0
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genetic admixture drives climate adaptation in the bank vole

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Genetic admixture introduces new variants at relatively high frequencies, potentially aiding rapid responses to environmental changes. Here, we evaluate its role in adaptive variation related to climatic conditions in bank voles ( Clethrionomys glareolus) in Britain, using whole-genome data. Our results reveal loci showing excess ancestry from one of the two postglacial colonist populations inconsistent with overall admixture patterns. Notably, loci associated with climate adaptation exhibit disproportionate amounts of excess ancestry, highlighting the impact of admixture between colonist populations on local adaptation. The results suggest strong and localized selection on climate-adaptive loci, as indicated by steep clines and/or shifted cline centres, during population replacement. A subset, including a haemoglobin gene, is associated with oxidative stress responses, underscoring a role of oxidative stress in local adaptation. Our study highlights the important contribution of admixture during secondary contact between populations from distinct climatic refugia enriching adaptive diversity. Understanding these dynamics is crucial for predicting future adaptive capacity to anthropogenic climate change.

          Abstract

          Genetic mixing among distinct bank vole populations in Britain has enhanced their adaptive diversity for climate adaptation, highlighting the importance of admixture in responding to environmental change.

          Related collections

          Most cited references90

          • Record: found
          • Abstract: found
          • Article: not found

          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast model-based estimation of ancestry in unrelated individuals.

              Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
                Bookmark

                Author and article information

                Contributors
                kotlik@iapg.cas.cz
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                15 July 2024
                15 July 2024
                2024
                : 7
                : 863
                Affiliations
                [1 ]Laboratory of Molecular Ecology, Institute of Animal Physiology and Genetics, Czech Academy of Sciences, ( https://ror.org/053avzc18) Liběchov, Czech Republic
                [2 ]Department of Biology, Program in Ecology & Evolutionary Biology, University of Oklahoma, ( https://ror.org/02aqsxs83) Norman, OK USA
                [3 ]Sam Noble Museum, University of Oklahoma, ( https://ror.org/02aqsxs83) Norman, OK USA
                [4 ]Department of Ecology and Evolutionary Biology, Cornell University, ( https://ror.org/05bnh6r87) Ithaca, NY USA
                Author information
                http://orcid.org/0000-0002-8396-4651
                http://orcid.org/0000-0001-9429-0667
                Article
                6549
                10.1038/s42003-024-06549-z
                11251159
                39009753
                7674aa9c-c4ec-465c-bbcc-f02af1a0162b
                © The Author(s) 2024

                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 July 2023
                : 3 July 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001824, Grantová Agentura České Republiky (Grant Agency of the Czech Republic);
                Award ID: 20-11058S
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                evolutionary genetics,evolutionary biology
                evolutionary genetics, evolutionary biology

                Comments

                Comment on this article