validated questionnaires (for FMS and CFS respectively) were used to assess the level of disease severity. To capture the overall effect of FMS symptomatology the FIQ with the 1997 and 2002 modifications was used to categorise FMS patients. The total values ranged from 0 to 100, with 100 representing the most severe cases¹²' ²⁰. For CFS patients the CSI is the recommended tool to document the occurrence, duration and severity of the CFS symptom complex²¹. Its subscale, Case Definition Score, reflects the frequency and intensity of symptoms according to the diagnostic criteria. We followed the evaluation criteria defined in it; the values range between 0 and 128, where 128 represents the highest severity¹³.

Based on experience²² and in accordance with other research teams we applied a cut-off value defining two sub-groups (severe vs. mild/moderate) for each disease, with the aggressive forms corresponding to the upper third of the scale (FIQ>76 and CSI>84)¹³' ²⁰' ^Z3. Using these criteria, 57.6 % (Λ/=68) and 15.8 % (Λ/=31) of the patients were classified as severely affected by FM and CFS respectively.

Genotypinq and Single Nucleotide Polymorphism (SNP) selection

Peripheral blood (10 ml) was obtained from each patient, placed in an EDTA-treated tube. Plasma DNA was extracted with the QIAamp DNA Blood MiniKit (Qiagen) following the manufacturer's specifications. Genotyping was carry out by SNPIex™ ²⁴ technology in the National Genotyping Center (Barcelona, Spain). A total of 107 SNPs belonging to neurotransmitters (dopamine and serotonin), Propiomelanocortin (POMC), Thioredoxin reductase, Glucocorticoid receptors, lnterleukins (IL), Nitric Oxide Synthetase (NOS), Tumor Necrosis Factor (TNF), Corticotropin receptors, Catechol-O-Methyltransferase (COMT) and Tryptophan hydroxylase (TPH) genes were genotyped for each patient. The SNP selection was based on previous published data^25"29, emerging pharmacological therapies^30"32 and our own research expertise²². The SNP selection in those genes was based on a minor allele frequency of 0.1, an homogeneous distribution along the gene and location inside the exons or near to them. Only "TagSNPs" (R²<0.8) were taken into account as this gave more statistical power by reducing the degrees of freedom (df) of our tests,

From the 107 SNPs genotyped, only one fraction was included in the stepwise logistic regression analysis to limit the overall false-positive rate. The sets of loci eventually included in the models were chosen according to the suggestions published by Hoh J. et a/.³³.

First of all, chi-squared (χ²) tests were performed in order to test the conformity with Hardy- Weinberg expectations (HWE) of the genetic polymorphisms under analysis. Tests of HWE were carried out for all loci among all the different phenotypes described. Only SNPs that conformed to HWE in both separate groups under analysis were included in the study. SNPs with extremely high deviations from the predictions of HWE (p values lower than 0.01) were excluded from the analysis since such deviations could indicate problems such as genotyping errors.

In addition, single locus association tests between SNP allele frequency (allelic associations) and patient status were carried out using the standard contingency χ² test, and p-values were determined, including Bonferroni correction for multiple testing. The possibility that deviations from HWE in our overall population (both phenotypes under analysis together) could be important in disease causation was also investigated by combining the effect of the allelic association and total HWE. The product of the HWE p-value and the allelic association p-value was used to rank the SNPs in order of importance. The ones with the smallest p-values were included in the regression analysis.

All the genetic analyses were carried out using HelixTree® software (Golden Helix, Inc., Bozeman, MT, USA).

Population stratification was tested using Partition software {http://www.genetix.univ- montp2. fr/partition/partition. htmf*.

Statistical modelling The statistical analysis was carried out between the FM and CFS patient groups to attempt discrimination both between the diseases, and within each group. Three different models were evaluated: one model to predict a profile to distinguish between FM and CFS (model 1); one model to predict FM prognosis (model 2) (FIQ>76 vs. FIQ≤76); and a final model (model 3) to predict CFS prognosis (CSI>84 vs. CSI≤84).

Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS Inc. Headquarters, Chicago, IL, USA) version 14.0.

