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Genotype-environment interaction in Nellore cattle for growth traits and visual scores using sequence data

Grant number: 19/06361-1
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): January 01, 2020
Effective date (End): July 31, 2022
Field of knowledge:Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
Principal Investigator:Roberto Carvalheiro
Grantee:Ivan Carvalho Filho
Home Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
Associated research grant:17/10630-2 - Genetic aspects of meat production quality, efficiency and sustainability in Nelore breed animals, AP.TEM

Abstract

The predominance of the Nellore cattle in Brazil is due to its greater adaptability, leading to its breeding in most parts of Brazilian territory, which makes genetic evaluations of this breed of great importance. However, in genetic evaluations, genotype-environment interaction is not usually incorporated into the model, leading to the selection of animals with sub-optimal genetic potential for determinate environments. Reaction norm models can be used to predict animal performance in different environments and with the inclusion of genomic information in the models it is possible to obtain can obtain more accurate genetic values in addition to discovery to the discovery of chromosomal regions of greater effect in determinate environments. The aim of this study is to evaluate the occurrence of genotype-environment interaction using genomic information from complete genome sequencing for growth and visual scores traits and to use genomic information to analyze prediction accuracy and to perform genomic association studies. Approximately one million phenotypic observations of herds distributed in different regions will be used. Approximately 7,000 animals were genotyped with a High-Density (HD) panel or had their genotypes imputed to HD and complete genome sequencing of approximately 150 bulls, all HD genotyped data will be imputed to the complete sequence. The studies of association and genomic prediction will be conducted considering the single-step GBLUP and BayesR methodology and the predictive ability of genomic selection will be evaluated using cross-validation, bias and mean square error between predicted values and pseudo- phenotypes. It is expected that this work will help identify the most appropriate reaction pattern models and the identification and selection of higher breeding value animals, as well as the identification of genomic regions related to environmental sensitivity, in order to better understand their influence on phenotypic expression. (AU)