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Yoo D, Rhie A, Hebbar P, Antonacci F, Logsdon GA, Solar SJ, Antipov D, Pickett BD, Safonova Y, Montinaro F, Luo Y, Malukiewicz J, Storer JM, Lin J, Sequeira AN, Mangan RJ, Hickey G, Monfort Anez G, Balachandran P, Bankevich A, Beck CR, Biddanda A, Borchers M, Bouffard GG, Brannan E, Brooks SY, Carbone L, Carrel L, Chan AP, Crawford J, Diekhans M, Engelbrecht E, Feschotte C, Formenti G, Garcia GH, de Gennaro L, Gilbert D, Green RE, Guarracino A, Gupta I, Haddad D, Han J, Harris RS, Hartley GA, Harvey WT, Hiller M, Hoekzema K, Houck ML, Jeong H, Kamali K, Kellis M, Kille B, Lee C, Lee Y, Lees W, Lewis AP, Li Q, Loftus M, Loh YHE, Loucks H, Ma J, Mao Y, Martinez JFI, Masterson P, McCoy RC, McGrath B, McKinney S, Meyer BS, Miga KH, Mohanty SK, Munson KM, Pal K, Pennell M, Pevzner PA, Porubsky D, Potapova T, Ringeling FR, Rocha JL, Ryder OA, Sacco S, Saha S, Sasaki T, Schatz MC, Schork NJ, Shanks C, Smeds L, Son DR, Steiner C, Sweeten AP, Tassia MG, Thibaud-Nissen F, Torres-González E, Trivedi M, Wei W, Wertz J, Yang M, Zhang P, Zhang S, Zhang Y, Zhang Z, et alYoo D, Rhie A, Hebbar P, Antonacci F, Logsdon GA, Solar SJ, Antipov D, Pickett BD, Safonova Y, Montinaro F, Luo Y, Malukiewicz J, Storer JM, Lin J, Sequeira AN, Mangan RJ, Hickey G, Monfort Anez G, Balachandran P, Bankevich A, Beck CR, Biddanda A, Borchers M, Bouffard GG, Brannan E, Brooks SY, Carbone L, Carrel L, Chan AP, Crawford J, Diekhans M, Engelbrecht E, Feschotte C, Formenti G, Garcia GH, de Gennaro L, Gilbert D, Green RE, Guarracino A, Gupta I, Haddad D, Han J, Harris RS, Hartley GA, Harvey WT, Hiller M, Hoekzema K, Houck ML, Jeong H, Kamali K, Kellis M, Kille B, Lee C, Lee Y, Lees W, Lewis AP, Li Q, Loftus M, Loh YHE, Loucks H, Ma J, Mao Y, Martinez JFI, Masterson P, McCoy RC, McGrath B, McKinney S, Meyer BS, Miga KH, Mohanty SK, Munson KM, Pal K, Pennell M, Pevzner PA, Porubsky D, Potapova T, Ringeling FR, Rocha JL, Ryder OA, Sacco S, Saha S, Sasaki T, Schatz MC, Schork NJ, Shanks C, Smeds L, Son DR, Steiner C, Sweeten AP, Tassia MG, Thibaud-Nissen F, Torres-González E, Trivedi M, Wei W, Wertz J, Yang M, Zhang P, Zhang S, Zhang Y, Zhang Z, Zhao SA, Zhu Y, Jarvis ED, Gerton JL, Rivas-González I, Paten B, Szpiech ZA, Huber CD, Lenz TL, Konkel MK, Yi SV, Canzar S, Watson CT, Sudmant PH, Molloy E, Garrison E, Lowe CB, Ventura M, O'Neill RJ, Koren S, Makova KD, Phillippy AM, Eichler EE. Complete sequencing of ape genomes. Nature 2025; 641:401-418. [PMID: 40205052 PMCID: PMC12058530 DOI: 10.1038/s41586-025-08816-3] [Show More Authors] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2024] [Accepted: 02/19/2025] [Indexed: 04/11/2025]
Abstract
The most dynamic and repetitive regions of great ape genomes have traditionally been excluded from comparative studies1-3. Consequently, our understanding of the evolution of our species is incomplete. Here we present haplotype-resolved reference genomes and comparative analyses of six ape species: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan and siamang. We achieve chromosome-level contiguity with substantial sequence accuracy (<1 error in 2.7 megabases) and completely sequence 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, to provide in-depth evolutionary insights. Comparative analyses enabled investigations of the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference genome. Such regions include newly minted gene families in lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes and subterminal heterochromatin. This resource serves as a comprehensive baseline for future evolutionary studies of humans and our closest living ape relatives.
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Affiliation(s)
- DongAhn Yoo
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arang Rhie
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Prajna Hebbar
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Francesca Antonacci
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, Italy
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven J Solar
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dmitry Antipov
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brandon D Pickett
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yana Safonova
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA, USA
| | - Francesco Montinaro
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, Italy
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yanting Luo
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC, USA
| | - Joanna Malukiewicz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
- German Primate Center, Primate Genetics Laboratory, Goettingen, Germany
| | - Jessica M Storer
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
| | - Jiadong Lin
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Riley J Mangan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genetics Training Program, Harvard Medical School, Boston, MA, USA
| | - Glenn Hickey
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | | | - Anton Bankevich
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA, USA
| | - Christine R Beck
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Arjun Biddanda
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Gerard G Bouffard
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emry Brannan
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Shelise Y Brooks
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lucia Carbone
- Department of Medicine, KCVI, Oregon Health Sciences University, Portland, OR, USA
- Division of Genetics, Oregon National Primate Research Center, Beaverton, OR, USA
| | - Laura Carrel
- PSU Medical School, Penn State University School of Medicine, Hershey, PA, USA
| | - Agnes P Chan
- The Translational Genomics Research Institute, City of Hope National Medical Center, Phoenix, AZ, USA
| | - Juyun Crawford
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Eric Engelbrecht
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Cedric Feschotte
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Giulio Formenti
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA
| | - Gage H Garcia
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Luciana de Gennaro
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, Italy
| | - David Gilbert
- San Diego Biomedical Research Institute, San Diego, CA, USA
| | - Richard E Green
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Ishaan Gupta
- Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA
| | - Diana Haddad
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Junmin Han
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Robert S Harris
- Department of Biology, Penn State University, University Park, PA, USA
| | | | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Michael Hiller
- LOEWE Centre for Translational Biodiversity Genomics, Frankfurt, Germany
- Senckenberg Research Institute, Frankfurt, Germany
- Institute of Cell Biology and Neuroscience, Faculty of Biosciences, Goethe University Frankfurt, Frankfurt, Germany
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Hyeonsoo Jeong
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Kaivan Kamali
- Department of Biology, Penn State University, University Park, PA, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bryce Kille
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Chul Lee
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Youngho Lee
- Laboratory of Bioinformatics and Population Genetics, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - William Lees
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Qiuhui Li
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Mark Loftus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Yong Hwee Eddie Loh
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Hailey Loucks
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Yafei Mao
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- Center for Genomic Research, International Institutes of Medicine, Fourth Affiliated Hospital, Zhejiang University, Yiwu, China
- Shanghai Jiao Tong University Chongqing Research Institute, Chongqing, China
| | - Juan F I Martinez
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA, USA
| | - Patrick Masterson
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Barbara McGrath
- Department of Biology, Penn State University, University Park, PA, USA
| | - Sean McKinney
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Britta S Meyer
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Karen H Miga
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Saswat K Mohanty
- Department of Biology, Penn State University, University Park, PA, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Karol Pal
- Department of Biology, Penn State University, University Park, PA, USA
| | - Matt Pennell
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Tamara Potapova
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Francisca R Ringeling
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
| | - Joana L Rocha
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | - Samuel Sacco
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Swati Saha
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Takayo Sasaki
- San Diego Biomedical Research Institute, San Diego, CA, USA
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Nicholas J Schork
- The Translational Genomics Research Institute, City of Hope National Medical Center, Phoenix, AZ, USA
| | - Cole Shanks
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Linnéa Smeds
- Department of Biology, Penn State University, University Park, PA, USA
| | - Dongmin R Son
- Department of Ecology, Evolution and Marine Biology, Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
| | | | - Alexander P Sweeten
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael G Tassia
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Mihir Trivedi
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Wenjie Wei
- School of Life Sciences, Westlake University, Hangzhou, China
- National Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Julie Wertz
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Muyu Yang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Panpan Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Shilong Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Zhenmiao Zhang
- Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA
| | - Sarah A Zhao
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yixin Zhu
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Erich D Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | - Iker Rivas-González
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Zachary A Szpiech
- Department of Biology, Penn State University, University Park, PA, USA
| | - Christian D Huber
- Department of Biology, Penn State University, University Park, PA, USA
| | - Tobias L Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Miriam K Konkel
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Soojin V Yi
- Department of Ecology, Evolution and Marine Biology, Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
- Department of Molecular, Cellular and Developmental Biology, Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Stefan Canzar
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Peter H Sudmant
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Erin Molloy
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Craig B Lowe
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC, USA
| | - Mario Ventura
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, Italy
| | - Rachel J O'Neill
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Sergey Koren
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kateryna D Makova
- Department of Biology, Penn State University, University Park, PA, USA.
| | - Adam M Phillippy
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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2
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Responte MA, Wu CY, Elias NU, Brown RM, Dai CY, Su YC. Recent Range Expansion and Genomic Admixture in a Kleptoparasitic Spider, Argyrodes lanyuensis: A Case of Adaptive Introgression on Small, Isolated Islands of the Taiwan-Philippine Transition Zone? Mol Ecol 2025; 34:e17630. [PMID: 39688644 DOI: 10.1111/mec.17630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 10/17/2024] [Accepted: 11/04/2024] [Indexed: 12/18/2024]
Abstract
Adaptive introgression involves the acquisition of advantageous genetic variants through hybridisation, which are subsequently favoured by natural selection due to their association with beneficial traits. Here, we analysed speciation patterns of the kleptoparasitic spider, Argyrodes lanyuensis, through genomic analyses and tested for possible genetic evidence of adaptive introgression at the Taiwan-Philippines transition zone. Our study used highly polymorphic SNPs to demonstrate that speciation occurred when the Hualien (on Taiwan Island + Green Island) and Orchid Island + Philippine lineages separated during the early to mid-Pleistocene. The best colonisation model suggested by approximate Bayesian computation and random forests and biogeographical analyses supported an inference of a bottleneck during speciation, an interpretation reinforced by observation of lower FST values and reduced genetic diversity of the Orchid Island + Philippines lineage. We also found the highest support for the occurrence of introgression on the youngest island (Green Island) of the Taiwan-Philippines transition zone based on the ABBA-BABA test. Our study highlights the inference of two noteworthy species (Hualien + Green Island and Orchid Island + Philippines) based on our species delimitation tests, with gene flow between Green Island and Orchid Island that indicates introgression. The potential adaptive alleles in Green Island population, which are under balancing selection, provide initial evidence of possible rare case of adaptive introgression. This could represent an evolutionary response to a newly formed niche (or novel geographical context) lying between the tropical climate of the Philippines and the subtropical climate of Hualien, Taiwan.
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Affiliation(s)
- Mae A Responte
- Graduate Institute Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Biological Sciences and Environmental Studies, College of Science and Mathematics, University of the Philippines Mindanao, Davao City, Philippines
| | - Cheng-Yu Wu
- Department of Biomedical Science and Environmental Biology, College of Life Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Noraya U Elias
- Department of Biomedical Science and Environmental Biology, College of Life Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- Mindanao State University-Malabang Community High School, Malabang, Lanao del Sur, Philippines
| | - Rafe M Brown
- Department of Ecology and Evolutionary Biology, Biodiversity Institute, University of Kansas, Lawrence, Kansas, USA
| | - Chia-Yen Dai
- Graduate Institute Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yong-Chao Su
- Department of Biomedical Science and Environmental Biology, College of Life Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
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3
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Han S, de Filippo C, Parra G, Meneu JR, Laurent R, Frandsen P, Hvilsom C, Gronau I, Marques-Bonet T, Kuhlwilm M, Andrés AM. Deep genetic substructure within bonobos. Curr Biol 2024; 34:5341-5348.e3. [PMID: 39413789 DOI: 10.1016/j.cub.2024.09.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/07/2024] [Accepted: 09/17/2024] [Indexed: 10/18/2024]
Abstract
Establishing the genetic and geographic structure of populations is fundamental, both to understand their evolutionary past and preserve their future. Nevertheless, the patterns of genetic population structure are unknown for most endangered species. This is the case for bonobos (Pan paniscus), which, together with chimpanzees (Pan troglodytes), are humans' closest living relatives. Chimpanzees live across equatorial Africa and are classified into four subspecies,1 with some genetic population substructure even within subspecies. Conversely, bonobos live exclusively in the Democratic Republic of Congo and are considered a homogeneous group with low genetic diversity,2 despite some population structure inferred from mtDNA. Nevertheless, mtDNA aside, their genetic structure remains unknown, hampering our understanding of the species and conservation efforts. Mapping bonobo genetic diversity in space is, however, challenging because, being endangered, only non-invasive sampling is possible for wild individuals. Here, we jointly analyze the exomes and mtDNA from 20 wild-born bonobos, the whole genomes of 10 captive bonobos, and the mtDNA of 136 wild individuals. We identify three genetically distinct bonobo groups of inferred Central, Western, and Far-Western geographic origin within the bonobo range. We estimate the split time between the central and western populations to be ∼145,000 years ago and genetic differentiation to be in the order of that of the closest chimpanzee subspecies. Furthermore, our estimated long-term Ne for Far-West (∼3,000) is among the lowest estimated for any great ape lineage. Our results highlight the need to attend to the bonobo substructure, both in terms of research and conservation.
