DOI: 10.5061/dryad.rq80k
title: Data from: Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP

description: 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.
description: Genotypes_26503SNPsFile with ID and genotypes for 26503 SNPs for 1285 animals (from three breeds).ID_BreedFile with the breeds for each of the 1285 animals. File contains: ID, breedcode (HF=Holstein Friesian, GWH=Groningen White Headed or MRY=Meuse-Rhine-Yssel).Phenotypes_GenCor_1Files with the simulated phenotypes (100 replicates) for the scenario using a genetic correlation of 1 between the breeds. Each of the files contains: ID, Phenotype with 3000 QTL, Phenotype with 300 QTL, Phenotype with 30 QTL, Phenotype with 3 QTL.Phenotypes_GenCor_0.8Files with the simulated phenotypes (100 replicates) for the scenario using a genetic correlation of 0.8 between the breeds. Each of the files contains: ID, Phenotype with 3000 QTL, Phenotype with 300 QTL, Phenotype with 30 QTL, Phenotype with 3 QTL.Phenotypes_GenCor_0.4Files with the simulated phenotypes (100 replicates) for the scenario using a genetic correlation of 0.4 between the breeds. Each of the files contains: ID, Phenotype with 3000 QTL, Phenotype with 300 QTL, Phenotype with 30 QTL, Phenotype with 3 QTL.