Mapping eQTL networks with mixed graphical Markov models
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- dc.contributor.author Tur Mongé, Imma, 1985-
- dc.contributor.author Roverato, Alberto
- dc.contributor.author Castelo Valdueza, Robert
- dc.date.accessioned 2025-06-06T07:16:37Z
- dc.date.available 2025-06-06T07:16:37Z
- dc.date.issued 2014
- dc.description.abstract 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.
- dc.description.sponsorship This work has been supported by a grant from the Spanish Ministry of Economy and Competitiveness to R.C. (ref. TIN2011-22826).
- dc.format.mimetype application/pdf
- dc.identifier.citation 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
- dc.identifier.doi http://dx.doi.org/10.1534/genetics.114.169573
- dc.identifier.issn 0016-6731
- dc.identifier.uri http://hdl.handle.net/10230/70637
- dc.language.iso eng
- dc.publisher Oxford University Press
- dc.relation.ispartof Genetics. 2014 Dec 1;198(4):1377-93
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TIN2011-22826
- dc.rights © 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.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword eQTL
- dc.subject.keyword Gene network
- dc.subject.keyword Exact-likelihood-ratio test
- dc.subject.keyword Conditional Gaussian distribution
- dc.subject.keyword Mixed graphical Markov model
- dc.title Mapping eQTL networks with mixed graphical Markov models
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/acceptedVersion