Integrating functional data to prioritize causal variants in statistical fine-mapping studies.

ABSTRACT: Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further

SUBMITTER: Kichaev G 

PROVIDER: S-EPMC4214605 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature