TY - JOUR T1 - A scalable Bayesian method for integrating functional information in genome-wide association studies JF - bioRxiv DO - 10.1101/101691 SP - 101691 AU - Jingjing Yang AU - Lars G. Fritsche AU - Xiang Zhou AU - Gonçalo Abecasis AU - International Age-related Macular Degeneration Genomics Consortium (IAMDGC) Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/01/19/101691.abstract N2 - Genome-wide association studies (GWASs) have identified many complex trait loci. To understand the biological mechanisms underlying these, we pair a flexible Bayesian method with efficient computational techniques to model functional information in GWASs. We model the effect-size distribution and probability of causality for variants with different annotation, explicitly allowing for multiple causal-variants per locus. In simulations, our method shows higher power to identify true causal-variants than competing methods. In a GWAS of age-related macular degeneration with 33,976 individuals and 9,857,286 variants, we find the strongest enrichment for causality among non-synonymous variants (54× more likely to be causal, 1.4× larger effect-sizes) and among variants in active promoters (7.8× more likely, 1.4× larger effect-sizes). Importantly, when multiple causal-variants reside in the same locus, our approach improves upon the list of candidate variants produced by sequential forward selection or methods only allowing for a single causal-variant per locus. In conclusion, our method is shown to efficiently integrate functional information in GWASs, helping identify causal-variants and underlying biology. ER -