GWAS-Assisted Genomic Prediction to Predict Resistance to Septoria Tritici Blotch in Nordic Winter Wheat at Seedling Stage

Abstract

Septoria tritici blotch (STB) disease caused by Zymoseptoria tritici is one of the most damaging diseases of wheat causing significant yield losses worldwide. Identification and employment of resistant germplasm is the most cost-effective method to control STB. In this study, we characterized seedling stage resistance to STB in 175 winter wheat landraces and old cultivars of Nordic origin. The study revealed significant (p < 0.05) phenotypic differences in STB severity in the germplasm. Genome-wide association analysis (GWAS) using five different algorithms identified ten significant markers on five chromosomes. Six markers were localized within a region of 2 cM that contained seven candidate genes on chromosome 1B. Genomic prediction (GP) analysis resulted in a model with an accuracy of 0.47. To further improve the prediction efficiency, significant markers identified by GWAS were included as fixed effects in the GP model. Depending on the number of fixed effect markers, the prediction accuracy improved from 0.47 (without fixed effects) to 0.62 (all non-redundant GWAS markers as fixed effects), respectively. The resistant genotypes and single-nucleotide polymorphism (SNP) markers identified in the present study will serve as a valuable resource for future breeding for STB resistance in wheat. The results also highlight the benefits of integrating GWAS with GP to further improve the accuracy of GP.

Genetic Analysis Using a Multi-Parent Wheat Population Identifies Novel Sources of Septoria Tritici Blotch Resistance

“…Therefore, resistance QTL may require prioritising for selection through successive cycles of breeding. Approaches such as genomic selection could be useful for quantitative resistance by estimating a line's breeding value using genome-wide markers, and QTL identified here and in other studies can be used as fixed effects during genomic selection [75,102]. For instance, the prediction accuracy for STB resistance in wheat has been previously reported to have improved from 0.47 (without using QTL as fixed effects) to 0.62 (with using QTL as fixed effects) [75].…”

“…Approaches such as genomic selection could be useful for quantitative resistance by estimating a line's breeding value using genome-wide markers, and QTL identified here and in other studies can be used as fixed effects during genomic selection [75,102]. For instance, the prediction accuracy for STB resistance in wheat has been previously reported to have improved from 0.47 (without using QTL as fixed effects) to 0.62 (with using QTL as fixed effects) [75]. Ultimately, a pyramiding of qualitative and quantitative resistance has been highlighted as a strategy to extend the life of resistant cultivars [103], thereby providing multiple barriers against STB and reducing the number of asexual cycles in the field during the cropping season.…”

“…The QTL identified were compared with previously reported Stb genes and QTL by projecting onto the physical map in The Triticeae Toolbox (T3) database to identify the co-located genes/QTL and determine the novelty of others. For our QTL co-location study, only QTL mapping and genome-wide association scan (GWAS) studies of STB using high-throughput marker platforms were considered [60,[74][75][76][77][78][79][80][81][82][83]…”

Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

“…A simulation study demonstrated that it improved prediction accuracy in the RR-BLUP model to include quantitative trait nucleotides, each explaining at least 10% of the genetic variance (Bernardo 2014). Real traits also demonstrated the same trend (Odilbekov et al 2019).…”

Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

“…Besides enhancing prediction accuracy, GS + GWAS does not require additional data because the same phenotypic and genotypic data set is used, and it can be more accessible to breeders as it does not require extensive knowledge of the underlying genetics of a trait of interest [68]. The benefits of integrating GWAS with GS to further improve the accuracy of GS in wheat are confirmed for rusts [69,70], Septoria tritici blotch [71,72], and yield [73]. Particularly, Daetwyler et al [69] and Rutkoski et al [70] demonstrated the advantage of including markers linked to large to moderate effect genes or loci previously found to affect the traits of interest.…”