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Affiliation(s)
- Sojung Han
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas, Universitat Pompeu Fabra, 08003 Barcelona, Spain; Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria; Human Evolution and Archaeological Sciences (HEAS), University of Vienna, 1030 Vienna, Austria.
| | - Cesare de Filippo
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria; Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Genís Parra
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany; Centre Nacional d'Anàlisi Genòmica (CNAG), Baldiri Reixac 4, 08028 Barcelona, Spain
| | - Juan Ramon Meneu
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Romain Laurent
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Peter Frandsen
- Conservation, Copenhagen Zoo, Roskildevej 38, 2000, Frederiksberg, Denmark
| | - Christina Hvilsom
- Conservation, Copenhagen Zoo, Roskildevej 38, 2000, Frederiksberg, Denmark
| | - Ilan Gronau
- The Efi Arazi School of Computer Science, Reichman University, 4610101 Herzliya, Israel
| | - Tomas Marques-Bonet
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas, Universitat Pompeu Fabra, 08003 Barcelona, Spain; CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain; Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, 08193 Barcelona, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010, Barcelona, Spain
| | - Martin Kuhlwilm
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas, Universitat Pompeu Fabra, 08003 Barcelona, Spain; Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria; Human Evolution and Archaeological Sciences (HEAS), University of Vienna, 1030 Vienna, Austria
| | - Aida M Andrés
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany; UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK.
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4
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Soni V, Terbot JW, Versoza CJ, Pfeifer SP, Jensen JD. A whole-genome scan for evidence of recent positive and balancing selection in aye-ayes ( Daubentonia madagascariensis) utilizing a well-fit evolutionary baseline model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.08.622667. [PMID: 39605496 PMCID: PMC11601216 DOI: 10.1101/2024.11.08.622667] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
The aye-aye (Daubentonia madagascariensis) is one of the 25 most endangered primate species in the world, maintaining amongst the lowest genetic diversity of any primate measured to date. Characterizing patterns of genetic variation within aye-aye populations, and the relative influences of neutral and selective processes in shaping that variation, is thus important for future conservation efforts. In this study, we performed the first whole-genome scans for recent positive and balancing selection in the species, utilizing high-coverage population genomic data from newly sequenced individuals. We generated null thresholds for our genomic scans by creating an evolutionarily appropriate baseline model that incorporates the demographic history of this aye-aye population, and identified a small number of candidate genes. Most notably, a suite of genes involved in olfaction - a key trait in these nocturnal primates - were identified as experiencing long-term balancing selection. We also conducted analyses to quantify the expected statistical power to detect positive and balancing selection in this population using site frequency spectrum-based inference methods, once accounting for the potentially confounding contributions of population history, recombination and mutation rate variation, and purifying and background selection. This work, presenting the first high-quality, genome-wide polymorphism data across the functional regions of the aye-aye genome, thus provides important insights into the landscape of episodic selective forces in this highly endangered species.
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Affiliation(s)
- Vivak Soni
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - John W. Terbot
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Cyril J. Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Susanne P. Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Jeffrey D. Jensen
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
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5
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Garrison E, Guarracino A, Heumos S, Villani F, Bao Z, Tattini L, Hagmann J, Vorbrugg S, Marco-Sola S, Kubica C, Ashbrook DG, Thorell K, Rusholme-Pilcher RL, Liti G, Rudbeck E, Golicz AA, Nahnsen S, Yang Z, Mwaniki MN, Nobrega FL, Wu Y, Chen H, de Ligt J, Sudmant PH, Huang S, Weigel D, Soranzo N, Colonna V, Williams RW, Prins P. Building pangenome graphs. Nat Methods 2024; 21:2008-2012. [PMID: 39433878 DOI: 10.1038/s41592-024-02430-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 08/26/2024] [Indexed: 10/23/2024]
Abstract
Pangenome graphs can represent all variation between multiple reference genomes, but current approaches to build them exclude complex sequences or are based upon a single reference. In response, we developed the PanGenome Graph Builder, a pipeline for constructing pangenome graphs without bias or exclusion. The PanGenome Graph Builder uses all-to-all alignments to build a variation graph in which we can identify variation, measure conservation, detect recombination events and infer phylogenetic relationships.
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Affiliation(s)
- Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Human Technopole, Milan, Italy
| | - Simon Heumos
- Quantitative Biology Center (QBiC) Tübingen, University of Tübingen, Tübingen, Germany
- Biomedical Data Science, Dept. of Computer Science, University of Tübingen, Tübingen, Germany
- M3 Research Center, University Hospital Tübingen, Tübingen, Germany
| | - Flavia Villani
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Zhigui Bao
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Lorenzo Tattini
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
- Data Science Department, EURECOM, Biot, France
| | | | - Sebastian Vorbrugg
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
| | - Santiago Marco-Sola
- Computer Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
- Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Christian Kubica
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
| | - David G Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Kaisa Thorell
- Chemistry and Molecular Biology, Faculty of Science, University of Gothenburg, Gothenburg, Sweden
| | | | - Gianni Liti
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
| | - Emilio Rudbeck
- Clinical Genomics Gothenburg, Bioinformatics and Data Centre, University of Gothenburg, Gothenburg, Sweden
| | - Agnieszka A Golicz
- Department of Plant Breeding, Justus Liebig University Giessen, Giessen, Germany
| | - Sven Nahnsen
- Quantitative Biology Center (QBiC) Tübingen, University of Tübingen, Tübingen, Germany
- Biomedical Data Science, Dept. of Computer Science, University of Tübingen, Tübingen, Germany
- M3 Research Center, University Hospital Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard-Karls University of Tübingen, Tübingen, Germany
| | - Zuyu Yang
- The Institute of Environmental Science and Research, Wellington, New Zealand
| | | | - Franklin L Nobrega
- School of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
| | - Yi Wu
- School of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
| | - Hao Chen
- Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Joep de Ligt
- Hartwig Medical Foundation, Amsterdam, the Netherlands
| | - Peter H Sudmant
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Sanwen Huang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Detlef Weigel
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University Tübingen, Tübingen, Germany
| | - Nicole Soranzo
- Human Technopole, Milan, Italy
- Wellcome Sanger Institute, Genome Campus, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, Cambridge Biomedical Campus, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Vincenza Colonna
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Institute of Genetics and Biophysics, National Research Council, Naples, Italy
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
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6
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Yoo D, Rhie A, Hebbar P, Antonacci F, Logsdon GA, Solar SJ, Antipov D, Pickett BD, Safonova Y, Montinaro F, Luo Y, Malukiewicz J, Storer JM, Lin J, Sequeira AN, Mangan RJ, Hickey G, Anez GM, Balachandran P, Bankevich A, Beck CR, Biddanda A, Borchers M, Bouffard GG, Brannan E, Brooks SY, Carbone L, Carrel L, Chan AP, Crawford J, Diekhans M, Engelbrecht E, Feschotte C, Formenti G, Garcia GH, de Gennaro L, Gilbert D, Green RE, Guarracino A, Gupta I, Haddad D, Han J, Harris RS, Hartley GA, Harvey WT, Hiller M, Hoekzema K, Houck ML, Jeong H, Kamali K, Kellis M, Kille B, Lee C, Lee Y, Lees W, Lewis AP, Li Q, Loftus M, Loh YHE, Loucks H, Ma J, Mao Y, Martinez JFI, Masterson P, McCoy RC, McGrath B, McKinney S, Meyer BS, Miga KH, Mohanty SK, Munson KM, Pal K, Pennell M, Pevzner PA, Porubsky D, Potapova T, Ringeling FR, Roha JL, Ryder OA, Sacco S, Saha S, Sasaki T, Schatz MC, Schork NJ, Shanks C, Smeds L, Son DR, Steiner C, Sweeten AP, Tassia MG, Thibaud-Nissen F, Torres-González E, Trivedi M, Wei W, Wertz J, Yang M, Zhang P, Zhang S, Zhang Y, Zhang Z, et alYoo D, Rhie A, Hebbar P, Antonacci F, Logsdon GA, Solar SJ, Antipov D, Pickett BD, Safonova Y, Montinaro F, Luo Y, Malukiewicz J, Storer JM, Lin J, Sequeira AN, Mangan RJ, Hickey G, Anez GM, Balachandran P, Bankevich A, Beck CR, Biddanda A, Borchers M, Bouffard GG, Brannan E, Brooks SY, Carbone L, Carrel L, Chan AP, Crawford J, Diekhans M, Engelbrecht E, Feschotte C, Formenti G, Garcia GH, de Gennaro L, Gilbert D, Green RE, Guarracino A, Gupta I, Haddad D, Han J, Harris RS, Hartley GA, Harvey WT, Hiller M, Hoekzema K, Houck ML, Jeong H, Kamali K, Kellis M, Kille B, Lee C, Lee Y, Lees W, Lewis AP, Li Q, Loftus M, Loh YHE, Loucks H, Ma J, Mao Y, Martinez JFI, Masterson P, McCoy RC, McGrath B, McKinney S, Meyer BS, Miga KH, Mohanty SK, Munson KM, Pal K, Pennell M, Pevzner PA, Porubsky D, Potapova T, Ringeling FR, Roha JL, Ryder OA, Sacco S, Saha S, Sasaki T, Schatz MC, Schork NJ, Shanks C, Smeds L, Son DR, Steiner C, Sweeten AP, Tassia MG, Thibaud-Nissen F, Torres-González E, Trivedi M, Wei W, Wertz J, Yang M, Zhang P, Zhang S, Zhang Y, Zhang Z, Zhao SA, Zhu Y, Jarvis ED, Gerton JL, Rivas-González I, Paten B, Szpiech ZA, Huber CD, Lenz TL, Konkel MK, Yi SV, Canzar S, Watson CT, Sudmant PH, Molloy E, Garrison E, Lowe CB, Ventura M, O’Neill RJ, Koren S, Makova KD, Phillippy AM, Eichler EE. Complete sequencing of ape genomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.605654. [PMID: 39131277 PMCID: PMC11312596 DOI: 10.1101/2024.07.31.605654] [Show More Authors] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
We present haplotype-resolved reference genomes and comparative analyses of six ape species, namely: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan, and siamang. We achieve chromosome-level contiguity with unparalleled sequence accuracy (<1 error in 500,000 base pairs), completely sequencing 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, providing more in-depth evolutionary insights. Comparative analyses, including human, allow us to investigate the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference. This includes newly minted gene families within lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes, and subterminal heterochromatin. This resource should serve as a definitive baseline for all future evolutionary studies of humans and our closest living ape relatives.
