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      Recombination rate variation shapes barriers to introgression across butterfly genomes

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          Abstract

          Hybridisation and introgression can dramatically alter the relationships among groups of species, leading to phylogenetic discordance across the genome and between populations. Introgression can also erode species differences over time, but selection against introgression at certain loci acts to maintain postmating species barriers. Theory predicts that species barriers made up of many loci throughout the genome should lead to a broad correlation between introgression and recombination rate, which determines the extent to which selection on deleterious foreign alleles will affect neutral alleles at physically linked loci. Here, we describe the variation in genealogical relationships across the genome among three species of Heliconius butterflies: H. melpomene ( mel), H. cydno ( cyd), and H. timareta ( tim), using whole genomes of 92 individuals, and ask whether this variation can be explained by heterogeneous barriers to introgression. We find that species relationships vary predictably at the chromosomal scale. By quantifying recombination rate and admixture proportions, we then show that rates of introgression are predicted by variation in recombination rate. This implies that species barriers are highly polygenic, with selection acting against introgressed alleles across most of the genome. In addition, long chromosomes, which have lower recombination rates, produce stronger barriers on average than short chromosomes. Finally, we find a consistent difference between two species pairs on either side of the Andes, which suggests differences in the architecture of the species barriers. Our findings illustrate how the combined effects of hybridisation, recombination, and natural selection, acting at multitudes of loci over long periods, can dramatically sculpt the phylogenetic relationships among species.

          Author summary

          Many species occasionally hybridise and share genetic material with related species. Interspecific gene flow may be counteracted by natural selection at particular ‘barrier loci’. As a result, a pair of species can end up sharing more genetic variation in some parts of their genome than in others, and the tree of relationships in a group of species can differ from one part of the genome to another. We studied relationships and barriers among three species of Heliconius butterflies using whole-genome sequences from nine populations. We find that species relationships vary dramatically and predictably across the genome because the species barriers are more porous in genomic regions with higher recombination rates. This occurs because recombination determines how broadly the surrounding genome is affected by a barrier locus. The genome-wide pattern suggests that barrier loci are widespread across the genome. One consequence is that smaller chromosomes, which have higher recombination rates, tend to have weaker species barriers than longer chromosomes. The relationships among populations on small chromosomes therefore tend to be predicted by geography, rather than by which species they belong to. Our work shows how hybridisation, recombination, and selection interact to reshape species’ relationships.

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          Neighbor-net: an agglomerative method for the construction of phylogenetic networks.

          We present Neighbor-Net, a distance based method for constructing phylogenetic networks that is based on the Neighbor-Joining (NJ) algorithm of Saitou and Nei. Neighbor-Net provides a snapshot of the data that can guide more detailed analysis. Unlike split decomposition, Neighbor-Net scales well and can quickly produce detailed and informative networks for several hundred taxa. We illustrate the method by reanalyzing three published data sets: a collection of 110 highly recombinant Salmonella multi-locus sequence typing sequences, the 135 "African Eve" human mitochondrial sequences published by Vigilant et al., and a collection of 12 Archeal chaperonin sequences demonstrating strong evidence for gene conversion. Neighbor-Net is available as part of the SplitsTree4 software package.
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            Testing for ancient admixture between closely related populations.

            One enduring question in evolutionary biology is the extent of archaic admixture in the genomes of present-day populations. In this paper, we present a test for ancient admixture that exploits the asymmetry in the frequencies of the two nonconcordant gene trees in a three-population tree. This test was first applied to detect interbreeding between Neandertals and modern humans. We derive the analytic expectation of a test statistic, called the D statistic, which is sensitive to asymmetry under alternative demographic scenarios. We show that the D statistic is insensitive to some demographic assumptions such as ancestral population sizes and requires only the assumption that the ancestral populations were randomly mating. An important aspect of D statistics is that they can be used to detect archaic admixture even when no archaic sample is available. We explore the effect of sequencing error on the false-positive rate of the test for admixture, and we show how to estimate the proportion of archaic ancestry in the genomes of present-day populations. We also investigate a model of subdivision in ancestral populations that can result in D statistics that indicate recent admixture.
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              The landscape of Neandertal ancestry in present-day humans

