DESEMPENHO DOS MÉTODOS DE ESTIMAÇÃO GENÔMICOS NA IDENTIFICAÇÃO DA RESISTÊNCIA DO ARROZ À BRUSONE

Zeferino Gomes da Silva Neto; Sebastião  Martins Filho; Lucas Souza da Silveira; Antônio Policarpo Souza Carneiro; Vinicius Silva dos Santos

doi:10.31413/nativa.v10i4.13448

Authors

Zeferino Gomes da Silva Neto zeferino.neto@ufv.br
Programa de Pós-Graduação em Estatística Aplicada e Biometria, Universidade Federal de Viçosa, Viçosa, MG, Brasil. https://orcid.org/0000-0002-8982-7375
Sebastião Martins Filho martinsfilho@ufv.br
Programa de Pós-Graduação em Estatística Aplicada e Biometria, Universidade Federal de Viçosa, Viçosa, MG, Brasil. https://orcid.org/0000-0002-8317-4318
Lucas Souza da Silveira lucas.s.silveira@ufv.br
Programa de Pós-Graduação em Estatística Aplicada e Biometria, Universidade Federal de Viçosa, Viçosa, MG, Brasil. https://orcid.org/0000-0003-4356-751X
Antônio Policarpo Souza Carneiro policarpo@ufv.br
Programa de Pós-Graduação em Estatística Aplicada e Biometria, Universidade Federal de Viçosa, Viçosa, MG, Brasil. https://orcid.org/0000-0002-9043-3242
Vinicius Silva dos Santos 2santosvinicius@gmail.com
Universidade Federal do Acre, Rio Branco, AC, Brasil. https://orcid.org/0000-0002-8387-2917

DOI:

10.31413/nativa.v10i4.13448

Keywords:

rice blast, statistical modelling, genomics wide selection, ROC analysis, accuracy

Abstract

In recent years, rice crop losses have increased due to biotic and abiotic stresses, among which brusone, which can result in 100% losses in susceptible rice cultivars. Therefore, it becomes strategic to identify methodologies that select resistant cultivars. In this work, we aimed to use ROC (Receiver operator characteristic) curve analysis and traditional measures to evaluate the performance of genomic estimation models (RR-BLUP, BLASSO and Bayes Cπ) in identifying rice resistance to brusone. The RR-BLUP and Bayes Cπ models were most accurate for the prediction of brusone resistance, while the best runtime was obtained by RR-BLUP. The area under the ROC curve was equivalent to traditional measures to evaluate the accuracy of the models, with the advantage of allowing graphical evaluation. By graphical analysis, BLASSO performed worst at high levels of specificity (>0.75). At lower levels of specificity, the sensitivity of the models was similar. The ROC methodology proved to be a good alternative for the evaluation of genomic prediction models, and can be used for the selection of rice cultivars resistant to brusone.

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