A Bayesian framework for multiple trait colocalization from summary association statistics

Claudia Giambartolomei, Jimmy Zhenli Liu, Wen Zhang, Mads Hauberg, Huwenbo Shi, James Boocock, Joe Pickrell, Andrew Jaffe, Bogdan Pasaniuc, Panos Roussos

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)2538-2545
Number of pages8
JournalBioinformatics
Volume34
Issue number15
DOIs
StatePublished - Jan 1 2018

Fingerprint

Quantitative Trait Loci
Genome-Wide Association Study
Genome
Methylation
Genes
Statistics
Association reactions
Gene
Schizophrenia
Functional Data
Epigenomics
Gene Expression
Locus
Framework
Probe
Availability
Integrate
Scenarios
Subset
Brain

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Giambartolomei, C., Liu, J. Z., Zhang, W., Hauberg, M., Shi, H., Boocock, J., ... Roussos, P. (2018). A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics, 34(15), 2538-2545. https://doi.org/10.1093/bioinformatics/bty147

A Bayesian framework for multiple trait colocalization from summary association statistics. / Giambartolomei, Claudia; Liu, Jimmy Zhenli; Zhang, Wen; Hauberg, Mads; Shi, Huwenbo; Boocock, James; Pickrell, Joe; Jaffe, Andrew; Pasaniuc, Bogdan; Roussos, Panos.

In: Bioinformatics, Vol. 34, No. 15, 01.01.2018, p. 2538-2545.

Research output: Contribution to journalArticle

Giambartolomei, C, Liu, JZ, Zhang, W, Hauberg, M, Shi, H, Boocock, J, Pickrell, J, Jaffe, A, Pasaniuc, B & Roussos, P 2018, 'A Bayesian framework for multiple trait colocalization from summary association statistics', Bioinformatics, vol. 34, no. 15, pp. 2538-2545. https://doi.org/10.1093/bioinformatics/bty147
Giambartolomei C, Liu JZ, Zhang W, Hauberg M, Shi H, Boocock J et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics. 2018 Jan 1;34(15):2538-2545. https://doi.org/10.1093/bioinformatics/bty147
Giambartolomei, Claudia ; Liu, Jimmy Zhenli ; Zhang, Wen ; Hauberg, Mads ; Shi, Huwenbo ; Boocock, James ; Pickrell, Joe ; Jaffe, Andrew ; Pasaniuc, Bogdan ; Roussos, Panos. / A Bayesian framework for multiple trait colocalization from summary association statistics. In: Bioinformatics. 2018 ; Vol. 34, No. 15. pp. 2538-2545.
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