Multiple genotype-phenotype associations were analysed by means of multivariate logistic regression (backward LR) with clinically determined disease phenotypes as dependent variables and the individual loci as independent variables³⁵. The goodness of fit of the models was evaluated using Hosmer-Lemeshow statistics and their accuracy was assessed by calculating the area under the curve (AUC) of the Receiver Operating Characteristic curve (ROC) with 95% confidence intervals. The explained variability of the models on the basis of the SNPs was evaluated by means of the R² Nalkerke. To measure the impact of the SNPs included in the models of the analysed phenotypes, the sensitivity, specificity, and positive likelihood ratio (LR+ = sensitivity/(1 -specificity)) were computed by means of ROC curves. Comparisons of mean probability function values between each of the compared phenotypes were performed using a f-test. The threshold for statistical significance was predefined as a p-level of 0.05.

Haplotvpe analysis

Estimation of haplotype frequencies for the SNPs included in the models was performed using the method of maximum likelihood from genotype data through the Expectation/Maximization (EM) algorithm (HelixTree®, Golden Helix, Inc., Bozeman, MT, USA). In addition, haplotype trend regression analysis was also carried out and compared to their single-locus allelic associations (χ² tests).

Results

The presence of population substructure in any genotype-phenotype association study can severely jeopardize its power and efficiency by generating false associations. In our study we recruited patients from all over the country to ensure homogeneous distribution of the sample population. Analysis of stratification with Partition software confirmed that we had a panmictic population (data not shown).

To differentiate between FM and CFS (model 1) 20 predictors SNPs were entered into the backward LR model. Five were excluded and the model based on the remaining 15 SNPs fitted the data well (p=0.897, Hosmer-Lemeshow statistic). For model 2 (FIQ>76 vs. FIQ<76) 13 predictors SNPs were entered in the model and 8 were retained (p=0.947, Hosmer-Lemeshow statistic). In model 3 (CSI>84 vs. CSI <84) only 6 out of the 11 SNPs initially included in the model remained (p=0.763, Hosmer-Lemeshow statistic). Information regarding the SNPs remaining in each function is shown in Table 3 (Figure 8). The contribution of genetic factors to FM and CFS can be further demonstrated by the substantial proportion of variance (R² Nagelkerke) explained by genetic factors (57.2% for model 1 ; 59.5% for model 2 and 52.7% for model 3).

Probability functions were obtained for each phenotype analysed and presented as box plots in Figure 10. Comparing mean probability function values, statistically significant differences were found between all subgroups: FM vs. CFS, p<0.0001; FIQ>76 vs. FIQ<76, p=0.0001; CSI>84 vs. CSI<84, p<0.0001. The sensitivity, specificity and positive likelihood ratios (LR+) of all the models are given in Table 4.

Haplotype regression analysis (Table 5) and frequency^' estimation (Table 6) for various marker combinations included in the models were estimated for each of the phenotypes separately. Table 5 displays the results of the overall haplotype association to the disease (via regression analysis) and compares it to the association of each individual locus (via χ² tests).

To confirm the validity of the three models described above a second independent study (validation study) was carried out. Both probability functions (Figure 10) and ROC curves (Table 4) were obtained. A comparison of the ROC-AUCs³⁷ of the first study and the validation study revealed no significant difference between the two models (Table 4).

In addition, a genetic analysis of case/control data was carried out to attempt a model for FMS and CFS diagnosis in the general population. For this analysis a group of 240 women recruited in Study 1 but who did not fit the criteria of strict ACR'90 and CDC'94 were chosen to participate as controls, and were compared to either FMS or CFS patients. The SNP selection criteria and statistical analysis was carried out as described before.

When we tried to predict the milder forms of FMS or CFS, the proportion of explained variance by genetic factors in the models was very low (18.1% and 30.5% respectively), as were their ROC- AUCs and LR+ (Table 4). The sensitivity of these models, which refers to the proportion of people with disease who have a positive test result, is very low, making these models ineffective for application as a diagnostic tool.

However, excellent discriminating models were obtained when the most aggressive cases (FIQ>76 and CDC>84) were analysed (Table 4). Not only the ROC-AUCs and LR+ obtained were very high, but also the amount of variation explained by genetic factors in the models was of 75.2 % and 69.7% for the aggressive forms of FMS and CFS respectively. The SNPs that define the models fitted well (Hosmer-Lemeshow statistic: p=0.820 for FIQ>76 and p=0.989 for CDC>84), and are shown in Table 3. Probability functions are illustrated in Figure 10.

