ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information.

ABSTRACT: Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach (called ClusterMI) to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilize

SUBMITTER: Cao X 

PROVIDER: S-EPMC6121365 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature