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      Parallel Alpine Differentiation in Arabidopsis arenosa

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          Abstract

          Parallel evolution provides powerful natural experiments for studying repeatability of evolution and genomic basis of adaptation. Well-documented examples from plants are, however, still rare, as are inquiries of mechanisms driving convergence in some traits while divergence in others. Arabidopsis arenosa, a predominantly foothill species with scattered morphologically distinct alpine occurrences is a promising candidate. Yet, the hypothesis of parallelism remained untested. We sampled foothill and alpine populations in all regions known to harbor the alpine ecotype and used SNP genotyping to test for repeated alpine colonization. Then, we combined field surveys and a common garden experiment to quantify phenotypic parallelism. Genetic clustering by region but not elevation and coalescent simulations demonstrated parallel origin of alpine ecotype in four mountain regions. Alpine populations exhibited parallelism in height and floral traits which persisted after two generations in cultivation. In contrast, leaf traits were distinctive only in certain region(s), reflecting a mixture of plasticity and genetically determined non-parallelism. We demonstrate varying degrees and causes of parallelism and non-parallelism across populations and traits within a plant species. Parallel divergence along a sharp elevation gradient makes A. arenosa a promising candidate for studying genomic basis of adaptation.

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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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            The genomic basis of adaptive evolution in threespine sticklebacks

            Summary Marine stickleback fish have colonized and adapted to innumerable streams and lakes formed since the last ice age, providing an exceptional opportunity to characterize genomic mechanisms underlying repeated ecological adaptation in nature. Here we develop a high quality reference genome assembly for threespine sticklebacks. By sequencing the genomes of 20 additional individuals from a global set of marine and freshwater populations, we identify a genome-wide set of loci that are consistently associated with marine-freshwater divergence. Our results suggest that reuse of globally-shared standing genetic variation, including chromosomal inversions, plays an important role in repeated evolution of distinct marine and freshwater sticklebacks, and in the maintenance of divergent ecotypes during early stages of reproductive isolation. Both coding and regulatory changes occur in the set of loci underlying marine-freshwater evolution, with regulatory changes likely predominating in this classic example of repeated adaptive evolution in nature.
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              fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets

              Tools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges. Here, we develop efficient algorithms for approximate inference of the model underlying the STRUCTURE program using a variational Bayesian framework. Variational methods pose the problem of computing relevant posterior distributions as an optimization problem, allowing us to build on recent advances in optimization theory to develop fast inference tools. In addition, we propose useful heuristic scores to identify the number of populations represented in a data set and a new hierarchical prior to detect weak population structure in the data. We test the variational algorithms on simulated data and illustrate using genotype data from the CEPH–Human Genome Diversity Panel. The variational algorithms are almost two orders of magnitude faster than STRUCTURE and achieve accuracies comparable to those of ADMIXTURE. Furthermore, our results show that the heuristic scores for choosing model complexity provide a reasonable range of values for the number of populations represented in the data, with minimal bias toward detecting structure when it is very weak. Our algorithm, fastSTRUCTURE, is freely available online at http://pritchardlab.stanford.edu/structure.html.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                08 December 2020
                2020
                : 11
                : 561526
                Affiliations
                [1] 1Department of Botany, Charles University , Prague, Czechia
                [2] 2Institute of Botany, The Czech Academy of Sciences , Průhonice, Czechia
                [3] 3Institute of Botany, Slovak Academy of Sciences , Bratislava, Slovakia
                [4] 4Department of Botany, University of Innsbruck , Innsbruck, Austria
                Author notes

                Edited by: Michael Eric Schranz, Wageningen University and Research, Netherlands

                Reviewed by: Tuomas Hämälä, University of Minnesota Twin Cities, United States; Karl Josef Schmid, University of Hohenheim, Germany

                This article was submitted to Plant Systematics and Evolution, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2020.561526
                7753741
                33363550
                0a4a484b-a8f4-405a-9936-7aec0f6e4231
                Copyright © 2020 Knotek, Konečná, Wos, Požárová, Šrámková, Bohutínská, Zeisek, Marhold and Kolář.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 May 2020
                : 16 November 2020
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 60, Pages: 12, Words: 0
                Funding
                Funded by: Grantová Agentura České Republiky 10.13039/501100001824
                Funded by: Grantová Agentura České Republiky 10.13039/501100001824
                Funded by: Univerzita Karlova v Praze 10.13039/100007397
                Funded by: Norges Forskningsråd 10.13039/501100005416
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                adaptation,alpine environments,arabidopsis,convergence,parallel evolution,phenotypic parallelism

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