1
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Raas MWD, Dutheil JY. The rate of adaptive molecular evolution in wild and domesticated Saccharomyces cerevisiae populations. Mol Ecol 2024; 33:e16980. [PMID: 37157166 DOI: 10.1111/mec.16980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/22/2023] [Accepted: 04/26/2023] [Indexed: 05/10/2023]
Abstract
Through its fermentative capacities, Saccharomyces cerevisiae was central in the development of civilisation during the Neolithic period, and the yeast remains of importance in industry and biotechnology, giving rise to bona fide domesticated populations. Here, we conduct a population genomic study of domesticated and wild populations of S. cerevisiae. Using coalescent analyses, we report that the effective population size of yeast populations decreased since the divergence with S. paradoxus. We fitted models of distributions of fitness effects to infer the rate of adaptive (ω a ) and non-adaptive (ω na ) non-synonymous substitutions in protein-coding genes. We report an overall limited contribution of positive selection to S. cerevisiae protein evolution, albeit with higher rates of adaptive evolution in wild compared to domesticated populations. Our analyses revealed the signature of background selection and possibly Hill-Robertson interference, as recombination was found to be negatively correlated withω na and positively correlated withω a . However, the effect of recombination onω a was found to be labile, as it is only apparent after removing the impact of codon usage bias on the synonymous site frequency spectrum and disappears if we control for the correlation withω na , suggesting that it could be an artefact of the decreasing population size. Furthermore, the rate of adaptive non-synonymous substitutions is significantly correlated with the residue solvent exposure, a relation that cannot be explained by the population's demography. Together, our results provide a detailed characterisation of adaptive mutations in protein-coding genes across S. cerevisiae populations.
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Affiliation(s)
- Maximilian W D Raas
- Research Group Molecular Systems Evolution, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Julien Y Dutheil
- Research Group Molecular Systems Evolution, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Unité Mixte de Recherche 5554 Institut des Sciences de l'Evolution, CNRS, IRD, EPHE, Université de Montpellier, Montpellier, France
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2
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Rivas-González I, Schierup MH, Wakeley J, Hobolth A. TRAILS: Tree reconstruction of ancestry using incomplete lineage sorting. PLoS Genet 2024; 20:e1010836. [PMID: 38330138 PMCID: PMC10880969 DOI: 10.1371/journal.pgen.1010836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 02/21/2024] [Accepted: 01/22/2024] [Indexed: 02/10/2024] Open
Abstract
Genome-wide genealogies of multiple species carry detailed information about demographic and selection processes on individual branches of the phylogeny. Here, we introduce TRAILS, a hidden Markov model that accurately infers time-resolved population genetics parameters, such as ancestral effective population sizes and speciation times, for ancestral branches using a multi-species alignment of three species and an outgroup. TRAILS leverages the information contained in incomplete lineage sorting fragments by modelling genealogies along the genome as rooted three-leaved trees, each with a topology and two coalescent events happening in discretized time intervals within the phylogeny. Posterior decoding of the hidden Markov model can be used to infer the ancestral recombination graph for the alignment and details on demographic changes within a branch. Since TRAILS performs posterior decoding at the base-pair level, genome-wide scans based on the posterior probabilities can be devised to detect deviations from neutrality. Using TRAILS on a human-chimp-gorilla-orangutan alignment, we recover speciation parameters and extract information about the topology and coalescent times at high resolution.
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Affiliation(s)
| | - Mikkel H. Schierup
- Bioinformatics Research Center (BiRC), Aarhus University, Aarhus, Denmark
| | - John Wakeley
- Department of Organismic and Evolutionary Biology, Harvard University, Massachusetts, United States of America
| | - Asger Hobolth
- Department of Mathematics, Aarhus University, Aarhus, Denmark
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3
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Pokharel K, Weldenegodguad M, Dudeck S, Honkatukia M, Lindeberg H, Mazzullo N, Paasivaara A, Peippo J, Soppela P, Stammler F, Kantanen J. Whole-genome sequencing provides novel insights into the evolutionary history and genetic adaptation of reindeer populations in northern Eurasia. Sci Rep 2023; 13:23019. [PMID: 38155192 PMCID: PMC10754820 DOI: 10.1038/s41598-023-50253-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023] Open
Abstract
Domestic reindeer (Rangifer tarandus) play a vital role in the culture and livelihoods of indigenous people across northern Eurasia. These animals are well adapted to harsh environmental conditions, such as extreme cold, limited feed availability and long migration distances. Therefore, understanding the genomics of reindeer is crucial for improving their management, conservation and utilisation. In this study, we have generated a new genome assembly for the Fennoscandian domestic reindeer with high contiguity, making it the most complete reference genome for reindeer to date. The new genome assembly was utilised to explore genetic diversity, population structure and selective sweeps in Eurasian Rangifer tarandus populations which was based on the largest population genomic dataset for reindeer, encompassing 58 individuals from diverse populations. Phylogenetic analyses revealed distinct genetic clusters, with the Finnish wild forest reindeer (Rangifer tarandus fennicus) standing out as a unique subspecies. Divergence time estimates suggested a separation of ~ 52 thousand years ago (Kya) between the northern European Rangifer tarandus fennicus and Rangifer tarandus tarandus. Our study identified four main genetic clusters: Fennoscandian, the eastern/northern Russian and Alaskan group, the Finnish forest reindeer, and the Svalbard reindeer. Furthermore, two independent reindeer domestication processes were inferred, suggesting separate origins for the domestic Fennoscandian and eastern/northern Russian reindeer. Notably, shared genes under selection, including retroviral genes, point towards molecular domestication processes that aided adaptation of this species to diverse environments.
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Affiliation(s)
- Kisun Pokharel
- Natural Resources Institute Finland (Luke), Myllytie 1, 31600, Jokioinen, Finland
| | - Melak Weldenegodguad
- Natural Resources Institute Finland (Luke), Myllytie 1, 31600, Jokioinen, Finland
| | - Stephan Dudeck
- Arctic Centre, University of Lapland, 96100, Rovaniemi, Finland
| | | | - Heli Lindeberg
- Natural Resources Institute Finland (Luke), 71750, Maaninka, Finland
| | - Nuccio Mazzullo
- Arctic Centre, University of Lapland, 96100, Rovaniemi, Finland
| | - Antti Paasivaara
- Natural Resources Institute Finland (Luke), Paavo Havaksentie 3, 90570, Oulu, Finland
| | - Jaana Peippo
- Natural Resources Institute Finland (Luke), Myllytie 1, 31600, Jokioinen, Finland
- NordGen-Nordic Genetic Resource Center, 1432, Ås, Norway
| | - Päivi Soppela
- Arctic Centre, University of Lapland, 96100, Rovaniemi, Finland
| | | | - Juha Kantanen
- Natural Resources Institute Finland (Luke), Myllytie 1, 31600, Jokioinen, Finland.
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4
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Pawar H, Rymbekova A, Cuadros-Espinoza S, Huang X, de Manuel M, van der Valk T, Lobon I, Alvarez-Estape M, Haber M, Dolgova O, Han S, Esteller-Cucala P, Juan D, Ayub Q, Bautista R, Kelley JL, Cornejo OE, Lao O, Andrés AM, Guschanski K, Ssebide B, Cranfield M, Tyler-Smith C, Xue Y, Prado-Martinez J, Marques-Bonet T, Kuhlwilm M. Ghost admixture in eastern gorillas. Nat Ecol Evol 2023; 7:1503-1514. [PMID: 37500909 PMCID: PMC10482688 DOI: 10.1038/s41559-023-02145-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 06/30/2023] [Indexed: 07/29/2023]
Abstract
Archaic admixture has had a substantial impact on human evolution with multiple events across different clades, including from extinct hominins such as Neanderthals and Denisovans into modern humans. In great apes, archaic admixture has been identified in chimpanzees and bonobos but the possibility of such events has not been explored in other species. Here, we address this question using high-coverage whole-genome sequences from all four extant gorilla subspecies, including six newly sequenced eastern gorillas from previously unsampled geographic regions. Using approximate Bayesian computation with neural networks to model the demographic history of gorillas, we find a signature of admixture from an archaic 'ghost' lineage into the common ancestor of eastern gorillas but not western gorillas. We infer that up to 3% of the genome of these individuals is introgressed from an archaic lineage that diverged more than 3 million years ago from the common ancestor of all extant gorillas. This introgression event took place before the split of mountain and eastern lowland gorillas, probably more than 40 thousand years ago and may have influenced perception of bitter taste in eastern gorillas. When comparing the introgression landscapes of gorillas, humans and bonobos, we find a consistent depletion of introgressed fragments on the X chromosome across these species. However, depletion in protein-coding content is not detectable in eastern gorillas, possibly as a consequence of stronger genetic drift in this species.
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Affiliation(s)
- Harvinder Pawar
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain
| | - Aigerim Rymbekova
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Wien, Austria
| | | | - Xin Huang
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Wien, Austria
| | - Marc de Manuel
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain
| | - Tom van der Valk
- Department of Bioinformatics and Genetics, Scilifelab, Swedish Museum of Natural History, Stockholm, Sweden
- Centre for Palaeogenetics, Stockholm, Sweden
| | - Irene Lobon
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain
| | | | - Marc Haber
- Institute of Cancer and Genomic Sciences, University of Birmingham, Dubai, United Arab Emirates
| | - Olga Dolgova
- Integrative Genomics Lab, CIC bioGUNE-Centro de Investigación Cooperativa en Biociencias, Parque Científico Tecnológico de Bizkaia building 801A, Derio, Spain
| | - Sojung Han
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Wien, Austria
| | | | - David Juan
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain
| | - Qasim Ayub
- Wellcome Sanger Institute, Hinxton, UK
- Monash University Malaysia Genomics Facility, School of Science, Monash University Malaysia, Selangor Darul Ehsan, Malaysia
| | | | - Joanna L Kelley
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA
| | - Omar E Cornejo
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA
| | - Oscar Lao
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain
| | - Aida M Andrés
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Katerina Guschanski
- Animal Ecology, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
- Science for Life Laboratory, Uppsala, Sweden
| | | | - Mike Cranfield
- Gorilla Doctors, Karen C. Drayer Wildlife Health Center, One Health Institute, University of California Davis, School of Veterinary Medicine, Davis, CA, USA
| | | | - Yali Xue
- Wellcome Sanger Institute, Hinxton, UK
| | - Javier Prado-Martinez
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain
- Wellcome Sanger Institute, Hinxton, UK
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain.
- Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, Barcelona, Spain.
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, Barcelona, Spain.
| | - Martin Kuhlwilm
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain.
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Wien, Austria.
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5
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Balmori-de la Puente A, Ventura J, Miñarro M, Somoano A, Hey J, Castresana J. Divergence time estimation using ddRAD data and an isolation-with-migration model applied to water vole populations of Arvicola. Sci Rep 2022; 12:4065. [PMID: 35260719 PMCID: PMC8904462 DOI: 10.1038/s41598-022-07877-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/21/2022] [Indexed: 01/18/2023] Open
Abstract
Molecular dating methods of population splits are crucial in evolutionary biology, but they present important difficulties due to the complexity of the genealogical relationships of genes and past migrations between populations. Using the double digest restriction-site associated DNA (ddRAD) technique and an isolation-with-migration (IM) model, we studied the evolutionary history of water vole populations of the genus Arvicola, a group of complex evolution with fossorial and semi-aquatic ecotypes. To do this, we first estimated mutation rates of ddRAD loci using a phylogenetic approach. An IM model was then used to estimate split times and other relevant demographic parameters. A set of 300 ddRAD loci that included 85 calibrated loci resulted in good mixing and model convergence. The results showed that the two populations of A. scherman present in the Iberian Peninsula split 34 thousand years ago, during the last glaciation. In addition, the much greater divergence from its sister species, A. amphibius, may help to clarify the controversial taxonomy of the genus. We conclude that this approach, based on ddRAD data and an IM model, is highly useful for analyzing the origin of populations and species.
