RT Journal Article
SR Electronic
T1 A Bayesian Framework for Multiple Trait Colocalization from Summary Association Statistics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 155481
DO 10.1101/155481
A1 Claudia Giambartolomei
A1 Jimmy Zhenli Liu
A1 Wen Zhang
A1 Mads Hauberg
A1 Huwenbo Shi
A1 James Boocock
A1 Joe Pickrell
A1 Andrew E. Jaffe
A1 the CommonMind Consortium
A1 Bogdan Pasaniuc
A1 Panos Roussos
YR 2017
UL http://biorxiv.org/content/early/2017/06/26/155481.abstract
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.