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      Genetic Diversity and Population Structure of a Medicinal Herb Houttuynia cordata Thunb. of North-East India

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          Abstract

          Intra-species genetic variability assessment is an effective tool in formulating genetic improvement and germplasm conservation strategies. Houttuynia cordata Thunb. is a semidomesticated medicinal herb consumed widely in traditional diet in North-Eastern India. In the present study, an effort has been made to assess the genetic diversity of H. cordata Thunb. from Brahmaputra valley of North-East India. A total of 545 genotypes from 18 populations of H. cordata Thunb. from four different regions, i.e. North-East, North-West, South-East and South-West, with respect to river Brahmaputra were collected and population genetic diversity and structure were analysed using ISSR molecular markers. Population genetic structure analysis using unweighted pair group method with averages (UPGMA)-based hierarchical cluster analysis, principal coordinate analysis (PCoA) and model-based clustering in STRUCTURE program revealed that the population of H. cordata Thunb. grouped according to regional distribution and forms four genetically distinct clusters. The analysis of molecular variance showed that differentiation among regions was significant with 60% genetic variation among region, 3% genetic variation among population within region and 37% genetic variation within population. We found wide variation in Nei’s gene diversity (Hj) ranging from 0.07782 in Margherita population to 0.13634 in Barapani population. Furthermore, Nei’s gene diversity within population (Nei’s Hs) and total gene diversity (Ht) were found to be 0.1081 and 0.1769 respectively. The genetic differentiation among 18 population was high (Fst = 0.3894; p < 0.001) with relatively restricted gene flow (Nm = 0.6564). Based on the result of this study, we suggest ex situ conservation could be an appropriate measure to adequately capture the total genetic diversity of H. cordata Thunb. populations of North-East India by selecting few individuals from different populations.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s11105-020-01260-9.

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          Most cited references 54

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          Estimation of average heterozygosity and genetic distance from a small number of individuals.

           M Nei (1978)
          The magnitudes of the systematic biases involved in sample heterozygosity and sample genetic distances are evaluated, and formulae for obtaining unbiased estimates of average heterozygosity and genetic distance are developed. It is also shown that the number of individuals to be used for estimating average heterozygosity can be very small if a large number of loci are studied and the average heterozygosity is low. The number of individuals to be used for estimating genetic distance can also be very small if the genetic distance is large and the average heterozygosity of the two species compared is low.
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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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              Is Open Access

              GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

              Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s D est and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. Contact: rod.peakall@anu.edu.au
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                Author and article information

                Contributors
                rajubh2@gmail.com
                Journal
                Plant Mol Biol Report
                Plant Mol Biol Report
                Plant Molecular Biology Reporter
                Springer US (New York )
                0735-9640
                1556-469X
                14 November 2020
                : 1-9
                Affiliations
                [1 ]GRID grid.419867.5, ISNI 0000 0001 0195 7806, Present Address: Environment and Industrial Biotechnology Division, The Energy and Resources Institute, , North Eastern Regional Centre, ; Guwahati, Assam India
                [2 ]GRID grid.467306.0, Life Science Division, , Institute of Advanced Study in Science and Technology, ; Paschim Boragaon, Garchuk, Guwahati, Assam India
                [3 ]GRID grid.440675.4, ISNI 0000 0001 0244 8958, Department of Molecular Biology and Biotechnology, , Cotton University, ; Guwahati, Assam India
                Article
                1260
                10.1007/s11105-020-01260-9
                7666406
                © Springer Science+Business Media, LLC, part of Springer Nature 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                Funding
                Funded by: Department of Biotechnology, Govt. of India
                Award ID: DBT-RA
                Categories
                Original Article

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