Genomic prediction of ordinal traits via application of the BLASSO and BayesCπ methods in simulated data

Authors

  • Geraldo Magela da Cruz Pereira Universidade Federal de Viçosa. http://orcid.org/0000-0001-6280-4870
  • Andrew de Paula Ribeiro Universidade Federal de Viçosa.
  • Sebastião Martins Filho Universidade Federal de Viçosa.

DOI:

https://doi.org/10.33837/msj.v3i1.1112

Keywords:

genomic selection, bayesian methods, ANOVA, ranking, molecular markers

Abstract

This paper aims at evaluating the use of BLASSO and BayesCπ methods for the genomic prediction of ordinal traits, studying factors that influence the performance of the models, and if there is a difference in the ranking of individuals. Genotypic and phenotypic information from a simulated population of 4,100 animals, genotyped by 10k markers (QTL-MAS Workshop) were used. 3,000 animals were used for estimation of the predictive ability and bias accessed through 5-fold cross-validation with five repetitions. The other animals were used as a population of selection. One ANOVA and the Ryan-Einot-Gabriel-Welch test were performed to verify, respectively, which factors influence significantly the genomic prediction and if there is a statistical difference between the models. The results show that the four main factors significantly (p < 0.05) affect the predictive ability of GEBVs (genomic estimated breeding values), and that heritability and the number of categories are the most influential factors. Only for ordinal trait 2, with a density of 9k, significant differences (p < 0.05) were observed between the predictive ability of the methods. In general, the BayesCπ method proved to be more efficient in the identification of relevant SNPs and in the ranking of individuals. Finally, there is a slight superiority of the BayesCπ method for the genomic prediction of ordinal traits.

Author Biographies

Geraldo Magela da Cruz Pereira, Universidade Federal de Viçosa.

Doutorando no programa de Pós-Graduação em Estatística Aplicada e Biometria, do Departamento de Estatística da Univerisdade Federal de Viçosa.

Andrew de Paula Ribeiro, Universidade Federal de Viçosa.

Graduando em Agronomia, pelo Depatamento de Fitotecnia da Univerisdade Federal de Viçosa.

Sebastião Martins Filho, Universidade Federal de Viçosa.

Professor Titular do Departamento de Estatística da Universidade Federal de Viçosa

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Published

2020-03-02

How to Cite

Pereira, G. M. da C., Ribeiro, A. de P., & Martins Filho, S. (2020). Genomic prediction of ordinal traits via application of the BLASSO and BayesCπ methods in simulated data. Multi-Science Journal (ISSN 2359-6902), 3(1), 1-7. https://doi.org/10.33837/msj.v3i1.1112

Issue

Section

Agricultural Sciences

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