Meta-analysis of genome-wide association studies.
ABSTRACT 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.
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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 · 15.15 Impact Factor
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ABSTRACT: Psychiatric disorders are among the most intractable enigmas in medicine. In the past 5 years, there has been unprecedented progress on the genetics of many of these conditions. In this Review, we discuss the genetics of nine cardinal psychiatric disorders (namely, Alzheimer's disease, attention-deficit hyperactivity disorder, alcohol dependence, anorexia nervosa, autism spectrum disorder, bipolar disorder, major depressive disorder, nicotine dependence and schizophrenia). Empirical approaches have yielded new hypotheses about aetiology and now provide data on the often debated genetic architectures of these conditions, which have implications for future research strategies. Further study using a balanced portfolio of methods to assess multiple forms of genetic variation is likely to yield many additional new findings.Nature Reviews Genetics 07/2012; 13(8):537-51. DOI:10.1038/nrg3240 · 39.79 Impact Factor
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ABSTRACT: Abstract Purpose: To describe methods to harmonize the classification of age-related macular degeneration (AMD) phenotypes across four population-based cohort studies: the Beaver Dam Eye Study (BDES), the Blue Mountains Eye Study (BMES), the Los Angeles Latino Eye Study (LALES), and the Rotterdam Study (RS). Methods: AMD grading protocols, definitions of categories, and grading forms from each study were compared to determine whether there were systematic differences in AMD severity definitions and lesion categorization among the three grading centers. Each center graded the same set of 60 images using their respective systems to determine presence and severity of AMD lesions. A common 5-step AMD severity scale and definitions of lesion measurement cutpoints and early and late AMD were developed from this exercise. Results: Applying this severity scale changed the age-sex adjusted prevalence of early AMD from 18.7% to 20.3% in BDES, from 4.7% to 14.4% in BMES, from 14.1% to 15.8% in LALES, and from 7.5% to 17.1% in RS. Age-sex adjusted prevalences of late AMD remained unchanged. Comparison of each center's grades of the 60 images converted to the consortium scale showed that exact agreement of AMD severity among centers varied from 61.0-81.4%, and one-step agreement varied from 84.7-98.3%. Conclusion: Harmonization of AMD classification reduced categorical differences in phenotypic definitions across the studies, resulted in a new 5-step AMD severity scale, and enhanced similarity of AMD prevalence among the four cohorts. Despite harmonization it may still be difficult to remove systematic differences in grading, if present.Ophthalmic epidemiology 02/2014; 21(1):14-23. DOI:10.3109/09286586.2013.867512 · 1.27 Impact Factor