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

Author(s)

Mota, Lucio F. M. ; Arikawa, Leonardo M. ; Santos, Samuel W. B. ; Fernandes, Gerardo A. ; Alves, Anderson A. C. ; Rosa, Guilherme J. M. ; Mercadante, Maria E. Z. ; Cyrillo, Joslaine N. S. G. ; Carvalheiro, Roberto ; Albuquerque, Lucia G.

• Document type: Journal article
• Source: SCIENTIFIC REPORTS; v. 14, n. 1, p. 14-pg., 2024-03-17.

Abstract

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 similar to 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. (AU)

• FAPESP's process: 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle
• Grantee: Lucia Galvão de Albuquerque
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/10630-2 - Genetic aspects of meat production quality, efficiency and sustainability in Nelore breed animals
• Grantee: Lucia Galvão de Albuquerque
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/20026-8 - Multi-user equipment approved in grant 2017/10630-2: server
• Grantee: Lucia Galvão de Albuquerque
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 16/24228-9 - Genomic wide association study for feed efficiency traits in Nellore cattle breeding programs
• Grantee: Samuel Wallace Boer dos Santos
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 17/13411-0 - Evaluation of benefits in combining two Nelore populations for genomic selection
• Grantee: Samuel Wallace Boer dos Santos
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree