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GENOMIC-METABOLOMIC INTEGRATION FOR ASSESSING FEED EFFICIENCY AND ENTERIC METHANE EMISSIONS IN NELLORE CATTLE

Grant number: 24/01607-0
Support Opportunities:Scholarships abroad - Research
Effective date (Start): June 01, 2024
Effective date (End): July 31, 2024
Field of knowledge:Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
Principal Investigator:Maria Eugênia Zerlotti Mercadante
Grantee:Maria Eugênia Zerlotti Mercadante
Host Investigator: Flavio Schramm Schenkel
Host Institution: Instituto de Zootecnia. Agência Paulista de Tecnologia dos Agronegócios (APTA). Secretaria de Agricultura e Abastecimento (São Paulo - Estado). Nova Odessa , SP, Brazil
Research place: University of Guelph, Canada  
Associated research grant:17/50339-5 - Institutional research development plan of the Animal Science Institute (PDIp), AP.PDIP

Abstract

The feeding costs represent the largest investment in livestock production systems. Feed efficiency is directly linked to increased meat production per unit of feed. Additionally, efficient animals are associated with the sustainability of the livestock system since they reduce the demand for food, consequently decreasing the impact generated by enteric methane emissions. In this context, this project proposes integrating genomic and metabolomic data into a genetic evaluation model to assess the traits of residual feed intake, enteric methane emissions and residual enteric methane emission in Nellore cattle. The information to be used has been collected over two decades in the Beef Cattle Research Center - Institute of Animal Science, and currently, the database comprises 2,150 animals phenotyped for residual feed intake, 870 animals phenotyped for enteric methane emission, and 3,226 genotyped animals. For this study, the mixed models' equations will be adapted to include the incidental effects of metabolite expression levels on the evaluated traits. Subsequently, an animal model will be used to estimate genetic and phenotypic variances and to obtain BLUP solutions, considering the fixed effect of the contemporary group, the random effect of metabolite expression levels, and the direct additive genetic effect. The results from this study will highlight the potential benefits of integrating different omics data for genetic evaluations, potentially increasing the accuracy of predictions.

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