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.
Highlights
Most common diseases, such as hypertension, cancer, diabetes, and heart disease, are resulting from the joint effects of various genetic variants, environmental factors, or their interactions
We propose a novel Bayesian method, named BAM, for simultaneously partitioning Single Nucleotide Polymorphisms (SNPs) into Linkage Disequilibrium (LD)-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases
With the candidate SNPs generated by Markov Chain Monte Carlo (MCMC), we apply the 2 statistic and the conditional 2 test to measure the significance for a module of SNPs
Summary
Most common diseases, such as hypertension, cancer, diabetes, and heart disease, are resulting from the joint effects of various genetic variants, environmental factors, or their interactions. It is of great interest to identify the genetic risk factors for understanding disease mechanisms to develop effective treatments and improve public health. Study (GWAS) has been proved to be a powerful genomic and statistical inference tool to identify genetic susceptibility on associations between traits of interests and genetic information of unrelated individuals[1, 2]. Xuan Guo is with the Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, To whom correspondence should be addressed. This work is an extension of Xuan Guo’s PhD dissertation at Georgia State University, Athens, GA, USA.
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