Genomic prediction ability for methane emission traits in Nellore cattle

• Grant number: 23/14482-9
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: September 01, 2024
• End date: July 31, 2026
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Maria Eugênia Zerlotti Mercadante
• Grantee: Tainara Luana da Silva Soares
• Host Institution: Instituto de Zootecnia. Agência Paulista de Tecnologia dos Agronegócios (APTA). Secretaria de Agricultura e Abastecimento (São Paulo - Estado). Nova Odessa , SP, Brazil

Abstract

The aim of this study will be to evaluate the genomic prediction ability of methane emission traits in the Nelore breed using different genomic prediction models, validation strategies and response variable. The database currently consists of 1,050 Nelore animals, from four different herds with phenotypic information for: methane emission (CH4; g/day), methane emission per kg of dry matter consumed (CH4CMS; g/kgCMS/day), methane emission per unit of average daily gain (CH4GMD; g/kgGMD/day), methane emission per unit of average live weight (CH4PVM; g/kgPVM/day) and residual methane (CH4RES; g/day). The animals were genotyped with BovineHD BeadChip (770k) and GGP Indicus (50 and 70k). Animals genotyped with lower density will be imputed for HD. The contemporary groups will be defined considering the methane collection groups composed of animals that participated in the feed efficiency test, of the same sex and year of birth. The genomic prediction accuracy of the methods: GBLUP, Bayes A and Bayes B will be evaluated. To evaluate the prediction ability of these models, different validation approaches will be used: random cross-validation, age and unrelated groups. The response variables used to evaluate the accuracy and prediction bias of the models will be: y corrected for fixed effects, traditional breeding value (EBV) and deregressed breeding value (DEBV). The prediction ability will be given by the correlation (r) between the GEBV estimated in the validation population and the response variable. The prediction bias will be obtained by regressing the response variable on GEBV and the mean square prediction error (QME) will be used to verify the fit of the models.