Meta-Analysis of Genome-Wide Association Studies
Individual genome-wide association studies have only limited power to find novel loci underlying complex traits and common diseases. With relatively modest sample and effect sizes, a true association between genotype and phenotype may never meet genome-wide statistical significance (P < 5 x 10(-8)) in a single study. Through meta-analysis, novel susceptibility loci can be discovered by effectively summing the statistical evidence of individually underpowered studies. Most genetic discoveries for complex traits are now made through meta-analysis collaborations, which so far have been restricted to single-locus analyses, testing for main effects at a single polymorphism at a time. A key benefit of this approach is that individual-level genotype (and phenotype) data do not need to be exchanged between research groups. In this article, we focus on meta-analysis at individual single-nucleotide polymorphisms (SNPs), paying particular attention to how imputation uncertainty can be incorporated into the association analysis and subsequent meta-analysis. Probably the most important aspect of genome-wide association meta-analysis is harmonization of the study results. As studies differ in design, sample collection, genotyping platforms, and association analysis methods, it is important that the association results (per SNP) of each study can be formatted, exchanged, and analyzed in such a way that the statistical evidence can be combined appropriately and that no valuable information is lost. Without minimizing the importance of having a clear phenotype definition (and corresponding measurements), we will assume that investigators representing the various studies have made sensible agreements about phenotype definitions, necessary sample exclusions, and appropriate covariate modeling.
Available from: Laura M Yerges-Armstrong
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ABSTRACT: A strong genetic influence on bone mineral density has been long established, and modern genotyping technologies have generated a flurry of new discoveries about the genetic determinants of bone mineral density (BMD) measured at a single time point. However, much less is known about the genetics of age-related bone loss. Identifying bone loss-related genes may provide new routes for therapeutic intervention and osteoporosis prevention.
A review of published peer-reviewed literature on the genetics of bone loss was performed. Relevant studies were summarized, most of which were drawn from the period 1990-2010.
Although bone loss is a challenging phenotype, available evidence supports a substantial genetic contribution. Some of the genes identified from recent genome-wide association studies of cross-sectional BMD are attractive candidate genes for bone loss, most notably genes in the nuclear factor κB and estrogen endocrine pathways. New insights into the biology of skeletal development and regulation of bone turnover have inspired new hypotheses about genetic regulation of bone loss and may provide new directions for identifying genes associated with bone loss.
Although recent genome-wide association and candidate gene studies have begun to identify genes that influence BMD, efforts to identify susceptibility genes specific for bone loss have proceeded more slowly. Nevertheless, clues are beginning to emerge on where to look, and as population studies accumulate, there is hope that important bone loss susceptibility genes will soon be identified.
The Journal of Clinical Endocrinology and Metabolism 02/2011; 96(5):1258-68. DOI:10.1210/jc.2010-2865 · 6.21 Impact Factor
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ABSTRACT: Corneal curvature (CC) is a key determinant of major eye diseases, such as keratoconus, myopia and corneal astigmatism. No prior studies have discovered the genes for CC. Here we report the findings from four genome-wide association studies of CC in 10 008 samples from three population groups in Singapore. Our discovery phase surveyed 2867 Chinese and 3072 Malays, allowing us to identify two loci that were associated with CC variation: FRAP1 on chromosome 1p36.2 and PDGFRA on chromosome 4q12. These findings were subsequently replicated in a validation study involving an additional 2953 Asian Indians and a further collection of 1116 Chinese children. The effect sizes of the identified variants were consistent across all four cohorts, with seven single nucleotide polymorphisms (SNPs) in FRAP1 (lead SNP: rs17036350, meta P-value = 4.06 × 10(-13)) and six SNPs in PDGFRA (lead SNP: rs2114039, meta P-value = 1.33 × 10(-9)) attaining genome-wide significance in the SNP-based meta-analysis of the four studies. This is the first genome-wide survey of CC variation and we have identified two implicated loci in three genetically diverse Asian populations, suggesting the presence of common genetic etiology across multiple populations.
Human Molecular Genetics 06/2011; 20(18):3693-8. DOI:10.1093/hmg/ddr269 · 6.39 Impact Factor
Available from: David TW Chen
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ABSTRACT: Meta-analyses of bipolar disorder (BD) genome-wide association studies (GWAS) have identified several genome-wide significant signals in European-ancestry samples, but so far account for little of the inherited risk. We performed a meta-analysis of ∼750 000 high-quality genetic markers on a combined sample of ∼14 000 subjects of European and Asian-ancestry (phase I). The most significant findings were further tested in an extended sample of ∼17 700 cases and controls (phase II). The results suggest novel association findings near the genes TRANK1 (LBA1), LMAN2L and PTGFR. In phase I, the most significant single nucleotide polymorphism (SNP), rs9834970 near TRANK1, was significant at the P=2.4 × 10(-11) level, with no heterogeneity. Supportive evidence for prior association findings near ANK3 and a locus on chromosome 3p21.1 was also observed. The phase II results were similar, although the heterogeneity test became significant for several SNPs. On the basis of these results and other established risk loci, we used the method developed by Park et al. to estimate the number, and the effect size distribution, of BD risk loci that could still be found by GWAS methods. We estimate that >63 000 case-control samples would be needed to identify the ∼105 BD risk loci discoverable by GWAS, and that these will together explain <6% of the inherited risk. These results support previous GWAS findings and identify three new candidate genes for BD. Further studies are needed to replicate these findings and may potentially lead to identification of functional variants. Sample size will remain a limiting factor in the discovery of common alleles associated with BD.Molecular Psychiatry advance online publication, 20 December 2011; doi:10.1038/mp.2011.157.
Molecular Psychiatry 12/2011; DOI:10.1038/mp.2011.157 · 14.50 Impact Factor
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