Evaluation of the Potential for Genomic Selection to Improve Spring Wheat Resistance to Fusarium Head Blight in the Pacific Northwest

Abstract

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 2-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.

Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

“…FHB incidence was observed to have the highest prediction accuracies and genotypes that were similarly related to the initial training population had a prediction accuracy of 0.60. The prediction accuracy was fairly high for FHB resistance overall, suggesting that GS could work well for FHB resistance [135].…”

Evaluation of Genomic Prediction for Pasmo Resistance in Flax

“…Therefore, diverse phenotypic and genetic variabilities within the flax core collection render it useful as a resource for breeding and as a TP for GP model construction. [11,14,42,[47][48][49]. For example, comparisons among RR-BLUP, Bayes-Cπ, and RKHSR showed no difference in accuracies in a wheat FHB study [19].…”

“…Large TPs provide the statistical power needed to improve prediction accuracy [38], especially for traits with low heritability [34,39].When TP size is sufficiently large, even low heritability traits can be accurately predicted [28,40], including the low heritability PS studied therein. Diversity of the population also affect prediction accuracy [21,29,34,[41][42][43]. A diverse TP may contain more QTL associated with selective traits and increase the correlation of the TP with validation populations (VPs) or test/prediction populations (PPs), resulting in a subsequent increase in prediction accuracy.…”

“…A diverse TP may contain more QTL associated with selective traits and increase the correlation of the TP with validation populations (VPs) or test/prediction populations (PPs), resulting in a subsequent increase in prediction accuracy. Although some breeding lines [11,30,44] and bi-parental derived lines [25,41,45,46] are used for TPs, many studies have opted for a more diverse TP germplasm [29,[41][42][43]. Our core collection TP preserves the variation present in the world collection of 3,378 accessions maintained by Plant Gene Resources of Canada (PGRC) and represents a broad range of geographical origins, different improvement statuses (landraces, historical and modern cultivars, breeding lines), and two morphotypes (linseed and fibre types) [1,3].…”

Biotechnological and Digital Revolution for Climate-Smart Plant Breeding

“…In wheat, GS models were largely developed to identify accessions that best adapt to the negative effects of climate change: FHB resistance [97], heading date as an important component of wheat adaptation [98] and water deficit stress [99]. Recently, Crain et al [100] disclosed several GS methods in relation to the phenotypic information derived from high-throughput phenotyping platforms.…”