A Bayesian Model for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer

TL;DR

This paper introduces a Bayesian model to improve genome-wide association studies by analyzing paired tumor and normal tissues, helping identify genetic factors in breast cancer.

Contribution

The novel Bayesian hierarchical model integrates multiple genetic markers to enhance detection of moderate-effect SNPs in cancer studies.

Findings

The Bayesian model improves detection of SNP sets grouped by genes or pathways.

Multiple-marker analysis yields more consistent results with external resources compared to single-marker analysis.

Application to breast cancer data from TCGA identifies associated genes with increased discovery potential.

Abstract

Complex human diseases, including cancer, are linked to genetic factors. Genome-wide association studies (GWASs) are powerful for identifying genetic variants associated with cancer but are limited by their reliance on case–control data. We propose approaches to expanding GWAS by using tumor and paired normal tissues to investigate somatic mutations. We apply penalized maximum likelihood estimation for single-marker analysis and develop a Bayesian hierarchical model to integrate multiple markers, identifying SNP sets grouped by genes or pathways, improving detection of moderate-effect SNPs. Applied to breast cancer data from The Cancer Genome Atlas (TCGA), both single- and multiple-marker analyses identify associated genes, with multiple-marker analysis providing more consistent results with external resources. The Bayesian model significantly increases the chance of new discoveries.

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