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

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 pai...

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Autores principales: Liu, Jie, Yu, Guoxian, Jiang, Yuan, Wang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485517/
https://www.ncbi.nlm.nih.gov/pubmed/28561745
http://dx.doi.org/10.3390/genes8060153
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author Liu, Jie
Yu, Guoxian
Jiang, Yuan
Wang, Jun
author_facet Liu, Jie
Yu, Guoxian
Jiang, Yuan
Wang, Jun
author_sort Liu, Jie
collection PubMed
description 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.
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spelling pubmed-54855172017-06-29 HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations Liu, Jie Yu, Guoxian Jiang, Yuan Wang, Jun Genes (Basel) Article 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. MDPI 2017-05-31 /pmc/articles/PMC5485517/ /pubmed/28561745 http://dx.doi.org/10.3390/genes8060153 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Jie
Yu, Guoxian
Jiang, Yuan
Wang, Jun
HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations
title HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations
title_full HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations
title_fullStr HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations
title_full_unstemmed HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations
title_short HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations
title_sort hiseeker: detecting high-order snp interactions based on pairwise snp combinations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485517/
https://www.ncbi.nlm.nih.gov/pubmed/28561745
http://dx.doi.org/10.3390/genes8060153
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