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Affiliation(s)
- Alfonso Balmori-de la Puente
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), Passeig Marítim de la Barceloneta 37, 08003, Barcelona, Spain
| | - Jacint Ventura
- Departament de Biologia Animal, de Biologia Vegetal i d'Ecologia, Facultat de Biociències, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain.,Àrea de Recerca en Petits Mamífers, Granollers Museum of Natural Sciences, Palaudàries, 102, 08402, Granollers, Barcelona, Spain
| | - Marcos Miñarro
- Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Ctra AS-267, PK 19, 33300, Villaviciosa, Asturias, Spain
| | - Aitor Somoano
- Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Ctra AS-267, PK 19, 33300, Villaviciosa, Asturias, Spain
| | - Jody Hey
- Department of Biology, Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA
| | - Jose Castresana
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), Passeig Marítim de la Barceloneta 37, 08003, Barcelona, Spain.
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6
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Städele V, Arandjelovic M, Nixon S, Bergl RA, Bradley BJ, Breuer T, Cameron KN, Guschanski K, Head J, Kyungu JC, Masi S, Morgan DB, Reed P, Robbins MM, Sanz C, Smith V, Stokes EJ, Thalmann O, Todd A, Vigilant L. The complex Y-chromosomal history of gorillas. Am J Primatol 2022; 84:e23363. [PMID: 35041228 DOI: 10.1002/ajp.23363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 12/27/2021] [Accepted: 01/08/2022] [Indexed: 11/10/2022]
Abstract
Studies of the evolutionary relationships among gorilla populations using autosomal and mitochondrial sequences suggest that male-mediated gene flow may have been important in the past, but data on the Y-chromosomal relationships among the gorilla subspecies are limited. Here, we genotyped blood and noninvasively collected fecal samples from 12 captives and 257 wild male gorillas of known origin representing all four subspecies (Gorilla gorilla gorilla, G. g. diehli, G. beringei beringei, and G. b. graueri) at 10 Y-linked microsatellite loci resulting in 102 unique Y-haplotypes for 224 individuals. We found that western lowland gorilla (G. g. gorilla) haplotypes were consistently more diverse than any other subspecies for all measures of diversity and comprised several genetically distinct groups. However, these did not correspond to geographical proximity and some closely related haplotypes were found several hundred kilometers apart. Similarly, our broad sampling of eastern gorillas revealed that mountain (G. b. beringei) and Grauer's (G. b. graueri) gorilla Y-chromosomal haplotypes did not form distinct clusters. These observations suggest structure in the ancestral population with subsequent mixing of differentiated haplotypes by male dispersal for western lowland gorillas, and postisolation migration or incomplete lineage sorting due to short divergence times for eastern gorillas.
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Affiliation(s)
- Veronika Städele
- School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, USA.,Institute of Human Origins, Arizona State University, Tempe, Arizona, USA.,Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Mimi Arandjelovic
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.,Evolutionary and Anthropocene Ecology, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Stuart Nixon
- Field Programmes and Conservation Science, Chester Zoo, North of England Zoological Society, Chester, UK
| | | | - Brenda J Bradley
- Department of Anthropology, Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, District of Columbia, USA
| | - Thomas Breuer
- WWF Germany, Berlin, Germany.,Mbeli Bai Study, Wildlife Conservation Society, Congo Program, Brazzaville, Republic of the Congo
| | | | - Katerina Guschanski
- Department of Ecology and Genetics/Animal Ecology, Uppsala University, Uppsala, Sweden.,Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Josephine Head
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | | | - Shelly Masi
- Eco-Anthropologie, Muséum National d'Histoire Naturelle, CNRS, Musée de l'Homme, Université de Paris, Paris, France
| | - David B Morgan
- Fisher Center for the Study and Conservation of Apes, Lincoln Park Zoo, Chicago, Illinois, USA
| | | | - Martha M Robbins
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Crickette Sanz
- Department of Anthropology, Washington University in Saint Louis, Saint Louis, Missouri, USA.,Wildlife Conservation Society, Congo Program, Brazzaville, Republic of the Congo
| | | | - Emma J Stokes
- Wildlife Conservation Society, Global Conservation Program, New York City, New York, USA
| | - Olaf Thalmann
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland
| | | | - Linda Vigilant
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
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7
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Termignoni-Garcia F, Kirchman JJ, Clark J, Edwards SV. Comparative Population Genomics of Cryptic Speciation and Adaptive Divergence in Bicknell's and Gray-Cheeked Thrushes (Aves: Catharus bicknelli and Catharus minimus). Genome Biol Evol 2022; 14:evab255. [PMID: 34999784 PMCID: PMC8743040 DOI: 10.1093/gbe/evab255] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/10/2021] [Indexed: 02/07/2023] Open
Abstract
Cryptic speciation may occur when reproductive isolation is recent or the accumulation of morphological differences between sister lineages is slowed by stabilizing selection preventing phenotypic differentiation. In North America, Bicknell's Thrush (Catharus bicknelli) and its sister species, the Gray-cheeked Thrush (Catharus minimus), are parapatrically breeding migratory songbirds, distinguishable in nature only by subtle differences in song and coloration, and were recognized as distinct species only in the 1990s. Previous molecular studies have estimated that the species diverged approximately 120,000-420,000 YBP and found very low levels of introgression despite their similarity and sympatry in the spring (prebreeding) migration. To further clarify the history, genetic divergence, genomic structure, and adaptive processes in C. bicknelli and C. minimus, we sequenced and assembled high-coverage reference genomes of both species and resequenced genomes from population samples of C. bicknelli, C. minimus, and two individuals of the Swainson's Thrush (Catharus ustulatus). The genome of C. bicknelli exhibits markedly higher abundances of transposable elements compared with other Catharus and chicken. Demographic and admixture analyses confirm moderate genome-wide differentiation (Fst ≈ 0.10) and limited gene flow between C. bicknelli and C. minimus, but suggest a more recent divergence than estimates based on mtDNA. We find evidence of rapid evolution of the Z-chromosome and elevated divergence consistent with natural selection on genomic regions near genes involved with neuronal processes in C. bicknelli. These genomes are a useful resource for future investigations of speciation, migration, and adaptation in Catharus thrushes.
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Affiliation(s)
- Flavia Termignoni-Garcia
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts, USA
| | | | - Johnathan Clark
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts, USA
| | - Scott V Edwards
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts, USA
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8
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Abstract
Alleles that introgress between species can influence the evolutionary and ecological fate of species exposed to novel environments. Hybrid offspring of different species are often unfit, and yet it has long been argued that introgression can be a potent force in evolution, especially in plants. Over the last two decades, genomic data have increasingly provided evidence that introgression is a critically important source of genetic variation and that this additional variation can be useful in adaptive evolution of both animals and plants. Here, we review factors that influence the probability that foreign genetic variants provide long-term benefits (so-called adaptive introgression) and discuss their potential benefits. We find that introgression plays an important role in adaptive evolution, particularly when a species is far from its fitness optimum, such as when they expand their range or are subject to changing environments.
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Affiliation(s)
- Nathaniel B Edelman
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA; .,Current affiliation: Yale Institute for Biospheric Studies and Yale School of the Environment, Yale University, New Haven, Connecticut 06511, USA;
| | - James Mallet
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA;
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9
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Chen ZH, Xu YX, Xie XL, Wang DF, Aguilar-Gómez D, Liu GJ, Li X, Esmailizadeh A, Rezaei V, Kantanen J, Ammosov I, Nosrati M, Periasamy K, Coltman DW, Lenstra JA, Nielsen R, Li MH. Whole-genome sequence analysis unveils different origins of European and Asiatic mouflon and domestication-related genes in sheep. Commun Biol 2021; 4:1307. [PMID: 34795381 PMCID: PMC8602413 DOI: 10.1038/s42003-021-02817-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 10/27/2021] [Indexed: 02/06/2023] Open
Abstract
The domestication and subsequent development of sheep are crucial events in the history of human civilization and the agricultural revolution. However, the impact of interspecific introgression on the genomic regions under domestication and subsequent selection remains unclear. Here, we analyze the whole genomes of domestic sheep and their wild relative species. We found introgression from wild sheep such as the snow sheep and its American relatives (bighorn and thinhorn sheep) into urial, Asiatic and European mouflons. We observed independent events of adaptive introgression from wild sheep into the Asiatic and European mouflons, as well as shared introgressed regions from both snow sheep and argali into Asiatic mouflon before or during the domestication process. We revealed European mouflons might arise through hybridization events between a now extinct sheep in Europe and feral domesticated sheep around 6000-5000 years BP. We also unveiled later introgressions from wild sheep to their sympatric domestic sheep after domestication. Several of the introgression events contain loci with candidate domestication genes (e.g., PAPPA2, NR6A1, SH3GL3, RFX3 and CAMK4), associated with morphological, immune, reproduction or production traits (wool/meat/milk). We also detected introgression events that introduced genes related to nervous response (NEURL1), neurogenesis (PRUNE2), hearing ability (USH2A), and placental viability (PAG11 and PAG3) into domestic sheep and their ancestral wild species from other wild species.
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Affiliation(s)
- Ze-Hui Chen
- grid.9227.e0000000119573309CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences (UCAS), Beijing, China ,grid.22935.3f0000 0004 0530 8290College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Ya-Xi Xu
- grid.22935.3f0000 0004 0530 8290College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xing-Long Xie
- grid.9227.e0000000119573309CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences (UCAS), Beijing, China
| | - Dong-Feng Wang
- grid.9227.e0000000119573309CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences (UCAS), Beijing, China
| | - Diana Aguilar-Gómez
- grid.47840.3f0000 0001 2181 7878Center for Computational Biology, University of California at Berkeley, Berkeley, CA 94720 USA
| | | | - Xin Li
- grid.9227.e0000000119573309CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences (UCAS), Beijing, China
| | - Ali Esmailizadeh
- grid.412503.10000 0000 9826 9569Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Vahideh Rezaei
- grid.412503.10000 0000 9826 9569Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Juha Kantanen
- grid.22642.300000 0004 4668 6757Natural Resources Institute Finland (Luke), Jokioinen, Finland
| | - Innokentyi Ammosov
- grid.495192.2Laboratory of Reindeer Husbandry and Traditional Industries, Yakut Scientific Research Institute of Agriculture, The Sakha Republic (Yakutia), Yakutsk, Russia
| | - Maryam Nosrati
- grid.412462.70000 0000 8810 3346Department of Agriculture, Payame Noor University, Tehran, Iran
| | - Kathiravan Periasamy
- grid.420221.70000 0004 0403 8399Animal Production and Health Laboratory, Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency, Vienna, Austria
| | - David W. Coltman
- grid.17089.37Department of Biological Sciences, University of Alberta, Edmonton, AB T6G2E9 Canada
| | - Johannes A. Lenstra
- grid.5477.10000000120346234Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Rasmus Nielsen
- Department of Integrative Biology, University of California at Berkeley, Berkeley, CA, 94720, USA. .,Department of Statistics, UC Berkeley, Berkeley, CA, 94707, USA. .,Globe Institute, University of Copenhagen, 1350, København K, Denmark.
| | - Meng-Hua Li
- College of Animal Science and Technology, China Agricultural University, Beijing, China.
