Abstract
In genome-wide association studies (GWAS), variants showing consistent effect directions across populations are considered as true discoveries. We model this information in an Effect Direction MEta-analysis (EDME) to quantify pleiotropy using GWAS of 34 Cholesky-decorrelated traits in 44,000+ cattle with sequence variants. The effect-direction agreement between independent bull and cow datasets was used to quantify the false discovery rate by effect direction (FDRed) and the number of affected traits for prioritised variants. Variants with multi-trait p < 1e–6 affected 1∼22 traits with an average of 10 traits. EDME assigns pleiotropic variants to each trait which informs the biology behind complex traits. New pleiotropic loci are identified, including signals from the cattle FTO locus mirroring its bystander effects on human obesity. When validated in the 1000-Bull Genome database, the prioritized pleiotropic variants consistently predicted expected phenotypic differences between dairy and beef cattle. EDME provides robust approaches to control GWAS FDR and quantify pleiotropy.
Highlights
In genome-wide association studies (GWAS), variants showing consistent effect directions across populations are considered as true discoveries
In this paper, we focus on finding pleiotropic variants and identifying the traits that each variant affects using Effect Direction MEta-analysis (EDME)
We use individual animal data rather than GWAS summaries. This is partly out of necessity because few summary data are available for cattle and most non-human species, and, because some information is lost in summaries, it is better to start with individual data when this is available
Summary
In genome-wide association studies (GWAS), variants showing consistent effect directions across populations are considered as true discoveries We model this information in an Effect Direction MEta-analysis (EDME) to quantify pleiotropy using GWAS of 34 Choleskydecorrelated traits in 44,000+ cattle with sequence variants. In this paper we directly use the ability to confirm a finding in an independent study to estimate the FDR by using the proportion of sequence variants which have an effect in the same direction in two independent datasets We call this FDRed for ‘effect direction’ based on a statistical model we call Effect Direction MEtaanalysis (EDME) for effect direction meta-analysis. EDME is extended to calculate the FDR by effect directions (FDRed) for a multi-trait GWAS This will allow us to know the number of traits that a significant variant is associated with and precisely which traits these are. A validation analysis with the 1000-bull genome database confirms the informativeness of pleiotropic variants prioritised by the EDME model
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