A Bayesian framework for multiple trait colocalization from summary association statistics

CommonMind Consortium

doi:10.1101/155481

A Bayesian framework for multiple trait colocalization from summary association statistics

CommonMind Consortium

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

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: 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.

Original language	English (US)
Journal	Unknown Journal
DOIs	https://doi.org/10.1101/155481
State	Published - Jun 26 2017

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
Immunology and Microbiology(all)
Neuroscience(all)
Pharmacology, Toxicology and Pharmaceutics(all)

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@article{d3a2ac4be5ec41a38e0ed66b3611786e,

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: 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 = "{CommonMind Consortium} and 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 and Menachem Fromer and Sieberts, {Solveig K.} and Johnson, {Jessica S.} and Ruderfer, {Douglas M.} and Shah, {Hardik R.} and Klei, {Lambertus L.} and Dang, {Kristen K.} and Perumal, {Thanneer M.} and Logsdon, {Benjamin A.} and Mahajan, {Milind C.} and Mangravite, {Lara M.} and Hiroyoshi Toyoshiba and Gur, {Raquel E.} and Hahn, {Chang Gyu} and Eric Schadt and Lewis, {David A.} and Vahram Haroutunian and Peters, {Mette A.} and Lipska, {Barbara K.} and Buxbaum, {Joseph D.} and Keisuke Hirai and Enrico Domenici and Bernie Devlin and Pamela Sklar",

note = "Publisher Copyright: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2017",

month = jun,

day = "26",

doi = "10.1101/155481",

language = "English (US)",

journal = "Advances in Water Resources",

issn = "0309-1708",

publisher = "Elsevier Limited",

}

TY - JOUR

T1 - A Bayesian framework for multiple trait colocalization from summary association statistics

AU - CommonMind Consortium

AU - Giambartolomei, Claudia

AU - Liu, Jimmy Zhenli

AU - Zhang, Wen

AU - Hauberg, Mads

AU - Shi, Huwenbo

AU - Boocock, James

AU - Pickrell, Joe

AU - Jaffe, Andrew E.

AU - Pasaniuc, Bogdan

AU - Roussos, Panos

AU - Fromer, Menachem

AU - Sieberts, Solveig K.

AU - Johnson, Jessica S.

AU - Ruderfer, Douglas M.

AU - Shah, Hardik R.

AU - Klei, Lambertus L.

AU - Dang, Kristen K.

AU - Perumal, Thanneer M.

AU - Logsdon, Benjamin A.

AU - Mahajan, Milind C.

AU - Mangravite, Lara M.

AU - Toyoshiba, Hiroyoshi

AU - Gur, Raquel E.

AU - Hahn, Chang Gyu

AU - Schadt, Eric

AU - Lewis, David A.

AU - Haroutunian, Vahram

AU - Peters, Mette A.

AU - Lipska, Barbara K.

AU - Buxbaum, Joseph D.

AU - Hirai, Keisuke

AU - Domenici, Enrico

AU - Devlin, Bernie

AU - Sklar, Pamela

N1 - Publisher Copyright: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2017/6/26

Y1 - 2017/6/26

N2 - 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: 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.

AB - 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: 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.

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U2 - 10.1101/155481

DO - 10.1101/155481

M3 - Article

AN - SCOPUS:85093562538

JO - Advances in Water Resources

JF - Advances in Water Resources

SN - 0309-1708

ER -