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Bayesian recursive mixed linear model for gene expression analyses with continuous covariates

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Bayesian recursive mixed linear model for gene expression analyses with continuous covariates

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dc.contributor.author Casellas, J. es_ES
dc.contributor.author Ibáñez-Escriche, Noelia es_ES
dc.date.accessioned 2020-03-12T06:51:47Z
dc.date.available 2020-03-12T06:51:47Z
dc.date.issued 2012-01 es_ES
dc.identifier.issn 0021-8812 es_ES
dc.identifier.uri http://hdl.handle.net/10251/138742
dc.description.abstract [EN] The analysis of microarray gene expression data has experienced a remarkable growth in scientific research over the last few years and is helping to decipher the genetic background of several productive traits. Nevertheless, most analytical approaches have relied on the comparison of 2 (or a few) well-defined groups of biological conditions where the continuous covariates have no sense (e. g., healthy vs. cancerous cells). Continuous effects could be of special interest when analyzing gene expression in animal production-oriented studies (e. g., birth weight), although very few studies address this peculiarity in the animal science framework. Within this context, we have developed a recursive linear mixed model where not only are linear covariates accounted for during gene expression analyses but also hierarchized and the effects of their genetic, environmental, and residual components on differential gene expression inferred independently. This parameterization allows a step forward in the inference of differential gene expression linked to a given quantitative trait such as birth weight. The statistical performance of this recursive model was exemplified under simulation by accounting for different sample sizes (n), heritabilities for the quantitative trait (h(2)), and magnitudes of differential gene expression (lambda). It is important to highlight that statistical power increased with n, h(2), and lambda, and the recursive model exceeded the standard linear mixed model with linear (nonrecursive) covariates in the majority of scenarios. This new parameterization would provide new insights about gene expression in the animal science framework, opening a new research scenario where within-covariate sources of differential gene expression could be individualized and estimated. The source code of the program accommodating these analytical developments and additional information about practical aspects on running the program are freely available by request to the corresponding author of this article. es_ES
dc.description.sponsorship This research was funded by grant AGL2008-04818-C03 (Ministerio de Ciencia e Innovacion, Madrid, Spain). The research contract of J. Casellas was partially financed by the Ministerio de Ciencia e Innovacion of Spain (program Ramon y Cajal, reference RYC-2009-04049). The authors are also indebted to 2 anonymous referees for their helpful comments on the manuscript. es_ES
dc.language Inglés es_ES
dc.publisher American Society of Animal Science es_ES
dc.relation MICINN/RYC-2009-04049 es_ES
dc.relation MICINN/AGL2008-04818-C03 es_ES
dc.relation.ispartof Journal of Animal Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Bayesian inference es_ES
dc.subject Gene expression es_ES
dc.subject Microarray es_ES
dc.subject Mixed model es_ES
dc.subject Recursive es_ES
dc.subject.classification PRODUCCION ANIMAL es_ES
dc.title Bayesian recursive mixed linear model for gene expression analyses with continuous covariates es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.2527/jas.2010-3750 es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal es_ES
dc.description.bibliographicCitation Casellas, J.; Ibáñez-Escriche, N. (2012). Bayesian recursive mixed linear model for gene expression analyses with continuous covariates. Journal of Animal Science. 90(1):67-75. https://doi.org/10.2527/jas.2010-3750 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.2527/jas.2010-3750 es_ES
dc.description.upvformatpinicio 67 es_ES
dc.description.upvformatpfin 75 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 90 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\395256 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES


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