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      Genome-Wide Association Analysis Identifies Resistance Loci for Bacterial Leaf Streak Resistance in Rice ( Oryza sativa L.)

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          Abstract

          Bacterial leaf streak (BLS) caused by Xanthomonas oryzae pv. oryzicola ( Xoc) is one of the most devastating diseases in rice production areas, especially in humid tropical and subtropical zones throughout Asia and worldwide. A genome-wide association study (GWAS) analysis conducted on a collection of 236 diverse rice accessions, mainly indica varieties, identified 12 quantitative trait loci (QTLs) on chromosomes 1, 2, 3, 4, 5, 8, 9 and 11, conferring resistance to five representative isolates of Thai Xoc. Of these, five QTLs conferred resistance to more than one Xoc isolates. Two QTLs, qBLS5.1 and qBLS2.3, were considered promising QTLs for broad-spectrum resistance to BLS. The xa5 gene was proposed as a potential candidate gene for qBLS5.1 and three genes, encoding pectinesterase inhibitor (OsPEI), eukaryotic zinc-binding protein ( OsRAR1), and NDP epimerase function, were proposed as candidate genes for qBLS2.3. Results from this study provide an insight into the potential QTLs and candidate genes for BLS resistance in rice. The recessive xa5 gene is suggested as a potential candidate for strong influence on broad-spectrum resistance and as a focal target in rice breeding programs for BLS resistance.

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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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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              The plant immune system.

              Many plant-associated microbes are pathogens that impair plant growth and reproduction. Plants respond to infection using a two-branched innate immune system. The first branch recognizes and responds to molecules common to many classes of microbes, including non-pathogens. The second responds to pathogen virulence factors, either directly or through their effects on host targets. These plant immune systems, and the pathogen molecules to which they respond, provide extraordinary insights into molecular recognition, cell biology and evolution across biological kingdoms. A detailed understanding of plant immune function will underpin crop improvement for food, fibre and biofuels production.
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                Author and article information

                Journal
                Plants (Basel)
                Plants (Basel)
                plants
                Plants
                MDPI
                2223-7747
                29 November 2020
                December 2020
                : 9
                : 12
                : 1673
                Affiliations
                [1 ]Plant Breeding Program, Faculty of Agriculture at Kamphaeng Saen, Kesetsart University, Nakhon Pathom 73140, Thailand; sattayachiti65@ 123456gmail.com
                [2 ]National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, PathumThani 12120, Thailand; samart.wan@ 123456biotec.or.th (S.W.); p.nubankoh@ 123456gmail.com (P.N.); cliveterence.dar@ 123456biotec.or.th (C.T.D.)
                [3 ]Department of Agronomy, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand; siwaret.a@ 123456ku.th (S.A.); vanavichit@ 123456gmail.com (A.V.)
                [4 ]Rice Science Center, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
                [5 ]Center of Excellence on Rice Precision Breeding for Food Security, Quality, and Nutrition, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
                [6 ]Department of Plant Pathology, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand; agrsujp@ 123456ku.ac.th
                Author notes
                [* ]Correspondence: theerayut@ 123456biotec.or.th
                [†]

                These authors contributed equally to the work.

                Author information
                https://orcid.org/0000-0002-5459-3425
                Article
                plants-09-01673
                10.3390/plants9121673
                7761455
                33260392
                eaa750af-0ac8-4582-b7a8-227707b1150f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 06 October 2020
                : 26 November 2020
                Categories
                Article

                bacterial leaf streak disease,xanthomonas oryzae pv. oryzicola (xoc),rice,genome-wide association (gwas),broad-spectrum resistance

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