Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines

Abstract

Genomic prediction is becoming a popular plant breeding method to predict the genetic merit of lines. While some genomic prediction results have been reported in canola, none have been evaluated for blackleg disease. Here, we report genomic prediction for seedling emergence, survival rate, and internal infection), using 532 Spring and Winter canola lines. These lines were phenotyped in two replicated blackleg disease nurseries grown at Wickliffe and Green Lake, Victoria, Australia. A transcriptome genotyping-by-sequencing approach revealed 98,054 single nucleotide polymorphisms (SNPs) after quality control. We assessed various genomic prediction scenarios based on Genomic Best Linear Unbiased Prediction (GBLUP), BayesR and BayesRC, which can make use of prior quantitative trait loci information, via cross-validation. Clustering based on genomic relationships showed that Winter and Spring lines were genetically distinct, indicating limited gene flow between sets. Genetic correlations within traits between Spring and Winter lines ranged from 0.68 and 0.90 (mean = 0.76). Based on GBLUP in the whole population, moderate to high genomic prediction accuracies were achieved within environments (0.35-0.74) and were reduced across environments (0.28-0.58). Prediction accuracy within the Spring set ranged from 0.30-0.69, and from 0.19-0.71 within the Winter set. The BayesR model resulted in slightly lower accuracy to GBLUP. The proportion of genetic variance explained by known blackleg quantitative trait loci (QTL) was < 30%, indicating that there is a large reservoir of genetic variation in blackleg traits that remains to be discovered, but can be captured with genomic prediction. However, providing prior information of known QTL in the BayesRC method resulted in an increased prediction accuracy for survival and internal infection, particularly with Spring lines. Overall, these promising results indicate that genomic prediction will be a valuable tool to make use of all genetic variation to improve blackleg resistance in canola.

Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.)

“…Jan et al, 2016 [14] used a canola reference population to evaluate genomic prediction accuracy of several quality and yield-related traits and found a high accuracy for seed oil content (0.81). The potential of GS for seedling emergence and blackleg disease resistance in a set of spring and winter lines were reported in Fikere et al, 2018 [15] demonstrating how GS effectively determines the most resistant lines from the field trial. One of the major factors for improving genomic prediction accuracy is the variability of plant performance across environments.…”

“…Trigold). The trials were sown in single rows (5 m long by 0.75 m apart) at 150 seeds per row, and two replications per site [15]. The same set of lines were sown in agronomic plot trials during the 2016 and 2017 under irrigated and rain-fed conditions.…”

“…Average internal infection (AvInf) for blackleg was measured in all years as the area of the stem infected by the fungus Laeptosphaeria maculans recorded as the percentage of affected area of the total stem cross-section, as described in the Spring Blackleg Management Guide [37]. In the 2015 disease nurseries, to estimate AvInf, a maximum of 20 randomly selected plants per row were cut with secateurs at the crown and the cross section of the stem was examined for fungal growth [15]. In the 2016 and 2017 agronomic trials, the blackleg AvInf was determined from a maximum of 5 consecutive plants cut at the crown level and observed.…”

“…The approach uses a reference population, which is phenotyped and genotyped, to predict the genetic values of selection candidates which are only genotyped. Recent studies in canola [12] demonstrated that GS accuracy is in the usable range for reducing the breeding cycle [13][14][15]. The role of GS in accelerating crop improvement, the factors affecting prediction accuracy, and possible strategies for future application have been reviewed [16][17][18].…”

Understanding Host–Pathogen Interactions in Brassica napus in the Omics Era

“…Genomics-assisted breeding, also known as genomic selection, is an advanced level of omics breeding in Brassica crops. Genomic selection incorporating multiple traits in crop breeding programs, with a focus on biotic stress, not only offers a promising strategy for developing high-quality Brassica crops resilient against a wide variety of pathogen types, but does so without compromising yield or crop quality [ 222 , 223 ]. A further area of interest is to screen for favourable alleles of diverse resistance genes sourced from wild relatives of Brassica species or, beyond that, wider members of the Brassicaceae family and expand from disease resistance genes to regulators such as small RNAs to find out how the disease resistance gene expression is being regulated [ 224 ].…”