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10
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Blischak PD, Barker MS, Gutenkunst RN. Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks. Mol Ecol Resour 2021; 21:2676-2688. [PMID: 33682305 PMCID: PMC8675098 DOI: 10.1111/1755-0998.13355] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 01/26/2021] [Accepted: 02/05/2021] [Indexed: 11/30/2022]
Abstract
Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their nonindependence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here, we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P1 , P2 ), P3 ), Out) and a matrix of pairwise nucleotide divergence (dXY ) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe-CNN, was able to accurately perform model selection for hybridization scenarios across a wide breath of parameter space. We then used HyDe-CNN to test models of admixture in Heliconius butterflies, as well as comparing it to phylogeny-based introgression statistics. Given the flexibility of our approach, the dropping cost of long-read sequencing and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.
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Affiliation(s)
- Paul D. Blischak
- Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
- Department of Molecular & Cellular Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Michael S. Barker
- Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Ryan N. Gutenkunst
- Department of Molecular & Cellular Biology, University of Arizona, Tucson, AZ, 85721, USA
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11
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Costa RJ, Wilkinson-Herbots HM. Inference of gene flow in the process of speciation: Efficient maximum-likelihood implementation of a generalised isolation-with-migration model. Theor Popul Biol 2021; 140:1-15. [PMID: 33736959 DOI: 10.1016/j.tpb.2021.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 02/28/2021] [Accepted: 03/01/2021] [Indexed: 11/21/2022]
Abstract
The 'isolation with migration' (IM) model has been extensively used in the literature to detect gene flow during the process of speciation. In this model, an ancestral population split into two or more descendant populations which subsequently exchanged migrants at a constant rate until the present. Of course, the assumption of constant gene flow until the present is often over-simplistic in the context of speciation. In this paper, we consider a 'generalised IM' (GIM) model: a two-population IM model in which migration rates and population sizes are allowed to change at some point in the past. By developing a maximum-likelihood implementation of this model, we enable inference on both historical and contemporary rates of gene flow between two closely related populations or species. The GIM model encompasses both the standard two-population IM model and the 'isolation with initial migration' (IIM) model as special cases, as well as a model of secondary contact. We examine for simulated data how our method can be used, by means of likelihood ratio tests or AIC scores, to distinguish between the following scenarios of population divergence: (a) divergence in complete isolation; (b) divergence with a period of gene flow followed by isolation; (c) divergence with a period of isolation followed by secondary contact; (d) divergence with ongoing gene flow. Our method is based on the coalescent and is suitable for data sets consisting of the number of nucleotide differences between one pair of DNA sequences at each of a large number of independent loci. As our method relies on an explicit expression for the likelihood, it is computationally very fast.
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12
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Abstract
The great apes play an important role as model organisms. They are our closest living relatives, allowing us to identify the genetic basis of phenotypic traits that we think of as characteristically human. However, the most significant asset of great apes as model organisms is that they share with humans most of their genetic makeup. This means that we can extend our vast knowledge of the human genome, its genes, and the associated phenotypes to these species. Comparative genomic studies of humans and apes thus reveal how very similar genomes react when exposed to different population genetic regimes. In this way, each species represents a natural experiment, where a genome highly similar to the human one, is differently exposed to the evolutionary forces of demography, population structure, selection, recombination, and admixture/hybridization. The initial sequencing of reference genomes for chimpanzee, orangutan, gorilla, the bonobo, each provided new insights and a second generation of sequencing projects has provided diversity data for all the great apes. In this chapter, we will outline some of the findings that population genomic analysis of great apes has provided, and how comparative studies have helped us understand how the fundamental forces in evolution have contributed to shaping the genomes and the genetic diversity of the great apes.
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Affiliation(s)
- David Castellano
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Kasper Munch
- Bioinformatics Research Centre, Aarhus University, Aarhus C, Denmark
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13
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Koch H, DeGiorgio M. Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure. Genome Biol Evol 2020; 12:3977-3995. [PMID: 32022857 PMCID: PMC7061232 DOI: 10.1093/gbe/evaa022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2020] [Indexed: 11/12/2022] Open
Abstract
Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI's performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community.
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Affiliation(s)
- Hillary Koch
- Department of Statistics, Pennsylvania State University
| | - Michael DeGiorgio
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University
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14
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Liu J, Liu Q, Yang Q. mstree: A Multispecies Coalescent Approach for Estimating Ancestral Population Size and Divergence Time during Speciation with Gene Flow. Genome Biol Evol 2020; 12:715-719. [PMID: 32365209 PMCID: PMC7259675 DOI: 10.1093/gbe/evaa087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2020] [Indexed: 11/28/2022] Open
Abstract
Gene flow between species may cause variations in branch length and topology of gene tree, which are beyond the expected variations from ancestral processes. These additional variations make it difficult to estimate parameters during speciation with gene flow, as the pattern of these additional variations differs with the relationship between isolation and migration. As far as we know, most methods rely on the assumption about the relationship between isolation and migration by a given model, such as the isolation-with-migration model, when estimating parameters during speciation with gene flow. In this article, we develop a multispecies coalescent approach which does not rely on any assumption about the relationship between isolation and migration when estimating parameters and is called mstree. mstree is available at https://github.com/liujunfengtop/MStree/ and uses some mathematical inequalities among several factors, which include the species divergence time, the ancestral population size, and the number of gene trees, to estimate parameters during speciation with gene flow. Using simulations, we show that the estimated values of ancestral population sizes and species divergence times are close to the true values when analyzing the simulation data sets, which are generated based on the isolation-with-initial-migration model, secondary contact model, and isolation-with-migration model. Therefore, our method is able to estimate ancestral population sizes and speciation times in the presence of different modes of gene flow and may be helpful to test different theories of speciation.
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Affiliation(s)
- Junfeng Liu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Qiao Liu
- Department of Automation, Tsinghua University, Beijing, China
| | - Qingzhu Yang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,Department of Automation, Tsinghua University, Beijing, China
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15
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Sellinger TPP, Abu Awad D, Moest M, Tellier A. Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data. PLoS Genet 2020; 16:e1008698. [PMID: 32251472 PMCID: PMC7173940 DOI: 10.1371/journal.pgen.1008698] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 04/21/2020] [Accepted: 02/24/2020] [Indexed: 02/04/2023] Open
Abstract
Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and non-overlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations' past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.
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Affiliation(s)
| | - Diala Abu Awad
- Department of Population Genetics, Technische Universitaet Muenchen, Freising, Germany
| | - Markus Moest
- Department of Ecology, University of Innsbruck, Innsbruck, Austria
| | - Aurélien Tellier
- Department of Population Genetics, Technische Universitaet Muenchen, Freising, Germany
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16
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Abstract
Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.
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Affiliation(s)
- Yujin Chung
- Department of Applied Statistics, Kyonggi University, Suwon 16227, Korea
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17
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Griffiths AG, Moraga R, Tausen M, Gupta V, Bilton TP, Campbell MA, Ashby R, Nagy I, Khan A, Larking A, Anderson C, Franzmayr B, Hancock K, Scott A, Ellison NW, Cox MP, Asp T, Mailund T, Schierup MH, Andersen SU. Breaking Free: The Genomics of Allopolyploidy-Facilitated Niche Expansion in White Clover. Plant Cell 2019; 31:1466-1487. [PMID: 31023841 PMCID: PMC6635854 DOI: 10.1105/tpc.18.00606] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 03/15/2019] [Accepted: 04/22/2019] [Indexed: 05/18/2023]
Abstract
The merging of distinct genomes, allopolyploidization, is a widespread phenomenon in plants. It generates adaptive potential through increased genetic diversity, but examples demonstrating its exploitation remain scarce. White clover (Trifolium repens) is a ubiquitous temperate allotetraploid forage crop derived from two European diploid progenitors confined to extreme coastal or alpine habitats. We sequenced and assembled the genomes and transcriptomes of this species complex to gain insight into the genesis of white clover and the consequences of allopolyploidization. Based on these data, we estimate that white clover originated ∼15,000 to 28,000 years ago during the last glaciation when alpine and coastal progenitors were likely colocated in glacial refugia. We found evidence of progenitor diversity carryover through multiple hybridization events and show that the progenitor subgenomes have retained integrity and gene expression activity as they traveled within white clover from their original confined habitats to a global presence. At the transcriptional level, we observed remarkably stable subgenome expression ratios across tissues. Among the few genes that show tissue-specific switching between homeologous gene copies, we found flavonoid biosynthesis genes strongly overrepresented, suggesting an adaptive role of some allopolyploidy-associated transcriptional changes. Our results highlight white clover as an example of allopolyploidy-facilitated niche expansion, where two progenitor genomes, adapted and confined to disparate and highly specialized habitats, expanded to a ubiquitous global presence after glaciation-associated allopolyploidization.
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Affiliation(s)
- Andrew G Griffiths
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Roger Moraga
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Marni Tausen
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus C, Denmark
- Bioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark
| | - Vikas Gupta
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus C, Denmark
| | - Timothy P Bilton
- AgResearch, Invermay Agricultural Centre, Mosgiel 9053, New Zealand
| | - Matthew A Campbell
- Bioinformatics and Statistics Group, Institute of Fundamental Sciences, Massey University, Palmerston North 4410, New Zealand
| | - Rachael Ashby
- AgResearch, Invermay Agricultural Centre, Mosgiel 9053, New Zealand
| | - Istvan Nagy
- Department of Molecular Biology and Genetics, Aarhus University, 200 Slagelse, Denmark
| | - Anar Khan
- AgResearch, Invermay Agricultural Centre, Mosgiel 9053, New Zealand
| | - Anna Larking
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Craig Anderson
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Benjamin Franzmayr
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Kerry Hancock
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Alicia Scott
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Nick W Ellison
- AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand
| | - Murray P Cox
- Bioinformatics and Statistics Group, Institute of Fundamental Sciences, Massey University, Palmerston North 4410, New Zealand
| | - Torben Asp
- Department of Molecular Biology and Genetics, Aarhus University, 200 Slagelse, Denmark
| | - Thomas Mailund
- Bioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark
| | - Mikkel H Schierup
- AgResearch, Invermay Agricultural Centre, Mosgiel 9053, New Zealand
- Department of Bioscience, Aarhus University, 8000 Aarhus C, Denmark
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18
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Rogers J, Raveendran M, Harris RA, Mailund T, Leppälä K, Athanasiadis G, Schierup MH, Cheng J, Munch K, Walker JA, Konkel MK, Jordan V, Steely CJ, Beckstrom TO, Bergey C, Burrell A, Schrempf D, Noll A, Kothe M, Kopp GH, Liu Y, Murali S, Billis K, Martin FJ, Muffato M, Cox L, Else J, Disotell T, Muzny DM, Phillips-Conroy J, Aken B, Eichler EE, Marques-Bonet T, Kosiol C, Batzer MA, Hahn MW, Tung J, Zinner D, Roos C, Jolly CJ, Gibbs RA, Worley KC. The comparative genomics and complex population history of Papio baboons. Sci Adv 2019; 5:eaau6947. [PMID: 30854422 PMCID: PMC6401983 DOI: 10.1126/sciadv.aau6947] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 12/06/2018] [Indexed: 05/26/2023]
Abstract
Recent studies suggest that closely related species can accumulate substantial genetic and phenotypic differences despite ongoing gene flow, thus challenging traditional ideas regarding the genetics of speciation. Baboons (genus Papio) are Old World monkeys consisting of six readily distinguishable species. Baboon species hybridize in the wild, and prior data imply a complex history of differentiation and introgression. We produced a reference genome assembly for the olive baboon (Papio anubis) and whole-genome sequence data for all six extant species. We document multiple episodes of admixture and introgression during the radiation of Papio baboons, thus demonstrating their value as a model of complex evolutionary divergence, hybridization, and reticulation. These results help inform our understanding of similar cases, including modern humans, Neanderthals, Denisovans, and other ancient hominins.
