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      Unweaving the population structure and genetic diversity of Canadian shrub willow

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          Abstract

          Perennial shrub willow are increasingly being promoted in short-rotation coppice systems as biomass feedstocks, for phytoremediation applications, and for the diverse ecosystem services that can accrue. This renewed interest has led to widespread willow cultivation, particularly of non-native varieties. However, Canadian willow species have not been widely adopted and their inherent diversity has not yet been thoroughly investigated. In this study, 324 genotypes of Salix famelica and Salix eriocephala collected from 33 sites of origin were analyzed using 26,016 single nucleotide polymorphisms to reveal patterns of population structure and genetic diversity. Analyses by Bayesian methods and principal component analysis detected five main clusters that appeared to be largely shaped by geoclimatic variables including mean annual precipitation and the number of frost-free days. The overall observed ( H O ) and expected ( H E ) heterozygosity were 0.126 and 0.179, respectively. An analysis of molecular variance revealed that the highest genetic variation occurred within genotypes (69%), while 8% of the variation existed among clusters and 23% between genotypes within clusters. These findings provide new insights into the extent of genetic variation that exists within native shrub willow species which could be leveraged in pan-Canadian willow breeding programs.

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          adegenet: a R package for the multivariate analysis of genetic markers.

          The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. Stable version is available from CRAN: http://cran.r-project.org/mirrors.html. Development version is available from adegenet website: http://adegenet.r-forge.r-project.org/. Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2).
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            Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

            Background The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results We introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.
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              ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE.

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                Author and article information

                Contributors
                raju.soolanayakanahally@agr.gc.ca
                shawn.mansfield@ubc.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 October 2022
                14 October 2022
                2022
                : 12
                : 17254
                Affiliations
                [1 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, Department of Wood Science, Faculty of Forestry, , University of British Columbia, ; Vancouver, BC Canada
                [2 ]GRID grid.419231.c, ISNI 0000 0001 2167 7174, Instituto Nacional de Tecnología Agropecuaria, Instituto de Recursos Biológicos, , Centro de Investigación en Recursos Naturales, ; Buenos Aires, Argentina
                [3 ]GRID grid.423606.5, ISNI 0000 0001 1945 2152, Consejo Nacional de Investigaciones Científicas y Técnicas, ; Buenos Aires, Argentina
                [4 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, Department of Forest and Conservation Sciences, Faculty of Forestry, , University of British Columbia, ; Vancouver, BC Canada
                [5 ]Indian Head Research Farm, Agriculture and Agri-Food Canada, Indian Head, SK Canada
                [6 ]GRID grid.55614.33, ISNI 0000 0001 1302 4958, Saskatoon Research and Development Centre, , Agriculture and Agri-Food Canada, ; Saskatoon, SK Canada
                [7 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, Department of Botany, , University of British Columbia, ; Vancouver, BC Canada
                Article
                20498
                10.1038/s41598-022-20498-9
                9568530
                36241753
                0b699b2b-f750-4264-b278-e23d9831ffb0
                © 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
                : 30 March 2022
                : 14 September 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000040, Agriculture and Agri-Food Canada;
                Funded by: NSERC
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                plant biotechnology,sequencing,plant evolution,biotechnology,plant sciences
                Uncategorized
                plant biotechnology, sequencing, plant evolution, biotechnology, plant sciences

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