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GENOME-WIDE SELECTION AND FEATURE SUBSET SELECTION OF MARKERS BY BAYESIAN NETWORKS IN BEEF CATTLE USING MULTIPLE-TRAIT MODEL

Grant number: 17/03221-9
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): January 01, 2018
Effective date (End): December 31, 2018
Field of knowledge:Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
Principal Investigator:Fernando Sebastián Baldi Rey
Grantee:Fernando Brito Lopes
Home Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil

Abstract

Several methods have been used for genome-enabled prediction, where multiple regression models describe a target variable with a linear function of a set or subset of covariates. Bayesian Networks has offered interesting tools for a more parsimonious representation of the join distribution of a set of variables, which are useful for prediction purposes, e.g. using Markov Blanket (MB) of the target variable. Genome prediction studies have been focused mostly on single-trait analyses. However, most of economically important traits are genetically correlated, and it is expected to increase the accuracy of prediction of genomic breeding values of genetically correlated traits using multiple-trait model. Thus, this study will be carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle using multiple-trait model on two different scenarios: i) genome-wide prediction using the whole SNP data; and ii) genomic prediction using subset of SNP markers selected using MB. It will be used data from 8,000 phenotyped and genotyped Nellore cattle. The animals have been genotyped using low-density SNP panel, and subsequently imputed for arrays with 54k and 777k SNPs. The MB learning method will be used to identify a minimum set of molecular markers associated with carcass and meat quality traits. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes CÀ and Bayes R, will be compared in terms of prediction accuracy using a five folds cross-validation. The results of this study will allow accessing the impact of genomic information upon genetic evaluations in beef cattle using multiple-trait model, which has shown advantageous regarding univariate models, because it takes into account the selection process through the use of several traits at the same time. Therefore, this project will be the technical and economic viability evaluation of multiple-trait genomic analyses, where there is information from both pedigree and genomic data.

Scientific publications
(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)
OLIVIERI, BIANCA FERREIRA; BRAZ, CAMILA URBANO; LOPES, FERNANDO BRITO; PERIPOLLI, ELISA; DE OLIVEIRA SILVA, RAFAEL MEDEIROS; PEREIRA DA SILVA CORTE, ROSANA RUEGGER; DE ALBUQUERQUE, LUCIA GALVAO; CRAVO PEREIRA, ANGELICA SIMONE; STAFUZZA, NEDENIA BONVINO; BALDI, FERNANDO. Differentially expressed genes identified through RNA-seq with extreme values of principal components for beef fatty acid in Nelore cattle. JOURNAL OF ANIMAL BREEDING AND GENETICS, MAY 2020. Web of Science Citations: 0.
BRITO LOPES, FERNANDO; MAGNABOSCO, CLAUDIO U.; PASSAFARO, TIAGO L.; BRUNES, LUDMILLA C.; COSTA, MARCOS F. O.; EIFERT, EDUARDO C.; NARCISO, MARCELO G.; ROSA, GUILHERME J. M.; LOBO, RAYSILDO B.; BALDI, FERNANDO. Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. JOURNAL OF ANIMAL BREEDING AND GENETICS, v. 137, n. 5 FEB 2020. Web of Science Citations: 1.

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