Supplementary data are available at Bioinformatics online.

44Most genetic variants implicated in complex diseases by genome-wide association 45 studies (GWAS) are non-coding, making it challenging to understand the causative 46 genes involved in disease. Integrating external information such as quantitative trait 47 locus (QTL) mapping of molecular traits (e.g., expression, methylation) is a powerful 48 approach to identify the subset of GWAS signals explained by regulatory effects. In 49 particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the 50 GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify 51 the epigenetic mechanisms that impact gene expression which in turn affect disease 52 risk. In this work we propose multiple-trait-coloc (moloc), a Bayesian statistical 53 framework that integrates GWAS summary data with multiple molecular QTL data to 54 identify regulatory effects at GWAS risk loci. We applied moloc to schizophrenia (SCZ) 55 and eQTL/mQTL data derived from human brain tissue and identified 52 candidate 56 genes that influence SCZ through methylation. Our method can be applied to any 57 GWAS and relevant functional data to help prioritize disease associated genes. 58 59 AUTHOR SUMMARY 60In this paper, we propose multiple-trait-coloc (moloc), a statistical method that can use 61 single variant summary statistics from multiple studies to test for colocalization. Using 62 moloc, we integrate risk variants identified through genome-wide association studies 63 (GWAS) in schizophrenia with quantitative trait loci that affect gene expression (eQTL) 64 and DNA methylation (mQTL). Our method links non-coding risk variants with 65