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Use of genetic variance in dynamic mechanistic models of growth to predict cattle performance and carcass composition under feedlot conditions

Grant number: 13/26902-0
Support type:Scholarships in Brazil - Master
Effective date (Start): May 01, 2014
Effective date (End): August 31, 2015
Field of knowledge:Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
Principal Investigator:José Bento Sterman Ferraz
Grantee:Mateus Castelani Freua
Home Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil

Abstract

The prediction of phenotypic variability among beef cattle may represent an opportunity to manage optimum slaughter endpoint, to target production to specific markets, and to reallocate animals throughout the supply chain. Dynamic mechanistic models of cattle growth have been used as decision support tools to assist individual cattle management systems. However, these models are still based on scaling factors to convert individual animal traits into a reference animal, with no further approach to sustain variability during simulations. By assuming that mechanistic models properly represent environmental deviations components of phenotypic variance and considering that SNPs models are able to predict the genetic component of this variance, this project aims to evolve towards a mathematical model that takes between-animal variance to its genetic level. Using hierarchical models, genetic variance will be incorporated directly into model parameters. This study will consider two mechanistic models (CDVS and DGM) to simultaneously estimate model parameters and their variance between different genotypes of the leptin and receptor genes. Genotype-specific vectors comprised of the effects of each genotype on model parameters will then be used for simulations instead of original model parameters. By doing this, we hope to develop a model capable of representing genetic variability among beef cattle under feedlot conditions and performing simulations with inputs from individuals' genotypes. This project is the first of this kind in Brazil and the first to use Bos indicus genotypes to study the use of genomics in the management of commercial livestock.

Scientific publications (5)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DE ALMEIDA SANTANA, MIGUEL HENRIQUE; GEBIM POLIZEL, GUILHERME HENRIQUE; GRIGOLETTO, LAIS; FERNANDES, ARICIA CHRISTOFARO; BONIN, MARINA DE NADAI; GOMES, RODRIGO DA COSTA; CARVALHO, MINOS ESPERANDIO; DA LUZ E SILVA, SAULO; LEME, PAULO ROBERTO; ROSSI JUNIOR, PAULO; STERMAN FERRAZ, JOSE BENTO. The genetic and genomic effects of Nellore lineages on feed efficiency, intake and performance. LIVESTOCK SCIENCE, v. 228, p. 104-108, OCT 2019. Web of Science Citations: 0.
GEBIM POLIZEL, GUILHERME HENRIQUE; GRIGOLETTO, LAIS; CARVALHO, MINOS ESPERANDIO; ROSSI JUNIOR, PAULO; STERMAN FERRAZ, JOSE BENTO; DE ALMEIDA SANTANA, MIGUEL HENRIQUE. Genetic correlations and heritability estimates for dry matter intake, weight gain and feed efficiency of Nellore cattle in feedlot. LIVESTOCK SCIENCE, v. 214, p. 209-210, AUG 2018. Web of Science Citations: 3.
FREUA, MATEUS CASTELANI; DE ALMEIDA SANTANA, MIGUEL HENRIQUE; VENTURA, RICARDO VIEIRA; TEDESCHI, LUIS ORLINDO; STERMAN FERRAZ, JOSE BENTO. Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits. JOURNAL OF APPLIED GENETICS, v. 58, n. 3, p. 393-400, AUG 2017. Web of Science Citations: 1.
FREUA, MATEUS CASTELANI; DE ALMEIDA SANTANA, MIGUEL HENRIQUE; STERMAN FERRAZ, JOSE BENTO. A new approach for applied nutritional models: Computing parameters of dynamic mechanistic growth models using genorne-wide prediction. LIVESTOCK SCIENCE, v. 190, p. 131-135, AUG 2016. Web of Science Citations: 1.
SANTANA, M. H. A.; FREUA, M. C.; DO, D. N.; VENTURA, R. V.; KADARMIDEEN, H. N.; FERRAZ, J. B. S. Systems genetics and genome-wide association approaches for analysis of feed intake, feed efficiency, and performance in beef cattle. Genetics and Molecular Research, v. 15, n. 4 2016. Web of Science Citations: 3.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.