The inventors also derived probability functions for determining each of the phenotypes described herein, using "minimal SNPs" for each phenotype. The minimal SNPs are selected from the lists in Table 3 for each phenotype and are described herein for each phenotype. The probability functions were calculated based on data from the population of 403 individuals and validated in the population of 282. The results (sensitivity, specificity and LR+ values) obtained by the inventors using the probability functions are described herein in relation to each phenotype. Details for calculation of probability functions from the minimal SNPs are given in Figure 15.

With these models it has been demonstrated that it is possible that a few genes can explain a major proportion of a complex disease and that genetic profiling can be an accurate discriminative and predictive tool for the most aggressive forms of FMS and CFS

Discussion

Even though significant differences in the prevalence of FMS and CFS (2-4 % for FM and 0.2-0.5 % for CFS) have been reported in almost all studies, many publications suggest an important overlap (40 - 60 %) between the two syndromes³⁸' ³⁹ indicating contradictions in the interpretation of the data.

The definition criteria for cases of FMS and CFS put particular emphasis on the severity of the symptoms to perform a diagnosis, instead of defining the diseases by their characteristics. Thus, in FMS widespread pain and sensitivity to pressure on specific tender points will lead us to the diagnosis¹⁸; in CFS major symptoms will be abnormal levels of physical and cognitive fatigue, with a high impact on the pre-morbid activities of the patient¹⁹. In this study 15 SNPs have been identified which could be used to discriminate between FMS and CFS patients with a high LR+. Likelihood ratios are a useful and practical way of expressing the power of diagnostic tests⁴⁰. In the three models shown in Figure 10 the inventors obtained LR+ ratios higher than 10, evidence of the capacity of the SNP combinations to predict each phenotype. The high ROC-AUCs obtained for all the models (>0.89) provides further evidence for the high discriminatory power of the SNP combinations used. The usefulness of the ROC-AUC magnitude as a tool for evaluating the strength of the relationship between genotypes and disease has been described previously³⁸, Using these SNPs to obtain a genetic profile of the patient therefore provides an extra tool for the physician to differentiate between the two diseases.

The stratification of the symptoms forms part of the diagnosis of both diseases and it is necessary for a correct therapeutic and prognostic orientation. The definition of disease subtypes using self referring tests requires underpinning with biological data¹¹. Up to date self referring questionnaires have been successfully used to assess the severity of the diseases. The models described herein are suitable for differentiating between severe and milder phenotypes (prognosis) of these diseases, e.g. in a female Spanish population. The models therefore allow identification of well defined patient subtypes. It is also important to address the fact that most of the predictors identified for the FMS prognosis were found in chromosomes 2, 17 and 22, whereas CFS predictors were found mostly in chromosomes 7 and 11, and that in all models more than 50% of the variation was explained by genetic factors. Therefore these models not only validate the usefulness of the self referring questionnaires for the stratification of FM and CFS, but also demonstrate for the first time the great differences in the genetic components of the two diseases.

To further test the validity of the SNPs, haplotype analysis was carried out. In all cases the haplotypes were more significant than single-locus associations, which highlights the fact that SNP combinations give more powerful models.

The present findings confirm that we are faced with two complex syndromes, of exclusively clinical definition until now, and indicate that there are well defined subtypes at both clinical and genetic level. References

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Claims

1. A method of diagnosing or prognosing a fibromyalgia syndrome (FMS) or chronic fatigue syndrome (CFS) phenotype in a subject, which comprises: (i) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs5746847, rs3794808 and rs2020942; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to differentially diagnose between FMS and CFS in the subject; and/or

(ii) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs6713532, rs11246226 and rs7224199; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to prognose FMS disease development in the subject; and/or

(iii) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs1474347 and rs489736; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to prognose CFS disease development in the subject; and/or

(iv) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs3794808 and rs11246226; thereby determining outcomes for each of the SNPs; and

(b) using the combination of outcomes determined in step (a) to diagnose severe FMS phenotype in the subject; and/or

(v) (a) determining the identity of the nucleotide in the genomic DNA of the subject at each of the following positions of single nucleotide polymorphism (SNPs): rs2020942 and rs1474347; thereby determining outcomes for each of the SNPs; and (b) using the combination of outcomes determined in step (a) to diagnose severe CFS phenotype in the subject.

