A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS)

Overview

Journal Ann Appl Stat

Date 2021 Apr 28

PMID 33907591

Citations 4

Authors

Laurent Briollais

Adrian Dobra

Jinnan Liu

Matt Friedlander

Hilmi Ozcelik

Helene Massam

Affiliations

Soon will be listed here.

Abstract

The analysis of GWAS data has long been restricted to simple models that cannot fully capture the genetic architecture of complex human diseases. As a shift from standard approaches, we propose here a general statistical framework for multi-SNP analysis of GWAS data based on a Bayesian graphical model. Our goal is to develop a general approach applicable to a wide range of genetic association problems, including GWAS and fine-mapping studies, and, more specifically, be able to: (1) Assess the joint effect of multiple SNPs that can be linked or unlinked and interact or not; (2) Explore the multi-SNP model space efficiently using the Mode Oriented Stochastic Search (MOSS) algorithm and determine the best models. We illustrate our new methodology with an application to the CGEM breast cancer GWAS data. Our algorithm selected several SNPs embedded in multi-locus models with high posterior probabilities. Most of the SNPs selected have a biological relevance. Interestingly, several of them have never been detected in standard single-SNP analyses. Finally, our approach has been implemented in the open source package genMOSS.

Citing Articles

Application of Bayesian genomic prediction methods to genome-wide association analyses.

Wolc A, Dekkers J Genet Sel Evol. 2022; 54(1):31.

PMID: 35562659 PMC: 9103490. DOI: 10.1186/s12711-022-00724-8.

A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS).

Briollais L, Dobra A, Liu J, Friedlander M, Ozcelik H, Massam H Ann Appl Stat. 2021; 10(2):786-811.

PMID: 33907591 PMC: 8075301. DOI: 10.1214/16-aoas909.

Bayesian Joint Spike-and-Slab Graphical Lasso.

Li Z, McCormick T, Clark S Proc Mach Learn Res. 2021; 97:3877-3885.

PMID: 33521648 PMC: 7845917.

Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.

Zhao Y, Zhu H, Lu Z, Knickmeyer R, Zou F Genetics. 2019; 212(2):397-415.

PMID: 31010934 PMC: 6553832. DOI: 10.1534/genetics.119.301906.

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