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Research article - Peer-reviewed, 2019

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

Odilbekov, Firuz; Armoniené, Rita; Koc, Alexander; Svensson, Jan; Chawade, Aakash

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.

Keywords

GWAS - genome-wide association study; genomic prediction (GP); genomic selection (GS); wheat; Septoria tritici blotch (STB); Quantitative trait loci (QTL)

Published in

Frontiers in Genetics
2019, Volume: 10, article number: 1224

      SLU Authors

      • Armoniené, Rita

        • Associated SLU-program

          SLU Plant Protection Network

          UKÄ Subject classification

          Agricultural Science

          Publication identifier

          DOI: https://doi.org/10.3389/fgene.2019.01224

          Permanent link to this page (URI)

          https://res.slu.se/id/publ/106675