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Affiliation(s)
- Jeffrey Rogers
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Muthuswamy Raveendran
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - R. Alan Harris
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Thomas Mailund
- Bioinformatics Research Centre, Aarhus University, CF Møllers Alle 8, DK-8000 Aarhus, Denmark
| | - Kalle Leppälä
- Bioinformatics Research Centre, Aarhus University, CF Møllers Alle 8, DK-8000 Aarhus, Denmark
| | - Georgios Athanasiadis
- Bioinformatics Research Centre, Aarhus University, CF Møllers Alle 8, DK-8000 Aarhus, Denmark
| | - Mikkel Heide Schierup
- Bioinformatics Research Centre, Aarhus University, CF Møllers Alle 8, DK-8000 Aarhus, Denmark
| | - Jade Cheng
- Bioinformatics Research Centre, Aarhus University, CF Møllers Alle 8, DK-8000 Aarhus, Denmark
| | - Kasper Munch
- Bioinformatics Research Centre, Aarhus University, CF Møllers Alle 8, DK-8000 Aarhus, Denmark
| | - Jerilyn A. Walker
- Department of Biological Sciences, 202 Life Sciences Building, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Miriam K. Konkel
- Department of Genetics and Biochemistry, 105 Collings Street, Clemson University, Clemson, SC 29634, USA
| | - Vallmer Jordan
- Department of Biological Sciences, 202 Life Sciences Building, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Cody J. Steely
- Department of Biological Sciences, 202 Life Sciences Building, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Thomas O. Beckstrom
- Department of Biological Sciences, 202 Life Sciences Building, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Christina Bergey
- Department of Anthropology, New York University, 25 Waverly Place, New York, NY 10003, USA
- Departments of Anthropology and Biology, Pennsylvania State University, 514 Carpenter Building, University Park, PA 16802, USA
| | - Andrew Burrell
- Department of Anthropology, New York University, 25 Waverly Place, New York, NY 10003, USA
| | - Dominik Schrempf
- Institut für Populationsgenetik, Veterinärmedizinische Universität Wien, Veterinärplatz 11210 Vienna, Austria
| | - Angela Noll
- Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Maximillian Kothe
- Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Gisela H. Kopp
- Cognitive Ethology Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
- Department of Biology, University of Konstanz, Universitätsstr. 10, 78467 Konstanz, Germany
- Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology, Am Obstberg 1, 78315 Radolfzell, Germany
| | - Yue Liu
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Shwetha Murali
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Department of Genome Sciences, University of Washington, 3720 15th Avenue NE, S413C, Box 355065, Seattle, WA 98195-5065, USA
- Howard Hughes Medical Institute, University of Washington, 3720 15th Avenue NE, S413C, Box 355065, Seattle, WA 98195-5065, USA
| | - Konstantinos Billis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Fergal J. Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Matthieu Muffato
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Laura Cox
- Southwest National Primate Research Center, Texas Biomedical Research Institute, 8715 W. Military Drive, San Antonio, TX 78227, USA
- Center for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA
| | - James Else
- Department of Pathology and Laboratory Medicine and Yerkes Primate Research Center, 954 Gatewood Road, Emory University, Atlanta, GA 30322, USA
| | - Todd Disotell
- Department of Anthropology, New York University, 25 Waverly Place, New York, NY 10003, USA
| | - Donna M. Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Jane Phillips-Conroy
- Department of Neuroscience, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA
- Department of Anthropology, Washington University, McMillan Hall, 1 Brookings Drive, St. Louis, MO 63130, USA
| | - Bronwen Aken
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington, 3720 15th Avenue NE, S413C, Box 355065, Seattle, WA 98195-5065, USA
- Howard Hughes Medical Institute, University of Washington, 3720 15th Avenue NE, S413C, Box 355065, Seattle, WA 98195-5065, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader, 88. 08003, Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Baldiri Reixac, 4, 08028, Barcelona, Spain
- Institut Catala de Paleontologia Miquel Crusafont, Universitat Autonoma de Barcelona, c/de les Columnes, s/n. Campus de la UAB. 08193–Cerdanyola del Vallès, Barcelona, Spain
| | - Carolin Kosiol
- Institut für Populationsgenetik, Veterinärmedizinische Universität Wien, Veterinärplatz 11210 Vienna, Austria
- Centre for Biological Diversity, School of Biology, University of St. Andrews, Dyers Brae House, Greenside Place, St Andrews, Fife, KY16 9TH, UK
| | - Mark A. Batzer
- Department of Biological Sciences, 202 Life Sciences Building, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Matthew W. Hahn
- Department of Biology and Department of Computer Science, Indiana University, 1001 E. 3rd Street, Bloomington, IN 47405, USA
| | - Jenny Tung
- Department of Biology, Duke University, Box 90338, Durham, NC 27708, USA
- Duke Population Research Institute, Duke University, Box 90989, Durham, NC 27708, USA
- Institute of Primate Research, P.O. Box 24481, Nairobi, Kenya
| | - Dietmar Zinner
- Cognitive Ethology Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Christian Roos
- Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Clifford J. Jolly
- Department of Anthropology, New York University, 25 Waverly Place, New York, NY 10003, USA
| | - Richard A. Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Kim C. Worley
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
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19
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Abstract
Phylogeny estimation is difficult for closely related populations and species, especially if they have been exchanging genes. We present a hierarchical Bayesian, Markov-chain Monte Carlo method with a state space that includes all possible phylogenies in a full Isolation-with-Migration model framework. The method is based on a new type of genealogy augmentation called a "hidden genealogy" that enables efficient updating of the phylogeny. This is the first likelihood-based method to fully incorporate directional gene flow and genetic drift for estimation of a species or population phylogeny. Application to human hunter-gatherer populations from Africa revealed a clear phylogenetic history, with strong support for gene exchange with an unsampled ghost population, and relatively ancient divergence between a ghost population and modern human populations, consistent with human/archaic divergence. In contrast, a study of five chimpanzee populations reveals a clear phylogeny with several pairs of populations having exchanged DNA, but does not support a history with an unsampled ghost population.
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Affiliation(s)
- Jody Hey
- Department of Biology, Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA
| | - Yujin Chung
- Department of Biology, Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA
- The Department of Applied Statistics, Kyonggi University, Suwon, South Korea
| | - Arun Sethuraman
- Department of Biology, Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA
- Department of Biological Sciences, California State University San Marcos, San Marcos, CA
| | - Joseph Lachance
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Georgia Institute of Technology, Atlanta, GA
| | - Sarah Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Vitor C Sousa
- Department of Genetics, Rutgers, the State University of New Jersey, Piscataway, NJ
- University of Lisbon, Lisboa, Portugal
| | - Yong Wang
- Department of Genetics, Rutgers, the State University of New Jersey, Piscataway, NJ
- Ancestry, San Francisco, CA
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20
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Wang K, Lenstra JA, Liu L, Hu Q, Ma T, Qiu Q, Liu J. Incomplete lineage sorting rather than hybridization explains the inconsistent phylogeny of the wisent. Commun Biol 2018; 1:169. [PMID: 30374461 DOI: 10.1038/s42003-018-0176-6] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 09/12/2018] [Indexed: 12/30/2022] Open
Abstract
The wisent or European bison is the largest European herbivore and is completely cross-fertile with its American relative. However, mtDNA genome of wisent is similar to that of cattle, which suggests that wisent emerged as a hybrid of bison and an extinct cattle-like species. Here, we analyzed nuclear whole-genome sequences of the bovine species, and found only a minor and recent gene flow between wisent and cattle. Furthermore, we identified an appreciable heterogeneity of the nuclear gene tree topologies of the bovine species. The relative frequencies of various topologies, including the mtDNA topology, were consistent with frequencies of incomplete lineage sorting (ILS) as estimated by tree coalescence analysis. This indicates that ILS has occurred and may well account for the anomalous wisent mtDNA phylogeny as the outcome of a rare event. We propose that ILS is a possible explanation of phylogenomic anomalies among closely related species. Kun Wang et al. present a genomic analysis identifying incomplete lineage sorting and hybridization in the mitochondrial DNA of the European bison (wisent). They find that incomplete lineage sorting is the most feasible explanation for the phylogenetic heterogeneity observed in Bovidae.
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21
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Mao Y, Economo EP, Satoh N. The Roles of Introgression and Climate Change in the Rise to Dominance of Acropora Corals. Curr Biol 2018; 28:3373-3382.e5. [PMID: 30344117 DOI: 10.1016/j.cub.2018.08.061] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/02/2018] [Accepted: 08/29/2018] [Indexed: 12/31/2022]
Abstract
Reef-building corals provide the structural basis for one of Earth's most spectacular and diverse-but increasingly threatened-ecosystems. Modern Indo-Pacific reefs are dominated by species of the staghorn coral genus Acropora, but the evolutionary and ecological factors associated with their diversification and rise to dominance are unclear. Recent work on evolutionary radiations has demonstrated the importance of introgression and ecological opportunity in promoting diversification and ecological success. Here, we analyze the genomes of five staghorn coral species to examine the roles of introgression and ecological opportunity in the rise to dominance of Acropora. We found evidence for a history marked by a major introgression event as well as recurrent gene flow across species. In addition, we found that genes with topologies mismatching the species tree are evolving faster, which is suggestive of a role for introgression in spreading adaptive genetic variation. Demographic analysis showed that Acropora lineages profited from climate-driven mass extinctions in the Plio-Pleistocene, indicating that Acropora exploited ecological opportunity opened by a new climatic regime favoring species that could cope with rapid sea-level changes. Collectively, the genomes of reef-building corals have recorded an evolutionary history shaped by introgression and climate change, suggesting that Acropora-among most vulnerable corals to stressors-may be critical for understanding how reefs track the impending rapid sea-level changes of the Anthropocene.
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Affiliation(s)
- Yafei Mao
- Marine Genomics Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan; Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan.
| | - Evan P Economo
- Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan.
| | - Noriyuki Satoh
- Marine Genomics Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan.
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22
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Beeravolu CR, Hickerson MJ, Frantz LAF, Lohse K. ABLE: blockwise site frequency spectra for inferring complex population histories and recombination. Genome Biol 2018; 19:145. [PMID: 30253810 PMCID: PMC6156964 DOI: 10.1186/s13059-018-1517-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 08/22/2018] [Indexed: 01/08/2023] Open
Abstract
We introduce ABLE (Approximate Blockwise Likelihood Estimation), a novel simulation-based composite likelihood method that uses the blockwise site frequency spectrum to jointly infer past demography and recombination. ABLE is explicitly designed for a wide variety of data from unphased diploid genomes to genome-wide multi-locus data (for example, RADSeq) and can also accommodate arbitrarily large samples. We use simulations to demonstrate the accuracy of this method to infer complex histories of divergence and gene flow and reanalyze whole genome data from two species of orangutan. ABLE is available for download at https://github.com/champost/ABLE.