2. A method of diagnosing or prognosing a fibromyalgia syndrome (FMS) or chronic fatigue syndrome (CFS) phenotype in a subject which comprises (i), (ii) and/or (iii) as defined in claim 1 and wherein the subject is clinically diagnosed as suffering from FMS or CFS.

3. A method of diagnosing or prognosing a fibromyalgia syndrome (FMS) or chronic fatigue syndrome (CFS) phenotype in a subject which comprises (iv) and (v) as defined in claim 1 wherein the subject is symptomatic for FMS or CFS.

4. A method according to any of the preceding claims wherein in part (i) step (a) additionally comprises determining outcomes for one or more SNPs selected from rs6713532, rs10194776, rs1549339, rs2168631 , rs2229094, rs1800797, rs2770296, rs2297518, rs933271 , rs4680, rs165815 and rs165774.

5. A method according to any of the preceding claims wherein in part (i) step (a) additionally comprises determining outcomes for one or more SNPs selected from rs1800797, rs2297518, rs2229094, rs2168631 , rs933271 , rs2770296, rs165774, rs10194776 and rs6713532.

6. A method according to any of the preceding claims wherein in part (i) step (a) comprises determining outcomes for all of: rs5746847, rs3794808, rs2020942 rs1800797, rs2297518, rs2229094, rs2168631 , rs933271, rs2770296, rs165774, rs10194776 and rs6713532.

7. A method according to any of the preceding claims wherein in part (i) step (a) comprises determining outcomes for all of: rs6713532, rs10194776, rs1549339, rs2168631 , rs2229094, rs1800797, rs2770296, rs2020942, rs3794808, rs2297518, rs5746847, rs933271, rs4680, rs165815 and rs165774.

8. A method according to any of the preceding claims wherein in part (i), the subject is symptomatic for meets the criteria to be clinically diagnosed as suffering from, or has been clinically diagnosed as suffering from, FMS or CFS.

9. A method according to any of the preceding claims wherein in part (ii) step (a) additionally comprises determining outcomes for one or more SNPs selected from rs10194776, rs324029, rs3794808, rs165774 and rs4680.

10. A method according to claim 9 wherein in part (ii) step (a) additionally comprises determining outcomes for one or more SNPs selected from rs324029 and rs3794808.

11. A method according to any of the preceding claims wherein in part (ii) step (a) comprises determining outcomes for: rs6713532, rs11246226, rs7224199 rs324029 and rs3794808.

12. A method according to any of the preceding claims wherein in part (ii) step (a) comprises determining outcomes for: rs10194776, rs6713532, rs324029, rs11246226, rs7224199, rs3794808, rs165774 and rs4680.

13. A method according to any of the preceding claims wherein in part (ii), the subject is symptomatic for, meets the criteria to be clinically diagnosed as suffering from, or has been clinically diagnosed as suffering from, FMS.

14. A method according to any of the preceding claims wherein in part (iii) step (a) additionally comprises determining outcomes for one or more SNPs selected from rs10488682, rs11246226, rs2020942 and rs2284217.

15. A method according to any of the preceding claims wherein in part (ii) step (a) comprises determining outcomes for rs10488682, rs11246226, rs2020942, rs1474347, rs2284217and rs489736.

16. A method according to any of the preceding claims wherein in part (iii), the subject is symptomatic for meets the criteria to be clinically diagnosed as suffering from, or has been clinically diagnosed as suffering from, CFS.

17. A method according to any of the preceding claims wherein in part (iv) step (a) additionally comprises determining outcomes for one or more SNPs selected from rs10194776, rs6713532, rs2770296, rs7224199, rs165774, rs4680 and rs2428721.

18. A method according to any of the preceding claims wherein in part (iv step (a) comprises determining outcomes for rs10194776, rs6713532, rs11246226, rs2770296, rs7224199, rs3794808, rs165774, rs4680 and rs2428721.

19. A method according to any of the preceding claims wherein in part (iv), the subject is asymptomatic for or not clinically diagnosed as suffering from FMS.

20. A method according to any of the preceding claims wherein in part (v) step (a) additionally comprises determining outcomes for one or more SNPs selected from rs2168631, rs2284217, rs2069827 and rs11246226.

21. A method according to any of the preceding claims wherein in part (v) step (a) comprises determining outcomes for rs2168631 , rs1474347, rs2284217, rs2069827, rs11246226 and rs2020942.

22. A method according to any of the preceding claims wherein in part (v), the subject is asymptomatic for or not clinically diagnosed as suffering from CFS.

