Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Wisconsin-
Autor(es): dc.contributorBeef Cattle Research Center-
Autor(es): dc.contributorNational Council for Science and Technological Development-
Autor(es): dc.creatorMota, Lucio F. M.-
Autor(es): dc.creatorArikawa, Leonardo M.-
Autor(es): dc.creatorSantos, Samuel W. B.-
Autor(es): dc.creatorFernandes Júnior, Gerardo A.-
Autor(es): dc.creatorAlves, Anderson A. C.-
Autor(es): dc.creatorRosa, Guilherme J. M.-
Autor(es): dc.creatorMercadante, Maria E. Z.-
Autor(es): dc.creatorCyrillo, Joslaine N. S. G.-
Autor(es): dc.creatorCarvalheiro, Roberto-
Autor(es): dc.creatorAlbuquerque, Lucia G.-
Data de aceite: dc.date.accessioned2025-08-21T22:54:38Z-
Data de disponibilização: dc.date.available2025-08-21T22:54:38Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-024-57234-4-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/297260-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/297260-
Descrição: dc.descriptionGenomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.-
Descrição: dc.descriptionSchool of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), SP-
Descrição: dc.descriptionDepartment of Animal and Dairy Sciences University of Wisconsin-
Descrição: dc.descriptionInstitute of Animal Science Beef Cattle Research Center, SP-
Descrição: dc.descriptionNational Council for Science and Technological Development, DF-
Descrição: dc.descriptionSchool of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleBenchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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