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

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          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.

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          Most cited references44

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          COI1: an Arabidopsis gene required for jasmonate-regulated defense and fertility.

          The coi1 mutation defines an Arabidopsis gene required for response to jasmonates, which regulate defense against insects and pathogens, wound healing, and pollen fertility. The wild-type allele, COI1, was mapped to a 90-kilobase genomic fragment and located by complementation of coi1-1 mutants. The predicted amino acid sequence of the COI1 protein contains 16 leucine-rich repeats and an F-box motif. It has similarity to the F-box proteins Arabidopsis TIR1, human Skp2, and yeast Grr1, which appear to function by targeting repressor proteins for removal by ubiquitination.
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            Exploiting genetic diversity from landraces in wheat breeding for adaptation to climate change.

            Climate change has generated unpredictability in the timing and amount of rain, as well as extreme heat and cold spells that have affected grain yields worldwide and threaten food security. Sources of specific adaptation related to drought and heat, as well as associated breeding of genetic traits, will contribute to maintaining grain yields in dry and warm years. Increased crop photosynthesis and biomass have been achieved particularly through disease resistance and healthy leaves. Similarly, sources of drought and heat adaptation through extended photosynthesis and increased biomass would also greatly benefit crop improvement. Wheat landraces have been cultivated for thousands of years under the most extreme environmental conditions. They have also been cultivated in lower input farming systems for which adaptation traits, particularly those that increase the duration of photosynthesis, have been conserved. Landraces are a valuable source of genetic diversity and specific adaptation to local environmental conditions according to their place of origin. Evidence supports the hypothesis that landraces can provide sources of increased biomass and thousand kernel weight, both important traits for adaptation to tolerate drought and heat. Evaluation of wheat landraces stored in gene banks with highly beneficial untapped diversity and sources of stress adaptation, once characterized, should also be used for wheat improvement. Unified development of databases and promotion of data sharing among physiologists, pathologists, wheat quality scientists, national programmes, and breeders will greatly benefit wheat improvement for adaptation to climate change worldwide.
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              Genomic selection using different marker types and densities.

              With the availability of high-density marker maps and cost-effective genotyping, genomic selection methods may provide faster genetic gain than can be achieved by current selection methods based on phenotypes and the pedigree. Here we investigate some of the factors driving the accuracy of genomic selection, namely marker density and marker type (i.e., microsatellite and SNP markers), and the use of marker haplotypes versus marker genotypes alone. Different densities were tested with marker densities equivalent to 2, 1, 0.5, and 0.25N(e) markers/morgan using microsatellites and 8, 4, 2, and 1N(e) markers/morgan using SNP, where 1N(e) markers/morgan means 100 markers per morgan, if effective size (N(e)) is 100. Marker characteristics and linkage disequilibria were obtained by simulating a population over 1,000 generations to achieve a mutation drift balance. The marker designs were evaluated for their accuracy of predicting breeding values from either estimating marker effects or estimating effects of haplotypes based upon combining 2 markers. Using microsatellites as direct marker effects, the accuracy of selection increased from 0.63 to 0.83 as the density increased from 0.25N(e)/morgan to 2N(e)/morgan. Using SNP markers as direct marker effects, the accuracy of selection increased from 0.69 to 0.86 as the density increased from 1N(e)/morgan to 8N(e)/morgan. The SNP markers required a 2 to 3 times greater density compared with using microsatellites to achieve a similar accuracy. The biases that genomic selection EBV often show are due to the prediction of marker effects instead of QTL effects, and hence, genomic selection EBV may need rescaling for practical use. Using haplotypes resulted in similar or reduced accuracies compared with using direct marker effects. In practical situations, this means that it is advantageous to use direct marker effects, because this avoids the estimation of marker phases with the associated errors. In general, the results showed that the accuracy remained responsive with small bias to increasing marker density at least up to 8N(e) SNP/morgan, where the effective population size was 100 and with the genomic model assumed. For a 30-morgan genome and N(e) = 100, this implies that about approximately 24,000 SNP are needed.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                26 November 2019
                2019
                : 10
                : 1224
                Affiliations
                [1] 1Department of Plant Breeding, Swedish University of Agricultural Sciences , Alnarp, Sweden
                [2] 2Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (LAMMC) , Akademija, Lithuania
                [3] 3Nordic Genetic Resource Centre , Alnarp, Sweden
                Author notes

                Edited by: Alison Bentley, National Institute of Agricultural Botany (NIAB), United Kingdom

                Reviewed by: Thomas Miedaner, University of Hohenheim, Germany; Matthew Rouse, United States Department of Agriculture, United States; Morten Lillemo, Norwegian University of Life Sciences, Norway

                *Correspondence: Aakash Chawade, aakash.chawade@ 123456slu.se

                This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2019.01224
                6901976
                31850073
                321d5cba-1e66-4979-82d4-c6848aa9fb8e
                Copyright © 2019 Odilbekov, Armoniené, Koc, Svensson and Chawade

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 June 2019
                : 05 November 2019
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 56, Pages: 10, Words: 5292
                Categories
                Genetics
                Original Research

                Genetics
                gwas - genome-wide association study,genomic prediction (gp),genomic selection (gs),wheat,septoria tritici blotch (stb),quantitative trait loci (qtl)

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