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      Genome-Wide Novel Genic Microsatellite Marker Resource Development and Validation for Genetic Diversity and Population Structure Analysis of Banana

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          Abstract

          Trait tagging through molecular markers is an important molecular breeding tool for crop improvement. SSR markers encoded by functionally relevant parts of a genome are well suited for this task because they may be directly related to traits. However, a limited number of these markers are known for Musa spp. Here, we report 35136 novel functionally relevant SSR markers (FRSMs). Among these, 17,561, 15,373 and 16,286 FRSMs were mapped in-silico to the genomes of Musa acuminata, M. balbisiana and M. schizocarpa, respectively. A set of 273 markers was validated using eight accessions of Musa spp., from which 259 markers (95%) produced a PCR product of the expected size and 203 (74%) were polymorphic. In-silico comparative mapping of FRSMs onto Musa and related species indicated sequence-based orthology and synteny relationships among the chromosomes of Musa and other plant species. Fifteen FRSMs were used to estimate the phylogenetic relationships among 50 banana accessions, and the results revealed that all banana accessions group into two major clusters according to their genomic background. Here, we report the first large-scale development and characterization of functionally relevant Musa SSR markers. We demonstrate their utility for germplasm characterization, genetic diversity studies, and comparative mapping in Musa spp. and other monocot species. The sequences for these novel markers are freely available via a searchable web interface called Musa Marker Database.

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          Most cited references81

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          Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

          S Altschul (1997)
          The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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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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                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                09 December 2020
                December 2020
                : 11
                : 12
                : 1479
                Affiliations
                [1 ]Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Tianhe District, Guangzhou 510640, China; yuxuanliunxyc@ 123456163.com (Y.L.); lichunyu881@ 123456163.com (C.L.); shengou6@ 123456126.com (O.S.); yiganjun@ 123456vip.163.com (G.Y.)
                [2 ]Department of Genetics, University of Leicester, Leicester LE1 7RH, UK; bagchi_econ@ 123456yahoo.com (M.B.); jennihari@ 123456um.edu.my (J.A.H.)
                [3 ]The College of Economics and Managements, South China Agricultural University, Guangzhou 510640, China
                [4 ]Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal 700064, India; bdcse12@ 123456gmail.com
                [5 ]University of Malaya, Kuala Lumpur 50603, Malaysia
                [6 ]Forschungsmuseum Alexander Koenig, Bonn, Adenauerallee 160, 53113 Bonn, Germany; c.mayer.zfmk@ 123456uni-bonn.de
                Author notes
                [* ]Correspondence: mkbcit@ 123456ymail.com (M.K.B.); dengguiming@ 123456gdaas.cn (G.D.)
                Author information
                https://orcid.org/0000-0001-9228-1927
                https://orcid.org/0000-0002-4687-709X
                https://orcid.org/0000-0003-3777-4598
                https://orcid.org/0000-0001-5104-6621
                Article
                genes-11-01479
                10.3390/genes11121479
                7763637
                33317074
                47c3d5a9-c793-4eff-b3a0-e9840e7c513a
                © 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
                : 25 September 2020
                : 20 November 2020
                Categories
                Article

                musa spp.,comparative mapping,functional domain,ssr markers

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