A cost-effective method for combining the power of genetic and epigenetic selection in animal production

17 pages, 8 figures, 1 table, supplementary data https://doi.org/10.1093/eep/dvaf027.-- Data availability: All data used in this study are in the supplementary material https://doi.org/10.20350/digitalCSIC/17856 or have been deposited in a publicly accessible database: NCBI Sequence Read Archive (Bi...

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Detalles Bibliográficos
Autores: Sánchez Baizán, Núria, Herlin, Marine, Millán, Adrián, Martínez, Paulino, López-Belluga, María, Piferrer, Francesc
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/411478
Acceso en línea:http://hdl.handle.net/10261/411478
Access Level:acceso abierto
Palabra clave:Epigenetic biomarkers
Breeding programs
Key performance indicators
Machine learning
Feature selection
Polygenic traits
http://metadata.un.org/sdg/14
Conserve and sustainably use the oceans, seas and marine resources for sustainable development
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oai_identifier_str oai:digital.csic.es:10261/411478
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv A cost-effective method for combining the power of genetic and epigenetic selection in animal production
title A cost-effective method for combining the power of genetic and epigenetic selection in animal production
spellingShingle A cost-effective method for combining the power of genetic and epigenetic selection in animal production
Sánchez Baizán, Núria
Epigenetic biomarkers
Breeding programs
Key performance indicators
Machine learning
Feature selection
Polygenic traits
http://metadata.un.org/sdg/14
Conserve and sustainably use the oceans, seas and marine resources for sustainable development
title_short A cost-effective method for combining the power of genetic and epigenetic selection in animal production
title_full A cost-effective method for combining the power of genetic and epigenetic selection in animal production
title_fullStr A cost-effective method for combining the power of genetic and epigenetic selection in animal production
title_full_unstemmed A cost-effective method for combining the power of genetic and epigenetic selection in animal production
title_sort A cost-effective method for combining the power of genetic and epigenetic selection in animal production
dc.creator.none.fl_str_mv Sánchez Baizán, Núria
Herlin, Marine
Millán, Adrián
Martínez, Paulino
López-Belluga, María
Piferrer, Francesc
author Sánchez Baizán, Núria
author_facet Sánchez Baizán, Núria
Herlin, Marine
Millán, Adrián
Martínez, Paulino
López-Belluga, María
Piferrer, Francesc
author_role author
author2 Herlin, Marine
Millán, Adrián
Martínez, Paulino
López-Belluga, María
Piferrer, Francesc
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Epigenetic biomarkers
Breeding programs
Key performance indicators
Machine learning
Feature selection
Polygenic traits
http://metadata.un.org/sdg/14
Conserve and sustainably use the oceans, seas and marine resources for sustainable development
topic Epigenetic biomarkers
Breeding programs
Key performance indicators
Machine learning
Feature selection
Polygenic traits
http://metadata.un.org/sdg/14
Conserve and sustainably use the oceans, seas and marine resources for sustainable development
description 17 pages, 8 figures, 1 table, supplementary data https://doi.org/10.1093/eep/dvaf027.-- Data availability: All data used in this study are in the supplementary material https://doi.org/10.20350/digitalCSIC/17856 or have been deposited in a publicly accessible database: NCBI Sequence Read Archive (BioProject PRJNA1103406). Additional information on the specific genomic location of the target sites can be requested for research purposes only to the corresponding author
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/411478
url http://hdl.handle.net/10261/411478
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-139096OB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/BES-2017-079744
https://doi.org/10.1093/eep/dvaf027
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
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spelling A cost-effective method for combining the power of genetic and epigenetic selection in animal productionSánchez Baizán, NúriaHerlin, MarineMillán, AdriánMartínez, PaulinoLópez-Belluga, MaríaPiferrer, FrancescEpigenetic biomarkersBreeding programsKey performance indicatorsMachine learningFeature selectionPolygenic traitshttp://metadata.un.org/sdg/14Conserve and sustainably use the oceans, seas and marine resources for sustainable development17 pages, 8 figures, 1 table, supplementary data https://doi.org/10.1093/eep/dvaf027.-- Data availability: All data used in this study are in the supplementary material https://doi.org/10.20350/digitalCSIC/17856 or have been deposited in a publicly accessible database: NCBI Sequence Read Archive (BioProject PRJNA1103406). Additional information on the specific genomic location of the target sites can be requested for research purposes only to the corresponding authorTraditional breeding programs have largely focused on genetics, often overlooking environmental and epigenetic influences on phenotypic variability. Current methods for developing epigenetic biomarkers (EBs) with machine learning (ML) algorithms require extensive data, making them costly and time-intensive. In this study, using a fish as a model, we analysed ~500 000 CpG loci in samples from 60 different families to develop EBs for broodstock selection. To address limited sample sizes at the sequencing stage, we combined careful sample selection, statistical filtering, and various feature selection and ML algorithms. As a result, we identified three heritable CpGs sites in sire sperm associated with three key performance indicators in their offspring: biomass, fast-growing females, and resistance to the masculinizing effects of high temperature. Then, we were able to build a model successfully predicting the best sire broodstock based on DNA methylation levels of these EBs. This model was validated across three independent trials, including one involving an external cohort of fish with differentiated genetic background, thereby confirming its robustness beyond the training population. Yield was increased up to 1.4-fold when including epigenetic selection into the genetic selection program as compared with genetic selection alone. In summary, we present a cost-effective strategy for integrating epigenetic and genetic selection in the context of animal production. Furthermore, this method also can be applied to assess the impact of environmental factors into the broodstock and on samples where obtaining information can be challenging, such as in the study of the epigenetic basis of rare diseases, and the application of epigenetic markers in conservation biologyThis study was supported by the ‘Centro para el Desarrollo Tecnológico e Industrial’ (CDTI) and Fondo Europeo Marítimo y de Pesca (FEMP) through the Project IDI-20220244 to ML and by Spanish Ministry of Science grants PID2019-108888RB-I00 and PID2022-139096OB-I00 to FP with funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). NS was supported by a Spanish Ministry of Science and Innovation predoctoral scholarship (BES-2017-079744)Peer reviewedOxford University PressEuropean CommissionMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Ministerio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/411478reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108888RB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-139096OB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/BES-2017-079744https://doi.org/10.1093/eep/dvaf027Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4114782026-05-22T06:33:51Z
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