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Open AccessArticle

HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations

by Jie Liu, Guoxian Yu, Yuan Jiang and Jun Wang *,†
College of Computer and Information Science, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Current address: College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, China
Genes 2017, 8(6), 153; https://doi.org/10.3390/genes8060153
Received: 31 March 2017 / Revised: 6 May 2017 / Accepted: 25 May 2017 / Published: 31 May 2017
(This article belongs to the Section Human Genomics and Genetic Diseases)
Detecting single nucleotide polymorphisms’ (SNPs) interaction is one of the most popular approaches for explaining the missing heritability of common complex diseases in genome-wide association studies. Many methods have been proposed for SNP interaction detection, but most of them only focus on pairwise interactions and ignore high-order ones, which may also contribute to complex traits. Existing methods for high-order interaction detection can hardly handle genome-wide data and suffer from low detection power, due to the exponential growth of search space. In this paper, we proposed a flexible two-stage approach (called HiSeeker) to detect high-order interactions. In the screening stage, HiSeeker employs the chi-squared test and logistic regression model to efficiently obtain candidate pairwise combinations, which have intermediate or significant associations with the phenotype for interaction detection. In the search stage, two different strategies (exhaustive search and ant colony optimization-based search) are utilized to detect high-order interactions from candidate combinations. The experimental results on simulated datasets demonstrate that HiSeeker can more efficiently and effectively detect high-order interactions than related representative algorithms. On two real case-control datasets, HiSeeker also detects several significant high-order interactions, whose individual SNPs and pairwise interactions have no strong main effects or pairwise interaction effects, and these high-order interactions can hardly be identified by related algorithms. View Full-Text
Keywords: genome-wide association studies; high-order SNP interactions; logistic regression model; ant colony optimization genome-wide association studies; high-order SNP interactions; logistic regression model; ant colony optimization
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Liu, J.; Yu, G.; Jiang, Y.; Wang, J. HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations. Genes 2017, 8, 153.

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