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Affiliation(s)
- DongAhn Yoo
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arang Rhie
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Prajna Hebbar
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Francesca Antonacci
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
| | - Glennis A. Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - Steven J. Solar
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Dmitry Antipov
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Brandon D. Pickett
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yana Safonova
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA 16801, USA
| | - Francesco Montinaro
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yanting Luo
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Joanna Malukiewicz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, 20146 Hamburg, Germany
| | - Jessica M. Storer
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
| | - Jiadong Lin
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Abigail N. Sequeira
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Riley J. Mangan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Genetics Training Program, Harvard Medical School, Boston, MA 02115, USA
| | - Glenn Hickey
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | | | | | - Anton Bankevich
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA 16801, USA
| | - Christine R. Beck
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Arjun Biddanda
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Matthew Borchers
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Gerard G. Bouffard
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Emry Brannan
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Shelise Y. Brooks
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lucia Carbone
- Department of Medicine, KCVI, Oregon Health Sciences University, Portland, OR, USA
- Division of Genetics, Oregon National Primate Research Center, Beaverton, OR, USA
| | - Laura Carrel
- PSU Medical School, Penn State University School of Medicine, Hershey, PA, USA
| | - Agnes P. Chan
- The Translational Genomics Research Institute, a part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Juyun Crawford
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Eric Engelbrecht
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Cedric Feschotte
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Giulio Formenti
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY 10021, USA
| | - Gage H. Garcia
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Luciana de Gennaro
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
| | - David Gilbert
- San Diego Biomedical Research Institute, San Diego, CA, USA
| | | | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Ishaan Gupta
- Department of Computer Science and Engineering, University of California San Diego, CA, USA
| | - Diana Haddad
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Junmin Han
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Robert S. Harris
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Gabrielle A. Hartley
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
| | - William T. Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Michael Hiller
- LOEWE Centre for Translational Biodiversity Genomics, Senckenberg Research Institute, Goethe University, Frankfurt, Germany
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Marlys L. Houck
- San Diego Zoo Wildlife Alliance, Escondido, CA, 92027-7000, USA
| | - Hyeonsoo Jeong
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Kaivan Kamali
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Bryce Kille
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Chul Lee
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Youngho Lee
- Laboratory of bioinformatics and population genetics, Interdisciplinary program in bioinformatics, Seoul National University, Republic of Korea
| | - William Lees
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Alexandra P. Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Qiuhui Li
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mark Loftus
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Yong Hwee Eddie Loh
- Neuroscience Research Institute, University of California, Santa Barbara, CA, USA
| | - Hailey Loucks
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, PA, USA
| | - Yafei Mao
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- Center for Genomic Research, International Institutes of Medicine, Fourth Affiliated Hospital, Zhejiang University, Yiwu, Zhejiang, China
- Shanghai Jiao Tong University Chongqing Research Institute, Chongqing, China
| | - Juan F. I. Martinez
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA 16801, USA
| | - Patrick Masterson
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Rajiv C. McCoy
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Barbara McGrath
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Sean McKinney
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Britta S. Meyer
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, 20146 Hamburg, Germany
| | - Karen H. Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Saswat K. Mohanty
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Katherine M. Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Karol Pal
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Pavel A. Pevzner
- Department of Computer Science and Engineering, University of California San Diego, CA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Tamara Potapova
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Francisca R. Ringeling
- Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany
| | - Joana L. Roha
- Department of Integrative Biology, University of California, Berkeley, Berkeley, USA
| | - Oliver A. Ryder
- San Diego Zoo Wildlife Alliance, Escondido, CA, 92027-7000, USA
| | - Samuel Sacco
- University of California Santa Cruz, Santa Cruz, CA, USA
| | - Swati Saha
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Takayo Sasaki
- San Diego Biomedical Research Institute, San Diego, CA, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Nicholas J. Schork
- The Translational Genomics Research Institute, a part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Cole Shanks
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Linnéa Smeds
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Dongmin R. Son
- Department of Ecology, Evolution and Marine Biology, Neuroscience Research Institute, University of California, Santa Barbara, CA, USA
| | - Cynthia Steiner
- San Diego Zoo Wildlife Alliance, Escondido, CA, 92027-7000, USA
| | - Alexander P. Sweeten
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael G. Tassia
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Mihir Trivedi
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Wenjie Wei
- School of Life Sciences, Westlake University, Hangzhou 310024, China
- National Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 430070, Wuhan, China
| | - Julie Wertz
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Muyu Yang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, PA, USA
| | - Panpan Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Shilong Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, PA, USA
| | - Zhenmiao Zhang
- Department of Computer Science and Engineering, University of California San Diego, CA, USA
| | - Sarah A. Zhao
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yixin Zhu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Erich D. Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | - Iker Rivas-González
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Zachary A. Szpiech
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Christian D. Huber
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Tobias L. Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, 20146 Hamburg, Germany
| | - Miriam K. Konkel
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Soojin V. Yi
- Department of Ecology, Evolution and Marine Biology, Department of Molecular, Cellular and Developmental Biology, Neuroscience Research Institute, University of California, Santa Barbara, CA, USA
| | - Stefan Canzar
- Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Peter H. Sudmant
- Department of Integrative Biology, University of California, Berkeley, Berkeley, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, USA
| | - Erin Molloy
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Craig B. Lowe
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Mario Ventura
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
| | - Rachel J. O’Neill
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
- Departments of Molecular and Cell Biology, UConn Storrs, CT, USA
| | - Sergey Koren
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kateryna D. Makova
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Adam M. Phillippy
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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7
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Garrison E, Guarracino A, Heumos S, Villani F, Bao Z, Tattini L, Hagmann J, Vorbrugg S, Marco-Sola S, Kubica C, Ashbrook DG, Thorell K, Rusholme-Pilcher RL, Liti G, Rudbeck E, Nahnsen S, Yang Z, Mwaniki MN, Nobrega FL, Wu Y, Chen H, de Ligt J, Sudmant PH, Soranzo N, Colonna V, Williams RW, Prins P. Building pangenome graphs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.05.535718. [PMID: 37066137 PMCID: PMC10104075 DOI: 10.1101/2023.04.05.535718] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Pangenome graphs can represent all variation between multiple reference genomes, but current approaches to build them exclude complex sequences or are based upon a single reference. In response, we developed the PanGenome Graph Builder (PGGB), a pipeline for constructing pangenome graphs without bias or exclusion. PGGB uses all-to-all alignments to build a variation graph in which we can identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships.
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8
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Ostridge HJ, Fontsere C, Lizano E, Soto DC, Schmidt JM, Saxena V, Alvarez-Estape M, Barratt CD, Gratton P, Bocksberger G, Lester JD, Dieguez P, Agbor A, Angedakin S, Assumang AK, Bailey E, Barubiyo D, Bessone M, Brazzola G, Chancellor R, Cohen H, Coupland C, Danquah E, Deschner T, Dotras L, Dupain J, Egbe VE, Granjon AC, Head J, Hedwig D, Hermans V, Hernandez-Aguilar RA, Jeffery KJ, Jones S, Junker J, Kadam P, Kaiser M, Kalan AK, Kambere M, Kienast I, Kujirakwinja D, Langergraber KE, Lapuente J, Larson B, Laudisoit A, Lee KC, Llana M, Maretti G, Martín R, Meier A, Morgan D, Neil E, Nicholl S, Nixon S, Normand E, Orbell C, Ormsby LJ, Orume R, Pacheco L, Preece J, Regnaut S, Robbins MM, Rundus A, Sanz C, Sciaky L, Sommer V, Stewart FA, Tagg N, Tédonzong LR, van Schijndel J, Vendras E, Wessling EG, Willie J, Wittig RM, Yuh YG, Yurkiw K, Vigilant L, Piel A, Boesch C, Kühl HS, Dennis MY, Marques-Bonet T, Arandjelovic M, Andrés AM. Local genetic adaptation to habitat in wild chimpanzees. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.601734. [PMID: 39026872 PMCID: PMC11257515 DOI: 10.1101/2024.07.09.601734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
How populations adapt to their environment is a fundamental question in biology. Yet we know surprisingly little about this process, especially for endangered species such as non-human great apes. Chimpanzees, our closest living relatives, are particularly interesting because they inhabit diverse habitats, from rainforest to woodland-savannah. Whether genetic adaptation facilitates such habitat diversity remains unknown, despite having wide implications for evolutionary biology and conservation. Using 828 newly generated exomes from wild chimpanzees, we find evidence of fine-scale genetic adaptation to habitat. Notably, adaptation to malaria in forest chimpanzees is mediated by the same genes underlying adaptation to malaria in humans. This work demonstrates the power of non-invasive samples to reveal genetic adaptations in endangered populations and highlights the importance of adaptive genetic diversity for chimpanzees.
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Affiliation(s)
- Harrison J Ostridge
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Claudia Fontsere
- Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Esther Lizano
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Daniela C Soto
- University of California, Davis, Genome Center, MIND Institute, Department of Biochemistry & Molecular Medicine, One Shields Drive, Davis, CA, 95616, USA
| | - Joshua M Schmidt
- Flinders Health and Medical Research Institute (FHMRI), Department of Ophthalmology, Flinders University Sturt Rd, Bedford Park South Australia 5042 Australia
| | - Vrishti Saxena
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Marina Alvarez-Estape
- University of California, Davis, Genome Center, MIND Institute, Department of Biochemistry & Molecular Medicine, One Shields Drive, Davis, CA, 95616, USA
| | - Christopher D Barratt
- Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, the Netherlands
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Paolo Gratton
- University of Rome "Tor Vergata" Department of Biology Via Cracovia, 1, Roma, Italia
| | - Gaëlle Bocksberger
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage, 60325 Frankfurt am Main, Germany
| | - Jack D Lester
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Paula Dieguez
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Anthony Agbor
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Samuel Angedakin
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Alfred Kwabena Assumang
- Department of Wildlife and Range Management, Faculty of Renewable Natural Resources, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Emma Bailey
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Donatienne Barubiyo
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Mattia Bessone
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- University of Konstanz, Centre for the Advanced Study of Collective Behaviour, Universitätsstraße 10, 78464, Konstanz, Germany
| | - Gregory Brazzola
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Rebecca Chancellor
- West Chester University, Depts of Anthropology & Sociology and Psychology, West Chester, PA, 19382 USA
| | - Heather Cohen
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Charlotte Coupland
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Emmanuel Danquah
- Department of Wildlife and Range Management, Faculty of Renewable Natural Resources, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Tobias Deschner
- Institute of Cognitive Science, University of Osnabrück, Artilleriestrasse 34, 49076 Osnabrück, Germany
| | - Laia Dotras
- Jane Goodall Institute Spain and Senegal, Dindefelo Biological Station, Dindefelo, Kedougou, Senegal
- Department of Social Psychology and Quantitative Psychology, Serra Hunter Programme, University of Barcelona, Barcelona, Spain
| | - Jef Dupain
- Antwerp Zoo Foundation, RZSA, Kon.Astridplein 26, 2018 Antwerp, Belgium
| | - Villard Ebot Egbe
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Anne-Céline Granjon
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Josephine Head
- The Biodiversity Consultancy, 3E Kings Parade, Cambridge, CB2 1SJ, UK
| | - Daniela Hedwig
- Elephant Listening Project, K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, 159 Sapsucker Woods Road, Ithaca, NY 14850, USA
| | - Veerle Hermans
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
| | - R Adriana Hernandez-Aguilar
- Jane Goodall Institute Spain and Senegal, Dindefelo Biological Station, Dindefelo, Kedougou, Senegal
- Department of Social Psychology and Quantitative Psychology, Serra Hunter Programme, University of Barcelona, Barcelona, Spain
| | - Kathryn J Jeffery
- School of Natural Sciences, University of Stirling, UK
- Agence National des Parcs Nationaux (ANPN) Batterie 4, BP20379, Libreville, Gabon
| | - Sorrel Jones
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Jessica Junker
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Parag Kadam
- Greater Mahale Ecosystem Research and Conservation Project
| | - Michael Kaiser
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Ammie K Kalan
- Department of Anthropology, University of Victoria, 3800 Finnerty Rd, Victoria, BC V8P 5C2, Canada
| | - Mbangi Kambere
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Ivonne Kienast
- Department of Natural Resources and the Environment, Cornell University, Ithaca, NY 14850, USA
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
| | - Deo Kujirakwinja
- Wildlife Conservation Society (WCS), 2300 Southern Boulevard. Bronx, New York 10460, USA
| | - Kevin E Langergraber
- School of Human Evolution and Social Change, Institute of Human Origins, Arizona State University, 777 East University Drive, Tempe, AZ 85287 Arizona State University, PO Box 872402, Tempe, AZ 85287-2402 USA
- Institute of Human Origins, Arizona State University, 900 Cady Mall, Tempe, AZ 85287 Arizona State University, PO Box 872402, Tempe, AZ 85287-2402 USA
| | - Juan Lapuente
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | | | | | - Kevin C Lee
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
| | - Manuel Llana
- Jane Goodall Institute Spain and Senegal, Dindefelo Biological Station, Dindefelo, Kedougou, Senegal
| | - Giovanna Maretti
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Rumen Martín
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Amelia Meier
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- Hawai'i Insititute of Marine Biology, University of Hawai'i at Manoa, 46-007 Lilipuna Place, Kaneohe, HI, 96744, USA
| | - David Morgan
- Lester E. Fisher Center for the Study and Conservation of Apes, Lincoln Park Zoo, 2001 North Clark Street, Chicago, Illinois 60614 USA
| | - Emily Neil
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Sonia Nicholl
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Stuart Nixon
- North of England Zoological Society, Chester Zoo, Upton by Chester, CH2 1LH, United Kingdom
| | | | - Christopher Orbell
- Panthera, 8 W 40TH ST, New York, NY 10018, USA
- School of Natural Sciences, University of Stirling, UK
| | - Lucy Jayne Ormsby
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Robinson Orume
- Korup Rainforest Conservation Society, c/o Korup National Park, P.O. Box 36 Mundemba, South West Region, Cameroon
| | - Liliana Pacheco
- Save the Dogs and Other Animals, DJ 223 Km 3, 905200 Cernavoda CT, Romania
| | - Jodie Preece
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | | | - Martha M Robbins
- Max Planck Institute for Evolutionary Anthropology, Department of Primate Behavior and Evolution, Deutscher Platz 6, 04103 Leipzig
| | - Aaron Rundus
- West Chester University, Depts of Anthropology & Sociology and Psychology, West Chester, PA, 19382 USA
| | - Crickette Sanz
- Washington University in Saint Louis, Department of Anthropology, One Brookings Drive, St. Louis, MO 63130, USA
- Congo Program, Wildlife Conservation Society, 151 Avenue Charles de Gaulle, Brazzaville, Republic of Congo
| | - Lilah Sciaky
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Volker Sommer
- University College London, Department of Anthropology, 14 Taviton Street, London WC1H 0BW, UK
| | - Fiona A Stewart
- University College London, Department of Anthropology, 14 Taviton Street, London WC1H 0BW, UK
- Department of Human Origins, Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Nikki Tagg
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
- Born Free Foundation, Floor 2 Frazer House, 14 Carfax, Horsham, RH12 1ER, UK
| | - Luc Roscelin Tédonzong
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
| | - Joost van Schijndel
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Elleni Vendras
- Frankfurt Zoological Society, Bernhard-Grzimek-Allee 1, 60316 Frankfurt, Germany
| | - Erin G Wessling
- Johann-Friedrich-Blumenbach Institute for Zoology and Anthropology, Georg-August-University Göttingen,Göttingen, Germany
- German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Jacob Willie
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
- Terrestrial Ecology Unit (TEREC), Department of Biology, Ghent University (UGent), K.L. Ledeganckstraat 35, 9000 Ghent, Belgium
| | - Roman M Wittig
- Ape Social Mind Lab, Institute for Cognitive Sciences Marc Jeannerod, CNRS UMR 5229 CNRS, 67 bd Pinel, 69675 Bron CEDEX, France
- Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques, BP 1301, Abidjan 01, CI
| | - Yisa Ginath Yuh
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Kyle Yurkiw
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Linda Vigilant
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Alex Piel
- University College London, Department of Anthropology, 14 Taviton Street, London WC1H 0BW, UK
| | | | - Hjalmar S Kühl
- Senckenberg Museum for Natural History Görlitz, Senckenberg - Member of the Leibniz Association Am Museum 1, 02826 Görlitz, Germany
- International Institute Zittau, Technische Universität Dresden, Markt 23, 02763 Zittau, Germany
| | - Megan Y Dennis
- University of California, Davis, Genome Center, MIND Institute, Department of Biochemistry & Molecular Medicine, One Shields Drive, Davis, CA, 95616, 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 (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Mimi Arandjelovic
- Max Planck Institute for Evolutionary Anthropology, Department of Primate Behavior and Evolution, Deutscher Platz 6, 04103 Leipzig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103
| | - Aida M Andrés
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
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9
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Braichenko S, Borges R, Kosiol C. Polymorphism-Aware Models in RevBayes: Species Trees, Disentangling Balancing Selection, and GC-Biased Gene Conversion. Mol Biol Evol 2024; 41:msae138. [PMID: 38980178 PMCID: PMC11272101 DOI: 10.1093/molbev/msae138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 04/19/2024] [Accepted: 07/06/2024] [Indexed: 07/10/2024] Open
Abstract
The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the Polymorphism-aware phylogenetic Models (PoMos) framework rooted in the Moran model, we introduce a PoMoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, and directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analyzing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in polymorphism-aware phylogenetic models' ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited color dimorphism in Drosophila erecta.
