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Objective Fibromyalgia (FM) represents a complex disorder that is characterized by widespread pain and tenderness and is frequently accompanied by additional somatic and cognitive/affective symptoms. Genetic risk factors are known to contribute to the etiology of the syndrome. The aim of this study was to examine >350 genes for association with FM, using a large-scale candidate gene approach. Methods The study group comprised 496 patients with FM (cases) and 348 individuals with no chronic pain (controls). Genotyping was performed using a dedicated gene array chip, the Pain Research Panel, which assays variants characterizing >350 genes known to be involved in the biologic pathways relevant to nociception, inflammation, and mood. Association testing was performed using logistic regression. Results Significant differences in allele frequencies between cases and controls were observed for 3 genes: GABRB3 (rs4906902; P = 3.65 × 10−6), TAAR1 (rs8192619; P = 1.11 × 10−5), and GBP1 (rs7911; P = 1.06 × 10−4). These 3 genes and 7 other genes with suggestive evidence for association were examined in a second, independent cohort of patients with FM and control subjects who were genotyped using the Perlegen 600K platform. Evidence of association in the replication cohort was observed for TAAR1, RGS4, CNR1, and GRIA4. Conclusion Variation in these 4 replicated genes may serve as a basis for development of new diagnostic approaches, and the products of these genes may contribute to the pathophysiology of FM and represent potential targets for therapeutic action. [ABSTRACT FROM AUTHOR]

In recent years, genome-wide association studies (GWAS) have uncovered a large number of susceptibility variants. Nevertheless, GWAS findings provide only tentative evidence of association, and replication studies are required to establish their validity. Due to this uncertainty, researchers often focus on top-ranking SNPs, instead of considering strict significance thresholds to guide replication efforts. The number of SNPs for replication is often determined ad hoc. We show how the rank-based approach can be used for sample size allocation in GWAS as well as for deciding on a number of SNPs for replication. The basis of this approach is the "ranking probability": chances that at least j true associations will rank among top u SNPs, when SNPs are sorted by P-value. By employing simple but accurate approximations for ranking probabilities, we accommodate linkage disequilibrium (LD) and evaluate consequences of ignoring LD. Further, we relate ranking probabilities to the proportion of false discoveries among top u SNPs. A study-specific proportion can be estimated from P-values, and its expected value can be predicted for study design applications. [ABSTRACT FROM AUTHOR]

The gene SCN9A is responsible for three human pain disorders. Non-sense mutations cause a complete absence of pain, whereas activating mutations cause severe episodic pain in paroxysmal extreme pain disorder and primary erythermalgia. This led us to investigate whether single nucleotide polymorphisms (SNPs) in SCN9A were associated with differing pain perception in the general population. We first genotyped 27 SCN9A SNP5 in 578 individuals with a radio- graphic diagnosis of osteoarthriiis and a pain score assessment. A significant association was found between pain score and SNP rs6746030; the rarer A allele was associated with increased pain scores compared to the commoner G allele (P = 0.016). This SNP was then further genotyped in 195 pain-assessed people with sciatica, 100 amputees with phantom pain, 179 individuals after lumbar discectomy, and 205 individuals with pancreatitis. The combined P value for increased A allele pain was 0.0001 in the five cohorts tested (1277 people in total). The two alleles of the SNP rs6746030 alter the coding sequence of the sodium channel Navi .7. Each was separately transfected into HEK293 cells and electrophysiologically assessed by patch-clamping. The two alleles showed a difference in the voltage-dependent slow inactivation (P = 0.042) where the A allele would be predicted to increase Nav1.7 activity. Finally, we genotyped 186 healthy females characterized by their responses to a diverse set of noxious stimuli. The A allele of rs6746030 was associated with an altered pain threshold and the effect mediated through C-fiber activation. We conclude that individuals experience differing amounts of pain, per nociceptive stimulus, on the basis of their SCN9A rs6746030 genotype. [ABSTRACT FROM AUTHOR]

The correlation between alleles at a pair of genetic loci is a measure of linkage disequilibrium. The square of the sample correlation multiplied by sample size provides the usual test statistic for the hypothesis of no disequilibrium for loci with two alleles and this relation has proved useful for study design and marker selection. Nevertheless, this relation holds only in a diallelic case, and an extension to multiple alleles has not been made. Here we introduce a similar statistic, R², which leads to a correlation-based test for loci with multiple alleles: for a pair of loci with k and m alleles, and a sample of n individuals, the approximate distribution of n(k- 1) (m-1)/(km)R² under independence between loci is X²(k-1)(m-1). One advantage of this statistic is thatitcan he interpreted as the total correlation between a pair of loci. When the phase of two-locus genotypes is known, the approach is equivalent to a test for the overall correlation between rows and columns in a contingency table. In the phase-known case, R² is the sum of the squared sample correlations for all km 2 X 2 subtables formed by collapsing to one allele vs. the rest at each locus. We examine the approximate distribution under the null of independence for R² and report its close agreement with the exact distribution obtained by permutation. The test for independence using R² is a strong competitor to approaches such as Pearson's chi square, Fisher's exact test, and a test based on Cressie and Read's power divergence statistic. We combine this approach with our previous composite-disequilibrium measures to address the case when the genotypic phase is unknown. Calculation of the new multiallele test statistic and its P-value is very simple and utilizes the approximate distribution of R². We provide a computer program that evaluates approximate as well as "exact" permutational P-values. [ABSTRACT FROM AUTHOR]