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Affiliation(s)
- Champak R Beeravolu
- Biology Department, The City College of New York, New York, 10031, NY, USA. .,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, 8057, Switzerland.
| | - Michael J Hickerson
- Biology Department, The City College of New York, New York, 10031, NY, USA.,The Graduate Center, The City University of New York, New York, 10016, NY, USA.,Division of Invertebrate Zoology, American Museum of Natural History, New York, 10024, NY, USA
| | - Laurent A F Frantz
- Paleogenomics and Bio-Archaeology Research Network, Research Laboratory for Archeology and History of Art, University of Oxford, Oxford, OX1 3QY, UK.,School of Biological and Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK
| | - Konrad Lohse
- Institute of Evolutionary Biology, University of Edinburgh, King's Buildings, Edinburgh, EH9 3FL, UK
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23
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Wu D, Ding X, Wang S, Wójcik JM, Zhang Y, Tokarska M, Li Y, Wang M, Faruque O, Nielsen R, Zhang Q, Zhang Y. Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nat Ecol Evol 2018; 2:1139-45. [DOI: 10.1038/s41559-018-0562-y] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 04/20/2018] [Indexed: 12/23/2022]
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24
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Ravinet M, Yoshida K, Shigenobu S, Toyoda A, Fujiyama A, Kitano J. The genomic landscape at a late stage of stickleback speciation: High genomic divergence interspersed by small localized regions of introgression. PLoS Genet 2018; 14:e1007358. [PMID: 29791436 PMCID: PMC5988309 DOI: 10.1371/journal.pgen.1007358] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 06/05/2018] [Accepted: 04/11/2018] [Indexed: 12/17/2022] Open
Abstract
Speciation is a continuous process and analysis of species pairs at different stages of divergence provides insight into how it unfolds. Previous genomic studies on young species pairs have revealed peaks of divergence and heterogeneous genomic differentiation. Yet less known is how localised peaks of differentiation progress to genome-wide divergence during the later stages of speciation in the presence of persistent gene flow. Spanning the speciation continuum, stickleback species pairs are ideal for investigating how genomic divergence builds up during speciation. However, attention has largely focused on young postglacial species pairs, with little knowledge of the genomic signatures of divergence and introgression in older stickleback systems. The Japanese stickleback species pair, composed of the Pacific Ocean three-spined stickleback (Gasterosteus aculeatus) and the Japan Sea stickleback (G. nipponicus), which co-occur in the Japanese islands, is at a late stage of speciation. Divergence likely started well before the end of the last glacial period and crosses between Japan Sea females and Pacific Ocean males result in hybrid male sterility. Here we use coalescent analyses and Approximate Bayesian Computation to show that the two species split approximately 0.68-1 million years ago but that they have continued to exchange genes at a low rate throughout divergence. Population genomic data revealed that, despite gene flow, a high level of genomic differentiation is maintained across the majority of the genome. However, we identified multiple, small regions of introgression, occurring mainly in areas of low recombination rate. Our results demonstrate that a high level of genome-wide divergence can establish in the face of persistent introgression and that gene flow can be localized to small genomic regions at the later stages of speciation with gene flow.
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Affiliation(s)
- Mark Ravinet
- Division of Ecological Genetics, Department of Population Genetics, National Institute of Genetics, Mishima, Shizuoka, Japan
- Centre for Ecological and Evolutionary Synthesis, University of Oslo, Oslo, Norway
| | - Kohta Yoshida
- Division of Ecological Genetics, Department of Population Genetics, National Institute of Genetics, Mishima, Shizuoka, Japan
- Integrative Evolutionary Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Shuji Shigenobu
- Functional Genomics Facility, National Institute for Basic Biology, Okazaki, Aichi, Japan
| | - Atsushi Toyoda
- Comparative Genomics Laboratory, National Institute of Genetics, Mishima, Shizuoka, Japan
| | - Asao Fujiyama
- Comparative Genomics Laboratory, National Institute of Genetics, Mishima, Shizuoka, Japan
| | - Jun Kitano
- Division of Ecological Genetics, Department of Population Genetics, National Institute of Genetics, Mishima, Shizuoka, Japan
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25
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Abstract
Phylogeographic methods can help reveal the movement of genes between populations of organisms. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for data sets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex data sets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. Although this solution is computationally unfeasible for large data sets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.
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Affiliation(s)
- Nicola F Müller
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - David A Rasmussen
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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26
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Palkopoulou E, Lipson M, Mallick S, Nielsen S, Rohland N, Baleka S, Karpinski E, Ivancevic AM, To TH, Kortschak RD, Raison JM, Qu Z, Chin TJ, Alt KW, Claesson S, Dalén L, MacPhee RDE, Meller H, Roca AL, Ryder OA, Heiman D, Young S, Breen M, Williams C, Aken BL, Ruffier M, Karlsson E, Johnson J, Di Palma F, Alfoldi J, Adelson DL, Mailund T, Munch K, Lindblad-Toh K, Hofreiter M, Poinar H, Reich D. A comprehensive genomic history of extinct and living elephants. Proc Natl Acad Sci U S A 2018; 115:E2566-E2574. [PMID: 29483247 PMCID: PMC5856550 DOI: 10.1073/pnas.1720554115] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Elephantids are the world's most iconic megafaunal family, yet there is no comprehensive genomic assessment of their relationships. We report a total of 14 genomes, including 2 from the American mastodon, which is an extinct elephantid relative, and 12 spanning all three extant and three extinct elephantid species including an ∼120,000-y-old straight-tusked elephant, a Columbian mammoth, and woolly mammoths. Earlier genetic studies modeled elephantid evolution via simple bifurcating trees, but here we show that interspecies hybridization has been a recurrent feature of elephantid evolution. We found that the genetic makeup of the straight-tusked elephant, previously placed as a sister group to African forest elephants based on lower coverage data, in fact comprises three major components. Most of the straight-tusked elephant's ancestry derives from a lineage related to the ancestor of African elephants while its remaining ancestry consists of a large contribution from a lineage related to forest elephants and another related to mammoths. Columbian and woolly mammoths also showed evidence of interbreeding, likely following a latitudinal cline across North America. While hybridization events have shaped elephantid history in profound ways, isolation also appears to have played an important role. Our data reveal nearly complete isolation between the ancestors of the African forest and savanna elephants for ∼500,000 y, providing compelling justification for the conservation of forest and savanna elephants as separate species.
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Affiliation(s)
- Eleftheria Palkopoulou
- Department of Genetics, Harvard Medical School, Boston, MA 02115;
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Mark Lipson
- Department of Genetics, Harvard Medical School, Boston, MA 02115
| | - Swapan Mallick
- Department of Genetics, Harvard Medical School, Boston, MA 02115
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Svend Nielsen
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus, Denmark
| | - Nadin Rohland
- Department of Genetics, Harvard Medical School, Boston, MA 02115
| | - Sina Baleka
- Unit of General Zoology-Evolutionary Adaptive Genomics, Institute of Biochemistry and Biology, Faculty of Mathematics and Life Sciences, University of Potsdam, 14476 Potsdam, Germany
| | - Emil Karpinski
- McMaster Ancient DNA Centre, Department of Anthropology, McMaster University, Hamilton, ON L8S 4L9, Canada
- Department of Biology, McMaster University, Hamilton, ON L8S 4K1, Canada
- Department of Biochemistry, McMaster University, Hamilton, ON L8S 4L8, Canada
- The Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Atma M Ivancevic
- Department of Genetics and Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, 5005 SA, Australia
| | - Thu-Hien To
- Department of Genetics and Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, 5005 SA, Australia
| | - R Daniel Kortschak
- Department of Genetics and Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, 5005 SA, Australia
| | - Joy M Raison
- Department of Genetics and Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, 5005 SA, Australia
| | - Zhipeng Qu
- Department of Genetics and Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, 5005 SA, Australia
| | - Tat-Jun Chin
- School of Computer Science, The University of Adelaide, 5005 SA, Australia
| | - Kurt W Alt
- Center of Natural and Cultural Human History, Danube Private University, A-3500 Krems, Austria
- Department of Biomedical Engineering, University Hospital Basel, University of Basel, CH-4123 Basel, Switzerland
- Integrative Prehistory and Archaeological Science, University of Basel, CH-4055 Basel, Switzerland
| | | | - Love Dalén
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, SE-10405 Stockholm, Sweden
| | - Ross D E MacPhee
- Division of Vertebrate Zoology/Mammalogy, American Museum of Natural History, New York, NY 10024
| | - Harald Meller
- State Office for Heritage Management and Archaeology, 06114 Halle (Saale), Germany
| | - Alfred L Roca
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Oliver A Ryder
- Institute for Conservation Research, San Diego Zoo, Escondido, CA 92027
| | - David Heiman
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Sarah Young
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Matthew Breen
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607
| | - Christina Williams
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607
| | - Bronwen L Aken
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, CB10 1SD Cambridge, United Kingdom
- Wellcome Sanger Institute, Hinxton, CB10 1SD Cambridge, United Kingdom
| | - Magali Ruffier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, CB10 1SD Cambridge, United Kingdom
- Wellcome Sanger Institute, Hinxton, CB10 1SD Cambridge, United Kingdom
| | - Elinor Karlsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655
| | | | | | | | - David L Adelson
- Department of Genetics and Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, 5005 SA, Australia
| | - Thomas Mailund
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus, Denmark
| | - Kasper Munch
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus, Denmark
| | - Kerstin Lindblad-Toh
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden
| | - Michael Hofreiter
- Unit of General Zoology-Evolutionary Adaptive Genomics, Institute of Biochemistry and Biology, Faculty of Mathematics and Life Sciences, University of Potsdam, 14476 Potsdam, Germany
| | - Hendrik Poinar
- McMaster Ancient DNA Centre, Department of Anthropology, McMaster University, Hamilton, ON L8S 4L9, Canada
- Department of Biology, McMaster University, Hamilton, ON L8S 4K1, Canada
- Department of Biochemistry, McMaster University, Hamilton, ON L8S 4L8, Canada
- The Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - David Reich
- Department of Genetics, Harvard Medical School, Boston, MA 02115;
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115
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27
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Springer MS, Gatesy J. Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets. Genes (Basel) 2018; 9:genes9030123. [PMID: 29495400 PMCID: PMC5867844 DOI: 10.3390/genes9030123] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 02/02/2018] [Accepted: 02/19/2018] [Indexed: 02/07/2023] Open
Abstract
coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset-the 'recombination ratchet'-is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d'etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful enough to infer the correct species tree for difficult phylogenetic problems in the anomaly zone, where concatenation is expected to fail because of ILS, then there should be a decreasing probability of inferring the correct species tree using longer loci with many intralocus recombination breakpoints (i.e., increased levels of concatenation).
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Affiliation(s)
- Mark S Springer
- Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, CA 92521, USA.
| | - John Gatesy
- Division of Vertebrate Zoology and Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY 10024, USA.
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28
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Tataru P, Simonsen M, Bataillon T, Hobolth A. Statistical Inference in the Wright-Fisher Model Using Allele Frequency Data. Syst Biol 2018; 66:e30-e46. [PMID: 28173553 PMCID: PMC5837693 DOI: 10.1093/sysbio/syw056] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Revised: 05/31/2016] [Accepted: 06/06/2016] [Indexed: 11/14/2022] Open
Abstract
The Wright–Fisher model provides an elegant mathematical framework for understanding allele frequency data. In particular, the model can be used to infer the demographic history of species and identify loci under selection. A crucial quantity for inference under the Wright–Fisher model is the distribution of allele frequencies (DAF). Despite the apparent simplicity of the model, the calculation of the DAF is challenging. We review and discuss strategies for approximating the DAF, and how these are used in methods that perform inference from allele frequency data. Various evolutionary forces can be incorporated in the Wright–Fisher model, and we consider these in turn. We begin our review with the basic bi-allelic Wright–Fisher model where random genetic drift is the only evolutionary force. We then consider mutation, migration, and selection. In particular, we compare diffusion-based and moment-based methods in terms of accuracy, computational efficiency, and analytical tractability. We conclude with a brief overview of the multi-allelic process with a general mutation model.
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Affiliation(s)
- Paula Tataru
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Maria Simonsen
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Thomas Bataillon
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Asger Hobolth
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
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29
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Abstract
With the advent of sequencing techniques population genomics took a major shift. The structure of data sets has evolved from a sample of a few loci in the genome, sequenced in dozens of individuals, to collections of complete genomes, virtually comprising all available loci. Initially sequenced in a few individuals, such genomic data sets are now reaching and even exceeding the size of traditional data sets in the number of haplotypes sequenced. Because all loci in a genome are not independent, this evolution of data sets is mirrored by a methodological change. The evolutionary processes that generate the observed sequences are now modeled spatially along genomes whereas it was previously described temporally (either in a forward or backward manner). Although the spatial process of sequence evolution is complex, approximations to the model feature Markovian properties, permitting efficient inference. In this chapter, we introduce these recent developments that enable the modeling of the evolutionary history of a sample of several individual genomes. Such models assume the occurrence of meiotic recombination, and therefore, to date, they are dedicated to the analysis of eukaryotic species.
