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Entropy
Volume 27
Issue 10
10.3390/e27101077
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Yashi Bu
1,
Min Chen
1,*,
Zhenyu Xuan
2 and
Xinlei Wang
3
1
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
2
Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
3
Department of Mathematics, University of Texas at Arlington, Arlington, TX 76019, USA
*
Author to whom correspondence should be addressed.
Entropy 2025, 27(10), 1077; https://doi.org/10.3390/e27101077 (registering DOI)
Submission received: 31 July 2025 / Revised: 13 October 2025 / Accepted: 15 October 2025 / Published: 18 October 2025
(This article belongs to the Special Issue Statistical Methods for Modeling High-Dimensional and Complex Data: Second Edition)
Versions Notes

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.
Keywords: single nucleotide polymorphism; somatic mutation; TCGA single nucleotide polymorphism; somatic mutation; TCGA

Share and Cite

MDPI and ACS Style

Bu, Y.; Chen, M.; Xuan, Z.; Wang, X. A Bayesian Model for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer. Entropy 2025, 27, 1077. https://doi.org/10.3390/e27101077

AMA Style

Bu Y, Chen M, Xuan Z, Wang X. A Bayesian Model for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer. Entropy. 2025; 27(10):1077. https://doi.org/10.3390/e27101077

Chicago/Turabian Style

Bu, Yashi, Min Chen, Zhenyu Xuan, and Xinlei Wang. 2025. "A Bayesian Model for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer" Entropy 27, no. 10: 1077. https://doi.org/10.3390/e27101077

APA Style

Bu, Y., Chen, M., Xuan, Z., & Wang, X. (2025). A Bayesian Model for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer. Entropy, 27(10), 1077. https://doi.org/10.3390/e27101077

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