Prediction of genomic values using Bayesian models and neural networks for endoparasite resistance traits in Santa Inês sheep

• Grant number: 18/01540-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2019
• End date: March 31, 2023
• Field of knowledge: Biological Sciences - Genetics - Animal Genetics
• Principal Investigator: Claudia Cristina Paro de Paz
• Grantee: Luara Afonso de Freitas Januário
• Host Institution: Instituto de Zootecnia. Agência Paulista de Tecnologia dos Agronegócios (APTA). Secretaria de Agricultura e Abastecimento (São Paulo - Estado). Nova Odessa , SP, Brazil
• Associated research grant: 16/14522-7 - Genomic studies associated with resistance to endoparasites traits in Santa Inês sheep, AP.TEM
• Associated scholarship(s): 20/03575-8 - Machine learning for predictive analysis in Santa Inês sheep: an example of application to predict resistant, resilient and susceptible animals, BE.EP.DR

Abstract

In ovine meat production one of the problems is susceptibility to gastrointestinal endoparasites, resulting in decreased production, resulting in decreased production. Fecal Egg Counts (FEC) and packed Cell Volume (CV) are traits used to evaluate the resistance of sheep to gastrointestinal nematode parasites. Genetically resistant animals are able to hinder the establishment of parasites and/or eliminate that settled. Genetic evaluations for choosing this type of animal can be made through the genomic prediction models, which include the information of molecular markers. The aims of this study will be: (1) to compare models of genomic prediction by the GBLUP method, Bayesian methods (BayesA, BayesB and LASSO Bayesiano) and Artificial neural networks as to the prediction accuracy for the genomic values for fecal egg counts and mean corpuscular volume in Santa Inês sheep, and (2) to evaluate changes in the prediction accuracy of the models as a function of the variation of the training set of the genomic selection models Genetically superior animals are expected to be identified for traits evaluated using the model (s) which will present higher prediction accuracy for FEC and CV and it is expected to verify the possibility of establishing a minimum number of animals in the training population in which the plateau of the predictive accuracy will be observed. (AU)

Scientific publications

(6)

(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)

OLIVEIRA, ELISA JUNQUEIRA; SAVEGNAGO, RODRIGO PELICIONI; FREITAS, ANIELLY DE PAULA; DE FREITAS, LUARA AFONSO; DE PAZ, ANA CAROLINA ALMEIDA ROLLO; EL FARO, LENIRA; SIMILI, FLAVIA FERNANDA; VERCESI FILHO, ANIBAL EUGENIO; DA COSTA, RICARDO LOPES DIAS; DE PAZ, CLAUDIA CRISTINA PARO. Genetic parameters for body weight and morphometric traits in Santa Ines sheep using Bayesian inference. Small Ruminant Research, v. 201, AUG 2021. (18/01540-2, 12/15982-0, 16/14522-7)

FREITAS, LUARA; SAVEGNAGO, RODRIGO; CARVALHO ALVES, ANDERSON A.; COSTA, RICARDO; ROSA, GUILHERME J. J. M.; PAZ, CLAUDIA. Classification Performance of Multinomial Logistic Regression for Identifying Resistance, Resilience, and Susceptibility to Gastrointestinal Nematode Infections in Sheep. JOURNAL OF ANIMAL SCIENCE, v. 100, p. 1-pg., 2022-09-22. (16/14522-7, 18/01540-2, 20/03575-8)

FREITAS, LUARA; FERREIRA, RAFAEL; SAVEGNAGO, RODRIGO; DOREA, JOAO R.; ROSA, GUILHERME J. J. M.; PAZ, CLAUDIA. Computer Vision System to Predict Famacha (c) Degree in Sheep from Ocular Conjunctiva Images. JOURNAL OF ANIMAL SCIENCE, v. 100, p. 1-pg., 2022-10-01. (16/14522-7, 18/01540-2, 20/03575-8)

MONTEIRO GAMA, MANUELA PIRES; SAVEGNAGO, RODRIGO PELICIONI; VENTURA, HENRIQUE TORRES; PEREIRA, MARIANA ALENCAR; FREITAS, LUARA AFONSO; PARO PAZ, CLAUDIA CRISTINA; EL FARO, LENIRA. stimates of genetic parameters, principal components and cluster analysis for milk yield and body weight in Guzera cattl. ANIMAL PRODUCTION SCIENCE, v. 62, n. 3, NOV 2021. (18/01540-2, 13/20091-0)

VILELA PIRES, BIANCA; DE FREITAS, LUARA AFONSO; VOLTARELI DA SILVA, GABRIELE; BRASIL GARCIA PIMENTA NEVES PEREIRA LIMA, SERGIO; DOS SANTOS GONCALVES CYRILLO, JOSLAINE NOELY; BONVINO STAFUZZA, NEDENIA; PEREIRA DE LIMA, MARIA LUCIA; PARO DE PAZ, CLAUDIA CRISTINA. Influence of calf vigour and suckling assistance from birth to weaning in Guzera beef cattle. ANIMAL PRODUCTION SCIENCE, v. 61, n. 8, MAR 2021. (19/10438-0, 17/50339-5, 18/01540-2)

DE FREITAS, LUARA AFONSO; SAVEGNAGO, RODRIGO PELICIONI; MENEGATTO, LEONARDO SARTORI; DO BEM, RICARDO DUTRA; STAFUZZA, NEDENIA BONVINO; ALMEIDA ROLLO DE PAZ, ANA CAROLINA; PIRES, BIANCA VILELA; DIAS DA COSTA, RICARDO LOPES; PARO DE PAZ, CLAUDIA CRISTINA. Cluster analysis to explore additive-genetic patterns for the identification of sheep resistant, resilient and susceptible to gastrointestinal nematodes. Veterinary Parasitology, v. 301, p. 5-pg., 2022-01-01. (16/14522-7, 18/01540-2, 12/15982-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

JANUÁRIO, Luara Afonso de Freitas. Optimization of resistance to gastrointestinal nematodes in Santa Inês sheep: a genomic selection, machine learning and image analysis approach. 2023. Doctoral Thesis - Universidade de São Paulo (USP). Faculdade de Medicina de Ribeirão Preto (PCARP/BC) Ribeirão Preto.