Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures

Abstract

Background: Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects.

Methods: Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations.

Results: Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure.

Conclusions: The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved. The reference population structure had a limited effect on long-term accuracy and response. Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off.

Figure 1

Accuracy of estimated breeding values. Accuracy of MEBV in generations 0 to 10 averaged over 30 replicates; panels show results from genetic architectures with a low number of QTL of unequal variance (row 1), a low number of QTL of equal variance (row 2), a high number of QTL of unequal variance (row 3) and a high number of QTL of equal variance (row 4); estimation methods are BM (column 1), PLSR (column 2), GBLUP (column 3) and pedigree BLUP (column 4); levels of accuracy are shown for selection with training on phenotypes from one generation (shallow reference population, red circles) or from five generations (deep reference population, blue diamonds); accuracies of MEBV under RANDOM selection are shown as gray triangles; symbols for some scenarios may be hidden if values overlap.