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Affiliation(s)
- Svitlana Braichenko
- Centre for Biological Diversity, School of Biology, University of St Andrews, Fife KY16 9TH, UK
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Rui Borges
- Institut für Populationsgenetik, Vetmeduni Vienna, Wien 1210, Austria
| | - Carolin Kosiol
- Centre for Biological Diversity, School of Biology, University of St Andrews, Fife KY16 9TH, UK
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10
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Marsh JI, Johri P. Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection. Mol Biol Evol 2024; 41:msae118. [PMID: 38874402 PMCID: PMC11245712 DOI: 10.1093/molbev/msae118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/05/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ancestral recombination graph (ARG)-based approaches to demographic inference in typical empirical analyses are susceptible to misinference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying, and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, although it could cause misinference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion, which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid misinference due to the linked effects of selection.
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Affiliation(s)
- Jacob I Marsh
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Parul Johri
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- Integrative Program for Biological and Genome Sciences, University of North Carolina, Chapel Hill, NC 27599, USA
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11
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Brand CM, Kuang S, Gilbertson EN, McArthur E, Pollard KS, Webster TH, Capra JA. Sequence-based machine learning reveals 3D genome differences between bonobos and chimpanzees. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.564272. [PMID: 37961120 PMCID: PMC10634871 DOI: 10.1101/2023.10.26.564272] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Phenotypic divergence between closely related species, including bonobos and chimpanzees (genus Pan), is largely driven by variation in gene regulation. The 3D structure of the genome mediates gene expression; however, genome folding differences in Pan are not well understood. Here, we apply machine learning to predict genome-wide 3D genome contact maps from DNA sequence for 56 bonobos and chimpanzees, encompassing all five extant lineages. We use a pairwise approach to estimate 3D divergence between individuals from the resulting contact maps in 4,420 1 Mb genomic windows. While most pairs were similar, ∼17% were predicted to be substantially divergent in genome folding. The most dissimilar maps were largely driven by single individuals with rare variants that produce unique 3D genome folding in a region. We also identified 89 genomic windows where bonobo and chimpanzee contact maps substantially diverged, including several windows harboring genes associated with traits implicated in Pan phenotypic divergence. We used in silico mutagenesis to identify 51 3D-modifying variants in these bonobo-chimpanzee divergent windows, finding that 34 or 66.67% induce genome folding changes via CTCF binding motif disruption. Our results reveal 3D genome variation at the population-level and identify genomic regions where changes in 3D folding may contribute to phenotypic differences in our closest living relatives.
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Affiliation(s)
- Colin M Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
| | - Shuzhen Kuang
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA
| | - Erin N Gilbertson
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA
| | - Evonne McArthur
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN
| | - Katherine S Pollard
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA
- Chan Zuckerberg Biohub, San Francisco, CA
| | | | - John A Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA
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12
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Zhao S, Chi L, Chen H. CEGA: a method for inferring natural selection by comparative population genomic analysis across species. Genome Biol 2023; 24:219. [PMID: 37789379 PMCID: PMC10548728 DOI: 10.1186/s13059-023-03068-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/20/2023] [Indexed: 10/05/2023] Open
Abstract
We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis.
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Affiliation(s)
- Shilei Zhao
- CAS Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- School of Future Technology, College of Life Sciences and Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lianjiang Chi
- CAS Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
| | - Hua Chen
- CAS Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- China National Center for Bioinformation, Beijing, 100101, China.
- School of Future Technology, College of Life Sciences and Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
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13
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Rivas-González I, Rousselle M, Li F, Zhou L, Dutheil JY, Munch K, Shao Y, Wu D, Schierup MH, Zhang G. Pervasive incomplete lineage sorting illuminates speciation and selection in primates. Science 2023; 380:eabn4409. [PMID: 37262154 DOI: 10.1126/science.abn4409] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/19/2023] [Indexed: 06/03/2023]
Abstract
Incomplete lineage sorting (ILS) causes the phylogeny of some parts of the genome to differ from the species tree. In this work, we investigate the frequencies and determinants of ILS in 29 major ancestral nodes across the entire primate phylogeny. We find up to 64% of the genome affected by ILS at individual nodes. We exploit ILS to reconstruct speciation times and ancestral population sizes. Estimated speciation times are much more recent than genomic divergence times and are in good agreement with the fossil record. We show extensive variation of ILS along the genome, mainly driven by recombination but also by the distance to genes, highlighting a major impact of selection on variation along the genome. In many nodes, ILS is reduced more on the X chromosome compared with autosomes than expected under neutrality, which suggests higher impacts of natural selection on the X chromosome. Finally, we show an excess of ILS in genes with immune functions and a deficit of ILS in housekeeping genes. The extensive ILS in primates discovered in this study provides insights into the speciation times, ancestral population sizes, and patterns of natural selection that shape primate evolution.
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Affiliation(s)
- Iker Rivas-González
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| | | | - Fang Li
- BGI-Research, BGI-Wuhan, Wuhan 430074, China
- Institute of Animal Sex and Development, ZhejiangWanli University, Ningbo 315104, China
- BGI-Research, BGI-Shenzhen, Shenzhen 518083, China
| | - Long Zhou
- Evolutionary & Organismal Biology Research Center, Zhejiang University School of Medicine, Hangzhou 310058, China
- Women's Hospital, School of Medicine, Zhejiang University, Shangcheng District, Hangzhou 310006, China
| | - Julien Y Dutheil
- Max Planck Institute for Evolutionary Biology, Plön, Germany
- Institute of Evolution Sciences of Montpellier (ISEM), CNRS, University of Montpellier, IRD, EPHE, 34095 Montpellier, France
| | - Kasper Munch
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Yong Shao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Dongdong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic and Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Mikkel H Schierup
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Guojie Zhang
- Evolutionary & Organismal Biology Research Center, Zhejiang University School of Medicine, Hangzhou 310058, China
- Women's Hospital, School of Medicine, Zhejiang University, Shangcheng District, Hangzhou 310006, China
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 311121, China
- Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark
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14
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Current advances in primate genomics: novel approaches for understanding evolution and disease. Nat Rev Genet 2023; 24:314-331. [PMID: 36599936 DOI: 10.1038/s41576-022-00554-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/07/2022] [Indexed: 01/05/2023]
Abstract
Primate genomics holds the key to understanding fundamental aspects of human evolution and disease. However, genetic diversity and functional genomics data sets are currently available for only a few of the more than 500 extant primate species. Concerted efforts are under way to characterize primate genomes, genetic polymorphism and divergence, and functional landscapes across the primate phylogeny. The resulting data sets will enable the connection of genotypes to phenotypes and provide new insight into aspects of the genetics of primate traits, including human diseases. In this Review, we describe the existing genome assemblies as well as genetic variation and functional genomic data sets. We highlight some of the challenges with sample acquisition. Finally, we explore how technological advances in single-cell functional genomics and induced pluripotent stem cell-derived organoids will facilitate our understanding of the molecular foundations of primate biology.
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15
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Gómez M, Casado A, de Diego M, Pastor JF, Potau JM. Anatomical and molecular analyses of the deltoid muscle in chimpanzees (Pan troglodytes) and modern humans (Homo sapiens): Similarities and differences due to the uses of the upper extremity. Am J Primatol 2022; 84:e23390. [PMID: 35561001 DOI: 10.1002/ajp.23390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/22/2022] [Accepted: 04/25/2022] [Indexed: 11/12/2022]
Abstract
In the deltoid muscles of Pan troglodytes and Homo sapiens, we have analyzed the muscle architecture and the expression of the myosin heavy chain (MHC) isoforms. Our aim was to identify differences between the two species that could be related to their different uses of the upper limb. The deltoid muscle of six adult Pan troglodytes and six adult Homo sapiens were dissected. The muscle fascicle length (MFL) and the physiological cross-sectional area (PCSA) of each muscle were calculated in absolute and normalized values. The expression pattern of the MHC-I, MHC-IIa and MHC-IIx isoforms was analyzed in the same muscles by real-time polymerase chain reaction. Only the acromial deltoid (AD) presented significant architectural differences between the two species, with higher MFL values in humans and higher PCSA values in chimpanzees. No significant differences in the expression pattern of the MHC isoforms were identified. The higher PCSA values in the AD of Pan troglodytes indicate a greater capacity of force generation in chimpanzees than in humans, which may be related to a greater use of the upper limb in locomotion, specifically in arboreal locomotion like vertical climbing. The functional differences between chimpanzees and humans in the deltoid muscle are more related to muscle architecture than to a differential expression of MHC isoforms.
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Affiliation(s)
- Mónica Gómez
- Department of Surgery and Surgical Specializations, Unit of Human Anatomy and Embryology, University of Barcelona, Barcelona, Spain
| | - Aroa Casado
- Department of Surgery and Surgical Specializations, Unit of Human Anatomy and Embryology, University of Barcelona, Barcelona, Spain.,Institut d'Arqueologia de la Universitat de Barcelona (IAUB), Faculty of Geography and History, University of Barcelona, Barcelona, Spain
| | - Marina de Diego
- Department of Surgery and Surgical Specializations, Unit of Human Anatomy and Embryology, University of Barcelona, Barcelona, Spain
| | | | - Josep Maria Potau
- Department of Surgery and Surgical Specializations, Unit of Human Anatomy and Embryology, University of Barcelona, Barcelona, Spain.,Institut d'Arqueologia de la Universitat de Barcelona (IAUB), Faculty of Geography and History, University of Barcelona, Barcelona, Spain
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16
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Pires E, Carvalho LDC, Shimada I, McCullagh J. Human Blood and Bird Egg Proteins Identified in Red Paint Covering a 1000-Year-Old Gold Mask from Peru. J Proteome Res 2021; 20:5212-5217. [PMID: 34582218 DOI: 10.1021/acs.jproteome.1c00472] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We analyzed a red paint sample from the surface of a gold mask excavated from a Middle Sicán elite tomb in Peru. The mask covered the face of the principal male and dates from ca. 1000 AD, a period when many painted precious metal objects were produced. The paint's inorganic pigment was identified more than 30 years ago as cinnabar (a mercuric sulfide scarlet-red to brown-red mineral), but the identity of the effective organic binder remained a mystery. Fourier transform infrared (FTIR) analysis of the sample indicated a proteinaceous composition, and no lipids were recovered from an N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) derivatized extract of the sample analyzed by gas chromatography-mass spectrometry (GC-MS). Proteomics analysis by nanoLC-MS/MS identified unique peptides in the sample, which were matched to human blood and bird egg proteins via Uniprot database searches. These included immunoglobulin heavy chain, immunoglobulin G, serum albumin, and ovomucoid. Cinnabar-based paints were typically used in the context of social elites and ritually important items. The presence of human blood would support previous ideas that red cinnabar paint may represent "life force" intended to support "rebirth". As the red paint sample came from the first scientifically excavated Sicán gold mask, the results suggest a method to authenticate similar unprovenanced masks now in private and museum collections. Proteomics data set identifier https://doi.org/10.5287/bodleian:1ajYbBgQP.