              Analyses of Neandertal genomes have revealed that Neandertals have contributed genetic variants to modern humans 1–2 . The antiquity of Neandertal gene flow into modern humans means that regions that derive from Neandertals in any one human today are usually less than a hundred kilobases in size. However, Neandertal haplotypes are also distinctive enough that several studies have been able to detect Neandertal ancestry at specific loci 1,3–8 . Here, we have systematically inferred Neandertal haplotypes in the genomes of 1,004 present-day humans 12 . Regions that harbor a high frequency of Neandertal alleles in modern humans are enriched for genes affecting keratin filaments suggesting that Neandertal alleles may have helped modern humans adapt to non-African environments. Neandertal alleles also continue to shape human biology, as we identify multiple Neandertal-derived alleles that confer risk for disease. We also identify regions of millions of base pairs that are nearly devoid of Neandertal ancestry and enriched in genes, implying selection to remove genetic material derived from Neandertals. Neandertal ancestry is significantly reduced in genes specifically expressed in testis, and there is an approximately 5-fold reduction of Neandertal ancestry on chromosome X, which is known to harbor a disproportionate fraction of male hybrid sterility genes 20–22 . These results suggest that part of the reduction in Neandertal ancestry near genes is due to Neandertal alleles that reduced fertility in males when moved to a modern human genetic background.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                7 February 2019
                February 2019
                7 February 2019
                : 17
                : 2
                : e2006288
                Affiliations
                [1 ] Department of Zoology, University of Cambridge, Cambridge, United Kingdom
                [2 ] Department of Biology, University of York, York, United Kingdom
                [3 ] Biology Program, Faculty of Natural Sciences and Mathematics, Universidad del Rosario, Bogota, Colombia
                Indiana University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-0747-7456
                Article
                pbio.2006288
                10.1371/journal.pbio.2006288
                6366726
                30730876
                3d54cee5-b4ac-48e5-8cf5-240db948b0fd
                © 2019 Martin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 April 2018
                : 7 January 2019
                Page count
                Figures: 5, Tables: 0, Pages: 28
                Funding
                European Research Council https://erc.europa.eu/ (grant number 339873). Funding awarded to CDJ. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Universidad del Rosario http://www.urosario.edu.co/ (grant number 2016-PIN-2017-001). Funding awarded to CS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Introgression
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromosomes
                Autosomes
                Biology and Life Sciences
                Genetics
                Heredity
                Gene Flow
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Gene Flow
                Biology and Life Sciences
                Genetics
                Population Genetics
                Gene Flow
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Gene Flow
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Systematics
                Phylogenetics
                Biology and Life Sciences
                Taxonomy
                Evolutionary Systematics
                Phylogenetics
                Computer and Information Sciences
                Data Management
                Taxonomy
                Evolutionary Systematics
                Phylogenetics
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromosomes
                Sex Chromosomes
                Z Chromosomes
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromosomes
                Sex Chromosomes
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Systematics
                Phylogenetics
                Phylogenetic Analysis
                Biology and Life Sciences
                Taxonomy
                Evolutionary Systematics
                Phylogenetics
                Phylogenetic Analysis
                Computer and Information Sciences
                Data Management
                Taxonomy
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                Phylogenetics
                Phylogenetic Analysis
                Custom metadata
                Resequencing fastq files for all 92 individuals are available from the European Nucleotide Archive (accession numbers are provided in S1 Table). Filtered SNP data (VCF) and all processed data files used to generate all empirical and simulation figures are available from the Dryad Digital Repository:  https://doi.org/10.5061/dryad.sk2pd88.

                Life sciences
                Life sciences

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