An information-theoretic machine learning approach to expression QTL analysis.

Expression Quantitative Trait Locus (eQTL) analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQ...

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Autores principales:	Tao Huang, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2da206a2eece4b43b4242aef3789e525
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spelling	oai:doaj.org-article:2da206a2eece4b43b4242aef3789e5252021-11-18T07:40:00ZAn information-theoretic machine learning approach to expression QTL analysis.1932-620310.1371/journal.pone.0067899https://doaj.org/article/2da206a2eece4b43b4242aef3789e5252013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23825689/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Expression Quantitative Trait Locus (eQTL) analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQTL), and on other chromosomes (trans-eQTL). Many traditional eQTL methods are based on a linear regression model. In this study, we propose a novel method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI) is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What's more, it can capture the heterogeneity of the population. Advanced feature selection methods, Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS), were applied to optimize the selection of the affected genes by the genetic marker. When we applied our method to a study of apoE-deficient mice, it was found that the cis-acting eQTLs are stronger than trans-acting eQTLs but there are more trans-acting eQTLs than cis-acting eQTLs. We compared our results (mRMR.eQTL) with R/qtl, and MatrixEQTL (modelLINEAR and modelANOVA). In female mice, 67.9% of mRMR.eQTL results can be confirmed by at least two other methods while only 14.4% of R/qtl result can be confirmed by at least two other methods. In male mice, 74.1% of mRMR.eQTL results can be confirmed by at least two other methods while only 18.2% of R/qtl result can be confirmed by at least two other methods. Our methods provide a new way to identify the association between genetic markers and gene expression. Our software is available from supporting information.Tao HuangYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 6, p e67899 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tao Huang Yu-Dong Cai An information-theoretic machine learning approach to expression QTL analysis.
description	Expression Quantitative Trait Locus (eQTL) analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQTL), and on other chromosomes (trans-eQTL). Many traditional eQTL methods are based on a linear regression model. In this study, we propose a novel method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI) is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What's more, it can capture the heterogeneity of the population. Advanced feature selection methods, Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS), were applied to optimize the selection of the affected genes by the genetic marker. When we applied our method to a study of apoE-deficient mice, it was found that the cis-acting eQTLs are stronger than trans-acting eQTLs but there are more trans-acting eQTLs than cis-acting eQTLs. We compared our results (mRMR.eQTL) with R/qtl, and MatrixEQTL (modelLINEAR and modelANOVA). In female mice, 67.9% of mRMR.eQTL results can be confirmed by at least two other methods while only 14.4% of R/qtl result can be confirmed by at least two other methods. In male mice, 74.1% of mRMR.eQTL results can be confirmed by at least two other methods while only 18.2% of R/qtl result can be confirmed by at least two other methods. Our methods provide a new way to identify the association between genetic markers and gene expression. Our software is available from supporting information.
format	article
author	Tao Huang Yu-Dong Cai
author_facet	Tao Huang Yu-Dong Cai
author_sort	Tao Huang
title	An information-theoretic machine learning approach to expression QTL analysis.
title_short	An information-theoretic machine learning approach to expression QTL analysis.
title_full	An information-theoretic machine learning approach to expression QTL analysis.
title_fullStr	An information-theoretic machine learning approach to expression QTL analysis.
title_full_unstemmed	An information-theoretic machine learning approach to expression QTL analysis.
title_sort	information-theoretic machine learning approach to expression qtl analysis.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/2da206a2eece4b43b4242aef3789e525
work_keys_str_mv	AT taohuang aninformationtheoreticmachinelearningapproachtoexpressionqtlanalysis AT yudongcai aninformationtheoreticmachinelearningapproachtoexpressionqtlanalysis AT taohuang informationtheoreticmachinelearningapproachtoexpressionqtlanalysis AT yudongcai informationtheoreticmachinelearningapproachtoexpressionqtlanalysis
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An information-theoretic machine learning approach to expression QTL analysis.

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