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Affiliation(s)
- Elisabete Pires
- Mass Spectrometry Research Facility, Department of Chemistry, University of Oxford, Oxford OX1 3TA, U.K
| | | | - Izumi Shimada
- Department of Anthropology, Southern Illinois University, Carbondale, Illinois 62901-6899, United States
| | - James McCullagh
- Mass Spectrometry Research Facility, Department of Chemistry, University of Oxford, Oxford OX1 3TA, U.K
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17
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Yousaf A, Liu J, Ye S, Chen H. Current Progress in Evolutionary Comparative Genomics of Great Apes. Front Genet 2021; 12:657468. [PMID: 34456962 PMCID: PMC8385753 DOI: 10.3389/fgene.2021.657468] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/15/2021] [Indexed: 12/04/2022] Open
Abstract
The availability of high-quality genome sequences of great ape species provides unprecedented opportunities for genomic analyses. Herein, we reviewed the recent progress in evolutionary comparative genomic studies of the existing great ape species, including human, chimpanzee, bonobo, gorilla, and orangutan. We elaborate discovery on evolutionary history, natural selection, structural variations, and new genes of these species, which is informative for understanding the origin of human-specific phenotypes.
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Affiliation(s)
- Aisha Yousaf
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,China National Center for Bioinformation, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Junfeng Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,China National Center for Bioinformation, Beijing, China
| | - Sicheng Ye
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,China National Center for Bioinformation, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Hua Chen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,China National Center for Bioinformation, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
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18
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Moreira A, Croze M, Delehelle F, Cussat-Blanc S, Luga H, Mollereau C, Balaresque P. Hearing Sensitivity of Primates: Recurrent and Episodic Positive Selection in Hair Cells and Stereocilia Protein-Coding Genes. Genome Biol Evol 2021; 13:6302699. [PMID: 34137817 PMCID: PMC8358225 DOI: 10.1093/gbe/evab133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2021] [Indexed: 12/29/2022] Open
Abstract
The large spectrum of hearing sensitivity observed in primates results from the impact of environmental and behavioral pressures to optimize sound perception and localization. Although evidence of positive selection in auditory genes has been detected in mammals including in Hominoids, selection has never been investigated in other primates. We analyzed 123 genes highly expressed in the inner ear of 27 primate species and tested to what extent positive selection may have shaped these genes in the order Primates tree. We combined both site and branch-site tests to obtain a comprehensive picture of the positively selected genes (PSGs) involved in hearing sensitivity, and drew a detailed description of the most affected branches in the tree. We chose a conservative approach, and thus focused on confounding factors potentially affecting PSG signals (alignment, GC-biased gene conversion, duplications, heterogeneous sequencing qualities). Using site tests, we showed that around 12% of these genes are PSGs, an α selection value consistent with average human genome estimates (10-15%). Using branch-site tests, we showed that the primate tree is heterogeneously affected by positive selection, with the black snub-nosed monkey, the bushbaby, and the orangutan, being the most impacted branches. A large proportion of these genes is inclined to shape hair cells and stereocilia, which are involved in the mechanotransduction process, known to influence frequency perception. Adaptive selection, and more specifically recurrent adaptive evolution, could have acted in parallel on a set of genes (ADGRV1, USH2A, PCDH15, PTPRQ, and ATP8A2) involved in stereocilia growth and the whole complex of bundle links connecting them, in species across different habitats, including high altitude and nocturnal environments.
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Affiliation(s)
- Andreia Moreira
- Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Faculté de Médecine Purpan, CNRS UMR5288, Université de Toulouse, Université Toulouse III Paul Sabatier, France.,Institut de Recherche en Informatique de Toulouse (IRIT), CNRS UMR5505, Université Toulouse III Paul Sabatier, France
| | - Myriam Croze
- Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Faculté de Médecine Purpan, CNRS UMR5288, Université de Toulouse, Université Toulouse III Paul Sabatier, France
| | - Franklin Delehelle
- Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Faculté de Médecine Purpan, CNRS UMR5288, Université de Toulouse, Université Toulouse III Paul Sabatier, France.,Institut de Recherche en Informatique de Toulouse (IRIT), CNRS UMR5505, Université Toulouse III Paul Sabatier, France
| | - Sylvain Cussat-Blanc
- Institut de Recherche en Informatique de Toulouse (IRIT), CNRS UMR5505, Université Toulouse III Paul Sabatier, France
| | - Hervé Luga
- Institut de Recherche en Informatique de Toulouse (IRIT), CNRS UMR5505, Université Toulouse III Paul Sabatier, France
| | - Catherine Mollereau
- Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Faculté de Médecine Purpan, CNRS UMR5288, Université de Toulouse, Université Toulouse III Paul Sabatier, France
| | - Patricia Balaresque
- Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Faculté de Médecine Purpan, CNRS UMR5288, Université de Toulouse, Université Toulouse III Paul Sabatier, France
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19
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Roycroft E, Achmadi A, Callahan CM, Esselstyn JA, Good JM, Moussalli A, Rowe KC. Molecular Evolution of Ecological Specialisation: Genomic Insights from the Diversification of Murine Rodents. Genome Biol Evol 2021; 13:6275684. [PMID: 33988699 PMCID: PMC8258016 DOI: 10.1093/gbe/evab103] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2021] [Indexed: 12/15/2022] Open
Abstract
Adaptive radiations are characterized by the diversification and ecological differentiation of species, and replicated cases of this process provide natural experiments for understanding the repeatability and pace of molecular evolution. During adaptive radiation, genes related to ecological specialization may be subject to recurrent positive directional selection. However, it is not clear to what extent patterns of lineage-specific ecological specialization (including phenotypic convergence) are correlated with shared signatures of molecular evolution. To test this, we sequenced whole exomes from a phylogenetically dispersed sample of 38 murine rodent species, a group characterized by multiple, nested adaptive radiations comprising extensive ecological and phenotypic diversity. We found that genes associated with immunity, reproduction, diet, digestion, and taste have been subject to pervasive positive selection during the diversification of murine rodents. We also found a significant correlation between genome-wide positive selection and dietary specialization, with a higher proportion of positively selected codon sites in derived dietary forms (i.e., carnivores and herbivores) than in ancestral forms (i.e., omnivores). Despite striking convergent evolution of skull morphology and dentition in two distantly related worm-eating specialists, we did not detect more genes with shared signatures of positive or relaxed selection than in a nonconvergent species comparison. Although a small number of the genes we detected can be incidentally linked to craniofacial morphology or diet, protein-coding regions are unlikely to be the primary genetic basis of this complex convergent phenotype. Our results suggest a link between positive selection and derived ecological phenotypes, and highlight specific genes and general functional categories that may have played an integral role in the extensive and rapid diversification of murine rodents.
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Affiliation(s)
- Emily Roycroft
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.,Sciences Department, Museums Victoria, Melbourne, Victoria, Australia.,Division of Ecology and Evolution, Research School of Biology, The Australian National University, Acton, Australian Capital Territory, Australia
| | - Anang Achmadi
- Museum Zoologicum Bogoriense, Research Center for Biology, Cibinong, Jawa Barat, Indonesia
| | - Colin M Callahan
- Division of Biological Sciences, University of Montana, Missoula, Montana, USA
| | - Jacob A Esselstyn
- Museum of Natural Science, Louisiana State University, Baton Rouge, Louisiana, USA.,Department of Biological Sciences, Louisiana State University, Baton Rouge, Los Angeles, USA
| | - Jeffrey M Good
- Division of Biological Sciences, University of Montana, Missoula, Montana, USA.,Wildlife Biology Program, University of Montana, Missoula, Montana, USA
| | - Adnan Moussalli
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.,Sciences Department, Museums Victoria, Melbourne, Victoria, Australia
| | - Kevin C Rowe
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.,Sciences Department, Museums Victoria, Melbourne, Victoria, Australia
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20
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Nye J, Mondal M, Bertranpetit J, Laayouni H. A fully integrated machine learning scan of selection in the chimpanzee genome. NAR Genom Bioinform 2021; 2:lqaa061. [PMID: 33575612 PMCID: PMC7671310 DOI: 10.1093/nargab/lqaa061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 06/11/2020] [Accepted: 07/31/2020] [Indexed: 11/13/2022] Open
Abstract
After diverging, each chimpanzee subspecies has been the target of unique selective pressures. Here, we employ a machine learning approach to classify regions as under positive selection or neutrality genome-wide. The regions determined to be under selection reflect the unique demographic and adaptive history of each subspecies. The results indicate that effective population size is important for determining the proportion of the genome under positive selection. The chimpanzee subspecies share signals of selection in genes associated with immunity and gene regulation. With these results, we have created a selection map for each population that can be displayed in a genome browser (www.hsb.upf.edu/chimp_browser). This study is the first to use a detailed demographic history and machine learning to map selection genome-wide in chimpanzee. The chimpanzee selection map will improve our understanding of the impact of selection on closely related subspecies and will empower future studies of chimpanzee.
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Affiliation(s)
- Jessica Nye
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Mayukh Mondal
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Jaume Bertranpetit
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Hafid Laayouni
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
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21
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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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22
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Kovalaskas S, Rilling JK, Lindo J. Comparative analyses of the Pan lineage reveal selection on gene pathways associated with diet and sociality in bonobos. GENES BRAIN AND BEHAVIOR 2020; 20:e12715. [PMID: 33200560 DOI: 10.1111/gbb.12715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/10/2020] [Accepted: 11/16/2020] [Indexed: 01/15/2023]
Abstract
Chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) diverged into distinct species approximately 1.7 million years ago when the ancestors of modern-day bonobo populations were separated by the Congo River. This geographic boundary separates the two species today and the associated ecological factors, including resource distribution and feeding competition, have likely shaped the divergent social behavior of both species. The most striking behavioral differences pertain to between group interactions in which chimpanzees behave aggressively towards unfamiliar conspecifics, while bonobos display remarkable tolerance. Several hypotheses attempt to explain how different patterns of social behavior have come to exist in the two species, some with specific genetic predictions, likening the evolution of bonobos to a process of domestication. Here, we utilize 73 ape genomes and apply linkage haplotype homozygosity and structure informed allele frequency differentiation methods to identify positively selected regions in bonobos since their split from a common pan ancestor to better understand the environment and processes that resulted in the behavioral differences observed today. We find novel evidence of selection in genetic regions that aid in starch digestion (AMY2) along with support for two genetic predictions related to self-domestication processes hypothesized to have occurred in the bonobo. We also find evidence for selection on neuroendocrine pathways associated with social behavior including the oxytocin, serotonin, and gonadotropin releasing hormone pathways.
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Affiliation(s)
- Sarah Kovalaskas
- Department of Anthropology, Emory University, Atlanta, Georgia, USA
| | - James K Rilling
- Department of Anthropology, Emory University, Atlanta, Georgia, USA.,Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia, USA.,Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA.,Center for Translational Social Neuroscience, Emory University, Atlanta, Georgia, USA.,Silvio O. Conte Center for Oxytocin and Social Cognition, Emory University, Atlanta, Georgia, USA
| | - John Lindo
- Department of Anthropology, Emory University, Atlanta, Georgia, USA
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23
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Bolter DR, Cameron N. Utilizing auxology to understand ontogeny of extinct hominins: A case study on Homo naledi. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2020; 173:368-380. [PMID: 32537780 DOI: 10.1002/ajpa.24088] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 02/03/2020] [Accepted: 05/10/2020] [Indexed: 02/05/2023]
Abstract
The methods used to study human growth and development (auxology) have not previously been applied within the setting of hominin maturation (ontogeny). Ontogeny is defined here as the pattern of biological change into an adult form, both at the individual and species level. The hominin fossil record has a lack of recovered immature materials, due to such factors as taphonomic processes that destroy pre-adults; the fragility of immature compared to adult bone; and the lower mortality rates of juveniles compared to adults. The recent discovery of pre-adult hominin skeletal material from a single, homogeneous Homo naledi species from the Rising Star cave system in South Africa provides the opportunity for a broader application of auxology methods and thus the need to understand their use in a modern context. Human auxology studies benefit from a robust database, across multiple populations, and with longitudinal studies in order to assess the patterns and variations in typical growth, development and life history stages. Here, we review the approach, vocabulary, and methods of these human studies, investigate commonalities in data with the fossil record, and then advance the reconstruction of ontogeny for the extinct hominin species H. naledi. To this end, we apply an auxology model into the paleontological context to broadly predict H. naledi birthweight of the offspring at 2.06 kg with a range (±1 SD) of 1.89 to 2.24 kg, with a length at birth 45.5 cm. We estimate a H. naledi juvenile partial skeleton DH7 to be a height of 111-125 cm at death.
