Data from: An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments

Predicting the accuracy of estimated genomic values using genome-wide
marker information is an important step in designing training populations.
Currently, different deterministic equations are available to predict
accuracy within populations, but not for multipopulation scenarios where
data from multiple breeds, lines or environments are combined. Therefore,
our objective was to develop and validate a deterministic equation to
predict the accuracy of genomic values when different populations are
combined in one training population. The input parameters of the derived
prediction equation are the number of individuals and the heritability
from each of the populations in the training population; the genetic
correlations between the populations, i.e., the correlation between allele
substitution effects of quantitative trait loci; the effective number of
chromosome segments across predicted and training populations; and the
proportion of the genetic variance in the predicted population captured by
the markers in each of the training populations. Validation was performed
based on real genotype information of 1033 Holstein–Friesian cows that
were divided into three different populations by combining half-sib
families in the same population. Phenotypes were simulated for multiple
scenarios, differing in heritability within populations and in genetic
correlations between the populations. Results showed that the derived
equation can accurately predict the accuracy of estimating genomic values
for different scenarios of multipopulation genomic prediction. Therefore,
the derived equation can be used to investigate the potential accuracy of
different multipopulation genomic prediction scenarios and to decide on
the most optimal design of training populations.