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

التفاصيل البيبلوغرافية
العنوان: Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.
المؤلفون: Mota LFM; School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil. flaviommota.zoo@gmail.com., Arikawa LM; School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil., Santos SWB; School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil., Fernandes Júnior GA; School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil., Alves AAC; School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil., Rosa GJM; Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, 53706, USA., Mercadante MEZ; Institute of Animal Science, Beef Cattle Research Center, Sertãozinho, SP, 14174-000, Brazil.; National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil., Cyrillo JNSG; Institute of Animal Science, Beef Cattle Research Center, Sertãozinho, SP, 14174-000, Brazil., Carvalheiro R; School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.; National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil., Albuquerque LG; School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil. galvao.albuquerque@unesp.br.; National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil. galvao.albuquerque@unesp.br.
المصدر: Scientific reports [Sci Rep] 2024 Mar 17; Vol. 14 (1), pp. 6404. Date of Electronic Publication: 2024 Mar 17.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Benchmarking* , Polymorphism, Single Nucleotide*, Cattle/genetics ; Animals ; Bayes Theorem ; Models, Genetic ; Phenotype ; Genomics/methods ; Genotype
مستخلص: Genomic 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.
(© 2024. The Author(s).)
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تواريخ الأحداث: Date Created: 20240317 Date Completed: 20240318 Latest Revision: 20240321
رمز التحديث: 20240321
مُعرف محوري في PubMed: PMC10944497
DOI: 10.1038/s41598-024-57234-4
PMID: 38493207
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-024-57234-4