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30
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31
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Ravinet M, Faria R, Butlin RK, Galindo J, Bierne N, Rafajlović M, Noor MAF, Mehlig B, Westram AM. Interpreting the genomic landscape of speciation: a road map for finding barriers to gene flow. J Evol Biol 2017; 30:1450-1477. [DOI: 10.1111/jeb.13047] [Citation(s) in RCA: 306] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 01/31/2017] [Accepted: 02/01/2017] [Indexed: 12/14/2022]
Affiliation(s)
- M. Ravinet
- Centre for Ecological and Evolutionary Synthesis; University of Oslo; Oslo Norway
- National Institute of Genetics; Mishima Shizuoka Japan
| | - R. Faria
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos; InBIO, Laboratório Associado; Universidade do Porto; Vairão Portugal
- Department of Experimental and Health Sciences; IBE, Institute of Evolutionary Biology (CSIC-UPF); Pompeu Fabra University; Barcelona Spain
- Department of Animal and Plant Sciences; University of Sheffield; Sheffield UK
| | - R. K. Butlin
- Department of Animal and Plant Sciences; University of Sheffield; Sheffield UK
- Department of Marine Sciences; Centre for Marine Evolutionary Biology; University of Gothenburg; Gothenburg Sweden
| | - J. Galindo
- Department of Biochemistry, Genetics and Immunology; University of Vigo; Vigo Spain
| | - N. Bierne
- CNRS; Université Montpellier; ISEM; Station Marine Sète France
| | - M. Rafajlović
- Department of Physics; University of Gothenburg; Gothenburg Sweden
| | | | - B. Mehlig
- Department of Physics; University of Gothenburg; Gothenburg Sweden
| | - A. M. Westram
- Department of Animal and Plant Sciences; University of Sheffield; Sheffield UK
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32
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Kumar V, Lammers F, Bidon T, Pfenninger M, Kolter L, Nilsson MA, Janke A. The evolutionary history of bears is characterized by gene flow across species. Sci Rep 2017; 7:46487. [PMID: 28422140 PMCID: PMC5395953 DOI: 10.1038/srep46487] [Citation(s) in RCA: 99] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 03/17/2017] [Indexed: 01/03/2023] Open
Abstract
Bears are iconic mammals with a complex evolutionary history. Natural bear hybrids and studies of few nuclear genes indicate that gene flow among bears may be more common than expected and not limited to polar and brown bears. Here we present a genome analysis of the bear family with representatives of all living species. Phylogenomic analyses of 869 mega base pairs divided into 18,621 genome fragments yielded a well-resolved coalescent species tree despite signals for extensive gene flow across species. However, genome analyses using different statistical methods show that gene flow is not limited to closely related species pairs. Strong ancestral gene flow between the Asiatic black bear and the ancestor to polar, brown and American black bear explains uncertainties in reconstructing the bear phylogeny. Gene flow across the bear clade may be mediated by intermediate species such as the geographically wide-spread brown bears leading to large amounts of phylogenetic conflict. Genome-scale analyses lead to a more complete understanding of complex evolutionary processes. Evidence for extensive inter-specific gene flow, found also in other animal species, necessitates shifting the attention from speciation processes achieving genome-wide reproductive isolation to the selective processes that maintain species divergence in the face of gene flow.
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Affiliation(s)
- Vikas Kumar
- Senckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany.,Goethe University Frankfurt, Institute for Ecology, Evolution &Diversity, Biologicum, Max-von-Laue-Str. 13, D-60439 Frankfurt am Main, Germany
| | - Fritjof Lammers
- Senckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany.,Goethe University Frankfurt, Institute for Ecology, Evolution &Diversity, Biologicum, Max-von-Laue-Str. 13, D-60439 Frankfurt am Main, Germany
| | - Tobias Bidon
- Senckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany.,Goethe University Frankfurt, Institute for Ecology, Evolution &Diversity, Biologicum, Max-von-Laue-Str. 13, D-60439 Frankfurt am Main, Germany
| | - Markus Pfenninger
- Senckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany.,Goethe University Frankfurt, Institute for Ecology, Evolution &Diversity, Biologicum, Max-von-Laue-Str. 13, D-60439 Frankfurt am Main, Germany
| | - Lydia Kolter
- AG Zoologischer Garten Cologne, Riehler Straße 173, 50735 Cologne, Germany
| | - Maria A Nilsson
- Senckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany
| | - Axel Janke
- Senckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany.,Goethe University Frankfurt, Institute for Ecology, Evolution &Diversity, Biologicum, Max-von-Laue-Str. 13, D-60439 Frankfurt am Main, Germany
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Yang M, He Z, Shi S, Wu CI. Can genomic data alone tell us whether speciation happened with gene flow? Mol Ecol 2017; 26:2845-2849. [PMID: 28345182 DOI: 10.1111/mec.14117] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 03/08/2017] [Accepted: 03/20/2017] [Indexed: 01/02/2023]
Abstract
The allopatric model, which requires a period of geographical isolation for speciation to complete, has been the standard model in the modern era. Recently, "speciation with gene flow" has been widely discussed in relation to the model of "strict allopatry" and the level of DNA divergence across genomic regions. We wish to caution that genomic data by themselves may only permit the rejection of the simplest form of allopatry. Even a slightly more complex and realistic model that starts with subdivided populations would be impossible to reject by the genomic data alone. To resolve this central issue of speciation, other forms of observations such as the sequencing of reproductive isolation genes or the identification of geographical barrier(s) will be necessary.
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Affiliation(s)
- Ming Yang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Ziwen He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Suhua Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Chung-I Wu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.,Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
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Dasmeh P, Kepp KP. Superoxide dismutase 1 is positively selected to minimize protein aggregation in great apes. Cell Mol Life Sci 2017; 74:3023-37. [DOI: 10.1007/s00018-017-2519-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 03/17/2017] [Accepted: 04/03/2017] [Indexed: 12/14/2022]
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Costa RJ, Wilkinson-Herbots H. Inference of Gene Flow in the Process of Speciation: An Efficient Maximum-Likelihood Method for the Isolation-with-Initial-Migration Model. Genetics 2017; 205:1597-1618. [PMID: 28193727 PMCID: PMC5378116 DOI: 10.1534/genetics.116.188060] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 01/25/2017] [Indexed: 12/03/2022] Open
Abstract
The isolation-with-migration (IM) model is commonly used to make inferences about gene flow during speciation, using polymorphism data. However, it has been reported that the parameter estimates obtained by fitting the IM model are very sensitive to the model's assumptions-including the assumption of constant gene flow until the present. This article is concerned with the isolation-with-initial-migration (IIM) model, which drops precisely this assumption. In the IIM model, one ancestral population divides into two descendant subpopulations, between which there is an initial period of gene flow and a subsequent period of isolation. We derive a very fast method of fitting an extended version of the IIM model, which also allows for asymmetric gene flow and unequal population sizes. This is a maximum-likelihood method, applicable to data on the number of segregating sites between pairs of DNA sequences from a large number of independent loci. In addition to obtaining parameter estimates, our method can also be used, by means of likelihood-ratio tests, to distinguish between alternative models representing the following divergence scenarios: (a) divergence with potentially asymmetric gene flow until the present, (b) divergence with potentially asymmetric gene flow until some point in the past and in isolation since then, and (c) divergence in complete isolation. We illustrate the procedure on pairs of Drosophila sequences from ∼30,000 loci. The computing time needed to fit the most complex version of the model to this data set is only a couple of minutes. The R code to fit the IIM model can be found in the supplementary files of this article.
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Affiliation(s)
- Rui J Costa
- Department of Statistical Science, University College London, WC1E 6BT, United Kingdom
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Abstract
Hybridization has played an important role in the evolution of many lineages. With the growing availability of genomic tools and advancements in genomic analyses, it is becoming increasingly clear that gene flow between divergent taxa can generate new phenotypic diversity, allow for adaptation to novel environments, and contribute to speciation. Hybridization can have immediate phenotypic consequences through the expression of hybrid vigor. On longer evolutionary time scales, hybridization can lead to local adaption through the introgression of novel alleles and transgressive segregation and, in some cases, result in the formation of new hybrid species. Studying both the abundance and the evolutionary consequences of hybridization has deep historical roots in plant biology. Many of the hypotheses concerning how and why hybridization contributes to biological diversity currently being investigated were first proposed tens and even hundreds of years ago. In this Update, we discuss how new advancements in genomic and genetic tools are revolutionizing our ability to document the occurrence of and investigate the outcomes of hybridization in plants.
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Affiliation(s)
- Benjamin E Goulet
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138 (B.E.G., F.R., R.H.); and
- Arnold Arboretum of Harvard University, Boston, Massachusetts 02131 (R.H.)
| | - Federico Roda
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138 (B.E.G., F.R., R.H.); and
- Arnold Arboretum of Harvard University, Boston, Massachusetts 02131 (R.H.)
| | - Robin Hopkins
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138 (B.E.G., F.R., R.H.); and
- Arnold Arboretum of Harvard University, Boston, Massachusetts 02131 (R.H.)
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Abascal F, Corvelo A, Cruz F, Villanueva-Cañas JL, Vlasova A, Marcet-Houben M, Martínez-Cruz B, Cheng JY, Prieto P, Quesada V, Quilez J, Li G, García F, Rubio-Camarillo M, Frias L, Ribeca P, Capella-Gutiérrez S, Rodríguez JM, Câmara F, Lowy E, Cozzuto L, Erb I, Tress ML, Rodriguez-Ales JL, Ruiz-Orera J, Reverter F, Casas-Marce M, Soriano L, Arango JR, Derdak S, Galán B, Blanc J, Gut M, Lorente-Galdos B, Andrés-Nieto M, López-Otín C, Valencia A, Gut I, García JL, Guigó R, Murphy WJ, Ruiz-Herrera A, Marques-Bonet T, Roma G, Notredame C, Mailund T, Albà MM, Gabaldón T, Alioto T, Godoy JA. Extreme genomic erosion after recurrent demographic bottlenecks in the highly endangered Iberian lynx. Genome Biol 2016; 17:251. [PMID: 27964752 PMCID: PMC5155386 DOI: 10.1186/s13059-016-1090-1] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 10/25/2016] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Genomic studies of endangered species provide insights into their evolution and demographic history, reveal patterns of genomic erosion that might limit their viability, and offer tools for their effective conservation. The Iberian lynx (Lynx pardinus) is the most endangered felid and a unique example of a species on the brink of extinction. RESULTS We generate the first annotated draft of the Iberian lynx genome and carry out genome-based analyses of lynx demography, evolution, and population genetics. We identify a series of severe population bottlenecks in the history of the Iberian lynx that predate its known demographic decline during the 20th century and have greatly impacted its genome evolution. We observe drastically reduced rates of weak-to-strong substitutions associated with GC-biased gene conversion and increased rates of fixation of transposable elements. We also find multiple signatures of genetic erosion in the two remnant Iberian lynx populations, including a high frequency of potentially deleterious variants and substitutions, as well as the lowest genome-wide genetic diversity reported so far in any species. CONCLUSIONS The genomic features observed in the Iberian lynx genome may hamper short- and long-term viability through reduced fitness and adaptive potential. The knowledge and resources developed in this study will boost the research on felid evolution and conservation genomics and will benefit the ongoing conservation and management of this emblematic species.
