RiVIERA-MT: A Bayesian model to infer risk variants in related traits using summary statistics and functional genomic annotations

Abstract

Dissecting the physiological circuitry underlying diverse human complex traits associated with heritable common mutations is an ongoing effort. The primary challenge involves identifying the relevant cell types and the causal variants among the vast majority of the associated mutations in the noncoding regions. To address this challenge, we developed an efficient probabilistic framework. First, we propose a sparse group-guided learning algorithm to infer cell-type-specific enrichments. Second, we propose a fine-mapping Bayesian model that incorporates as Bayesian priors the sparse enrichments to infer risk variants. Using the proposed framework to analyze 32 complex human traits revealed meaningful tissue-specific epigenomic enrichments indicative of the relevant disease pathologies. The prioritized variants exhibit prominent tissue-specific epigenomic signatures and significant enrichments for eQTL and conserved elements. Together, we demonstrate the general benefits of the proposed integrative framework in elucidating meaningful tissue-specific epigenomic elements from large-scale correlated annotations and the implicated functional variants for future experimental interrogation.