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      Genome-Wide Association Mapping of Loci for Resistance to Stripe Rust in North American Elite Spring Wheat Germplasm

      1 , 1 , 1 , 1
      Phytopathology®
      Scientific Societies

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          Abstract

          Stripe rust, caused by Puccinia striiformis f. sp. tritici, is a major yield-limiting foliar disease of wheat (Triticum aestivum) worldwide. In this study, the genetic variability of elite spring wheat germplasm from North America was investigated to characterize the genetic basis of effective all-stage and adult plant resistance (APR) to stripe rust. A genome-wide association study was conducted using 237 elite spring wheat lines genotyped with an Illumina Infinium 90K single-nucleotide polymorphism array. All-stage resistance was evaluated at seedling stage in controlled conditions and field evaluations were conducted under natural disease pressure in eight environments across Washington State. High heritability estimates and correlations between infection type and severity were observed. Ten loci for race-specific all-stage resistance were confirmed from previous mapping studies. Three potentially new loci associated with race-specific all-stage resistance were identified on chromosomes 1D, 2A, and 5A. For APR, 11 highly significant quantitative trait loci (QTL) (false discovery rate < 0.01) were identified, of which 3 QTL on chromosomes 3A, 5D, and 7A are reported for the first time. The QTL identified in this study can be used to enrich the current gene pool and improve the diversity of resistance to stripe rust disease.

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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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            STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

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              A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.

              As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.
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                Author and article information

                Journal
                Phytopathology®
                Phytopathology®
                Scientific Societies
                0031-949X
                1943-7684
                February 2018
                February 2018
                : 108
                : 2
                : 234-245
                Affiliations
                [1 ]First, second, and fourth authors: Department of Crop and Soil Sciences, Washington State University, Pullman 99164-6420; and third author: United States Department of Agriculture–Agricultural Research Service and Department of Plant Pathology, Washington State University, Pullman 99164-6430.
                Article
                10.1094/PHYTO-06-17-0195-R
                28952421
                d0e8983c-5ca3-4860-a96d-34d6f790443d
                © 2018
                History

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