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Simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association study of rheumatoid arthritis.

ABSTRACT: The availability of very large number of markers by modern technology makes genome-wide association studies very popular. The usual approach is to test single-nucleotide polymorphisms (SNPs) one at a time for association with disease status. However, it may not be possible to detect marginally significant effects by single-SNP analysis. Simultaneous analysis of SNPs enables detection of even those SNPs with small effect by evaluating the collective impact of several neighboring SNPs. Also, false-positive signals may be weakened by the presence of other neighboring SNPs included in the analysis. We analyzed the North American Rheumatoid Arthritis Consortium data of Genetic Analysis Workshop 16 using HLasso, a new method for simultaneous analysis of SNPs. The simultaneous analysis approach has excellent control of type I error, and many of the previously reported results of single-SNP analyses were confirmed by this approach.

SUBMITTER: Mathew G

PROVIDER: S-EPMC2795881 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association study of rheumatoid arthritis.

Mathew George G Xu Hongyan H George Varghese V

BMC proceedings 20091215

The availability of very large number of markers by modern technology makes genome-wide association studies very popular. The usual approach is to test single-nucleotide polymorphisms (SNPs) one at a time for association with disease status. However, it may not be possible to detect marginally significant effects by single-SNP analysis. Simultaneous analysis of SNPs enables detection of even those SNPs with small effect by evaluating the collective impact of several neighboring SNPs. Also, false ...[more]

PMID: 20017974

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Project description:IntroductionTo determine whether IL4R single-nucleotide polymorphisms (SNPs) rs1805010 (I50V) and rs1801275 (Q551R), which have been associated with disease severity in rheumatoid arthritis (RA) patients of European ancestry, relate to the presence of rheumatoid nodules and radiographic erosions in African Americans.MethodsTwo IL4R SNPs, rs1805010 and rs1801275, were genotyped in 749 patients from the Consortium for Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis (CLEAR) registries. End points were rheumatoid nodules defined as present either by physical examination or by chest radiography and radiographic erosions (radiographs of hands/wrists and feet were scored using the modified Sharp/van der Heijde system). Statistical analyses were performed by using logistic regression modeling adjusted for confounding factors.ResultsOf the 749 patients with RA, 156 (20.8%) had rheumatoid nodules, with a mean age of 47.0 years, 84.6% female gender, and median disease duration of 1.9 years. Of the 461 patients with available radiographic data, 185 (40.1%) had erosions (score>0); their mean age was 46.7 years; 83.3% were women; and median disease duration was 1.5 years. Patients positive for HLA-DRB1 shared epitope (SE) and autoantibodies (rheumatoid factor (RF) or anti-cyclic citrullinated peptide (CCP)) had a higher risk of developing rheumatoid nodules in the presence of the AA and AG alleles of rs1801275 (odds ratio (OR)adj=8.08 (95% confidence interval (CI): 1.60-40.89), P=0.01 and ORadj=2.97 (95% CI, 1.08 to 8.17), P=0.04, respectively). Likewise, patients positive for the HLA-DRB1 SE and RF alone had a higher risk of developing rheumatoid nodules in presence of the AA and AG alleles of rs1801275 (ORadj=8.45 (95% CI, 1.57 to 45.44), P=0.01, and ORadj=3.57 (95% CI, 1.18 to 10.76), P=0.02, respectively) and in the presence of AA allele of rs1805010 (ORadj=4.52 (95% CI, 1.20 to 17.03), P=0.03). No significant association was found between IL4R and radiographic erosions or disease susceptibility, although our statistical power was limited by relatively small numbers of cases and controls.ConclusionsWe found that IL4R SNPs, rs1801275 and rs1805010, are associated with rheumatoid nodules in autoantibody-positive African-American RA patients with at least one HLA-DRB1 allele encoding the SE. These findings highlight the need for analysis of genetic factors associated with clinical RA phenotypes in different racial/ethnic populations.

Project description:GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.

Project description:ObjectiveCandidate gene association studies and genome-wide association studies (GWAs) have identified a large number of single nucleotide polymorphisms (SNPs) loci affecting susceptibility to rheumatoid arthritis (RA). However, for the same locus, some studies have yielded inconsistent results. To assess all the available evidence for association, we performed a meta-analysis on previously published case-control studies investigating the association between SNPs and RA.MethodsTwo hundred and sixteen studies, involving 125 SNPs, were reviewed. For each SNP, three genetic models were considered: the allele, dominant and recessive effects models. For each model, the effect summary odds ratio (OR) and 95% CIs were calculated. Cochran's Q-statistics were used to assess heterogeneity. If the heterogeneity was high, a random effects model was used for meta-analysis, otherwise a fixed effects model was used.ResultsThe meta-analysis results showed that: (1) 30, 28 and 26 SNPs were significantly associated with RA (P<0.01) for the allele, dominant, and recessive models, respectively. (2) rs2476601 (PTPN22) showed the strongest association for all the three models: OR = 1.605, 95% CI: 1.540-1.672, P<1.00E-15 for the T-allele; OR = 1.638, 95% CI: 1.565-1.714, P<1.00E-15 for the T/T+T/C genotype and OR = 2.544, 95% CI: 2.173-2.978, P<1.00E-15 for the T/T genotype. (3) Only 23 (18.4%), 13 (10.4%) and 15 (12.0%) SNPs had high heterogeneity (P<0.01) for the three models, respectively. (4) For some of the SNPs, there was no publication bias according to Funnel plots and Egger's regression tests (P<0.01). For the other SNPs, the associations were tested in only a few studies, and may have been subject to publication bias. More studies on these loci are required.ConclusionOur meta-analysis provides a comprehensive evaluation of the RA association studies from the past two decades. The detailed meta-analysis results are available at: http://210.46.85.180/DRAP/index.php/Metaanalysis/index.

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Simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association study of rheumatoid arthritis.

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Simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association study of rheumatoid arthritis.

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