23. A method according to any of the preceding claims wherein in any of parts (i), (ii), (iii), (iv) and/or (v), step (b) uses only the outcomes determined in step (a) to diagnose or prognose phenotype.

24. A method according to any of the preceding claims wherein in any of parts (i), (ii), (iii), (iv) and/or (v), step (a) of determining outcomes for SNP variables comprises microarray analysis or sequencing.

25. A method according to any of the preceding claims wherein in any of parts (i), (ii), (iii), (iv) and/or (v), step (a) of determining outcomes for SNP variables comprises the use of one or more oligonucleotide probe pairs listed in Figure 14 or Figure 17A.

26. A method according to any of the preceding claims wherein in any of parts (i), (ii), (iii), (iv) and/or (v), step (a) of determining outcomes for SNP variables comprises the use of a microarray comprising one or more oligonucleotide probe pairs listed in Figure 14 or Figure 17A..

27. A method according to claim 26 wherein the microarray comprises one or more of the 4-probe sets listed in Figure 14 or Figure 17A.

28. A method according to claim 27 wherein the microarray comprises the probes listed in Figure 14 or Figure 17A.

29. A method according to any one of the preceding claims wherein in any of parts (i), (ii), (iii), (iv) and/or (v), step (a) of determining outcomes for SNP variables comprises amplification of nucleic acid obtained from the subject.

30. A method according to claim 29 which comprises use of one or more oligonucleotide primer pairs listed in Figure 13 or Figure 17B.

31. A method according to claim 30 which comprises use of the primer pairs listed in Figure 13 or Figure 17B.

32. A method according to any one of the preceding claims wherein in any of parts (i), (ii), (iii), (iv) and/or (v), step (b) comprises : (i) inputting the outcomes determined in step(a) into a probability function thereby calculating a probability function value; and

(ii) comparing the probability function value with probability function values calculated for individuals of known phenotype.

33. A method of selecting a suitable treatment for treating FMS or CFS in a subject, the method comprising:

(a) determining a FMS or CFS phenotype in the subject by a method according to any of the preceding claims; and (b) selecting a treatment which is suitable for the determined phenotype.

34. A method according to claim 33 wherein the treatment comprises providing a statement of laboral incapacity,

35. A method of treating FMS or CFS in a subject comprising:

(a) determining a FMS or CFS phenotype in the subject by a method according to any of claims 1 to 30; and

(b) administering to the subject a treatment which is suitable for the determined phenotype.

36. A microarray comprising oligonucleotide probes suitable for determining the allele in a sample nucleic acid at SNPs selected from: the FMS vs CFS discriminating SNPs in Table 3; and/or the FWlS prognosis discriminating SNPs in Table 3; and/or the CFS discriminating SNPs in Table 3; and/or the FMS severe diagnosis discriminating SNPs in Table 3; and/or the CFS severe discriminating SNPs in Table 3.

37. A microarray according to claim 36 wherein the said oligonucleotide probes make up at least 50% of the oligonucleotide probes on the array.

38. A microarray according to claim 36 or 37 wherein the said probes are selected from the probes in Figure 14 or Figure 17A.

39. A microarray according to any of claims 36 to 38 wherein the said probes comprise the oligonucleotide probes in Figure 14 or Figure 17A.

40. An oligonucleotide probe, probe pair, or 4-probe set listed in Figure 6, Figure 14 or Figure 17A.

41. An oligonucleotide primer or primer pair listed in Figure 7, Figure 13 or Figure 17B.

42. A kit for diagnosing or prognosing an FM and/or CFS phenotype in a subject by a method according to any one of the preceding claims, the kit comprising:

(i) means for determining the selected outcomes; and

(ii) instructions for determining the FM or CFS phenotype from the outcomes.

43. A kit according to claim 42 wherein the means comprise one or more probe pairs or 4-probe sets listed in Figure 14 or Figure 17A.

44. A kit according to claim 42 or 43 wherein the means comprises a microarray according to any of claims 36 to 39.

45. A kit according to any one of claims 42 to 44 which further comprises one or more oligonucleotide primer pairs for amplification of nucleic acid obtained from the subject.

46. A kit according to claim 45 wherein the primers comprise one or more of the primer pairs listed in Figure 13 or Figure 17B.

