Imputation aware meta‐analysis of genome‐wide association studies

Zaitlen, Noah; Eskin, Eleazar

doi:10.1002/gepi.20507

Cited by 22 publications

(25 citation statements)

References 12 publications

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“…These loci were associated with risk to disease at a genome-wide P value threshold of <10 −8 . Because imputation can sometimes result in underestimation of effect sizes 52,53 , we also performed the analysis at less stringent thresholds. At an uncorrected P value of < 0.001, for 57% of outside variant gene targets, we did not identify a common SNP that could account for the effects of outside variants on both expression and risk.…”

Section: Resultsmentioning

confidence: 99%

Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry

et al. 2016

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SNPs associated with disease susceptibility often reside in clusters of gene enhancers, or super enhancers. Constituents of these enhancer clusters cooperate to regulate target genes, and often extend beyond the linkage disequilibrium blocks containing GWAS risk SNPs. We identified “outside variants”, defined as SNPs in weak LD with GWAS risk SNPs that physically interact with risk SNPs as part of the target gene’s regulatory circuitry. These outside variants explain additional target gene expression variation beyond that of GWAS associated SNPs. Additionally, the clinical risk associated with the GWAS SNPs is considerably modified by the genotype of the outside variant. Collectively, these findings suggest a potential model whereby outside variants and GWAS SNPs that physically interact in 3D chromatin collude to influence target transcript levels as well as clinical risk. This model offers an additional hypothesis for the source of missing heritability of complex traits.

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Section: Resultsmentioning

confidence: 99%

Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry

et al. 2016

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“…I 2 measures the percentage of variability in effect estimates that is attributed to heterogeneity rather than chance (28). Heterogeneity in GWA studies can be attributed to differences between the included studies such as different populations, different linkage disequilibrium patterns, different environmental exposures, different genotyping platforms and different imputation accuracies, or it may represent unexplained statistical heterogeneity (27, 40, 80, 103). …”

Section: Part 1: Empirical Appraisal Of Published Gwa Meta-analysesmentioning

confidence: 99%

The Power of Meta-Analysis in Genome-Wide Association Studies

Panagiotou

Willer

Hirschhorn

et al. 2013

Annu. Rev. Genom. Hum. Genet.

116

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Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the last few years. The main advantage of this technique is the maximization of power to detect the subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review we systematically appraised and evaluated the characteristics of GWA meta-analyses with 10,000 or more subjects published until June 2012. We overview the current landscape of variants discovered by GWA meta-analyses and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.

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“…The opposing trends demonstrated in stratified analyses of the characteristics of controls have indicated that different design factors (Kim-Cohen et al, 2006;Tang, 2006;Pereira et al, 2009), even different genotyping platforms or different genotyping errors (Zaitlen and Eskin, 2010), potentially hinder the confirmation of the study (Han and Eskin, 2012). Ideal controls are composed of a general group of subjects without the disease of interest from which qualified cases arise once diagnosed.…”

Section: Discussionmentioning

confidence: 99%

Association between the epithelial cadherin -160C/A gene polymorphism and diffuse gastric cancer risk: a meta-analysis

Chen¹,

Sun²,

Wang³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. Several previous studies have investigated whether the -160C/A epithelial cadherin promoter polymorphism confers an increased risk of diffuse gastric cancer (DGC), but conflicting results have been reported. To explore further the association of this polymorphism with DGC susceptibility, we performed an extensive search of relevant studies and conducted a meta-analysis to obtain a more precise estimate. We conducted a systematic literature search using the databases EMBASE, PubMed, and Web of Knowledge for reports published before August 2012 that met certain criteria. Information was carefully and independently extracted from all eligible publications by 2 of the authors. Twelve distinct data sets from 10 case-control studies were analyzed. They included 1115 cases of DGC and 2965 controls. Although none of the genotypes was associated with DGC risk, a slight trend of increased risk was found among A allele carriers [odds ratio (OR) = 1.237, 95% confidence interval (95%CI), 0.940-1.627], CA heterozygotes (OR = 1.229, 95%CI = 0.938-1.610), and AA homozygotes (OR = 1.146, E-cadherin polymorphism and diffuse gastric cancer risk 95%CI = 0.684-1.918). However, when the cases were stratified by ethnicity, a diverging trend occurred in AA homozygotes between the Asian group (OR = 0.710, 95%CI = 0.328-1.536) and its Caucasian counterpart (OR = 1.434, 95%CI = 0.657-3.131). Taken together, the summarized analyses of these case-control studies demonstrated that the -160A of the epithelial cadherin gene exhibited no significant association with susceptibility for DGC; however, the results suggested that it is a potential genetic risk factor in both Asians and Caucasians. Additional large-scale, well-designed studies are necessary to confirm whether AA homozygosity is a protective factor in Asians.

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Imputation aware meta‐analysis of genome‐wide association studies

Cited by 22 publications

References 12 publications

Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry

Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry

The Power of Meta-Analysis in Genome-Wide Association Studies

Association between the epithelial cadherin -160C/A gene polymorphism and diffuse gastric cancer risk: a meta-analysis

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