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Affiliation(s)
- Debra R Bolter
- Department of Anthropology, Modesto Junior College, Modesto, California, USA
- Evolutionary Studies Institute and Centre for Excellence in PalaeoSciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Noel Cameron
- Evolutionary Studies Institute and Centre for Excellence in PalaeoSciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
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24
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Vangenot C, Nunes JM, Doxiadis GM, Poloni ES, Bontrop RE, de Groot NG, Sanchez-Mazas A. Similar patterns of genetic diversity and linkage disequilibrium in Western chimpanzees (Pan troglodytes verus) and humans indicate highly conserved mechanisms of MHC molecular evolution. BMC Evol Biol 2020; 20:119. [PMID: 32933484 PMCID: PMC7491122 DOI: 10.1186/s12862-020-01669-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 08/06/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Many species are threatened with extinction as their population sizes decrease with changing environments or face novel pathogenic threats. A reduction of genetic diversity at major histocompatibility complex (MHC) genes may have dramatic effects on populations' survival, as these genes play a key role in adaptive immunity. This might be the case for chimpanzees, the MHC genes of which reveal signatures of an ancient selective sweep likely due to a viral epidemic that reduced their population size a few million years ago. To better assess how this past event affected MHC variation in chimpanzees compared to humans, we analysed several indexes of genetic diversity and linkage disequilibrium across seven MHC genes on four cohorts of chimpanzees and we compared them to those estimated at orthologous HLA genes in a large set of human populations. RESULTS Interestingly, the analyses uncovered similar patterns of both molecular diversity and linkage disequilibrium across the seven MHC genes in chimpanzees and humans. Indeed, in both species the greatest allelic richness and heterozygosity were found at loci A, B, C and DRB1, the greatest nucleotide diversity at loci DRB1, DQA1 and DQB1, and both significant global linkage disequilibrium and the greatest proportions of haplotypes in linkage disequilibrium were observed at pairs DQA1 ~ DQB1, DQA1 ~ DRB1, DQB1 ~ DRB1 and B ~ C. Our results also showed that, despite some differences among loci, the levels of genetic diversity and linkage disequilibrium observed in contemporary chimpanzees were globally similar to those estimated in small isolated human populations, in contrast to significant differences compared to large populations. CONCLUSIONS We conclude, first, that highly conserved mechanisms shaped the diversity of orthologous MHC genes in chimpanzees and humans. Furthermore, our findings support the hypothesis that an ancient demographic decline affecting the chimpanzee populations - like that ascribed to a viral epidemic - exerted a substantial effect on the molecular diversity of their MHC genes, albeit not more pronounced than that experienced by HLA genes in human populations that underwent rapid genetic drift during humans' peopling history. We thus propose a model where chimpanzees' MHC genes regenerated molecular variation through recombination/gene conversion and/or balancing selection after the selective sweep.
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Affiliation(s)
- Christelle Vangenot
- Laboratory of Anthropology, Genetics and Peopling History, Department of Genetics and Evolution, Anthropology Unit, University of Geneva, Geneva, Switzerland
| | - José Manuel Nunes
- Laboratory of Anthropology, Genetics and Peopling History, Department of Genetics and Evolution, Anthropology Unit, University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
| | - Gaby M Doxiadis
- Comparative Genetics and Refinement, Biomedical Primate Research Centre, 2288, GJ, Rijswijk, The Netherlands
| | - Estella S Poloni
- Laboratory of Anthropology, Genetics and Peopling History, Department of Genetics and Evolution, Anthropology Unit, University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
| | - Ronald E Bontrop
- Comparative Genetics and Refinement, Biomedical Primate Research Centre, 2288, GJ, Rijswijk, The Netherlands
| | - Natasja G de Groot
- Comparative Genetics and Refinement, Biomedical Primate Research Centre, 2288, GJ, Rijswijk, The Netherlands
| | - Alicia Sanchez-Mazas
- Laboratory of Anthropology, Genetics and Peopling History, Department of Genetics and Evolution, Anthropology Unit, University of Geneva, Geneva, Switzerland. .,Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland.
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25
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Miller SE, Sheehan MJ, Reeve HK. Coevolution of cognitive abilities and identity signals in individual recognition systems. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190467. [PMID: 32420843 PMCID: PMC7331018 DOI: 10.1098/rstb.2019.0467] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2020] [Indexed: 12/24/2022] Open
Abstract
Social interactions are mediated by recognition systems, meaning that the cognitive abilities or phenotypic diversity that facilitate recognition may be common targets of social selection. Recognition occurs when a receiver compares the phenotypes produced by a sender with a template. Coevolution between sender and receiver traits has been empirically reported in multiple species and sensory modalities, though the dynamics and relative exaggeration of traits from senders versus receivers have received little attention. Here, we present a coevolutionary dynamic model that examines the conditions under which senders and receivers should invest effort in facilitating individual recognition. The model predicts coevolution of sender and receiver traits, with the equilibrium investment dependent on the relative costs of signal production versus cognition. In order for recognition to evolve, initial sender and receiver trait values must be above a threshold, suggesting that recognition requires some degree of pre-existing diversity and cognitive abilities. The analysis of selection gradients demonstrates that the strength of selection on sender signals and receiver cognition is strongest when the trait values are furthest from the optima. The model provides new insights into the expected strength and dynamics of selection during the origin and elaboration of individual recognition, an important feature of social cognition in many taxa. This article is part of the theme issue 'Signal detection theory in recognition systems: from evolving models to experimental tests'.
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Affiliation(s)
| | - Michael J. Sheehan
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - H. Kern Reeve
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
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26
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Naval-Sanchez M, McWilliam S, Evans B, Yáñez JM, Houston RD, Kijas JW. Changed Patterns of Genomic Variation Following Recent Domestication: Selection Sweeps in Farmed Atlantic Salmon. Front Genet 2020; 11:264. [PMID: 32318091 PMCID: PMC7147387 DOI: 10.3389/fgene.2020.00264] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/05/2020] [Indexed: 12/30/2022] Open
Abstract
The introduction of wild Atlantic salmon into captivity, and their subsequent artificial selection for production traits, has caused phenotypic differences between domesticated fish and their wild counterparts. Identification of regions of the genome underling these changes offers the promise of characterizing the early biological consequences of domestication. In the current study, we sequenced a population of farmed European Atlantic salmon and compared the observed patterns of SNP variation to those found in conspecific wild populations. This identified 139 genomic regions that contained significantly elevated SNP homozygosity in farmed fish when compared to their wild counterparts. The most extreme was adjacent to versican, a gene involved in control of neural crest cell migration. To control for false positive signals, a second and independent dataset of farmed and wild European Atlantic salmon was assessed using the same methodology. A total of 81 outlier regions detected in the first dataset showed significantly reduced homozygosity within the second one, strongly suggesting the genomic regions identified are enriched for true selection sweeps. Examination of the associated genes identified a number previously characterized as targets of selection in other domestic species and that have roles in development, behavior and olfactory system. These include arcvf, sema6, errb4, id2-like, and 6n1-like genes. Finally, we searched for evidence of parallel sweeps using a farmed population of North American origin. This failed to detect a convincing overlap to the putative sweeps present in European populations, suggesting the factors that drive patterns of variation under domestication and early artificial selection were largely independent. This is the first analysis on domestication of aquaculture species exploiting whole-genome sequence data and resulted in the identification of sweeps common to multiple independent populations of farmed European Atlantic salmon.
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Affiliation(s)
| | | | - Bradley Evans
- Salmon Enterprises of Tasmania Pty. Limited, Wayatinah, TAS, Australia
| | - José M Yáñez
- Faculty of Veterinary and Animal Sciences, University of Chile, Santiago, Chile
| | - Ross D Houston
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, United Kingdom
| | - James W Kijas
- CSIRO Agriculture and Food, Brisbane, QLD, Australia
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27
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Identification of Structural Variation in Chimpanzees Using Optical Mapping and Nanopore Sequencing. Genes (Basel) 2020; 11:genes11030276. [PMID: 32143403 PMCID: PMC7140787 DOI: 10.3390/genes11030276] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 02/29/2020] [Accepted: 02/29/2020] [Indexed: 12/19/2022] Open
Abstract
Recent efforts to comprehensively characterize great ape genetic diversity using short-read sequencing and single-nucleotide variants have led to important discoveries related to selection within species, demographic history, and lineage-specific traits. Structural variants (SVs), including deletions and inversions, comprise a larger proportion of genetic differences between and within species, making them an important yet understudied source of trait divergence. Here, we used a combination of long-read and -range sequencing approaches to characterize the structural variant landscape of two additional Pan troglodytes verus individuals, one of whom carries 13% admixture from Pan troglodytes troglodytes. We performed optical mapping of both individuals followed by nanopore sequencing of one individual. Filtering for larger variants (>10 kbp) and combined with genotyping of SVs using short-read data from the Great Ape Genome Project, we identified 425 deletions and 59 inversions, of which 88 and 36, respectively, were novel. Compared with gene expression in humans, we found a significant enrichment of chimpanzee genes with differential expression in lymphoblastoid cell lines and induced pluripotent stem cells, both within deletions and near inversion breakpoints. We examined chromatin-conformation maps from human and chimpanzee using these same cell types and observed alterations in genomic interactions at SV breakpoints. Finally, we focused on 56 genes impacted by SVs in >90% of chimpanzees and absent in humans and gorillas, which may contribute to chimpanzee-specific features. Sequencing a greater set of individuals from diverse subspecies will be critical to establish the complete landscape of genetic variation in chimpanzees.
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28
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Mugal CF, Kutschera VE, Botero-Castro F, Wolf JBW, Kaj I. Polymorphism Data Assist Estimation of the Nonsynonymous over Synonymous Fixation Rate Ratio ω for Closely Related Species. Mol Biol Evol 2020; 37:260-279. [PMID: 31504782 PMCID: PMC6984366 DOI: 10.1093/molbev/msz203] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The ratio of nonsynonymous over synonymous sequence divergence, dN/dS, is a widely used estimate of the nonsynonymous over synonymous fixation rate ratio ω, which measures the extent to which natural selection modulates protein sequence evolution. Its computation is based on a phylogenetic approach and computes sequence divergence of protein-coding DNA between species, traditionally using a single representative DNA sequence per species. This approach ignores the presence of polymorphisms and relies on the indirect assumption that new mutations fix instantaneously, an assumption which is generally violated and reasonable only for distantly related species. The violation of the underlying assumption leads to a time-dependence of sequence divergence, and biased estimates of ω in particular for closely related species, where the contribution of ancestral and lineage-specific polymorphisms to sequence divergence is substantial. We here use a time-dependent Poisson random field model to derive an analytical expression of dN/dS as a function of divergence time and sample size. We then extend our framework to the estimation of the proportion of adaptive protein evolution α. This mathematical treatment enables us to show that the joint usage of polymorphism and divergence data can assist the inference of selection for closely related species. Moreover, our analytical results provide the basis for a protocol for the estimation of ω and α for closely related species. We illustrate the performance of this protocol by studying a population data set of four corvid species, which involves the estimation of ω and α at different time-scales and for several choices of sample sizes.
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Affiliation(s)
- Carina F Mugal
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
| | - Verena E Kutschera
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden.,Science for Life Laboratory, Stockholm University, Stockholm, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Fidel Botero-Castro
- Division of Evolutionary Biology, Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Jochen B W Wolf
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden.,Division of Evolutionary Biology, Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Ingemar Kaj
- Department of Mathematics, Uppsala University, Uppsala, Sweden
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29
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Castellano D, Eyre-Walker A, Munch K. Impact of Mutation Rate and Selection at Linked Sites on DNA Variation across the Genomes of Humans and Other Homininae. Genome Biol Evol 2020; 12:3550-3561. [PMID: 31596481 PMCID: PMC6944223 DOI: 10.1093/gbe/evz215] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2019] [Indexed: 12/23/2022] Open
Abstract
DNA diversity varies across the genome of many species. Variation in diversity across a genome might arise from regional variation in the mutation rate, variation in the intensity and mode of natural selection, and regional variation in the recombination rate. We show that both noncoding and nonsynonymous diversity are positively correlated to a measure of the mutation rate and the recombination rate and negatively correlated to the density of conserved sequences in 50 kb windows across the genomes of humans and nonhuman homininae. Interestingly, we find that although noncoding diversity is equally affected by these three genomic variables, nonsynonymous diversity is mostly dominated by the density of conserved sequences. The positive correlation between diversity and our measure of the mutation rate seems to be largely a direct consequence of regions with higher mutation rates having more diversity. However, the positive correlation with recombination rate and the negative correlation with the density of conserved sequences suggest that selection at linked sites also affect levels of diversity. This is supported by the observation that the ratio of the number of nonsynonymous to noncoding polymorphisms is negatively correlated to a measure of the effective population size across the genome. We show these patterns persist even when we restrict our analysis to GC-conservative mutations, demonstrating that the patterns are not driven by GC biased gene conversion. In conclusion, our comparative analyses describe how recombination rate, gene density, and mutation rate interact to produce the patterns of DNA diversity that we observe along the hominine genomes.