With the recent advances in high-throughput genotyping techniques, it is now possible to perform whole-genorne association studies to fine map causal polymorphisms underlying important traits that influence susceptibility to human diseases and efficacy of drugs. Once a genome scan is completed the results can be sorted by the association statistic value. What is the probability that true positives will l)e encountered among the first most associated markers? When a particular polymorphism is found associated with the trait, there is a chance that it represents either a "true" or a "false" association (TA vs. FA). Setting appropriate significance thresholds has been considered to provide assurance of sufficient odds that the associations found to be significant are genuine. However, the problem with genome scans involving thousands of markers is that the statistic values of FAs can reach quite extreme magnitudes. In such situations, the distributions corresponding to TAs and the most extreme FAs become comparable and significance thresholds tend to penalize TM and FM in a similar fashion. When sorting between true and false associations, the "typical" place (i.e., rank) of TAs among the most significant outcomes becomes important, ordered by the association statistic value. The distribution of ranks that we study here allows calculation of several useful quantities. In particular, it gives the number of most significant markers needed for a follow-up study to guarantee that a true association is included with certain probability. This can be calculated conditionally on having applied a multiple-testing correction. Effects of multilocus (e.g., haplotype association) tests and impact of linkage disequilibrium on the distribution of ranks associated with TM are evaluated and can be taken into account. [ABSTRACT FROM AUTHOR]

The genotyping of closely spaced single-nucleotide polymorphism (SNP) markers frequently yields highly correlated data, owing to extensive linkage disequilibrium (LD) between markers. The extent of LD varies widely across the genome and drives the number of frequent haplotypes observed in small regions. Several studies have illustrated the possibility that LD or haplotype data could be used to select a subset of SNPs that optimize the information retained in a genomic region while reducing the genotyping effort and simplifying the analysis. We propose a method based on the spectral decomposition of the matrices of pairwise LD between markers, and we select markers on the basis of their contributions to the total genetic variation. We also modify Clayton's "haplotype tagging SNP" selection method, which utilizes haplotype information. For both methods, we propose sliding window-based algorithms that allow the methods to be applied to large chromosomal regions. Our procedures require genotype information about a small number of individuals for an initial set of SNPs and selection of an optimum subset of SNPs that could be efficiently genotyped on larger numbers of samples while retaining most of the genetic variation in samples. We identify suitable parameter combinations for the procedures, and we show that a sample size of 50-100 individuals achieves consistent results in studies of simulated data sets in linkage equilibrium and LD. When applied to experimental data sets, both procedures were similarly effective at reducing the genotyping requirement while maintaining the genetic information content throughout the regions. We also show that haplotype-association results that Hosking et al. obtained near CYP2D6 were almost identical before and after marker selection. [ABSTRACT FROM AUTHOR]

Abstract: Human association studies of common genetic polymorphisms have identified many loci that are associated with risk of complex diseases, although individual loci typically have small effects. However, by envisaging genetic associations in terms of cellular pathways, rather than any specific polymorphism, combined effects of many biologically relevant alleles can be detected. The effects are likely to be most apparent in investigations of phenotypically homogenous subtypes of complex diseases. We report findings from a case-control, genetic association study of relationships between 2925 single nucleotide polymorphisms (SNPs) and 2 subtypes of a commonly occurring chronic facial pain condition, temporomandibular disorder (TMD): 1) localized TMD and 2) TMD with widespread pain. When compared to healthy controls, cases with localized TMD differed in allelic frequency of SNPs that mapped to a serotonergic receptor pathway (P =0.0012), while cases of TMD with widespread pain differed in allelic frequency of SNPs that mapped to a T-cell receptor pathway (P =0.0014). A risk index representing combined effects of 6 SNPs from the serotonergic pathway was associated with greater odds of localized TMD (odds ratio 2.7, P =1.3 E-09), and the result was reproduced in a replication case-control cohort study of 639 people (odds ratio 1.6, P =0.014). A risk index representing combined effects of 8 SNPs from the T-cell receptor pathway was associated with greater odds of TMD with widespread pain (P =1.9 E-08), although the result was not significant in the replication cohort. These findings illustrate potential for clinical classification of chronic pain based on distinct molecular profiles and genetic background. [Copyright &y& Elsevier]