47. A method of deriving a probability function for use in determining a FMS or CFS phenotype in a subject, comprising:

(i) providing a study population of individuals, wherein each individual is of known clinically determined phenotype with respect to the FMS or CFS phenotype;

(ii) determining, for each individual in the population, the identity of the nucleotide in the individual's genomic DNA at each SNP in a set of SNPs, thereby obtaining a set of outcomes for each individual;

(iii) applying stepwise multiple logistic regression analysis to the outcomes obtained in (ii) and the known phenotypes referred to in (i); and

(iv) thereby deriving a probability function which produces a statistically significant separation between individuals of different phenotype in the population; wherein:

(a) the probability function is for distinguishing or differentially diagnosing FMS and CFS, and the set of SNPs is selected from the set of FMSvsCFS discriminating SNPs in Table 3;

(b) the probability function is for prognosing FMS disease development and the set of SNPs is selected from the set of FMS prognosis discriminating SNPs in Table 3;

(c) the probability function is for prognosing CFS disease development and the set of SNPs is selected from the set of CFS prognosis discriminating SNPs in Table 3;

(d) the probability function is for diagnosing severe FMS and the set of SNPs is selected from the set of severe FMS diagnosis discriminating SNPs in Table 3; and/or (e) the probability function is for diagnosing severe CFS and the set of SNPs is selected from the set of severe CFS diagnosis discriminating SNPs in Table 3.

48. A computational method of deriving a probability function for use in determining FMS or CFS phenotype in a subject which method comprises applying stepwise multiple logistic regression analysis to outcomes data and phenotype data obtained from a suitable study population of individuals, wherein each individual is of known clinically determined phenotype with respect to the FMS or CFS phenotype, thereby deriving a probability function which produces a statistically significant separation between individuals of different phenotype in the population; wherein: (i) the phenotype data comprises the known clinically determined phenotype of each individual; (ii) the outcomes data for each individual comprises the genotype of the individual at each SNP in a set of SNPs; and wherein:

(a) the probability function is for distinguishing or differentially diagnosing FMS and CFS, and the set of SNPs is selected from the set of FMSvsCFS discriminating SNPs in Table 3;

(b) the probability function is for prognosing FMS disease development and the set of SNPs is selected from the set of FMS prognosis discriminating SNPs in Table 3;

(c) the probability function is for prognosing CFS disease development and the set of SNPs is selected from the set of CFS prognosis discriminating SNPs in Table 3; (d) the probability function is for diagnosing severe FMS and the set of SNPs is selected from the set of severe FMS diagnosis discriminating SNPs in Table 3; and/or

(e) the probability function is for diagnosing severe CFS and the set of SNPs is selected from the set of severe CFS diagnosis discriminating SNPs in Table 3.

49. A method according to claim 47 or 48, wherein in (a) the set of SNPs comprises rs5746847, rs3794808 and rs2020942.

50. A method according to claim 49 wherein in (a) the set of SNPs comprises rs6713532, rs10194776, rs1549339, rs2168631, rs2229094, rs1800797, rs2770296, rs2020942, rs3794808, rs2297518, rs5746847, rs933271 , rs4680, rs165815 and rs165774.

51. A method according to any of claims 47 to 50 wherein in (b) the set of SNPs comprises rs6713532, rs11246226 and rs7224199.

52. A method according to any of claims 51 wherein in (b the set of SNPs comprises rs10194776, rs6713532, rs324029, rs11246226, rs7224199, rs3794808, rs165774 and rs4680.

53. A method according to any of claims 47 to 52 wherein in (c) the set of SNPs comprises rs1474347 and rs489736.

54. A method according to any of claims 47 to 53 wherein in (c) the set of SNPs comprises rs10488682, rs11246226, rs2020942, rs1474347, rs2284217and rs489736.

55. A method according to any of claims 47 to 54 wherein in (d) the set of SNPs comprises rs3794808 and rs11246226.

56. A method according to any of claims 55 wherein in (d) the set of SNPs comprises rs10194776, rs6713532, rs11246226, rs2770296, rs7224199, rs3794808, rs165774, rs4680 and rs2428721.

57. A method according to any of claims 47 to 56 wherein in (e) the set of SNPs comprises rs2020942 and rs1474347.

58. A method according to any of claims 57 wherein in (e) the set of SNPs comprises rs2168631 , rs1474347, rs2284217, rs2069827, rs11246226 and rs2020942.

