Comprehensive Genetic and Genomic Analysis of Longevity in Nellore Beef Cattle

• Grant number: 24/16681-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: March 01, 2025
• End date: February 29, 2028
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Miguel Henrique de Almeida Santana
• Grantee: Felipe Eguti de Carvalho
• Host Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil

Abstract

Increasing the profitability of beef cattle systems depends on accurately defining and incorporating longevity into herd genetic evaluations. This study aims to perform comprehensive genetic and genomic analyses using random regression models to estimate genetic parameters, correlations, and genetic architecture, along with prediction accuracy for key indicators of longevity in Nellore cattle. Two longevity definitions will be used: 1) traditional longevity, defined as the period from first calving to slaughter; and 2) functional longevity, which measures the duration a cow remains productive after her first calving. In addition, this study will investigate traits correlated with longevity, such as herd retention (STAY) and reproductive ability (REB), and their potential to be used as longevity indicators. The analysis will be performed using phenotypic records from approximately 570,000 Nellore cows and heifers. Genotypic data will come from 11,012 animals genotyped using different SNP panels. Reproductive performance traits (STAY and REB) and longevity definitions will be analyzed using Bayesian random regression models (RRM). Genetic correlations between different ages under the longevity definitions will be obtained from single-trait analyses, while correlations between longevity definitions and reproductive performance will be obtained from two-trait analyses. Genome-wide association studies (GWAS) will be performed using all genotyped animals, using linear mixed models computed via the postGWAS using BLUPF90 family programs. In addition, genomic prediction will be performed using the single-step genomic BLUP (ssGBLUP) method. Accuracy and bias of genomic predictions will be obtained through the linear regression (LR) approach. The findings of this research are expected to provide essential insights to support the selection of more productive and longer-lived cows, ultimately advancing genetic improvement efforts in the Brazilian beef cattle industry.