Application of weighted genomic matrix in predicting reproductive performance in Nelore cattle

• Grant number: 23/08817-8
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: January 08, 2024
• End date: July 06, 2024
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Fernando Sebastián Baldi Rey
• Grantee: Larissa Bordin Temp
• Supervisor: Daniela Andressa Lino Lourenço
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Institution abroad: University of Georgia, Athens (UGA), United States
• Associated to the scholarship: 22/12134-0 - Genomic prediction for sexual precocity and longevity traits using metafunders information through the blup genomic single step method of Nellore breed, BP.MS

Abstract

The aim of this project is to evaluate the use of the weighted genomic matrix for the allelic frequencies of the reproductive traits stayability (STAY72) and probability of early calving (PP30) in females, and scrotal circumference (SC365) in males of the Nelore breed using the single-step genomic BLUP (ssGBLUP) method. For the studied traits, an available data set of approximately 505.000, 152.000, and 240.000 animals will be used for STAY72, 3P, and SC365, respectively. The pedigree contains information on 1.5 million animals born between 1932 and 2022. Information on approximately 60,000 genotyped animals with a low-density panel containing more than 30,000 markers will also be used, and genotypes will be imputed to a panel with 70,000 markers using the FIMPUTE 2.2 software. For all genomic analyses, the ssGBLUP method will be used, where the genomic relationship matrix (G) and pedigree relationship matrix (A) are combined into one matrix H. To evaluate the prediction ability of different scenarios, the dataset will be divided into complete and partial subsets according to the LR method (linear regression). For animals in the partial subset, the accuracy of genomic values obtained in the traditional genomic evaluation approach will be compared with the G matrix centered and scaled for the specific allelic frequencies of the Nelore breed. (AU)