Genomic selection for wheat traits and trait stability

Trait and trait stability are important for wheat breeding. Our objectives were to assess the relative efficiency of genomic selection (GS) for various wheat traits and trait stability using four different models. Genotyping was conducted with a 90K SNP chip panel. SNP tagging was used to obtain a subset of 3,919 relatively independent markers for downstream analysis. Phenotyping was conducted on a population consisting of 273 lines, from seven different soft red winter wheat breeding programs in the U.S.A. Eberhart and Russells’ regression and additive main effects and multiplicative interaction (AMMI) models were used to assess trait stability. GS accuracy (r) was assessed for ridge regression best linear unbiased prediction (rr-BLUP), Bayesian ridge regression (BRR), reproducing kernel hilbert space (RKHS), and elastic net (EN). Across all models, GS produced a wide range of accuracies for trait stability (0.1 to 0.65) that varied by trait and stability method. Accuracy was 0.35 for yield and 0.44 for yield stability using AMMI, indicating the viability of GS in selecting lines with both high and stable yield. Our findings lay the foundation for wheat breeding programs in northeastern U.S.A. to implement GS. It also provides useful information for wheat rust researchers: as phenotypic selection for rust resistance can be both expensive and time consuming and rapid evolution of rust pathogens require an emphasis on durable resistance controlled by multiple genes, the GS approach applicable for complex traits thus has potential to achieve higher gains per unit time than traditional breeding for rust resistance.