PT - JOURNAL ARTICLE AU - Claudia Giambartolomei AU - Jimmy Zhenli Liu AU - Wen Zhang AU - Mads Hauberg AU - Huwenbo Shi AU - James Boocock AU - Joe Pickrell AU - Andrew E. Jaffe AU - the CommonMind Consortium AU - Bogdan Pasaniuc AU - Panos Roussos TI - A Bayesian Framework for Multiple Trait Colocalization from Summary Association Statistics AID - 10.1101/155481 DP - 2017 Jan 01 TA - bioRxiv PG - 155481 4099 - http://biorxiv.org/content/early/2017/06/26/155481.short 4100 - http://biorxiv.org/content/early/2017/06/26/155481.full AB - Most genetic variants implicated in complex diseases by genome-wide association studies (GWAS) are non-coding, making it challenging to understand the causative genes involved in disease. Integrating external information such as quantitative trait locus (QTL) mapping of molecular traits (e.g., expression, methylation) is a powerful approach to identify the subset of GWAS signals explained by regulatory effects. In particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify the epigenetic mechanisms that impact gene expression which in turn affect disease risk. In this work we propose multiple-trait-coloc (moloc), a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci. We applied moloc to schizophrenia (SCZ) and eQTL/mQTL data derived from human brain tissue and identified 56 candidate genes that influence SCZ through methylation. Our method can be applied to any GWAS and relevant functional data to help prioritize diseases associated genes.