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      Assessment of genetic diversity and volatile content of commercially grown banana ( Musa spp.) cultivars

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          Abstract

          Banana is an important fruit crop in the tropics and subtropics; however, limited information on biomarkers and signature volatiles is available for selecting commercial cultivars. Clonal fidelity is a major contributor to banana yield and aroma; however, there are no useful biomarkers available to validate clonal fidelity. In this study, we performed the molecular profiling of 20 banana cultivars consisting of diploid (AA or AB) and triploid (AAA or AAB or ABB) genomic groups. We screened 200 molecular markers, of which 34 markers (11 RAPD, 11 ISSR, and 12 SSR) yielded unequivocally scorable biomarker profiles. About 75, 69, and 24 allelic loci per marker were detected for RAPD, ISSR, and SSR markers, respectively. The statistical analysis of molecular variance (AMOVA) exhibited a high genetic difference of 77% with a significant FST value of 0.23 ( p < 0.001). Interestingly, the UBC-858 and SSR CNMPF-13 markers were unique to Grand Nain and Ardhapuri cultivars, respectively, which could be used for clonal fidelity analysis. Furthermore, the analysis of banana fruit volatilome using headspace solid-phase microextraction-gas chromatography-tandem mass spectrometry (HS-SPME-GCMS) revealed a total of fifty-four volatile compounds in nine banana cultivars with 56% of the total volatile compounds belonging to the ester group as the significant contributor of aroma. The study assumes significance with informative biomarkers and signature volatiles which could be helpful in breeding and for the authentic identification of commercial banana cultivars.

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

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            Analysis of Gene Diversity in Subdivided Populations

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              Inferring weak population structure with the assistance of sample group information.

              Genetic clustering algorithms require a certain amount of data to produce informative results. In the common situation that individuals are sampled at several locations, we show how sample group information can be used to achieve better results when the amount of data is limited. New models are developed for the structure program, both for the cases of admixture and no admixture. These models work by modifying the prior distribution for each individual's population assignment. The new prior distributions allow the proportion of individuals assigned to a particular cluster to vary by location. The models are tested on simulated data, and illustrated using microsatellite data from the CEPH Human Genome Diversity Panel. We demonstrate that the new models allow structure to be detected at lower levels of divergence, or with less data, than the original structure models or principal components methods, and that they are not biased towards detecting structure when it is not present. These models are implemented in a new version of structure which is freely available online at http://pritch.bsd.uchicago.edu/structure.html. © 2009 Blackwell Publishing Ltd.
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                Author and article information

                Contributors
                ulhasskadam@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 May 2022
                13 May 2022
                2022
                : 12
                : 7979
                Affiliations
                [1 ]GRID grid.444647.1, ISNI 0000 0001 2158 1375, Department of Plant Biotechnology, , Vilasrao Deshmukh College of Agricultural Biotechnology (Vasantrao Naik Marathwada Agricultural University, Parbhani), ; Latur, Maharashtra India
                [2 ]GRID grid.256681.e, ISNI 0000 0001 0661 1492, Division of Applied Life Science, Plant Molecular Biology and Biotechnology Research Center, , Gyeongsang National University, ; Jinju, Gyeongnam 52828 Republic of Korea
                [3 ]GRID grid.410498.0, ISNI 0000 0001 0465 9329, Agricultural Research Organization (ARO), , The Volcani Institute, ; P. O. Box 15159, 7505101 Rishon LeZion, Israel
                [4 ]GRID grid.418304.a, ISNI 0000 0001 0674 4228, Homi Bhabha National Institute (HBNI) and Nuclear Agriculture and Biotechnology Division, , Bhabha Atomic Research Center, ; Mumbai, India
                [5 ]GRID grid.256681.e, ISNI 0000 0001 0661 1492, Present Address: Division of Applied Life Science, Plant Molecular Biology and Biotechnology Research Center, , Gyeongsang National University, ; Jinju, Gyeongnam 52828 Republic of Korea
                Article
                11992
                10.1038/s41598-022-11992-1
                9106755
                35562398
                408a3ea0-8e1c-4655-8b42-184cc472c97a
                © The Author(s) 2022

                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
                : 20 January 2022
                : 4 May 2022
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                © The Author(s) 2022

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                genetic techniques,mass spectrometry,metabolomics,plant biotechnology,natural variation in plants,plant breeding,plant molecular biology

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