59. A computer system comprising a processor and means for controlling the processor to carry out the computational method of any of claims 48 to 58.

60. A computer program comprising computer program code which when run on a computer or computer network causes the computer or computer network to carry out the computational method of any of claims 48 to 58.

61. A computer program according to claim 60 which is stored on a computer readable medium.

62. A method of diagnosing or prognosing a FMS or CFS phenotype in a subject comprising determining the genotype of the subject at one or more positions of single nucleotide polymorphism selected from the SNPs in Tables 2 and 3.

63. A method according to claim 62 which is for differentially diagnosing between FMS and CFS and wherein the one or more SNPs is selected from rs6713532, rs10194776, rs1549339, rs2168631 , rs2229094, rs1800797, rs2770296, rs2020942, rs3794808, rs2297518, rs5746847, rs933271, rs4680, rs165815 and rs165774.

64. A method according to claim 62 which is for prognosing FMS disease development and wherein the one or more SNPs is selected from rs10194776, rs6713532, rs324029, rs11246226, rs7224199, rs3794808, rs165774 and rs4680.

65. A method according to claim 62 which is for prognosing CFS disease development and wherein the one or more SNPs is selected from rs10488682, rs11246226, rs2020942, rs1474347, rs2284217and rs489736.

66. A method according to claim 62 which is for diagnosing severe FMS and wherein the one or more SNPs is selected from rs10194776, rs6713532, rs11246226, rs2770296, rs7224199, rs3794808, rs165774, rs4680 and rs2428721.

67. A method according to claim 62 which is for diagnosing severe CFS and wherein the one or more SNPs is selected from rs2168631, rs1474347, rs2284217, rs2069827, rs11246226 and rs2020942.

68. A method according to claim 62 which is for differentially diagnosing between FWIS and CFS wherein the one or more SNPs is selected from the SNPs having a P value of ≤O.Oδ in the FMSvs CFS column in Figure 8C and the SNPs having a P value of ≤0.05 in Figure 16A.

69. A method according to claim 62 which is for prognosing FMS disease development wherein the one or more SNPs is selected from the SNPs having a P value of ≤Q.0.5 in the FIQ<76vsFIQ>76 column in Figure 8C and the SNPs having a P value of ≤0.05 in Figure 16B.

70. A method according to claim 62 which is for prognosing CFS disease development wherein the one or more SNPs is selected from the SNPs having a P value of ≤O.Oδ in the CSI<84vsCSI>84 column in Figure 8C and the SNPs having a P value of ≤O.Oδ in Figure 16C.

71. A method according to claim 62 which is for differentially diagnosing between FMS and CFS wherein the one or more SNPs is selected from the SNPs having a bP* value of ≤0.05 in the "Discriminating SNPs for FMS vs CFS" section in Table 3 and the SNPs having a bP value of ≤O.Oδ in Figure 16A.

72. A method according to claim 62 which is for prognosing FMS disease development wherein the one or more SNPs is selected from the SNPs having a bP* value of ≤0.05 in the "Discriminating SNPs for FMS prognosis" section in Table 3 and the SNPs having a bP value of ≤0.05 in Figure 16B.

73. A method according to claim 62 which is for prognosing CFS disease development wherein the one or more SNPs is selected from the SNPs having a bP* value of ≤O.Oδ in the "Discriminating SNPs for CFS prognosis" in Table 3 and the SNPs having a bP value of ≤O.Oδ in Figure 16C.

74. A method of diagnosing or prognosing a FMS or CFS phenotype in a subject comprising determining the haplotype of the subject with respect to one or more of the haplotypes listed in Table 5 or 6.

76. A method according to claim 74 which is for differentially diagnosing between FMS and CFS and which comprises determining the haplotype of the subject with respect to hap 1 and/or hap2 and/or hap 3 listed in Tables δ and 6.

76. A method according to claim 74 which is for prognosing FMS disease development and which comprises determining the haplotype of the subject with respect to hap 4 and/or hapδ and/or hap 6 listed in Tables 6 and 6.

77. A method according to claim 74 which is for prognosing CFS disease development and which comprises determining the haplotype of the subject with respect to hap 7 and/or hap 8 listed in Tables 5 and 6.

78. A method according to any one of claims 1 to 35 or 47 to 58 which comprises use of a probability function derived using the data in any of the Tables in Figure 15.