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

作　　者：Guanying Wu Xuan Guo Baohua Xu 

机构地区：[1]the Dental Center of China-Japan Friendship Hospital,Beijing 100029,China [2]the Department of Computer Science and Engineering,University of North Texas,Denton,TX 76203,USA

出　　处：《Tsinghua Science and Technology》2020年第5期678-689,共12页清华大学学报（自然科学版（英文版）

摘　　要：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.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.

关 键 词：disease association study EPISTASIS Linkage Disequilibrium(LD)block Bayesian methods 

分 类 号：R440[医药卫生—诊断学] O212.8[医药卫生—临床医学]