Investigating the effect of heat stress on feed efficiency-related traits in Nellore cattle based on genomic reaction norm models

• Grant number: 23/13417-9
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: February 29, 2024
• End date: February 27, 2025
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Fernando Sebastián Baldi Rey
• Grantee: João Barbosa da Silva Neto
• Supervisor: Luiz Fernando Brito
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Institution abroad: Purdue University, United States
• Associated to the scholarship: 22/15385-4 - GENOMIC REACTION NORM MODELS AND GENETIC NETWORK ANALYSIS EXPLORING THE EFFECTS OF GENOTYPE X ENVIRONMENT INTERACTION ON FEED EFFICIENCY TRAITS IN NELORE CATTLE, BP.DR

Abstract

Global warming is a major threat to livestock production and under heat stress conditions feed intake and efficiency is altered. In this context, this research project aims to: 1) investigate the occurrence of genotype-by-environment interactions (GxE) for dry matter intake (DMI) and residual feed intake (RFI) in Nellore cattle based on genomic reaction norm models; 2) estimate variance components for DMI and RFI and identify genomic regions associated with environmental plasticity and pleiotropic regions across different environmental gradients; and, 3) assess the genetic relationship between DMI and RFI across environmental gradients. The phenotypic information for feed efficiency-related traits in Nellore cattle will be provided by the National Association of Breeders and Researchers (ANCP, Ribeirão Preto, SP, Brazil). The datasets will include information from >300 feeding trials and ~20,000 animals with phenotypic records for average daily gain (ADG), DMI, and RFI. Over 40,000 animals born between 2014 and 2022 were genotyped with an SNP panel containing more than 30,000 markers, which will be imputed to 735,044 markers. The reference population for imputation will consist of 963 representative sires of the main Nellore lineages imported from India during the twentieth century, born between 1995 and 2015, and genotyped with the Illumina BovineHD BeadChip. The genomic quality control will be performed using the pregsf90 software. Samples and SNPs with a call rate <0.90 will be removed from the dataset. Markers with Mendelian conflicts >1%, with redundant position, minor allele frequency (MAF) <0.05, and those located in non-autosomal chromosomes will also be removed. The temperature and humidity index (THI) will be used for deriving the environmental gradients. The climatic variables will be retrieved from NASA POWER based on each herd's geographical coordinates. The single-step linear animal model (ssGBLUP) combining pedigree and genomic information will be used to evaluate the traits related to feed efficiency. The analyses will be performed using the gibbs2F90 program. The effects of SNP markers will be estimated at each level of EG from a linear transformation of the predicted genomic breeding values for the genotyped animals in each environmental group. Based on these effects, possible changes or pleiotropic effects of candidate genes will be investigated, as well as biological pathways of significant importance on the phenotypic expression of feed efficiency indicator traits. The present study will enable the evaluation of genetic parameter estimates for DMI and RFI traits under different climatic conditions based on THI as an environmental gradient. We will determine the genomic background of heat stress sensitivity based on variability in feed efficiency traits in Nellore cattle, which will enable a better understanding of the biological mechanisms regulating the phenotypic expression of DMI and RFI traits at different environmental levels. Altogether, this project will contribute to the optimization of Nellore cattle breeding programs for improved feed efficiency under challenging environmental conditions.