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      Contrasting signatures of genomic divergence during sympatric speciation

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          Abstract

          The transition from ‘well-marked varieties’ of a single species into ‘well-defined species’—especially in the absence of geographic barriers to gene flow (sympatric speciation)—has puzzled evolutionary biologists ever since Darwin 1, 2 . Gene flow counteracts the buildup of genome-wide differentiation, which is a hallmark of speciation and increases the likelihood of the evolution of irreversible reproductive barriers (incompatibilities) that complete the speciation process 3 . Theory predicts that the genetic architecture of divergently selected traits can influence whether sympatric speciation occurs 4 , but empirical tests of this theory are scant because comprehensive data are difficult to collect and synthesize across species, owing to their unique biologies and evolutionary histories 5 . Here, within a young species complex of neotropical cichlid fishes ( Amphilophus spp.), we analysed genomic divergence among populations and species. By generating a new genome assembly and re-sequencing 453 genomes, we uncovered the genetic architecture of traits that have been suggested to be important for divergence. Species that differ in monogenic or oligogenic traits that affect ecological performance and/or mate choice show remarkably localized genomic differentiation. By contrast, differentiation among species that have diverged in polygenic traits is genomically widespread and much higher overall, consistent with the evolution of effective and stable genome-wide barriers to gene flow. Thus, we conclude that simple trait architectures are not always as conducive to speciation with gene flow as previously suggested, whereas polygenic architectures can promote rapid and stable speciation in sympatry.

          Abstract

          Population genomic analyses of Midas cichlid fishes in young Nicaraguan crater lakes suggest that sympatric speciation is promoted by polygenic architectures.

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          Most cited references116

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Contributors
                axel.meyer@uni-konstanz.de
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                28 October 2020
                28 October 2020
                2020
                : 588
                : 7836
                : 106-111
                Affiliations
                [1 ]GRID grid.9811.1, ISNI 0000 0001 0658 7699, Department of Biology, , University of Konstanz, ; Konstanz, Germany
                [2 ]GRID grid.419537.d, ISNI 0000 0001 2113 4567, Max Planck Institute of Molecular Cell Biology and Genetics, ; Dresden, Germany
                [3 ]GRID grid.495510.c, Center for Systems Biology Dresden, ; Dresden, Germany
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Present Address: Department of Organismic and Evolutionary Biology, , Harvard University, ; Cambridge, MA USA
                [5 ]GRID grid.10863.3c, ISNI 0000 0001 2164 6351, Present Address: Department of Functional Biology, Area of Genetics, , University of Oviedo, ; Oviedo, Spain
                [6 ]Present Address: Argentine Dryland Research Institute of the National Council for Scientific Research (IADIZA-CONICET), Mendoza, Argentina
                [7 ]GRID grid.8536.8, ISNI 0000 0001 2294 473X, Present Address: Department of Genetics, Institute of Biology, , Federal University of Rio de Janeiro (UFRJ), ; Rio de Janeiro, Brazil
                [8 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Present Address: Division of Biological Sciences, Section of Ecology, Behavior & Evolution, , University of California San Diego, ; La Jolla, CA USA
                Author information
                http://orcid.org/0000-0001-7792-0735
                http://orcid.org/0000-0002-5646-3114
                http://orcid.org/0000-0002-4805-5575
                http://orcid.org/0000-0003-2729-6246
                http://orcid.org/0000-0003-2894-5041
                http://orcid.org/0000-0002-8184-1463
                http://orcid.org/0000-0002-8134-5929
                http://orcid.org/0000-0002-6580-7839
                http://orcid.org/0000-0002-0888-8193
                Article
                2845
                10.1038/s41586-020-2845-0
                7759464
                33116308
                b50f4892-9993-4c15-887e-9b9a836a6bac
                © The Author(s) 2020

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 March 2020
                : 23 July 2020
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                © The Author(s), under exclusive licence to Springer Nature Limited 2020

                Uncategorized
                evolutionary genetics,molecular evolution,adaptive radiation,genomics,population genetics

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