The contribution of dominance to phenotype prediction in a pine breeding and simulated population.
Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive-dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.
School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA.|Graduate Program in Genetics and Improvement, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.|Department of Zootecnia, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.
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JE
de Almeida Filho
School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA.|Graduate Program in Genetics and Improvement, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.|Department of Zootecnia, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.
J F R
JF
Guimarães
Department of Zootecnia, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.
F F
FF
E Silva
EMBRAPA Florestas/Department of Statistics, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.
M D V
MD
de Resende
Agronomy Department, University of Florida, Gainesville, FL, USA.
P
P
Muñoz
University of Florida Genetics Institute, University of Florida, Gainesville, FL, USA.|School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA.
M
M
Kirst
RAPiD Genomics LLC, Gainesville, FL, USA.
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MF
Resende
D000465:Algorithms; D001947:Breeding; D003198:Computer Simulation; D005799:Genes, Dominant; D005828:Genetics, Population; D040582:Inheritance Patterns; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D028223:Pinus / Q000235:genetics*; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results