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Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions

Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene inte...

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Autores principales: Yang, Cheng-Hong, Chuang, Li-Yeh, Lin, Yu-Da
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5634479/
https://www.ncbi.nlm.nih.gov/pubmed/28993686
http://dx.doi.org/10.1038/s41598-017-12773-x
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author Yang, Cheng-Hong
Chuang, Li-Yeh
Lin, Yu-Da
author_facet Yang, Cheng-Hong
Chuang, Li-Yeh
Lin, Yu-Da
author_sort Yang, Cheng-Hong
collection PubMed
description Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.
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spelling pubmed-56344792017-10-18 Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions Yang, Cheng-Hong Chuang, Li-Yeh Lin, Yu-Da Sci Rep Article Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies. Nature Publishing Group UK 2017-10-09 /pmc/articles/PMC5634479/ /pubmed/28993686 http://dx.doi.org/10.1038/s41598-017-12773-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Yang, Cheng-Hong
Chuang, Li-Yeh
Lin, Yu-Da
Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions
title Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions
title_full Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions
title_fullStr Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions
title_full_unstemmed Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions
title_short Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions
title_sort multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5634479/
https://www.ncbi.nlm.nih.gov/pubmed/28993686
http://dx.doi.org/10.1038/s41598-017-12773-x
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