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

Dental Center of China-Japan Friendship Hospital, Beijing 100029, China.
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA.
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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 studyepistasisLinkage Disequilibrium (LD) blockBayesian methods

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Tsinghua Science and Technology
Volume 25 Issue 5,
October 2020
Pages 678-689
DOI: 10.26599/TST.2019.9010064
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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10.26599/TST.2019.9010064.T1Odds tables of disease Models 1 - 4.

BBBbbb
Model 1AA μ μ μ
Aa μ μ⁢(1+σ) μ⁢(1+σ)2
aa μ μ⁢(1+σ)2 μ⁢(1+σ)4
Model 2AA μ μ⁢(1+σ) μ⁢(1+σ)
Aa μ⁢(1+σ) μ μ
aa μ⁢(1+σ) μ μ
Model 3AA μ μ μ⁢(1+σ)
Aa μ μ⁢(1+σ) μ
aa μ⁢(1+σ) μ μ
Model 4AA μ μ⁢(1+σ) μ
Aa μ⁢(1+σ) μ μ⁢(1+σ)
aa μ μ⁢(1+σ) μ

Note: The disease prevalence p⁢(D), the genetic heritability h2, and the MAFs determine the parameters ( μ and σ) as in Ref. [41]. In the simulation, we set p⁢(D)=0.1 for all models, h2=0.03 for Model 1, h2=0.02 for Models 2 - 4, and the MAFs of disease-associated SNPs to be 0.1, 0.2, and 0.4.