The Accuracy and Bias of Single-Step Genomic Prediction for Populations Under Selection

In single-step analyses, missing genotypes are explicitly or implicitly imputed, and this requires centering the observed genotypes using the means of the unselected founders. If genotypes are only available for selected individuals, centering on the unselected founder mean is not straightforward. Here, computer simulation is used to study an alternative analysis that does not require centering genotypes but fits the mean $${\mu }_{g}$$ of unselected individuals as a fixed effect. Starting with observed diplotypes from 721 cattle, a five-generation population was simulated with sire selection to produce 40,000 individuals with phenotypes, of which the 1000 sires had genotypes. The next generation of 8000 genotyped individuals was used for validation. Evaluations were undertaken with (J) or without (N) $${\mu }_{g}$$ when marker covariates were not centered; and with (JC) or without (C) $${\mu }_{g}$$ when all observed and imputed marker covariates were centered. Centering did not influence accuracy of genomic prediction, but fitting $${\mu }_{g}$$ did. Accuracies were improved when the panel comprised only quantitative trait loci (QTL); models JC and J had accuracies of 99.4%, whereas models C and N had accuracies of 90.2%. When only markers were in the panel, the 4 models had accuracies of 80.4%. In panels that included QTL, fitting $${\mu }_{g}$$ in the model improved accuracy, but had little impact when the panel contained only markers. In populations undergoing selection, fitting $${\mu }_{g}$$ in the model is recommended to avoid bias and reduction in prediction accuracy due to selection.

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