A meta-analysis on the effects of marker coverage, status number, and size of training set on predictive accuracy and heritability estimates from genomic selection in tree breeding

Abstract

AbstractGenomic selection (GS) is increasingly used in tree breeding because of the possibility to hasten breeding cycles, increase selection intensity or facilitate multi-trait selection, and to obtain less biased estimates of quantitative genetic parameters such as heritability. However, tree breeders are aiming to obtain accurate estimates of such parameters and breeding values while optimizing sampling and genotyping costs. We conducted a metadata analysis of results from 28 GS studies totalling 115 study-traits. We found that heritability estimates obtained using DNA marker-based information for a variety of traits and species were not significantly related to variation in the total number of markers ranging from about 1500 to 116 000, nor by the marker density, ranging from about 1 to 60 markers/centimorgan, nor by the status number of the breeding populations ranging from about 10 to 620, nor by the size of the training set ranging from 236 to 2458. However, the predictive accuracy of breeding values was generally higher when the status number of the breeding population was smaller, which was expected given the higher level of relatedness in small breeding populations, and the increased ability of a given number of markers to trace the long-range linkage disequilibrium in such conditions. According to expectations, the predictive accuracy also increased with the size of the training set used to build marker-based models. Genotyping arrays with a few to many thousand markers exist for several tree species and with the actual costs, GS could thus be efficiently implemented in many more tree breeding programs, delivering less biased genetic parameters and more accurate estimates of breeding values.