Genomic prediction of ordinal traits via application of the BLASSO and BayesCπ methods in simulated data
DOI:
https://doi.org/10.33837/msj.v3i1.1112Palabras clave:
genomic selection, bayesian methods, ANOVA, ranking, molecular markersResumen
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.
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