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Bioinformatics. 2018 Aug 1;34(15):2538-2545. doi: 10.1093/bioinformatics/bty147.

A Bayesian framework for multiple trait colocalization from summary association statistics.

Author information

Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
New York Genome Center, New York, NY, USA.
Department of Computational Biology and Genomics, Biogen, Cambridge, MA, USA.
Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Biomedicine, The Lundbeck Foundation Initiative of Integrative Psychiatric Research (iPSYCH), Aarhus University, Aarhus, Denmark.
Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.
Departments of Mental Health and Biostatistics, Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA.



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 52 candidate genes that influence SCZ through methylation. Our method can be applied to any GWAS and relevant functional data to help prioritize disease associated genes. Availability and implementation: moloc is available for download as an R package ( We also developed a web site to visualize the biological findings ( The browser allows searches by gene, methylation probe and scenario of interest.

Supplementary information:

Supplementary data are available at Bioinformatics online.

[Available on 2019-08-01]

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