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Affiliation(s)
- Federico Abascal
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - André Corvelo
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
| | - Fernando Cruz
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
- Department of Integrative Ecology, Doñana Biological Station (EBD), Spanish National Research Council (CSIC), C/ Americo Vespucio, s/n, 41092, Sevilla, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - José L Villanueva-Cañas
- Evolutionary Genomics Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute (IMIM), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Anna Vlasova
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Marina Marcet-Houben
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Begoña Martínez-Cruz
- Department of Integrative Ecology, Doñana Biological Station (EBD), Spanish National Research Council (CSIC), C/ Americo Vespucio, s/n, 41092, Sevilla, Spain
| | - Jade Yu Cheng
- Bioinformatics Research Centre, Aarhus University, C.F. Møllers Allé 8, 8000, Aarhus, Denmark
| | - Pablo Prieto
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Víctor Quesada
- Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, 33006, Oviedo, Spain
| | - Javier Quilez
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, PRBB, Doctor Aiguader, 88, 08003, Barcelona, Spain
| | - Gang Li
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine, Texas A&M University, College Station, TX, 77843, USA
| | - Francisca García
- Servei de Cultius Cel.lulars (SCC, SCAC), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Miriam Rubio-Camarillo
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - Leonor Frias
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
| | - Paolo Ribeca
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
| | - Salvador Capella-Gutiérrez
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - José M Rodríguez
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
- National Bioinformatics Institute (INB), Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - Francisco Câmara
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Ernesto Lowy
- Bioinformatics Core Facility, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Luca Cozzuto
- Bioinformatics Core Facility, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Ionas Erb
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Michael L Tress
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - Jose L Rodriguez-Ales
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Jorge Ruiz-Orera
- Evolutionary Genomics Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute (IMIM), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Ferran Reverter
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Mireia Casas-Marce
- Department of Integrative Ecology, Doñana Biological Station (EBD), Spanish National Research Council (CSIC), C/ Americo Vespucio, s/n, 41092, Sevilla, Spain
| | - Laura Soriano
- Department of Integrative Ecology, Doñana Biological Station (EBD), Spanish National Research Council (CSIC), C/ Americo Vespucio, s/n, 41092, Sevilla, Spain
| | - Javier R Arango
- Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, 33006, Oviedo, Spain
| | - Sophia Derdak
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
| | - Beatriz Galán
- Department of Environmental Biology, Center for Biological Research (CIB), Spanish National Research Council (CSIC), Ramiro de Maeztu 9, 28040, Madrid, Spain
| | - Julie Blanc
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
| | - Marta Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
| | - Belen Lorente-Galdos
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, PRBB, Doctor Aiguader, 88, 08003, Barcelona, Spain
| | - Marta Andrés-Nieto
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain
| | - Carlos López-Otín
- Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, 33006, Oviedo, Spain
| | - Alfonso Valencia
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
- National Bioinformatics Institute (INB), Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - Ivo Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
| | - José L García
- Department of Environmental Biology, Center for Biological Research (CIB), Spanish National Research Council (CSIC), Ramiro de Maeztu 9, 28040, Madrid, Spain
| | - Roderic Guigó
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
- Computational Genomics Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute (IMIM), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - William J Murphy
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine, Texas A&M University, College Station, TX, 77843, USA
| | - Aurora Ruiz-Herrera
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain
- Departament de Biologia Cel.lular, Fisiologia i Immunologia, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain
| | - Tomas Marques-Bonet
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, PRBB, Doctor Aiguader, 88, 08003, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010, Barcelona, Spain
| | - Guglielmo Roma
- Bioinformatics Core Facility, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Cedric Notredame
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Thomas Mailund
- Bioinformatics Research Centre, Aarhus University, C.F. Møllers Allé 8, 8000, Aarhus, Denmark
| | - M Mar Albà
- Evolutionary Genomics Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute (IMIM), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010, Barcelona, Spain
| | - Toni Gabaldón
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010, Barcelona, Spain
| | - Tyler Alioto
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003, Barcelona, Spain
| | - José A Godoy
- Department of Integrative Ecology, Doñana Biological Station (EBD), Spanish National Research Council (CSIC), C/ Americo Vespucio, s/n, 41092, Sevilla, Spain.
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Gautier M, Moazami-Goudarzi K, Levéziel H, Parinello H, Grohs C, Rialle S, Kowalczyk R, Flori L. Deciphering the Wisent Demographic and Adaptive Histories from Individual Whole-Genome Sequences. Mol Biol Evol 2016; 33:2801-2814. [PMID: 27436010 PMCID: PMC5062319 DOI: 10.1093/molbev/msw144] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
As the largest European herbivore, the wisent (Bison bonasus) is emblematic of the continent wildlife but has unclear origins. Here, we infer its demographic and adaptive histories from two individual whole-genome sequences via a detailed comparative analysis with bovine genomes. We estimate that the wisent and bovine species diverged from 1.7 × 106 to 850,000 years before present (YBP) through a speciation process involving an extended period of limited gene flow. Our data further support the occurrence of more recent secondary contacts, posterior to the Bos taurus and Bos indicus divergence (∼150,000 YBP), between the wisent and (European) taurine cattle lineages. Although the wisent and bovine population sizes experienced a similar sharp decline since the Last Glacial Maximum, we find that the wisent demography remained more fluctuating during the Pleistocene. This is in agreement with a scenario in which wisents responded to successive glaciations by habitat fragmentation rather than southward and eastward migration as for the bovine ancestors. We finally detect 423 genes under positive selection between the wisent and bovine lineages, which shed a new light on the genome response to different living conditions (temperature, available food resource, and pathogen exposure) and on the key gene functions altered by the domestication process.
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Affiliation(s)
- Mathieu Gautier
- CBGP, INRA, CIRAD, IRD, Supagro, Montferrier-sur-Lez, France IBC, Institut de Biologie Computationnelle, Montpellier, France
| | | | | | - Hugues Parinello
- MGX-Montpellier GenomiX, c/o Institut de Génomique Fonctionnelle, Montpellier, France
| | - Cécile Grohs
- GABI, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Stéphanie Rialle
- MGX-Montpellier GenomiX, c/o Institut de Génomique Fonctionnelle, Montpellier, France
| | - Rafał Kowalczyk
- Mammal Research Institute, Polish Academy of Sciences, Białowieża, Poland
| | - Laurence Flori
- GABI, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France INTERTRYP, CIRAD, IRD, Montpellier, France
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Hejase HA, Liu KJ. A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation. BMC Bioinformatics 2016; 17:422. [PMID: 27737628 PMCID: PMC5064893 DOI: 10.1186/s12859-016-1277-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 09/22/2016] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Branching events in phylogenetic trees reflect bifurcating and/or multifurcating speciation and splitting events. In the presence of gene flow, a phylogeny cannot be described by a tree but is instead a directed acyclic graph known as a phylogenetic network. Both phylogenetic trees and networks are typically reconstructed using computational analysis of multi-locus sequence data. The advent of high-throughput sequencing technologies has brought about two main scalability challenges: (1) dataset size in terms of the number of taxa and (2) the evolutionary divergence of the taxa in a study. The impact of both dimensions of scale on phylogenetic tree inference has been well characterized by recent studies; in contrast, the scalability limits of phylogenetic network inference methods are largely unknown. RESULTS In this study, we quantify the performance of state-of-the-art phylogenetic network inference methods on large-scale datasets using empirical data sampled from natural mouse populations and a range of simulations using model phylogenies with a single reticulation. We find that, as in the case of phylogenetic tree inference, the performance of leading network inference methods is negatively impacted by both dimensions of dataset scale. In general, we found that topological accuracy degrades as the number of taxa increases; a similar effect was observed with increased sequence mutation rate. The most accurate methods were probabilistic inference methods which maximize either likelihood under coalescent-based models or pseudo-likelihood approximations to the model likelihood. The improved accuracy obtained with probabilistic inference methods comes at a computational cost in terms of runtime and main memory usage, which become prohibitive as dataset size grows past twenty-five taxa. None of the probabilistic methods completed analyses of datasets with 30 taxa or more after many weeks of CPU runtime. CONCLUSIONS We conclude that the state of the art of phylogenetic network inference lags well behind the scope of current phylogenomic studies. New algorithmic development is critically needed to address this methodological gap.
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Affiliation(s)
- Hussein A. Hejase
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, MI USA
| | - Kevin J. Liu
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, MI USA
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41
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Rougemont Q, Gagnaire PA, Perrier C, Genthon C, Besnard AL, Launey S, Evanno G. Inferring the demographic history underlying parallel genomic divergence among pairs of parasitic and nonparasitic lamprey ecotypes. Mol Ecol 2016; 26:142-162. [PMID: 27105132 DOI: 10.1111/mec.13664] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Revised: 03/22/2016] [Accepted: 04/06/2016] [Indexed: 12/20/2022]
Abstract
Understanding the evolutionary mechanisms generating parallel genomic divergence patterns among replicate ecotype pairs remains an important challenge in speciation research. We investigated the genomic divergence between the anadromous parasitic river lamprey (Lampetra fluviatilis) and the freshwater-resident nonparasitic brook lamprey (Lampetra planeri) in nine population pairs displaying variable levels of geographic connectivity. We genotyped 338 individuals with RAD sequencing and inferred the demographic divergence history of each population pair using a diffusion approximation method. Divergence patterns in geographically connected population pairs were better explained by introgression after secondary contact, whereas disconnected population pairs have retained a signal of ancient migration. In all ecotype pairs, models accounting for differential introgression among loci outperformed homogeneous migration models. Generating neutral predictions from the inferred divergence scenarios to detect highly differentiated markers identified greater proportions of outliers in disconnected population pairs than in connected pairs. However, increased similarity in the most divergent genomic regions was found among connected ecotype pairs, indicating that gene flow was instrumental in generating parallelism at the molecular level. These results suggest that heterogeneous genomic differentiation and parallelism among replicate ecotype pairs have partly emerged through restricted introgression in genomic islands.
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Affiliation(s)
- Quentin Rougemont
- INRA, UMR 985 Ecologie et Santé des Ecosystèmes, 35042, Rennes, France.,Agrocampus Ouest, UMR ESE, 65 rue de Saint-Brieuc, 35042, Rennes, France
| | - Pierre-Alexandre Gagnaire
- Institut des Sciences de l'Evolution (UMR 5554), CNRS-UM2-IRD, Place Eugène Bataillon, F-34095, Montpellier, France.,Station Méditerranéenne de l'Environnement Littoral, Université de Montpellier, 2 Rue des Chantiers, F-34200, Sète, France
| | - Charles Perrier
- CEFE-CNRS, Centre D'Ecologie Fonctionnelle et Evolutive, Route de Mende, 34090, Montpellier, France
| | - Clémence Genthon
- Plateforme génomique INRA GenoToul Chemin de Borderouge - Auzeville, 31320, Castanet-Tolosan, France
| | - Anne-Laure Besnard
- INRA, UMR 985 Ecologie et Santé des Ecosystèmes, 35042, Rennes, France.,Agrocampus Ouest, UMR ESE, 65 rue de Saint-Brieuc, 35042, Rennes, France
| | - Sophie Launey
- INRA, UMR 985 Ecologie et Santé des Ecosystèmes, 35042, Rennes, France.,Agrocampus Ouest, UMR ESE, 65 rue de Saint-Brieuc, 35042, Rennes, France
| | - Guillaume Evanno
- INRA, UMR 985 Ecologie et Santé des Ecosystèmes, 35042, Rennes, France.,Agrocampus Ouest, UMR ESE, 65 rue de Saint-Brieuc, 35042, Rennes, France
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42
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Abstract
Hybridization among diverging lineages is common in nature. Genomic data provide a special opportunity to characterize the history of hybridization and the genetic basis of speciation. We review existing methods and empirical studies to identify recent advances in the genomics of hybridization, as well as issues that need to be addressed. Notable progress has been made in the development of methods for detecting hybridization and inferring individual ancestries. However, few approaches reconstruct the magnitude and timing of gene flow, estimate the fitness of hybrids or incorporate knowledge of recombination rate. Empirical studies indicate that the genomic consequences of hybridization are complex, including a highly heterogeneous landscape of differentiation. Inferred characteristics of hybridization differ substantially among species groups. Loci showing unusual patterns - which may contribute to reproductive barriers - are usually scattered throughout the genome, with potential enrichment in sex chromosomes and regions of reduced recombination. We caution against the growing trend of interpreting genomic variation in summary statistics across genomes as evidence of differential gene flow. We argue that converting genomic patterns into useful inferences about hybridization will ultimately require models and methods that directly incorporate key ingredients of speciation, including the dynamic nature of gene flow, selection acting in hybrid populations and recombination rate variation.
