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Tsinghua Science and Technology 2020, 25(5): 678-689 https://doi.org/10.26599/TST.2019.9010064
Open Access | Issue | Published: 16 March 2020

BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases

Show Author's Information Guanying Wu,  Xuan Guo( ),  Baohua Xu( )
Dental Center of China-Japan Friendship Hospital, Beijing 100029, China.
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA.
Keywords:
disease association study, epistasis, Linkage Disequilibrium (LD) block, Bayesian methods
Cite this article:
Wu G, Guo X, Xu B. BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases. Tsinghua Science and Technology, 2020, 25(5): 678-689. https://doi.org/10.26599/TST.2019.9010064
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Abstract Full text About this article

Many human diseases involve multiple genes in complex interactions. Large Genome-Wide Association Studies (GWASs) have been considered to hold promise for unraveling such interactions. However, statistic tests for high-order epistatic interactions ( ⩾2 Single Nucleotide Polymorphisms (SNPs)) raise enormous computational and analytical challenges. It is well known that the block-wise structure exists in the human genome due to Linkage Disequilibrium (LD) between adjacent SNPs. In this paper, we propose a novel Bayesian method, named BAM, for simultaneously partitioning SNPs into LD-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient. We also applied BAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and accurately recovered the LD-block structure. Therefore, we believe that BAM is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.


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About this article

BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases

Show Author's information Guanying Wu, Xuan Guo( ), Baohua Xu( )
Dental Center of China-Japan Friendship Hospital, Beijing 100029, China.
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA.

Abstract

Many human diseases involve multiple genes in complex interactions. Large Genome-Wide Association Studies (GWASs) have been considered to hold promise for unraveling such interactions. However, statistic tests for high-order epistatic interactions ( ⩾2 Single Nucleotide Polymorphisms (SNPs)) raise enormous computational and analytical challenges. It is well known that the block-wise structure exists in the human genome due to Linkage Disequilibrium (LD) between adjacent SNPs. In this paper, we propose a novel Bayesian method, named BAM, for simultaneously partitioning SNPs into LD-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient. We also applied BAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and accurately recovered the LD-block structure. Therefore, we believe that BAM is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

Keywords: disease association study, epistasis, Linkage Disequilibrium (LD) block, Bayesian methods

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Received: 29 October 2019
Revised: 05 December 2019
Accepted: 02 January 2020
Published: 16 March 2020
Issue date: October 2020

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