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dc.contributor.authorRamayo-Caldas, Yuliaxis
dc.contributor.authorMármol-Sánchez, Emilio
dc.contributor.authorBallester, Maria
dc.contributor.authorPablo Sánchez, Juan
dc.contributor.authorGonzález-Prendes, Rayner
dc.contributor.authorAmills, Marcel
dc.contributor.authorQuintanilla, Raquel
dc.contributor.otherProducció Animalca
dc.date.accessioned2019-12-03T16:15:50Z
dc.date.available2019-12-03T16:15:50Z
dc.date.issued2019-09-02
dc.identifier.citationRamayo-Caldas, Y., Mármol-Sánchez, E., Ballester, M., Sánchez, J., González-Prendes, R., Amills, M., & Quintanilla, R. (2019). Integrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigs. Genetics Selection Evolution, 51(1). doi:10.1186/s12711-019-0490-6ca
dc.identifier.issn0999-193Xca
dc.identifier.urihttp://hdl.handle.net/20.500.12327/572
dc.description.abstractBackground: Feed efciency (FE) has a major impact on the economic sustainability of pig production. We used a systems‑based approach that integrates single nucleotide polymorphism (SNP) co‑association and gene‑expression data to identify candidate genes, biological pathways, and potential predictors of FE in a Duroc pig population. Results: We applied an association weight matrix (AWM) approach to analyse the results from genome‑wide associa‑ tion studies (GWAS) for nine FE associated and production traits using 31K SNPs by defning residual feed intake (RFI) as the target phenotype. The resulting co‑association network was formed by 829 SNPs. Additive efects of this SNP panel explained 61% of the phenotypic variance of RFI, and the resulting phenotype prediction accuracy estimated by cross‑validation was 0.65 (vs. 0.20 using pedigree‑based best linear unbiased prediction and 0.12 using the 31K SNPs). Sixty‑eight transcription factor (TF) genes were identifed in the co‑association network; based on the lossless approach, the putative main regulators were COPS5, GTF2H5, RUNX1, HDAC4, ESR1, USP16, SMARCA2 and GTF2F2. Fur‑ thermore, gene expression data of the gluteus medius muscle was explored through diferential expression and mul‑ tivariate analyses. A list of candidate genes showing functional and/or structural associations with FE was elaborated based on results from both AWM and gene expression analyses, and included the aforementioned TF genes and other ones that have key roles in metabolism, e.g. ESRRG, RXRG, PPARGC1A, TCF7L2, LHX4, MAML2, NFATC3, NFKBIZ, TCEA1, CDCA7L, LZTFL1 or CBFB. The most enriched biological pathways in this list were associated with behaviour, immunity, nervous system, and neurotransmitters, including melatonin, glutamate receptor, and gustation pathways. Finally, an expression GWAS allowed identifying 269 SNPs associated with the candidate genes’ expression (eSNPs). Addition of these eSNPs to the AWM panel of 829 SNPs did not improve the accuracy of genomic predictions. Conclusions: Candidate genes that have a direct or indirect efect on FE‑related traits belong to various biological processes that are mainly related to immunity, behaviour, energy metabolism, and the nervous system. The pituitary gland, hypothalamus and thyroid axis, and estrogen signalling play fundamental roles in the regulation of FE in pigs. The 829 selected SNPs explained 61% of the phenotypic variance of RFI, which constitutes a promising perspective for applying genetic selection on FE relying on molecular‑based prediction.ca
dc.format.extent17ca
dc.language.isoengca
dc.publisherBMCca
dc.relation.ispartofGenetics Selection Evolutionca
dc.rightsAttribution 4.0 Internationalca
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleIntegrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigsca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.relation.projectIDMINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2013-48742-C2-2-R/ES/FISIOLOGIA GENOMICA DEL DEPOSITO DE GRASA INTRAMUSCULAR EN PORCINO/ca
dc.relation.projectIDMINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2013-48742-C2-1-R/ES/FISIOLOGIA GENOMICA DEL DEPOSITO DE GRASA INTRAMUSCULAR EN PORCINO/ca
dc.relation.projectIDMICINN/Proyectos de Investigación Fundamental/AGL2010-22208-C02-01/ES/ESTUDIO DE CARACTERES RELACIONADOS CON EL METABOLISMO LIPIDICO Y LA CALIDAD EN PORCINO MEDIANTE EL ANALISIS INTEGRAL DE DATOS MASIVOS DE GENOTIPOS Y EXPRESION GENICA/ca
dc.relation.projectIDMICINN/Proyectos de Investigación Fundamental/AGL2010-22208-C02-02/ES/ESTUDIO DE CARACTERES RELACIONADOS CON EL METABOLISMO LIPIDICO Y LA CALIDAD EN PORCINO MEDIANTE EL ANALISIS INTEGRAL DE DATOS MASIVOS DE GENOTIPOS Y EXPRESION GENICA/ca
dc.relation.projectIDEC/H2020/665919/EU/Opening Sphere UAB-CEI to PostDoctoral Fellows/P-SPHEREca
dc.relation.projectIDMINECO/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/RYC‑2013‑12573/ES/ /ca
dc.subject.udc619ca
dc.identifier.doihttps://doi.org/10.1186/s12711-019-0490-6ca
dc.contributor.groupGenètica i Millora Animalca


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/