Using transcriptomic data to improve the prediction of immunity traits in pigs

Considering health-related traits among breeding selection criteria has been proposed as a way to improve pig robustness. This study investigated the potential of whole blood RNA-sequencing data for predicting immunity-related traits, stress indicators and carcass weight, using data from 255 pigs be...

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Autores: Jové-Juncà, Teodor, Haas, V.P., Calus, M.P.L., Ballester Devis, Maria, Quintanilla, Raquel
Tipo de recurso: artículo
Fecha de publicación:2025
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:20.500.12327/5015
Acceso en línea:https://hdl.handle.net/20.500.12327/5015
https://doi.org/10.1016/j.animal.2025.101742
Access Level:acceso abierto
Palabra clave:577
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spelling Using transcriptomic data to improve the prediction of immunity traits in pigsJové-Juncà, TeodorHaas, V.P.Calus, M.P.L.Ballester Devis, MariaQuintanilla, Raquel577Considering health-related traits among breeding selection criteria has been proposed as a way to improve pig robustness. This study investigated the potential of whole blood RNA-sequencing data for predicting immunity-related traits, stress indicators and carcass weight, using data from 255 pigs belonging to a commercial Duroc population. The prediction performance of mixed models fitting either genomic (G), transcriptomic (T) or both effects as independent (GT) was evaluated and compared. Three additional models addressing the redundant information between G and T were also evaluated: the GTC model that subtracts the genetic effect from the transcriptome, the GTCi model that makes this correction based on the estimated heritability of T effects, and a multiomic model that weights G and T effects in a multiomics relationship matrix. The models including gene expression information captured a higher proportion of variance than the genomic model for all studied traits but carcass weight. Adding transcriptomic effects improved both model fit and phenotypic prediction of all immunity traits, particularly those with a high transcriptomic contribution such as the abundance of T helper and γδ T cells, the haptoglobin concentration and the leukocyte counts. Considering the interaction between genomic and transcriptomic effects led to greater prediction accuracies, with the GTCi model performing the best. Our work demonstrates the value of considering gene expression data to predict immunity traits as well as the importance of adequately modelling the interaction between genomic and transcriptomic effects.This study was funded by grants PID2020-112677RB-C21 and PID2023-148961OB-C21 awarded by MCIN/AEI/10.13039/501100011033. Jové-Juncà, T. was supported by an IRTA fellowship (CPI1221).info:eu-repo/semantics/publishedVersionElsevierProducció AnimalGenètica i Millora Animal2025info:eu-repo/semantics/article13https://hdl.handle.net/20.500.12327/5015https://doi.org/10.1016/j.animal.2025.101742reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAnimalMICINN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I y Programa Estatal de I+D+I orientada a los retos de la sociedad/PID2020-112677RB-C21/ES/FISIOLOGIA MOLECULAR DEL INMUNOMETABOLISMO EN PORCINO: BASES PARA LA SELECCION DE POBLACIONES MAS ROBUSTAS/MICINN/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/PID2023-148961OB-C21/ES/MEJORA GENETICA DE LA SALUD PORCINA: IDENTIFICACION Y VALIDACION DE BIOMARCADORES Y MODELOS PREDICTIVOS DE LA INMUNOCOMPETENCIA/Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:20.500.12327/50152026-05-29T05:05:01Z
dc.title.none.fl_str_mv Using transcriptomic data to improve the prediction of immunity traits in pigs
title Using transcriptomic data to improve the prediction of immunity traits in pigs
spellingShingle Using transcriptomic data to improve the prediction of immunity traits in pigs
Jové-Juncà, Teodor
577
title_short Using transcriptomic data to improve the prediction of immunity traits in pigs
title_full Using transcriptomic data to improve the prediction of immunity traits in pigs
title_fullStr Using transcriptomic data to improve the prediction of immunity traits in pigs
title_full_unstemmed Using transcriptomic data to improve the prediction of immunity traits in pigs
title_sort Using transcriptomic data to improve the prediction of immunity traits in pigs
dc.creator.none.fl_str_mv Jové-Juncà, Teodor
Haas, V.P.
Calus, M.P.L.
Ballester Devis, Maria
Quintanilla, Raquel
author Jové-Juncà, Teodor
author_facet Jové-Juncà, Teodor
Haas, V.P.
Calus, M.P.L.
Ballester Devis, Maria
Quintanilla, Raquel
author_role author
author2 Haas, V.P.
Calus, M.P.L.
Ballester Devis, Maria
Quintanilla, Raquel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Producció Animal
Genètica i Millora Animal
dc.subject.none.fl_str_mv 577
topic 577
description Considering health-related traits among breeding selection criteria has been proposed as a way to improve pig robustness. This study investigated the potential of whole blood RNA-sequencing data for predicting immunity-related traits, stress indicators and carcass weight, using data from 255 pigs belonging to a commercial Duroc population. The prediction performance of mixed models fitting either genomic (G), transcriptomic (T) or both effects as independent (GT) was evaluated and compared. Three additional models addressing the redundant information between G and T were also evaluated: the GTC model that subtracts the genetic effect from the transcriptome, the GTCi model that makes this correction based on the estimated heritability of T effects, and a multiomic model that weights G and T effects in a multiomics relationship matrix. The models including gene expression information captured a higher proportion of variance than the genomic model for all studied traits but carcass weight. Adding transcriptomic effects improved both model fit and phenotypic prediction of all immunity traits, particularly those with a high transcriptomic contribution such as the abundance of T helper and γδ T cells, the haptoglobin concentration and the leukocyte counts. Considering the interaction between genomic and transcriptomic effects led to greater prediction accuracies, with the GTCi model performing the best. Our work demonstrates the value of considering gene expression data to predict immunity traits as well as the importance of adequately modelling the interaction between genomic and transcriptomic effects.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12327/5015
https://doi.org/10.1016/j.animal.2025.101742
url https://hdl.handle.net/20.500.12327/5015
https://doi.org/10.1016/j.animal.2025.101742
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Animal
MICINN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I y Programa Estatal de I+D+I orientada a los retos de la sociedad/PID2020-112677RB-C21/ES/FISIOLOGIA MOLECULAR DEL INMUNOMETABOLISMO EN PORCINO: BASES PARA LA SELECCION DE POBLACIONES MAS ROBUSTAS/
MICINN/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/PID2023-148961OB-C21/ES/MEJORA GENETICA DE LA SALUD PORCINA: IDENTIFICACION Y VALIDACION DE BIOMARCADORES Y MODELOS PREDICTIVOS DE LA INMUNOCOMPETENCIA/
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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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score 15,812455

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