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Affiliation(s)
- David Castellano
- Bioinformatics Research Centre, Aarhus University, Denmark
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, Barcelona, Spain
| | - Adam Eyre-Walker
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Kasper Munch
- Bioinformatics Research Centre, Aarhus University, Denmark
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30
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Schmidt JM, de Manuel M, Marques-Bonet T, Castellano S, Andrés AM. The impact of genetic adaptation on chimpanzee subspecies differentiation. PLoS Genet 2019; 15:e1008485. [PMID: 31765391 PMCID: PMC6901233 DOI: 10.1371/journal.pgen.1008485] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 12/09/2019] [Accepted: 10/17/2019] [Indexed: 12/25/2022] Open
Abstract
Chimpanzees, humans' closest relatives, are in danger of extinction. Aside from direct human impacts such as hunting and habitat destruction, a key threat is transmissible disease. As humans continue to encroach upon their habitats, which shrink in size and grow in density, the risk of inter-population and cross-species viral transmission increases, a point dramatically made in the reverse with the global HIV/AIDS pandemic. Inhabiting central Africa, the four subspecies of chimpanzees differ in demographic history and geographical range, and are likely differentially adapted to their particular local environments. To quantitatively explore genetic adaptation, we investigated the genic enrichment for SNPs highly differentiated between chimpanzee subspecies. Previous analyses of such patterns in human populations exhibited limited evidence of adaptation. In contrast, chimpanzees show evidence of recent positive selection, with differences among subspecies. Specifically, we observe strong evidence of recent selection in eastern chimpanzees, with highly differentiated SNPs being uniquely enriched in genic sites in a way that is expected under recent adaptation but not under neutral evolution or background selection. These sites are enriched for genes involved in immune responses to pathogens, and for genes inferred to differentiate the immune response to infection by simian immunodeficiency virus (SIV) in natural vs. non-natural host species. Conversely, central chimpanzees exhibit an enrichment of signatures of positive selection only at cytokine receptors, due to selective sweeps in CCR3, CCR9 and CXCR6 -paralogs of CCR5 and CXCR4, the two major receptors utilized by HIV to enter human cells. Thus, our results suggest that positive selection has contributed to the genetic and phenotypic differentiation of chimpanzee subspecies, and that viruses likely play a predominate role in this differentiation, with SIV being a likely selective agent. Interestingly, our results suggest that SIV has elicited distinctive adaptive responses in these two chimpanzee subspecies.
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MESH Headings
- Adaptation, Physiological/genetics
- Adaptation, Physiological/immunology
- Animals
- Demography
- Genetic Drift
- Genetic Speciation
- HIV/genetics
- HIV/immunology
- HIV/pathogenicity
- Humans
- Immunity, Innate/genetics
- Pan troglodytes/genetics
- Pan troglodytes/immunology
- Pan troglodytes/virology
- Polymorphism, Single Nucleotide/genetics
- Receptors, CCR/genetics
- Receptors, CCR3/genetics
- Receptors, CCR5/genetics
- Receptors, CXCR4/genetics
- Receptors, CXCR6/immunology
- Selection, Genetic/genetics
- Simian Immunodeficiency Virus/genetics
- Simian Immunodeficiency Virus/immunology
- Simian Immunodeficiency Virus/pathogenicity
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Affiliation(s)
- Joshua M. Schmidt
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
- Max Planck Institute for Evolutionary Anthropology, Department of Evolutionary Genetics, Leipzig, Germany
- * E-mail: (JMS); (AMA)
| | - Marc de Manuel
- Institut de Biologia Evolutiva (Consejo Superior de Investigaciones Científicas–Universitat Pompeu Fabra), Barcelona, Spain
| | - Tomas Marques-Bonet
- Institut de Biologia Evolutiva (Consejo Superior de Investigaciones Científicas–Universitat Pompeu Fabra), Barcelona, Spain
- National Centre for Genomic Analysis–Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Sergi Castellano
- Max Planck Institute for Evolutionary Anthropology, Department of Evolutionary Genetics, Leipzig, Germany
- Genetics and Genomic Medicine Programme, Great Ormond Street Institute of Child Health, University College London (UCL), London, United Kingdom
- UCL Genomics, London, United Kingdom
| | - Aida M. Andrés
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
- Max Planck Institute for Evolutionary Anthropology, Department of Evolutionary Genetics, Leipzig, Germany
- * E-mail: (JMS); (AMA)
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31
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Castellano D, Macià MC, Tataru P, Bataillon T, Munch K. Comparison of the Full Distribution of Fitness Effects of New Amino Acid Mutations Across Great Apes. Genetics 2019; 213:953-966. [PMID: 31488516 PMCID: PMC6827385 DOI: 10.1534/genetics.119.302494] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 08/29/2019] [Indexed: 12/31/2022] Open
Abstract
The distribution of fitness effects (DFE) is central to many questions in evolutionary biology. However, little is known about the differences in DFE between closely related species. We use >9000 coding genes orthologous one-to-one across great apes, gibbons, and macaques to assess the stability of the DFE across great apes. We use the unfolded site frequency spectrum of polymorphic mutations (n = 8 haploid chromosomes per population) to estimate the DFE. We find that the shape of the deleterious DFE is strikingly similar across great apes. We confirm that effective population size (Ne ) is a strong predictor of the strength of negative selection, consistent with the nearly neutral theory. However, we also find that the strength of negative selection varies more than expected given the differences in Ne between species. Across species, mean fitness effects of new deleterious mutations covaries with Ne , consistent with positive epistasis among deleterious mutations. We find that the strength of negative selection for the smallest populations, bonobos and western chimpanzees, is higher than expected given their Ne This may result from a more efficient purging of strongly deleterious recessive variants in these populations. Forward simulations confirm that these findings are not artifacts of the way we are inferring Ne and DFE parameters. All findings are replicated using only GC-conservative mutations, thereby confirming that GC-biased gene conversion is not affecting our conclusions.
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Affiliation(s)
- David Castellano
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Moisès Coll Macià
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Paula Tataru
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Thomas Bataillon
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Kasper Munch
- Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark
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32
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Kanton S, Boyle MJ, He Z, Santel M, Weigert A, Sanchís-Calleja F, Guijarro P, Sidow L, Fleck JS, Han D, Qian Z, Heide M, Huttner WB, Khaitovich P, Pääbo S, Treutlein B, Camp JG. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature 2019; 574:418-422. [DOI: 10.1038/s41586-019-1654-9] [Citation(s) in RCA: 312] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 09/06/2019] [Indexed: 12/22/2022]
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33
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Coronado-Zamora M, Salvador-Martínez I, Castellano D, Barbadilla A, Salazar-Ciudad I. Adaptation and Conservation throughout the Drosophila melanogaster Life-Cycle. Genome Biol Evol 2019; 11:1463-1482. [PMID: 31028390 PMCID: PMC6535812 DOI: 10.1093/gbe/evz086] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2019] [Indexed: 01/09/2023] Open
Abstract
Previous studies of the evolution of genes expressed at different life-cycle stages of Drosophila melanogaster have not been able to disentangle adaptive from nonadaptive substitutions when using nonsynonymous sites. Here, we overcome this limitation by combining whole-genome polymorphism data from D. melanogaster and divergence data between D. melanogaster and Drosophila yakuba. For the set of genes expressed at different life-cycle stages of D. melanogaster, as reported in modENCODE, we estimate the ratio of substitutions relative to polymorphism between nonsynonymous and synonymous sites (α) and then α is discomposed into the ratio of adaptive (ωa) and nonadaptive (ωna) substitutions to synonymous substitutions. We find that the genes expressed in mid- and late-embryonic development are the most conserved, whereas those expressed in early development and postembryonic stages are the least conserved. Importantly, we found that low conservation in early development is due to high rates of nonadaptive substitutions (high ωna), whereas in postembryonic stages it is due, instead, to high rates of adaptive substitutions (high ωa). By using estimates of different genomic features (codon bias, average intron length, exon number, recombination rate, among others), we also find that genes expressed in mid- and late-embryonic development show the most complex architecture: they are larger, have more exons, more transcripts, and longer introns. In addition, these genes are broadly expressed among all stages. We suggest that all these genomic features are related to the conservation of mid- and late-embryonic development. Globally, our study supports the hourglass pattern of conservation and adaptation over the life-cycle.
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Affiliation(s)
- Marta Coronado-Zamora
- Genomics, Bioinformatics and Evolution, Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Irepan Salvador-Martínez
- Evo-Devo Helsinki Community, Centre of Excellence in Experimental and Computational Developmental Biology, Institute of Biotechnology, University of Helsinki, Finland.,Department of Genetics, Evolution and Environment, University College London, United Kingdom
| | | | - Antonio Barbadilla
- Genomics, Bioinformatics and Evolution, Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Isaac Salazar-Ciudad
- Genomics, Bioinformatics and Evolution, Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.,Evo-Devo Helsinki Community, Centre of Excellence in Experimental and Computational Developmental Biology, Institute of Biotechnology, University of Helsinki, Finland.,Centre de Recerca Matemàtica, Cerdanyola del Vallès, Spain
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34
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Turan ZG, Parvizi P, Dönertaş HM, Tung J, Khaitovich P, Somel M. Molecular footprint of Medawar's mutation accumulation process in mammalian aging. Aging Cell 2019; 18:e12965. [PMID: 31062469 PMCID: PMC6612638 DOI: 10.1111/acel.12965] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 02/14/2019] [Accepted: 03/28/2019] [Indexed: 12/20/2022] Open
Abstract
Medawar's mutation accumulation hypothesis explains aging by the declining force of natural selection with age: Slightly deleterious germline mutations expressed in old age can drift to fixation and thereby lead to aging‐related phenotypes. Although widely cited, empirical evidence for this hypothesis has remained limited. Here, we test one of its predictions that genes relatively highly expressed in old adults should be under weaker purifying selection than genes relatively highly expressed in young adults. Combining 66 transcriptome datasets (including 16 tissues from five mammalian species) with sequence conservation estimates across mammals, here we report that the overall conservation level of expressed genes is lower at old age compared to young adulthood. This age‐related decrease in transcriptome conservation (ADICT) is systematically observed in diverse mammalian tissues, including the brain, liver, lung, and artery, but not in others, most notably in the muscle and heart. Where observed, ADICT is driven partly by poorly conserved genes being up‐regulated during aging. In general, the more often a gene is found up‐regulated with age among tissues and species, the lower its evolutionary conservation. Poorly conserved and up‐regulated genes have overlapping functional properties that include responses to age‐associated tissue damage, such as apoptosis and inflammation. Meanwhile, these genes do not appear to be under positive selection. Hence, genes contributing to old age phenotypes are found to harbor an excess of slightly deleterious alleles, at least in certain tissues. This supports the notion that genetic drift shapes aging in multicellular organisms, consistent with Medawar's mutation accumulation hypothesis.
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Affiliation(s)
- Zeliha Gözde Turan
- Department of Biological Sciences Middle East Technical University Ankara Turkey
| | - Poorya Parvizi
- Department of Biological Sciences Middle East Technical University Ankara Turkey
- Usher Institute of Population Health Sciences and Informatics University of Edinburgh Edinburgh UK
| | - Handan Melike Dönertaş
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL‐EBI Wellcome Trust Genome Campus Cambridge UK
| | - Jenny Tung
- Department of Evolutionary Anthropology Duke University Durham North Carolina
- Department of Biology Duke University Durham North Carolina
- Duke Population Research Institute Duke University Durham North Carolina
| | - Philipp Khaitovich
- Center for Neurobiology and Brain Restoration Skolkovo Institute of Science and Technology Moscow Russia
- CAS Key Laboratory of Computational Biology, CAS‐MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China
| | - Mehmet Somel
- Department of Biological Sciences Middle East Technical University Ankara Turkey
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35
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Zhao S, Zhang T, Liu Q, Wu H, Su B, Shi P, Chen H. Identifying Lineage-Specific Targets of Natural Selection by a Bayesian Analysis of Genomic Polymorphisms and Divergence from Multiple Species. Mol Biol Evol 2019; 36:1302-1315. [PMID: 30840083 DOI: 10.1093/molbev/msz046] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
We present a method that jointly analyzes the polymorphism and divergence sites in genomic sequences of multiple species to identify the genes under natural selection and pinpoint the occurrence time of selection to a specific lineage of the species phylogeny. This method integrates population genetics models using a Bayesian Poisson random field framework and combines information over all gene loci to boost the power for detecting selection. The method provides posterior distributions of the fitness effects of each gene along with parameters associated with the evolutionary history, including the species divergence time and effective population size of external species. The results of simulations demonstrate that our method achieves a high power to identify genes under positive selection for a wide range of selection intensity and provides reasonably accurate estimates of the population genetic parameters. The proposed method is applied to genomic sequences of humans, chimpanzees, gorillas, and orangutans and identifies a list of lineage-specific targets of positive selection. The positively selected genes in the human lineage are enriched in pathways of gene expression regulation, immune system and metabolism, etc. Our analysis provides insights into natural evolution in the coding regions of humans and great apes and thus serves as a basis for further molecular and functional studies.
