| 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). 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) | |
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