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      Complex signatures of genomic variation of two non-model marine species in a homogeneous environment

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          Abstract

          Background

          Genomic tools are increasingly being used on non-model organisms to provide insights into population structure and variability, including signals of selection. However, most studies are carried out in regions with distinct environmental gradients or across large geographical areas, in which local adaptation is expected to occur. Therefore, the focus of this study is to characterize genomic variation and selective signals over short geographic areas within a largely homogeneous region. To assess adaptive signals between microhabitats within the rocky shore, we compared genomic variation between the Cape urchin ( Parechinus angulosus), which is a low to mid-shore species, and the Granular limpet ( Scutellastra granularis), a high shore specialist.

          Results

          Using pooled restriction site associated DNA (RAD) sequencing, we described patterns of genomic variation and identified outlier loci in both species. We found relatively low numbers of outlier SNPs within each species, and identified outlier genes associated with different selective pressures than those previously identified in studies conducted over larger environmental gradients. The number of population-specific outlier loci differed between species, likely owing to differential selective pressures within the intertidal environment. Interestingly, the outlier loci were highly differentiated within the two northernmost populations for both species, suggesting that unique evolutionary forces are acting on marine invertebrates within this region.

          Conclusions

          Our study provides a background for comparative genomic studies focused on non-model species, as well as a baseline for the adaptive potential of marine invertebrates along the South African west coast. We also discuss the caveats associated with Pool-seq and potential biases of sequencing coverage on downstream genomic metrics. The findings provide evidence of species-specific selective pressures within a homogeneous environment, and suggest that selective forces acting on small scales are just as crucial to acknowledge as those acting on larger scales. As a whole, our findings imply that future population genomic studies should expand from focusing on model organisms and/or studying heterogeneous regions to better understand the evolutionary processes shaping current and future biodiversity patterns, particularly when used in a comparative phylogeographic context.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-4721-y) contains supplementary material, which is available to authorized users.

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          Most cited references108

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            Climate change and evolution: disentangling environmental and genetic responses.

            Rapid climate change is likely to impose strong selection pressures on traits important for fitness, and therefore, microevolution in response to climate-mediated selection is potentially an important mechanism mitigating negative consequences of climate change. We reviewed the empirical evidence for recent microevolutionary responses to climate change in longitudinal studies emphasizing the following three perspectives emerging from the published data. First, although signatures of climate change are clearly visible in many ecological processes, similar examples of microevolutionary responses in literature are in fact very rare. Second, the quality of evidence for microevolutionary responses to climate change is far from satisfactory as the documented responses are often - if not typically - based on nongenetic data. We reinforce the view that it is as important to make the distinction between genetic (evolutionary) and phenotypic (includes a nongenetic, plastic component) responses clear, as it is to understand the relative roles of plasticity and genetics in adaptation to climate change. Third, in order to illustrate the difficulties and their potential ubiquity in detection of microevolution in response to natural selection, we reviewed the quantitative genetic studies on microevolutionary responses to natural selection in the context of long-term studies of vertebrates. The available evidence points to the overall conclusion that many responses perceived as adaptations to changing environmental conditions could be environmentally induced plastic responses rather than microevolutionary adaptations. Hence, clear-cut evidence indicating a significant role for evolutionary adaptation to ongoing climate warming is conspicuously scarce.
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              Applications of next generation sequencing in molecular ecology of non-model organisms.

              As most biologists are probably aware, technological advances in molecular biology during the last few years have opened up possibilities to rapidly generate large-scale sequencing data from non-model organisms at a reasonable cost. In an era when virtually any study organism can 'go genomic', it is worthwhile to review how this may impact molecular ecology. The first studies to put the next generation sequencing (NGS) to the test in ecologically well-characterized species without previous genome information were published in 2007 and the beginning of 2008. Since then several studies have followed in their footsteps, and a large number are undoubtedly under way. This review focuses on how NGS has been, and can be, applied to ecological, population genetic and conservation genetic studies of non-model species, in which there is no (or very limited) genomic resources. Our aim is to draw attention to the various possibilities that are opening up using the new technologies, but we also highlight some of the pitfalls and drawbacks with these methods. We will try to provide a snapshot of the current state of the art for this rapidly advancing and expanding field of research and give some likely directions for future developments.
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                Author and article information

                Contributors
                esnielsen@sun.ac.za
                romhe@aqua.dtu.dk
                rjtoonen@gmail.com
                Ingrid.knapp16@gmail.com
                baochengguo@gmail.com
                svdh@sun.ac.za
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                9 May 2018
                9 May 2018
                2018
                : 19
                : 347
                Affiliations
                [1 ]ISNI 0000 0001 2214 904X, GRID grid.11956.3a, Evolutionary Genomics Group, Department of Botany and Zoology, , University of Stellenbosch, ; Private Bag X1, Matieland,, 7602 South Africa
                [2 ]ISNI 0000 0001 2188 0957, GRID grid.410445.0, Hawaiʻi Institute of Marine Biology, School of Ocean and Earth Science and Technology, , University of Hawaiʻi at Mānoa, ; Kāneʻohe, HI 96744 USA
                [3 ]ISNI 0000 0004 1792 6416, GRID grid.458458.0, The Key Laboratory of Zoological Systematics and Evolution, , Institute of Zoology Chinese Academy of Sciences, ; Beijing, 100101 China
                Author information
                http://orcid.org/0000-0001-9166-976X
                Article
                4721
                10.1186/s12864-018-4721-y
                5944137
                29743012
                df3596ba-bf70-498f-8004-5e79783bece7
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 21 November 2017
                : 23 April 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001321, National Research Foundation;
                Award ID: 92788
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1416889
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Genetics
                genomics,rad-seq,comparative phylogeography,snp,local adaptation,population differentiation
                Genetics
                genomics, rad-seq, comparative phylogeography, snp, local adaptation, population differentiation

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