PT - JOURNAL ARTICLE AU - Yue Li AU - Manolis Kellis TI - RiVIERA-MT: A Bayesian model to infer risk variants in related traits using summary statistics and functional genomic annotations AID - 10.1101/059345 DP - 2016 Jan 01 TA - bioRxiv PG - 059345 4099 - http://biorxiv.org/content/early/2016/06/16/059345.short 4100 - http://biorxiv.org/content/early/2016/06/16/059345.full AB - Fine-mapping causal variants is challenging due to linkage disequilibrium and the lack of interpretation of noncoding mutations. Existing fine-mapping methods do not scale well on inferring multiple causal variants per locus and causal variants across multiple related diseases. Moreover, many complex traits are not only genetically related but also potentially share causal mechanisms. We develop a novel integrative Bayesian fine-mapping model named RiVIERA-MT. The key features of RiVIERA-MT include 1) ability to model epigenomic covariance of multiple related traits; 2) efficient posterior inference of causal configuration; 3) efficient full Bayesian inference of enrichment parameters, allowing incorporation of large number of functional annotations; 4) simultaneously modeling the underlying heritability parameters. We conducted a comprehensive simulation studies using 1000 Genome and ENCODE/Roadmap epige-nomic data to demonstrate that RiVIERA-MT compares quite favorably with existing methods. In particular, the efficient inference of multiple causal variants per locus led to significantly improved estimation of causal posterior and functional enrichments compared to the state-of-the-art fine-mapping methods. Furthermore, joint modeling multiple traits confers further improvement over the single-trait mode of the same model, which is attributable to the more robust estimation of the enrichment parameters especially when the annotation measurements (i.e., ChIP-seq) themselves are noisy. We applied RiVIERA-MT to separately and jointly model 7 well-powered GWAS traits including body mass index, coronary artery disease, four lipid traits, and type 2 diabetes. To leverage potential tissue-specific epigenomic co-enrichments among these traits, we harness 52 baseline functional annotations and 220 tissue-specific epigenomic annotations from well-characterized cell types compiled from ENCODE/Roadmap consortium. Overall, we observed an improved enrichments for GTEx whole blood and tissue-specific eQTL SNPs based on the prioritized SNPs by RiVIERA-MT compared to existing methods.