Mapping eQTL networks with mixed graphical Markov models

Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect...

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Detalles Bibliográficos
Autores: Tur Mongé, Imma, 1985-, Roverato, Alberto, Castelo Valdueza, Robert
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2014
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/70637
Acceso en línea:http://hdl.handle.net/10230/70637
http://dx.doi.org/10.1534/genetics.114.169573
Access Level:acceso abierto
Palabra clave:eQTL
Gene network
Exact-likelihood-ratio test
Conditional Gaussian distribution
Mixed graphical Markov model
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spelling Mapping eQTL networks with mixed graphical Markov modelsTur Mongé, Imma, 1985-Roverato, AlbertoCastelo Valdueza, RoberteQTLGene networkExact-likelihood-ratio testConditional Gaussian distributionMixed graphical Markov modelExpression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene–gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes.This work has been supported by a grant from the Spanish Ministry of Economy and Competitiveness to R.C. (ref. TIN2011-22826).Oxford University Press202520252014info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/70637http://dx.doi.org/10.1534/genetics.114.169573http://hdl.handle.net/10230/70637reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésGenetics. 2014 Dec 1;198(4):1377-93info:eu-repo/grantAgreement/ES/3PN/TIN2011-22826© Oxford University Press. This is a pre-copyedited, author-produced version of an article accepted for publication in Genetics following peer review. The version of record Tur I, Roverato A, Castelo R. Mapping eQTL networks with mixed graphical Markov models. Genetics. 2014 Dec 1;198(4):1377-93. DOI: 10.1534/genetics.114.169573 is available online at: https://academic.oup.com/genetics/article/198/4/1377/5935964 and https://doi.org/10.1534/genetics.114.169573.info:eu-repo/semantics/openAccessoai:recercat.cat:10230/706372026-05-29T05:05:01Z
dc.title.none.fl_str_mv Mapping eQTL networks with mixed graphical Markov models
title Mapping eQTL networks with mixed graphical Markov models
spellingShingle Mapping eQTL networks with mixed graphical Markov models
Tur Mongé, Imma, 1985-
eQTL
Gene network
Exact-likelihood-ratio test
Conditional Gaussian distribution
Mixed graphical Markov model
title_short Mapping eQTL networks with mixed graphical Markov models
title_full Mapping eQTL networks with mixed graphical Markov models
title_fullStr Mapping eQTL networks with mixed graphical Markov models
title_full_unstemmed Mapping eQTL networks with mixed graphical Markov models
title_sort Mapping eQTL networks with mixed graphical Markov models
dc.creator.none.fl_str_mv Tur Mongé, Imma, 1985-
Roverato, Alberto
Castelo Valdueza, Robert
author Tur Mongé, Imma, 1985-
author_facet Tur Mongé, Imma, 1985-
Roverato, Alberto
Castelo Valdueza, Robert
author_role author
author2 Roverato, Alberto
Castelo Valdueza, Robert
author2_role author
author
dc.subject.none.fl_str_mv eQTL
Gene network
Exact-likelihood-ratio test
Conditional Gaussian distribution
Mixed graphical Markov model
topic eQTL
Gene network
Exact-likelihood-ratio test
Conditional Gaussian distribution
Mixed graphical Markov model
description Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene–gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes.
publishDate 2014
dc.date.none.fl_str_mv 2014
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/70637
http://dx.doi.org/10.1534/genetics.114.169573
http://hdl.handle.net/10230/70637
url http://hdl.handle.net/10230/70637
http://dx.doi.org/10.1534/genetics.114.169573
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Genetics. 2014 Dec 1;198(4):1377-93
info:eu-repo/grantAgreement/ES/3PN/TIN2011-22826
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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