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      Adapting to climate with limited genetic diversity: Nucleotide, DNA methylation and microbiome variation among populations of the social spider Stegodyphus dumicola

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          Abstract

          Understanding the role of genetic and nongenetic variants in modulating phenotypes is central to our knowledge of adaptive responses to local conditions and environmental change, particularly in species with such low population genetic diversity that it is likely to limit their evolutionary potential. A first step towards uncovering the molecular mechanisms underlying population‐specific responses to the environment is to carry out environmental association studies. We associated climatic variation with genetic, epigenetic and microbiome variation in populations of a social spider with extremely low standing genetic diversity. We identified genetic variants that are associated strongly with environmental variation, particularly with average temperature, a pattern consistent with local adaptation. Variation in DNA methylation in many genes was strongly correlated with a wide set of climate parameters, thereby revealing a different pattern of associations than that of genetic variants, which show strong correlations to a more restricted range of climate parameters. DNA methylation levels were largely independent of cis‐genetic variation and of overall genetic population structure, suggesting that DNA methylation can work as an independent mechanism. Microbiome composition also correlated with environmental variation, but most strong associations were with precipitation‐related climatic factors. Our results suggest a role for both genetic and nongenetic mechanisms in shaping phenotypic responses to local environments.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

              The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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                Author and article information

                Contributors
                anneaagaard@bio.au.dk
                Journal
                Mol Ecol
                Mol Ecol
                10.1111/(ISSN)1365-294X
                MEC
                Molecular Ecology
                John Wiley and Sons Inc. (Hoboken )
                0962-1083
                1365-294X
                02 October 2022
                November 2022
                : 31
                : 22 ( doiID: 10.1111/mec.v31.22 )
                : 5765-5783
                Affiliations
                [ 1 ] Section for Genetics, Ecology & Evolution, Department of Biology Aarhus University Aarhus C Denmark
                [ 2 ] Centre for Ecology & Conservation, School of Biosciences University of Exeter Penryn Campus UK
                [ 3 ] Section for Microbiology, Department of Biology Aarhus University Aarhus C Denmark
                [ 4 ] Terrestrial Ecology Department Netherlands Institute of Ecology (NIOO‐KNAW) Wageningen The Netherlands
                Author notes
                [*] [* ] Correspondence

                Anne Aagaard, Section for Genetics, Ecology & Evolution, Department of Biology, Aarhus University, Aarhus C, Denmark.

                Email: anneaagaard@ 123456bio.au.dk

                Author information
                https://orcid.org/0000-0001-7044-5177
                https://orcid.org/0000-0003-4182-2222
                https://orcid.org/0000-0003-1543-1009
                https://orcid.org/0000-0002-7614-9616
                https://orcid.org/0000-0003-3002-4102
                https://orcid.org/0000-0003-3273-0174
                https://orcid.org/0000-0002-0341-161X
                Article
                MEC16696 MEC-22-0151.R1
                10.1111/mec.16696
                9827990
                36112081
                175be316-b44b-4d97-93d7-f446df6c78dc
                © 2022 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 01 September 2022
                : 10 February 2022
                : 06 September 2022
                Page count
                Figures: 6, Tables: 0, Pages: 19, Words: 14215
                Funding
                Funded by: Natur og Univers, Det Frie Forskningsråd , doi 10.13039/100008394;
                Award ID: 6108‐00565
                Funded by: Novo Nordisk Foundation Interdisciplinary Synergy Grant
                Award ID: NNF16OC0021110
                Categories
                Original Article
                ORIGINAL ARTICLES
                Ecological Genomics
                Custom metadata
                2.0
                November 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.3 mode:remove_FC converted:09.01.2023

                Ecology
                adaptation,dna methylation,low evolutionary potential,microbiome,phenotypic plasticity,social spiders

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