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      Dissection of the multigenic wheat stem rust resistance present in the Montenegrin spring wheat accession PI 362698

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          Abstract

          Background

          Research to identify and characterize stem rust resistance genes in common wheat, Triticum aestivum, has been stimulated by the emergence of Ug99-lineage races of the wheat stem rust pathogen, Puccinia graminis f. sp. tritici ( Pgt), in Eastern Africa. The Montenegrin spring wheat landrace PI 362698 was identified as a source of Pgt resistance. This accession exhibits resistance to multiple Ug99-lineage and North American Pgt races at seedling and adult-plant stages. A recombinant inbred population was developed by crossing the susceptible line LMPG-6 with a single plant selection of PI 362698. A genetic map was constructed using the Illumina iSelect 90 K wheat assay and the markers csLv34, NB-LRR3, and wMAS000003 and quantitative trait locus (QTL) analysis was performed.

          Results

          QTL analysis identified five significant QTLs (α = 0.05) on chromosomes 2B, 3B, 6A, 6D, and 7A associated with wheat stem rust resistance. The QTL on chromosome 3B was identified using both field data from Kenya ( Pgt Ug99-lineage races) and seedling data from Pgt race MCCF. This QTL potentially corresponds to Sr12 or a new allele of Sr12. The multi-pathogen resistance gene Sr57 located on chromosome 7D is present in PI 362698 according to the diagnostic markers csLv34 and wMAS000003, however a significant QTL was not detected at this locus. The QTLs on chromosomes 2B, 6A, and 6D were identified during seedling trials and are thought to correspond to Sr16, Sr8a, and Sr5, respectively. The QTL identified on chromosome 7A was detected using MCCF seedling data and may be Sr15 or a potentially novel allele of recently detected Ug99 resistance QTLs.

          Conclusions

          The combination of resistance QTLs found in PI 362698 is like the resistance gene combination present in the broadly resistant cultivar Thatcher. As such, PI 362698 may not be a landrace as previously thought. PI 362698 has been crossed with North Dakota wheat germplasm for future breeding efforts. Additional work is needed to fully understand why the combination of genes present in PI 362698 and ‘Thatcher’ provide such durable resistance.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-4438-y) contains supplementary material, which is available to authorized users.

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          Most cited references61

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          Precision mapping of quantitative trait loci.

          Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.
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            A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.

            The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.
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              Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.

              The advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.
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                Author and article information

                Contributors
                +1-607-255-1455 , ma934@cornell.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                22 January 2018
                22 January 2018
                2018
                : 19
                : 67
                Affiliations
                [1 ]ISNI 0000 0001 2293 4611, GRID grid.261055.5, Department of Plant Pathology, , North Dakota State University, ; Fargo, ND USA
                [2 ]ISNI 0000 0004 0404 0958, GRID grid.463419.d, USDA-ARS, , National Clonal Germplasm Repository, ; Corvallis, OR USA
                [3 ]ISNI 0000000419368657, GRID grid.17635.36, USDA-ARS, Cereal Disease Laboratory, and Department of Plant Pathology, , University of Minnesota, ; St. Paul, MN USA
                [4 ]ISNI 0000 0004 0404 0958, GRID grid.463419.d, USDA-ARS, , Cereal Crops Research Unit, ; Fargo, ND USA
                [5 ]GRID grid.473294.f, Kenya Agricultural and Livestock Research Organization, ; Njoro, Kenya
                [6 ]Agriculture and Agri-Food Canada, Morden, MB Canada
                [7 ]ISNI 0000 0001 2284 638X, GRID grid.412219.d, Department of Plant Sciences, , University of the Free State, ; Bloemfontein, South Africa
                [8 ]ISNI 0000 0004 0404 0958, GRID grid.463419.d, USDA-ARS, , Small Grains and Potato Germplasm Research Unit, ; Aberdeen, ID USA
                [9 ]ISNI 000000041936877X, GRID grid.5386.8, International Programs, College of Agriculture and Life Sciences, , Cornell University, ; Mann Library B-75, Ithaca, NY 14853 USA
                Article
                4438
                10.1186/s12864-018-4438-y
                5776780
                29357813
                9ef0f418-d573-4999-bcb7-a76d302e7d9c
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 26 June 2017
                : 4 January 2018
                Funding
                Funded by: North Dakota Wheat Commission
                Funded by: USDA-ARS National Plant Disease Recovery System
                Award ID: 2050-21000-029-00D
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Genetics
                food security,yield protection,mesothetic resistance,infinium,snp,kasp
                Genetics
                food security, yield protection, mesothetic resistance, infinium, snp, kasp

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