Evaluation of the Potential for Genomic Selection to Improve Spring Wheat Resistance to Fusarium Head Blight in the Pacific Northwest - Dong, Haixiao; Wang, Rui; Yuan, Yaping; Anderson, James; Pumphrey, Michael; Zhang, Zhiwu; Chen, Jianli (Frontiers in Plant Science, 2018)
Fusarium Head Blight (FHB) has emerged in spring wheat production in Pacific Northwest during the last decade due to factors including climate changes, crop rotations, and tillage practices. A breeding population with 170 spring wheat lines was established and screened over a two-year period in multiple locations for FHB incidence (INC), severity (SEV), and deposition of the mycotoxin, deoxynivalenol (DON). A genome-wide association study suggested that the detectable number of genetic loci and effects are limited for marker-assisted selection. In conjunction with the success of breeding on FHB resistance in other programs, genomic selection (GS) was suggested as a better option. To evaluate the prediction accuracy of GS in the current breeding population, we conducted a variety of validations by varying proportions of testing populations and cohorts based on both FHB resistance and market class, including soft white spring (SWS), hard white spring (HWS), and hard red spring (HRS). We found that INC had higher heritability, higher correlation across years and locations, and higher prediction accuracy than SEV and DON. Prediction accuracy varied among the scenarios that restricted the testing population to a certain cohort. For a small set of newly developed or introduced lines (< 17), prediction accuracy will be about 60% if the lines have similar genetic relationships as those among the current 170-line training population. However, we expect a lower prediction accuracy if new lines are selected for a specific characteristic, such as FHB resistance or market class. With the exception of DON in the SWS lines, the current training population is capable of making reasonably accurate predictions for FHB-resistant lines in most of the major market classes. For SWS, adding more lines or further phenotyping is required to improve prediction accuracy. These results demonstrate the potential and challenges of GS, especially for developing FHB-resistant varieties in the SWS market class.