Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods

Anderson Antonio Carvalho Alves, Rebeka Magalhes Da Costa, Tiago Bresolin, Gerardo Alves Fernandes, Rafael Espigolan, Andra Mauric Frossard Ribeiro, Roberto Carvalheiro, Lucia Galvo De Albuquerque

Research output: Contribution to journalArticlepeer-review


The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populations presenting different levels of dominance effects. Simulated genome comprised 50k SNP and 300 QTL, both biallelic and randomly distributed across 29 autosomes. A total of six traits were simulated considering different values for the narrow and broad-sense heritability. In the purely additive scenario with low heritability (h2= 0.10), the predictive ability obtained using GBLUP was slightly higher than the other methods whereas ANN provided the highest accuracies for scenarios with moderate heritability (h2= 0.30). The accuracies of dominance deviations predictions varied from 0.180 to 0.350 in GBLUP extended for dominance effects (GBLUP-D), from 0.06 to 0.185 in RF and they were null using the ANN and SVM methods. Although RF has presented higher accuracies for total genetic effect predictions, the meansquared error values in such a model were worse than those observed for GBLUP-D in scenarios with large additive and dominance variances. When applied to prescreen important regions, the RF approach detected QTL with high additive and/or dominance effects. Among machine learning methods, only the RF was capable to cover implicitly dominance effects without increasing the number of covariates in the model, resulting in higher accuracies for the total genetic and phenotypic values as the dominance ratio increases. Nevertheless, whether the interest is to infer directly on dominance effects, GBLUP-D could be a more suitable method.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalJournal of animal science
Issue number6
StatePublished - 2020
Externally publishedYes


  • Artificial Neural Network
  • Genomic Selection
  • Nonadditive Effects
  • Random Forestsupport Vector Machine

ASJC Scopus subject areas

  • Food Science
  • Animal Science and Zoology
  • Genetics


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