Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software

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cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.affiliationColegio de Postgraduados, Mexicoen
cg.contributor.affiliationAardevo B.V.en
cg.contributor.affiliationCornell Universityen
cg.contributor.affiliationLouisiana State Universityen
cg.contributor.affiliationUniversity of Californiaen
cg.contributor.affiliationUniversidad de Quintana Rooen
cg.contributor.affiliationResearch Center for Cereal and Industrial Cropsen
cg.contributor.affiliationAustralian National Universityen
cg.contributor.affiliationNorwegian University of Life Sciencesen
cg.contributor.affiliationSwedish University of Agricultural Sciencesen
cg.contributor.affiliationUniversidad de Colimaen
cg.contributor.affiliationUniversidad de Guadalajaraen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeAccelerated Breeding
cg.creator.identifierJose Crossa: 0000-0001-9429-5855en
cg.creator.identifierJohannes Martini: 0000-0003-0628-6794en
cg.creator.identifierPaolo Vitale: 0000-0002-4353-5828en
cg.creator.identifierPaulino Pérez-Rodríguez: 0000-0002-3202-1784en
cg.creator.identifierGermano Costa Neto: 0000-0003-1137-6786en
cg.creator.identifierRoberto Fritsche-Neto: 0000-0003-4310-0047en
cg.creator.identifierDaniel Runcie: 0000-0002-3008-9312en
cg.creator.identifierJaime Cuevas: 0000-0002-0685-2867en
cg.creator.identifierFernando HenriqueToledo: 0000-0003-0158-643Xen
cg.creator.identifierHuihuiLi: 0000-0002-9117-5011en
cg.creator.identifierPasquale De Vita: 0000-0002-9573-0510en
cg.creator.identifierGuillermo Gerard: 0000-0002-9112-3588en
cg.creator.identifierSusanne Dreisigacker: 0000-0002-3546-5989en
cg.creator.identifierLeonardo Abdiel Crespo Herrera: 0000-0003-0506-4700en
cg.creator.identifierCarolina Saint Pierre: 0000-0003-1291-7468en
cg.creator.identifierAlison Bentley: 0000-0001-5519-4357en
cg.creator.identifierMorten Lillemo: 0000-0002-8594-8794en
cg.creator.identifierRodomiro Ortiz: 0000-0002-1739-7206en
cg.creator.identifierOsval A. Montesinos-López: 0000-0002-3973-6547en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1016/j.tplants.2024.12.009en
cg.isijournalISI Journalen
cg.issn1360-1385en
cg.issn1878-4372en
cg.journalTrends in Plant Scienceen
cg.reviewStatusPeer Reviewen
dc.contributor.authorCrossa, Joséen
dc.contributor.authorMartini, Johannes W.R.en
dc.contributor.authorVitale, Paoloen
dc.contributor.authorPerez-Rodriguez, Paulinoen
dc.contributor.authorCosta-Neto, Germanoen
dc.contributor.authorFritsche-Neto, Robertoen
dc.contributor.authorRuncie, Daniel E.en
dc.contributor.authorCuevas, Jaimeen
dc.contributor.authorToledo, Fernando H.en
dc.contributor.authorHuihui Lien
dc.contributor.authorDe Vita, Pasqualeen
dc.contributor.authorGerard, Guillermo S.en
dc.contributor.authorDreisigacker, Susanneen
dc.contributor.authorCrespo-Herrera, Leonardo A.en
dc.contributor.authorSaint Pierre, Carolinaen
dc.contributor.authorBentley, Alison R.en
dc.contributor.authorLillemo, Mortenen
dc.contributor.authorOrtiz, Rodomiroen
dc.contributor.authorMontesinos-Lopez, Osval A.en
dc.contributor.authorMontesinos-López, Abelardoen
dc.date.accessioned2025-03-25T15:01:38Zen
dc.date.available2025-03-25T15:01:38Zen
dc.identifier.urihttps://hdl.handle.net/10568/173853
dc.titleExpanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced softwareen
dcterms.abstractWith growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.en
dcterms.accessRightsOpen Access
dcterms.available2025-01-30en
dcterms.bibliographicCitationCrossa, J., Martini, J. W. R., Vitale, P., Pérez-Rodríguez, P., Costa-Neto, G., Fritsche-Neto, R., Runcie, D., Cuevas, J., Toledo, F., Li, H., De Vita, P., Gerard, G., Dreisigacker, S., Crespo-Herrera, L., Saint Pierre, C., Bentley, A., Lillemo, M., Ortiz, R., Montesinos-López, O. A., & Montesinos-López, A. (2025). Expanding genomic prediction in plant breeding: Harnessing big data, machine learning, and advanced software. Trends in Plant Science, S1360138524003455. https://doi.org/10.1016/j.tplants.2024.12.009en
dcterms.hasVersionhttps://hdl.handle.net/10883/35536en
dcterms.issued2025en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherElsevieren
dcterms.subjectmodelsen
dcterms.subjectplant breedingen
dcterms.subjectgenomicsen
dcterms.subjectforecastingen
dcterms.subjectsoftware developmenten
dcterms.subjectmarker-assisted selectionen
dcterms.subjectstatistical methodsen
dcterms.subjectmachine learningen
dcterms.typeJournal Article

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