Multiracial genetic evaluation strategies for feed efficiency traits

• Grant number: 24/17795-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: February 01, 2025
• End date: December 31, 2028
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Fernando Sebastián Baldi Rey
• Grantee: Larissa Bordin Temp
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil

Abstract

This research project aims to evaluate different strategies for assessing a metapopulation composed of various breeds, such as Angus, Braford, Brangus, Charolais, Hereford, Montana Composite, and Nellore, using the single-step genomic BLUP method for traits related to feed efficiency, including residual feed intake (RFI), residual weight gain (RWG), and dry matter intake (DMI). Phenotypic and genotypic information will be used from various breeding programs, including the Beef Cattle Improvement Program (PROMEBO) of Embrapa Pecuária Sul, the Montana Tropical Composite program - CFM Leachman Pecuária Ltda., the Nellore Brasil program of the National Association of Breeders and Researchers (ANCP, Ribeirão Preto, SP), Brangus+ from the Brazilian Brangus Association (ABB) and Embrapa Pecuária Sul, PampaPlus from the Brazilian Hereford and Braford Association (ABHB). Heritabilities and variance components will be estimated without genomic information using Gibbs sampling by GIBBSF90+ software from the BLUPF90 program suite. Additionally, multi-trait analyses will be conducted to assess genetic and phenotypic correlations. Different scenarios will be proposed to assess the feasibility of evaluating the breeds together or separately, including metafounders or constructing the G matrix with allele frequencies specific to each breed. A joint evaluation of the subpopulations can provide these advantages, making it highly relevant for Brazilian livestock. The central hypothesis is that integrating these databases will contribute to a better understanding of the genetic architecture of the breeds being studied, allowing for more precise monitoring of genetic diversity and more accurate genomic evaluations, thereby promoting sustainable improvement.