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      Genome-wide association analysis permits characterization of Stagonospora nodorum blotch (SNB) resistance in hard winter wheat

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          Abstract

          Stagonospora nodorum blotch (SNB) is an economically important wheat disease caused by the necrotrophic fungus Parastagonospora nodorum. SNB resistance in wheat is controlled by several quantitative trait loci (QTLs). Thus, identifying novel resistance/susceptibility QTLs is crucial for continuous improvement of the SNB resistance. Here, the hard winter wheat association mapping panel (HWWAMP) comprising accessions from breeding programs in the Great Plains region of the US, was evaluated for SNB resistance and necrotrophic effectors (NEs) sensitivity at the seedling stage. A genome-wide association study (GWAS) was performed to identify single‐nucleotide polymorphism (SNP) markers associated with SNB resistance and effectors sensitivity. We found seven significant associations for SNB resistance/susceptibility distributed over chromosomes 1B, 2AL, 2DS, 4AL, 5BL, 6BS, and 7AL. Two new QTLs for SNB resistance/susceptibility at the seedling stage were identified on chromosomes 6BS and 7AL, whereas five QTLs previously reported in diverse germplasms were validated. Allele stacking analysis at seven QTLs explained the additive and complex nature of SNB resistance. We identified accessions (‘Pioneer-2180’ and ‘Shocker’) with favorable alleles at five of the seven identified loci, exhibiting a high level of resistance against SNB. Further, GWAS for sensitivity to NEs uncovered significant associations for SnToxA and SnTox3, co-locating with previously identified host sensitivity genes ( Tsn1 and Snn3). Candidate region analysis for SNB resistance revealed 35 genes of putative interest with plant defense response-related functions. The QTLs identified and validated in this study could be easily employed in breeding programs using the associated markers to enhance the SNB resistance in hard winter wheat.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Detecting the number of clusters of individuals using the software structure: a simulation study

            The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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              Inference of Population Structure Using Multilocus Genotype Data

              We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
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                Author and article information

                Contributors
                sunish.sehgal@sdstate.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 June 2021
                15 June 2021
                2021
                : 11
                : 12570
                Affiliations
                [1 ]GRID grid.263791.8, ISNI 0000 0001 2167 853X, Department of Agronomy, Horticulture and Plant Science, , South Dakota State University, ; Brookings, SD 57007 USA
                [2 ]GRID grid.261055.5, ISNI 0000 0001 2293 4611, Department of Plant Pathology, , North Dakota State University, ; Fargo, ND 58108 USA
                Article
                91515
                10.1038/s41598-021-91515-6
                8206080
                34131169
                9f34b706-c7cb-45cb-99a0-4be4ed0b0b2a
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 January 2021
                : 24 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100009928, Higher Committee for Education Development in Iraq;
                Funded by: FundRef http://dx.doi.org/10.13039/100005825, National Institute of Food and Agriculture;
                Award ID: 2011-68002-30029
                Award ID: 2017-67007-25939
                Award ID: 2019-67013-29015
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000199, U.S. Department of Agriculture;
                Award ID: SD00H538-15
                Award ID: SD00H695-20
                Award Recipient :
                Funded by: South Dakota Wheat Commission
                Award ID: 3X0281
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                plant breeding,plant genetics,agricultural genetics,quantitative trait
                Uncategorized
                plant breeding, plant genetics, agricultural genetics, quantitative trait

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