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Affiliation(s)
- Bret A. Payseur
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Loren H. Rieseberg
- Department of Botany, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
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43
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Hallast P, Maisano Delser P, Batini C, Zadik D, Rocchi M, Schempp W, Tyler-Smith C, Jobling MA. Great ape Y Chromosome and mitochondrial DNA phylogenies reflect subspecies structure and patterns of mating and dispersal. Genome Res 2016; 26:427-39. [PMID: 26883546 PMCID: PMC4817767 DOI: 10.1101/gr.198754.115] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 01/25/2016] [Indexed: 12/30/2022]
Abstract
The distribution of genetic diversity in great ape species is likely to have been affected by patterns of dispersal and mating. This has previously been investigated by sequencing autosomal and mitochondrial DNA (mtDNA), but large-scale sequence analysis of the male-specific region of the Y Chromosome (MSY) has not yet been undertaken. Here, we use the human MSY reference sequence as a basis for sequence capture and read mapping in 19 great ape males, combining the data with sequences extracted from the published whole genomes of 24 additional males to yield a total sample of 19 chimpanzees, four bonobos, 14 gorillas, and six orangutans, in which interpretable MSY sequence ranges from 2.61 to 3.80 Mb. This analysis reveals thousands of novel MSY variants and defines unbiased phylogenies. We compare these with mtDNA-based trees in the same individuals, estimating time-to-most-recent common ancestor (TMRCA) for key nodes in both cases. The two loci show high topological concordance and are consistent with accepted (sub)species definitions, but time depths differ enormously between loci and (sub)species, likely reflecting different dispersal and mating patterns. Gorillas and chimpanzees/bonobos present generally low and high MSY diversity, respectively, reflecting polygyny versus multimale–multifemale mating. However, particularly marked differences exist among chimpanzee subspecies: The western chimpanzee MSY phylogeny has a TMRCA of only 13.2 (10.8–15.8) thousand years, but that for central chimpanzees exceeds 1 million years. Cross-species comparison within a single MSY phylogeny emphasizes the low human diversity, and reveals species-specific branch length variation that may reflect differences in long-term generation times.
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Affiliation(s)
- Pille Hallast
- Department of Genetics, University of Leicester, Leicester LE1 7RH, United Kingdom; Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia
| | | | - Chiara Batini
- Department of Genetics, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Daniel Zadik
- Department of Genetics, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Mariano Rocchi
- Department of Biology, University of Bari, 70124 Bari, Italy
| | - Werner Schempp
- Institute of Human Genetics, University of Freiburg, 79106 Freiburg, Germany
| | - Chris Tyler-Smith
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom
| | - Mark A Jobling
- Department of Genetics, University of Leicester, Leicester LE1 7RH, United Kingdom
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44
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Abstract
Recent studies of eukaryotes including human and Neandertal, mice, and butterflies have highlighted the major role that interspecific introgression has played in adaptive trait evolution. A common question arises in each case: what is the genomic architecture of the introgressed traits? One common approach that can be used to address this question is association mapping, which looks for genotypic markers that have significant statistical association with a trait. It is well understood that sample relatedness can be a confounding factor in association mapping studies if not properly accounted for. Introgression and other evolutionary processes (e.g., incomplete lineage sorting) typically introduce variation among local genealogies, which can also differ from global sample structure measured across all genomic loci. In contrast, state-of-the-art association mapping methods assume fixed sample relatedness across the genome, which can lead to spurious inference. We therefore propose a new association mapping method called Coal-Map, which uses coalescent-based models to capture local genealogical variation alongside global sample structure. Using simulated and empirical data reflecting a range of evolutionary scenarios, we compare the performance of Coal-Map against EIGENSTRAT, a leading association mapping method in terms of its popularity, power, and type I error control. Our empirical data makes use of hundreds of mouse genomes for which adaptive interspecific introgression has recently been described. We found that Coal-Map's performance is comparable or better than EIGENSTRAT in terms of statistical power and false positive rate. Coal-Map's performance advantage was greatest on model conditions that most closely resembled empirically observed scenarios of adaptive introgression. These conditions had: (1) causal SNPs contained in one or a few introgressed genomic loci and (2) varying rates of gene flow - from high rates to very low rates where incomplete lineage sorting dominated as a primary cause of local genealogical variation.
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Affiliation(s)
- Hussein A Hejase
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, MI, USA.
| | - Kevin J Liu
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, MI, USA.
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45
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Edwards SV, Xi Z, Janke A, Faircloth BC, Mccormack JE, Glenn TC, Zhong B, Wu S, Lemmon EM, Lemmon AR, Leaché AD, Liu L, Davis CC. Implementing and testing the multispecies coalescent model: A valuable paradigm for phylogenomics. Mol Phylogenet Evol 2016; 94:447-62. [DOI: 10.1016/j.ympev.2015.10.027] [Citation(s) in RCA: 265] [Impact Index Per Article: 33.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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46
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Lohse K, Chmelik M, Martin SH, Barton NH. Efficient Strategies for Calculating Blockwise Likelihoods Under the Coalescent. Genetics 2016; 202:775-86. [PMID: 26715666 DOI: 10.1534/genetics.115.183814] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 12/15/2015] [Indexed: 01/08/2023] Open
Abstract
The inference of demographic history from genome data is hindered by a lack of efficient computational approaches. In particular, it has proved difficult to exploit the information contained in the distribution of genealogies across the genome. We have previously shown that the generating function (GF) of genealogies can be used to analytically compute likelihoods of demographic models from configurations of mutations in short sequence blocks (Lohse et al. 2011). Although the GF has a simple, recursive form, the size of such likelihood calculations explodes quickly with the number of individuals and applications of this framework have so far been mainly limited to small samples (pairs and triplets) for which the GF can be written by hand. Here we investigate several strategies for exploiting the inherent symmetries of the coalescent. In particular, we show that the GF of genealogies can be decomposed into a set of equivalence classes that allows likelihood calculations from nontrivial samples. Using this strategy, we automated blockwise likelihood calculations for a general set of demographic scenarios in Mathematica. These histories may involve population size changes, continuous migration, discrete divergence, and admixture between multiple populations. To give a concrete example, we calculate the likelihood for a model of isolation with migration (IM), assuming two diploid samples without phase and outgroup information. We demonstrate the new inference scheme with an analysis of two individual butterfly genomes from the sister species Heliconius melpomene rosina and H. cydno.
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47
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Lipson M, Loh PR, Sankararaman S, Patterson N, Berger B, Reich D. Calibrating the Human Mutation Rate via Ancestral Recombination Density in Diploid Genomes. PLoS Genet 2015; 11:e1005550. [PMID: 26562831 PMCID: PMC4642934 DOI: 10.1371/journal.pgen.1005550] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 09/03/2015] [Indexed: 01/06/2023] Open
Abstract
The human mutation rate is an essential parameter for studying the evolution of our species, interpreting present-day genetic variation, and understanding the incidence of genetic disease. Nevertheless, our current estimates of the rate are uncertain. Most notably, recent approaches based on counting de novo mutations in family pedigrees have yielded significantly smaller values than classical methods based on sequence divergence. Here, we propose a new method that uses the fine-scale human recombination map to calibrate the rate of accumulation of mutations. By comparing local heterozygosity levels in diploid genomes to the genetic distance scale over which these levels change, we are able to estimate a long-term mutation rate averaged over hundreds or thousands of generations. We infer a rate of 1.61 ± 0.13 × 10-8 mutations per base per generation, which falls in between phylogenetic and pedigree-based estimates, and we suggest possible mechanisms to reconcile our estimate with previous studies. Our results support intermediate-age divergences among human populations and between humans and other great apes.
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Affiliation(s)
- Mark Lipson
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (ML), (DR)
| | - Po-Ru Loh
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Sriram Sankararaman
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Nick Patterson
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Bonnie Berger
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - David Reich
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (ML), (DR)
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48
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49
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Hara Y. Tempo and mode of genomic mutations unveil human evolutionary history. Genes Genet Syst 2015; 90:123-31. [PMID: 26510567 DOI: 10.1266/ggs.90.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Mutations that have occurred in human genomes provide insight into various aspects of evolutionary history such as speciation events and degrees of natural selection. Comparing genome sequences between human and great apes or among humans is a feasible approach for inferring human evolutionary history. Recent advances in high-throughput or so-called 'next-generation' DNA sequencing technologies have enabled the sequencing of thousands of individual human genomes, as well as a variety of reference genomes of hominids, many of which are publicly available. These sequence data can help to unveil the detailed demographic history of the lineage leading to humans as well as the explosion of modern human population size in the last several thousand years. In addition, high-throughput sequencing illustrates the tempo and mode of de novo mutations, which are producing human genetic variation at this moment. Pedigree-based human genome sequencing has shown that mutation rates vary significantly across the human genome. These studies have also provided an improved timescale of human evolution, because the mutation rate estimated from pedigree analysis is half that estimated from traditional analyses based on molecular phylogeny. Because of the dramatic reduction in sequencing cost, sequencing on-demand samples designed for specific studies is now also becoming popular. To produce data of sufficient quality to meet the requirements of the study, it is necessary to set an explicit sequencing plan that includes the choice of sample collection methods, sequencing platforms, and number of sequence reads.
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Affiliation(s)
- Yuichiro Hara
- Phyloinformatics Unit, RIKEN Center for Life Science Technologies
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50
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Lohse K, Clarke M, Ritchie MG, Etges WJ. Genome-wide tests for introgression between cactophilic Drosophila implicate a role of inversions during speciation. Evolution 2015; 69:1178-90. [PMID: 25824653 PMCID: PMC5029762 DOI: 10.1111/evo.12650] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 03/17/2015] [Indexed: 12/25/2022]
Abstract
Models of speciation‐with‐gene‐flow have shown that the reduction in recombination between alternative chromosome arrangements can facilitate the fixation of locally adaptive genes in the face of gene flow and contribute to speciation. However, it has proven frustratingly difficult to show empirically that inversions have reduced gene flow and arose during or shortly after the onset of species divergence rather than represent ancestral polymorphisms. Here, we present an analysis of whole genome data from a pair of cactophilic fruit flies, Drosophila mojavensis and D. arizonae, which are reproductively isolated in the wild and differ by several large inversions on three chromosomes. We found an increase in divergence at rearranged compared to colinear chromosomes. Using the density of divergent sites in short sequence blocks we fit a series of explicit models of species divergence in which gene flow is restricted to an initial period after divergence and may differ between colinear and rearranged parts of the genome. These analyses show that D. mojavensis and D. arizonae have experienced postdivergence gene flow that ceased around 270 KY ago and was significantly reduced in chromosomes with fixed inversions. Moreover, we show that these inversions most likely originated around the time of species divergence which is compatible with theoretical models that posit a role of inversions in speciation with gene flow.
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Affiliation(s)
- Konrad Lohse
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom.
| | - Magnus Clarke
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom
| | - Michael G Ritchie
- School of Biology, University of St. Andrews, St. Andrews KY16 9TH, United Kingdom
| | - William J Etges
- Program in Ecology and Evolutionary Biology, Department of Biological Sciences, University of Arkansas, Fayetteville, Arkansas 72701
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