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GWA Studies (gwa + studies)

Distribution by Scientific Domains

Medical Sciences	33%
Life Sciences	33%

Selected Abstracts

Improved correction for population stratification in genome-wide association studies by identifying hidden population structures,

GENETIC EPIDEMIOLOGY, Issue 3 2008
Qizhai Li
Abstract Hidden population substructure can cause population stratification and lead to false-positive findings in population-based genome-wide association (GWA) studies. Given a large panel of markers scanned in a GWA study, it becomes increasingly feasible to uncover the hidden population substructure within the study sample based on measured genotypes across the genome. Recognizing that population substructure can be displayed as clustered and/or continuous patterns of genetic variation, we propose a method that aims at the detection and correction of the confounding effect resulting from both patterns of population substructure. The proposed method is an extension of the EIGENSTRAT method (Price et al. [2006] Nat Genet 38:904,909). This approach is computationally feasible and easily applied to large-scale GWA studies. We show through simulation studies that, compared with the EIGENSTRAT method, the new method requires a smaller number of markers and yields a more appropriate correction for population stratification. Genet. Epidemiol. 2007. Published 2007 Wiley-Liss, Inc. [source]

Genome-wide association studies of cardiovascular risk factors: design, conduct and interpretation

JOURNAL OF THROMBOSIS AND HAEMOSTASIS, Issue 2009
J. C. BIS
Summary., Relying on known biology, candidate-gene studies have been only modestly successful in identifying genetic variants associated with cardiovascular risk factors. Genome-wide association (GWA) studies, in contrast, allow broad scans across millions of loci in search of unsuspected genetic associations with phenotypes. The large numbers of statistical tests in GWA studies and the large sample sizes required to detect modest-sized associations have served as a powerful incentive for the development of large collaborative efforts such as the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium [1]. This article uses published data on three phenotypes, fibrinogen, uric acid, and electrocardiographic QT interval duration, from the CHARGE Consortium to describe several methodologic issues in the design, conduct, and interpretation of GWA studies, including the use of imputation and the need for additional genotyping. Even with large studies, novel genetic loci explain only a small proportion of the variance of cardiovascular phenotypes. [source]

Joint Identification of Multiple Genetic Variants via Elastic-Net Variable Selection in a Genome-Wide Association Analysis

ANNALS OF HUMAN GENETICS, Issue 5 2010
Seoae Cho
Summary Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome-wide association (GWA) studies have been conducted with large-scale data sets of genetic variants. Most of those studies have relied on single-marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and would provide better prediction on complex traits since it utilizes combined information across variants. Here we propose a multi-stage approach for GWA analysis: (1) prescreening, (2) joint identification of putative SNPs based on elastic-net variable selection, and (3) empirical replication using bootstrap samples. Our approach enables an efficient joint search for genetic associations in GWA analysis. The suggested empirical replication method can be beneficial in GWA studies because one can avoid a costly, independent replication study while eliminating false-positive associations and focusing on a smaller number of replicable variants. We applied the proposed approach to a GWA analysis, and jointly identified 129 genetic variants having an association with adult height in a Korean population. [source]

The association of a nonsynonymous single-nucleotide polymorphism in TNFAIP3 with systemic lupus erythematosus and rheumatoid arthritis in the Japanese population

ARTHRITIS & RHEUMATISM, Issue 2 2010
Kenichi Shimane
Objective Genome-wide association (GWA) studies in systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) in Caucasian populations have independently identified risk variants in and near the tumor necrosis factor , (TNF,),induced protein 3 gene (TNFAIP3), which is crucial for the regulation of TNF-mediated signaling and Toll-like receptor signaling. The aim of this study was to assess the role of TNFAIP3 in the development of SLE and RA in Japanese subjects. Methods We selected 2 single-nucleotide polymorphisms (SNPs) from previous GWA studies. Rs2230926 is a nonsynonymous SNP in TNFAIP3 and is associated with SLE, while rs10499194 is an intergenic SNP associated with RA. We then performed 2 independent sets of SLE case,control comparisons (717 patients and 1,362 control subjects) and 3 sets of RA case,control comparisons (3,446 patients and 2,344 control subjects) using Japanese subjects. We genotyped SNPs using TaqMan assays. Results We observed a significant association between rs2230926 and an increased risk of SLE and RA in the Japanese population (for SLE, odds ratio [OR] 1.92, 95% confidence interval [95% CI] 1.53,2.41, P = 1.9 × 10,8; for RA, OR 1.35, 95% CI 1.18,1.56, P = 2.6 × 10,5). The intergenic SNP rs10499194 was also associated with SLE and RA, while the risk allele for RA in Caucasians was protective against the diseases in our population. Conclusion We demonstrated a significant association between the nonsynonymous variant in TNFAIP3 and the risk for SLE and RA in the Japanese population. TNFAIP3, similar to STAT4 and IRF5, may be a common genetic risk factor for SLE and RA that is shared between the Caucasian and Japanese populations. [source]

A Robust Genome-Wide Scan Statistic of the Wellcome Trust Case,Control Consortium

BIOMETRICS, Issue 4 2009
Jungnam Joo
Summary In genome-wide association (GWA) studies, test statistics that are efficient and robust across various genetic models are preferable, particularly for studying multiple diseases in the Wellcome Trust Case,Control Consortium (WTCCC, 2007,,Nature,447, 661,678). A new test statistic, the minimum of the p-values of the trend test and Pearson's test, was considered by the WTCCC. It is referred to here as MIN2. Because the minimum of two p-values is no longer a valid p-value itself, the WTCCC only used it to rank single nucleotide polymorphisms (SNPs) but did not report the p-values of the associated SNPs when MIN2 was used for ranking. Given its importance in practice, we derive the asymptotic null distribution of MIN2, study some of its analytical properties related to GWA studies, and compare it with existing methods (the trend test, Pearson's test, MAX3, and the constrained likelihood ratio test [CLRT]) by simulations across a wide range of possible genetic models: the recessive (REC), additive (ADD), multiplicative (MUL), dominant (DOM), and overdominant models. The results show that MAX3 and CLRT have greater efficiency robustness than other tests when the REC, ADD/MUL, and DOM models are possible, whereas Pearson's test and MIN2 have greater efficiency robustness if the possible genetic models also include the overdominant model. We conclude that robust tests (MAX3, MIN2, CLRT, and Pearson's test) are preferable to a single trend test for initial GWA studies. The four robust tests are applied to more than 100 SNPs associated with 11 common diseases identified by the two WTCCC GWA studies. [source]