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Including Dominance Effects in the Genomic BLUP Method for Genomic Evaluation

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  • Motohide Nishio
  • Masahiro Satoh

Abstract

We evaluated the performance of GBLUP including dominance genetic effect (GBLUP-D) by estimating variances and predicting genetic merits in a computer simulation and 2 actual traits (T4 and T5) in pigs. In simulation data, GBLUP-D explained more than 50% of dominance genetic variance. Moreover, GBLUP-D yielded estimated total genetic effects over 1.2% more accurate than those yielded by GBLUP. In particular, when the dominance genetic variance was large, the accuracy could be substantially improved by increasing the number of markers. The dominance genetic variances in T4 and T5 accounted for 9.6% and 6.3% of the phenotypic variances, respectively. Estimates of such small dominance genetic variances contributed little to the improvement of the accuracies of estimated total genetic effects. In both simulation and pig data, there were nearly no differences in the estimates of additive genetic effects or their variance between GBLUP-D and GBLUP. Therefore, we conclude GBLUP-D is a feasible approach to improve genetic performance in crossbred populations with large dominance genetic variation and identify mating systems with good combining ability.

Suggested Citation

  • Motohide Nishio & Masahiro Satoh, 2014. "Including Dominance Effects in the Genomic BLUP Method for Genomic Evaluation," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.
    • Handle: RePEc:plo:pone00:0085792
    DOI: 10.1371/journal.pone.0085792
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    References listed on IDEAS

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    1. Guosheng Su & Ole F Christensen & Tage Ostersen & Mark Henryon & Mogens S Lund, 2012. "Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
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