Abstract
BackgroundThe theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.MethodsSimulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.ResultsThis study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.ConclusionsOur results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.
Highlights
The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers
The highest accuracy was achieved by the Bayesian variable selection model (Bayes B) method when genetic variation was controlled by a few QTL with relatively large effects (100 QTL)
Our results suggest that Bayes B is a superior method to gBLUP to estimate breeding values from genomic data
Summary
The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. There is increasing evidence that GS relies on “relationships” between individuals to accurately predict genetic values [2], because genomic predictions are more accurate when Given this debate, a better understanding of what GS is predicting is relevant for several reasons. The LD/QTL paradigm suggests that accurate predictions of breeding values will persist for several generations into the future allowing for a reduced number of phenotypic measurements [3] It assumes that higher marker densities may allow for the prediction of breeding values across breeds [4] In contrast, if the relationship paradigm is true, the predictive ability based on genomic data would persist only for one or two generations ahead. Continuous measurements of phenotypes of individuals that are related to selection candidates would be needed
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