@article{7d417498f16b4a3e99e41831f7f1662c,
title = "A Bayesian framework for multiple trait colocalization from summary association statistics",
abstract = "Motivation: 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. Results: 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 (https://github. com/clagiamba/moloc). We also developed a web site to visualize the biological findings (icahn.mssm.edu/moloc). The browser allows searches by gene, methylation probe and scenario of interest.",
author = "Claudia Giambartolomei and Liu, {Jimmy Zhenli} and Wen Zhang and Mads Hauberg and Huwenbo Shi and James Boocock and Joe Pickrell and Jaffe, {Andrew E.} and Bogdan Pasaniuc and Panos Roussos",
note = "Funding Information: This work was supported by the National Institutes of Health (R01AG050986 Roussos and R01MH109677 Roussos), Brain Behavior Research Foundation (20540 Roussos), Alzheimer{\textquoteright}s Association (NIRG-340998 Roussos) and the Veterans Affairs (Merit grant BX002395 Roussos). Additionally, this work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Data were generated as part of Funding Information: This work was funded in part by National Institutes of Health (NIH) under awards R01HG009120, R01HG006399, U01CA194393, T32NS048004. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding Information: the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer{\textquoteright}s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. This work was also funded in part by the National Institutes of Health (NIH) awards R01HG009120, R01HG006399, U01CA194393, T32NS048004. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} The Author(s) 2018. Published by Oxford University Press. All rights reserved.",
year = "2018",
month = aug,
day = "1",
doi = "10.1093/bioinformatics/bty147",
language = "English (US)",
volume = "34",
pages = "2538--2545",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "15",
}