Data from: Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP

Background: The use of information across populations is an attractive
approach to increase the accuracy of genomic prediction for numerically
small populations. However, accuracies of across population genomic
prediction, in which reference and selection individuals are from
different populations, are currently disappointing. It has been shown for
within population genomic prediction that Bayesian variable selection
models outperform GBLUP models when the number of QTL underlying the trait
is low. Therefore, our objective was to identify across population genomic
prediction scenarios in which Bayesian variable selection models
outperform GBLUP in terms of prediction accuracy. In this study, high
density genotype information of 1033 Holstein Friesian, 105 Groningen
White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were
simulated using two changing variables: (1) the number of QTL underlying
the trait (3000, 300, 30, 3), and (2) the correlation between allele
substitution effects of QTL across populations, i.e. the genetic
correlation of the simulated trait between the populations (1.0, 0.8,
0.4). Results: The accuracy obtained by the Bayesian variable selection
model was depending on the number of QTL underlying the trait, with a
higher accuracy when the number of QTL was lower. This trend was more
pronounced for across population genomic prediction than for within
population genomic prediction. It was shown that Bayesian variable
selection models have an advantage over GBLUP when the number of QTL
underlying the simulated trait was small. This advantage disappeared when
the number of QTL underlying the simulated trait was large. The point
where the accuracy of Bayesian variable selection and GBLUP became similar
was approximately the point where the number of QTL was equal to the
number of independent chromosome segments (M e ) across the populations.
Conclusion: Bayesian variable selection models outperform GBLUP when the
number of QTL underlying the trait is smaller than M e . Across
populations, M e is considerably larger than within populations. So, it is
more likely to find a number of QTL underlying a trait smaller than M e
across populations than within population. Therefore Bayesian variable
selection models can help to improve the accuracy of across population
genomic prediction.