Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program

Abstract

Genomic selection (GS) uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotypes of lines from each cross before conducting GS. This will prolong the selection cycle and may result in lower gains per year than approaches that estimate marker-effects with multiple families from previous selection cycles. In this study, phenotypic selection (PS), conventional marker-assisted selection (MAS), and GS prediction accuracy were compared for 13 agronomic traits in a population of 374 winter wheat (Triticum aestivum L.) advanced-cycle breeding lines. A cross-validation approach that trained and validated prediction accuracy across years was used to evaluate effects of model selection, training population size, and marker density in the presence of genotype × environment interactions (G×E). The average prediction accuracies using GS were 28% greater than with MAS and were 95% as accurate as PS. For net merit, the average accuracy across six selection indices for GS was 14% greater than for PS. These results provide empirical evidence that multifamily GS could increase genetic gain per unit time and cost in plant breeding.