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A search for pleotropic effects on reproductive traits in Nellore cattle considering genotype by environment interaction

Grant number: 17/02291-3
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): August 01, 2017
Effective date (End): July 31, 2018
Field of knowledge:Agronomical Sciences - Animal Husbandry
Principal Investigator:Lucia Galvão de Albuquerque
Grantee:Lúcio Flávio Macedo Mota
Supervisor abroad: Guilherme Jordao de Magalhaes Rosa
Home Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
Local de pesquisa : University of Wisconsin-Madison (UW-Madison), United States  
Associated to the scholarship:15/25356-8 - Estimation of genotype-environment interaction using genomic reaction norm and analysis of gene network for reproductive traits in Nellore cattle, BP.DR


The main objective of the present research project is to identify candidate genes affecting more than one trait related to reproductive traits in Nellore cattle and to investigate whether the identified candidate genes change their effects according to the environmental conditions. Phenotypic and genotypic data of Nellore animals from different commercial breeding programs will be used. The traits associated with reproductive traits are: age at first calving (AFC), heifer early pregnancy (HP), and scrotal circumference (SC). A total of 128,994; 94,339 and 151,553 animals for AFC, HR and SC, respectively, will be used. The genotypic data of 1,900 females, 1,500 males genotyped with 777,962 SNP markers and 1,756 males with 70,000 SNP markers, will be used. Males genotyped with panel 70K will be imputed to the HD panel using FImpute. Environment gradient (EG) will be defined according to animals born in the same herd and year, from the same management group (from birth to yearling). EG levels will be standardized to present a mean equal 0 and variance of 1, and with values ranging from -3 to +3 SD units. Firstly, it will be fitted a reaction norm model for each trait in order to obtain genetic parameters and SNP effects for the traits in each one of the three environment levels considered (Low, Intermediate and High). Then, candidate genes with possibly pleiotropic effects will be identified using a Multi-Trait Meta-Analysis which simultaneously considers the SNP effects for all traits (AFC, EP, and SC) in each environment level (Low, Medium and High). Thus, gene pleiotropic effect studies are important to detect gene showing multiple functions and specific effects between traits, with goal ascertaining how such genes may affect the performance and GxE interaction in animals.