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Affiliation(s)
- Shilei Zhao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Tao Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Qi Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Hao Wu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Peng Shi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Hua Chen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
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36
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Han S, Andrés AM, Marques-Bonet T, Kuhlwilm M. Genetic Variation in Pan Species Is Shaped by Demographic History and Harbors Lineage-Specific Functions. Genome Biol Evol 2019; 11:1178-1191. [PMID: 30847478 PMCID: PMC6482415 DOI: 10.1093/gbe/evz047] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/05/2019] [Indexed: 01/08/2023] Open
Abstract
Chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) are the closest living relatives of humans, but the two species show distinct behavioral and physiological differences, particularly regarding female reproduction. Despite their recent rapid decline, the demographic histories of the two species have been different during the past 1–2 Myr, likely having an impact on their genomic diversity. Here, we analyze the inferred functional consequences of genetic variation across 69 individuals, making use of the most complete data set of genomes in the Pan clade to date. We test to which extent the demographic history influences the efficacy of purifying selection in these species. We find that small historical effective population sizes (Ne) correlate not only with low levels of genetic diversity but also with a larger number of deleterious alleles in homozygosity and an increased proportion of deleterious changes at low frequencies. To investigate the putative genetic basis for phenotypic differences between chimpanzees and bonobos, we exploit the catalog of putatively deleterious protein-coding changes in each lineage. We show that bonobo-specific nonsynonymous changes are enriched in genes related to age at menarche in humans, suggesting that the prominent physiological differences in the female reproductive system between chimpanzees and bonobos might be explained, in part, by putatively adaptive changes on the bonobo lineage.
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Affiliation(s)
- Sojung Han
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas-Universitat Pompeu Fabra, Barcelona, Spain
| | - Aida M Andrés
- Department of Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.,Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, United Kingdom
| | - Tomas Marques-Bonet
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas-Universitat Pompeu Fabra, Barcelona, Spain.,Institucio Catalana de Recerca i Estudis Avançats (ICREA), 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
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas-Universitat Pompeu Fabra, Barcelona, Spain
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37
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Humans and Chimpanzees Display Opposite Patterns of Diversity in Arylamine N-Acetyltransferase Genes. G3-GENES GENOMES GENETICS 2019; 9:2199-2224. [PMID: 31068377 PMCID: PMC6643899 DOI: 10.1534/g3.119.400223] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Among the many genes involved in the metabolism of therapeutic drugs, human arylamine N-acetyltransferases (NATs) genes have been extensively studied, due to their medical importance both in pharmacogenetics and disease epidemiology. One member of this small gene family, NAT2, is established as the locus of the classic human acetylation polymorphism in drug metabolism. Current hypotheses hold that selective processes favoring haplotypes conferring lower NAT2 activity have been operating in modern humans’ recent history as an adaptation to local chemical and dietary environments. To shed new light on such hypotheses, we investigated the genetic diversity of the three members of the NAT gene family in seven hominid species, including modern humans, Neanderthals and Denisovans. Little polymorphism sharing was found among hominids, yet all species displayed high NAT diversity, but distributed in an opposite fashion in chimpanzees and bonobos (Pan genus) compared to modern humans, with higher diversity in Pan species at NAT1 and lower at NAT2, while the reverse is observed in humans. This pattern was also reflected in the results returned by selective neutrality tests, which suggest, in agreement with the predicted functional impact of mutations detected in non-human primates, stronger directional selection, presumably purifying selection, at NAT1 in modern humans, and at NAT2 in chimpanzees. Overall, the results point to the evolution of divergent functions of these highly homologous genes in the different primate species, possibly related to their specific chemical/dietary environment (exposome) and we hypothesize that this is likely linked to the emergence of controlled fire use in the human lineage.
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Gokhman D, Kelman G, Amartely A, Gershon G, Tsur S, Carmel L. Gene ORGANizer: linking genes to the organs they affect. Nucleic Acids Res 2019; 45:W138-W145. [PMID: 28444223 PMCID: PMC5570240 DOI: 10.1093/nar/gkx302] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 04/14/2017] [Indexed: 12/16/2022] Open
Abstract
One of the biggest challenges in studying how genes work is understanding their effect on the physiology and anatomy of the body. Existing tools try to address this using indirect features, such as expression levels and biochemical pathways. Here, we present Gene ORGANizer (geneorganizer.huji.ac.il), a phenotype-based tool that directly links human genes to the body parts they affect. It is built upon an exhaustive curated database that links >7000 genes to ∼150 anatomical parts using >150 000 gene-organ associations. The tool offers user-friendly platforms to analyze the anatomical effects of individual genes, and identify trends within groups of genes. We demonstrate how Gene ORGANizer can be used to make new discoveries, showing that chromosome X is enriched with genes affecting facial features, that positive selection targets genes with more constrained phenotypic effects, and more. We expect Gene ORGANizer to be useful in a variety of evolutionary, medical and molecular studies aimed at understanding the phenotypic effects of genes.
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Affiliation(s)
- David Gokhman
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel
| | - Guy Kelman
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel
| | - Adir Amartely
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel
| | - Guy Gershon
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel
| | - Shira Tsur
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel
| | - Liran Carmel
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel.,Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
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Kostka D, Holloway AK, Pollard KS. Developmental Loci Harbor Clusters of Accelerated Regions That Evolved Independently in Ape Lineages. Mol Biol Evol 2019; 35:2034-2045. [PMID: 29897475 PMCID: PMC6063267 DOI: 10.1093/molbev/msy109] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Some of the fastest evolving regions of the human genome are conserved noncoding elements with many human-specific DNA substitutions. These human accelerated regions (HARs) are enriched nearby regulatory genes, and several HARs function as developmental enhancers. To investigate if this evolutionary signature is unique to humans, we quantified evidence of accelerated substitutions in conserved genomic elements across multiple lineages and applied this approach simultaneously to the genomes of five apes: human, chimpanzee, gorilla, orangutan, and gibbon. We find roughly similar numbers and genomic distributions of lineage-specific accelerated regions (linARs) in all five apes. In particular, apes share an enrichment of linARs in regulatory DNA nearby genes involved in development, especially transcription factors and other regulators. Many developmental loci harbor clusters of nonoverlapping linARs from multiple apes, suggesting that accelerated evolution in each species affected distinct regulatory elements that control a shared set of developmental pathways. Our statistical tests distinguish between GC-biased and unbiased accelerated substitution rates, allowing us to quantify the roles of different evolutionary forces in creating linARs. We find evidence of GC-biased gene conversion in each ape, but unbiased acceleration consistent with positive selection or loss of constraint is more common in all five lineages. It therefore appears that similar evolutionary processes created independent accelerated regions in the genomes of different apes, and that these lineage-specific changes to conserved noncoding sequences may have differentially altered expression of a core set of developmental genes across ape evolution.
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Affiliation(s)
- Dennis Kostka
- Departments of Developmental Biology and Computational & Systems Biology, Pittsburgh Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Alisha K Holloway
- Gladstone Institutes, San Francisco, CA.,Phylos Bioscience, Portland, OR.,Department of Epidemiology & Biostatistics, Institute for Human Genetics, Quantitative Biology Institutes, and Institute for Computational Health Sciences, University of California, San Francisco, CA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA.,Department of Epidemiology & Biostatistics, Institute for Human Genetics, Quantitative Biology Institutes, and Institute for Computational Health Sciences, University of California, San Francisco, CA.,Chan-Zuckerberg Biohub, San Francisco, CA
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40
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A metaanalysis of bat phylogenetics and positive selection based on genomes and transcriptomes from 18 species. Proc Natl Acad Sci U S A 2019; 116:11351-11360. [PMID: 31113885 PMCID: PMC6561249 DOI: 10.1073/pnas.1814995116] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
This work represents a large, order-wide evolutionary analysis of the order Chiroptera (bats). Our pipeline for assembling sequence data and curating orthologous multiple sequence alignments includes methods for improving results when combining genomic and transcriptomic data sources. The resulting phylogenetic tree divides the order Chiroptera into Yinpterochiroptera and Yangochiroptera, in disagreement with the previous division into Megachiroptera and Microchiroptera and in agreement with some other recent molecular studies, and also provides evidence for other contested branch placements. We also performed a genome-wide analysis of positive selection and found 181 genes with signatures of positive selection. Enrichment analysis shows these positively selected genes to be primarily related to immune responses but also, surprisingly, collagen formation. Historically, the evolution of bats has been analyzed using a small number of genetic loci for many species or many genetic loci for a few species. Here we present a phylogeny of 18 bat species, each of which is represented in 1,107 orthologous gene alignments used to build the tree. We generated a transcriptome sequence of Hypsignathus monstrosus, the African hammer-headed bat, and additional transcriptome sequence for Rousettus aegyptiacus, the Egyptian fruit bat. We then combined these data with existing genomic and transcriptomic data from 16 other bat species. In the analysis of such datasets, there is no clear consensus on the most reliable computational methods for the curation of quality multiple sequence alignments since these public datasets represent multiple investigators and methods, including different source materials (chromosomal DNA or expressed RNA). Here we lay out a systematic analysis of parameters and produce an advanced pipeline for curating orthologous gene alignments from combined transcriptomic and genomic data, including a software package: the Mismatching Isoform eXon Remover (MIXR). Using this method, we created alignments of 11,677 bat genes, 1,107 of which contain orthologs from all 18 species. Using the orthologous gene alignments created, we assessed bat phylogeny and also performed a holistic analysis of positive selection acting in bat genomes. We found that 181 genes have been subject to positive natural selection. This list is dominated by genes involved in immune responses and genes involved in the production of collagens.
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Ancient admixture from an extinct ape lineage into bonobos. Nat Ecol Evol 2019; 3:957-965. [PMID: 31036897 DOI: 10.1038/s41559-019-0881-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 03/21/2019] [Indexed: 01/28/2023]
Abstract
Admixture is a recurrent phenomenon in humans and other great ape populations. Genetic information from extinct hominins allows us to study historical interactions with modern humans and discover adaptive functions of gene flow. Here, we investigate whole genomes from bonobo and chimpanzee populations for signatures of gene flow from unknown archaic populations, finding evidence for an ancient admixture event between bonobos and a divergent lineage. This result reveals a complex population history in our closest living relatives, probably several hundred thousand years ago. We reconstruct up to 4.8% of the genome of this 'ghost' ape, which represents genomic data of an extinct great ape population. Genes contained in archaic fragments might confer functional consequences for the immunity, behaviour and physiology of bonobos. Finally, comparing the landscapes of introgressed regions in humans and bonobos, we find that a recurrent depletion of introgression is rare, suggesting that genomic incompatibilities arose seldom in these lineages.
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Mattle-Greminger MP, Bilgin Sonay T, Nater A, Pybus M, Desai T, de Valles G, Casals F, Scally A, Bertranpetit J, Marques-Bonet T, van Schaik CP, Anisimova M, Krützen M. Genomes reveal marked differences in the adaptive evolution between orangutan species. Genome Biol 2018; 19:193. [PMID: 30428903 PMCID: PMC6237011 DOI: 10.1186/s13059-018-1562-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 10/09/2018] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Integrating demography and adaptive evolution is pivotal to understanding the evolutionary history and conservation of great apes. However, little is known about the adaptive evolution of our closest relatives, in particular if and to what extent adaptions to environmental differences have occurred. Here, we used whole-genome sequencing data from critically endangered orangutans from North Sumatra (Pongo abelii) and Borneo (P. pygmaeus) to investigate adaptive responses of each species to environmental differences during the Pleistocene. RESULTS Taking into account the markedly disparate demographic histories of each species after their split ~ 1 Ma ago, we show that persistent environmental differences on each island had a strong impact on the adaptive evolution of the genus Pongo. Across a range of tests for positive selection, we find a consistent pattern of between-island and species differences. In the more productive Sumatran environment, the most notable signals of positive selection involve genes linked to brain and neuronal development, learning, and glucose metabolism. On Borneo, however, positive selection comprised genes involved in lipid metabolism, as well as cardiac and muscle activities. CONCLUSIONS We find strikingly different sets of genes appearing to have evolved under strong positive selection in each species. In Sumatran orangutans, selection patterns were congruent with well-documented cognitive and behavioral differences between the species, such as a larger and more complex cultural repertoire and higher degrees of sociality. However, in Bornean orangutans, selective responses to fluctuating environmental conditions appear to have produced physiological adaptations to generally lower and temporally more unpredictable food supplies.
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Affiliation(s)
- Maja P. Mattle-Greminger
- Evolutionary Genetics Group, Department of Anthropology, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Tugce Bilgin Sonay
- Evolutionary Genetics Group, Department of Anthropology, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015 Lausanne, Switzerland
| | - Alexander Nater
- Evolutionary Genetics Group, Department of Anthropology, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Lehrstuhl für Zoologie und Evolutionsbiologie, Department of Biology, University of Konstanz, Universitätsstrasse 10, 78457 Konstanz, Germany
| | - Marc Pybus
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Spain
| | - Tariq Desai
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH UK
| | - Guillem de Valles
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Spain
| | - Ferran Casals
- Servei de Genòmica, Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Spain
| | - Aylwyn Scally
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH UK
| | - Jaume Bertranpetit
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Spain
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, Doctor Aiguader 88, Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys 23, Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, Cerdanyola del Vallès, Barcelona, Spain
| | - Carel P. van Schaik
- Evolutionary Genetics Group, Department of Anthropology, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Maria Anisimova
- Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015 Lausanne, Switzerland
- Institute of Applied Simulations, School of Life Sciences and Facility Management, Zurich University of Applied Sciences ZHAW, Einsiedlerstrasse 31a, 8820 Wädenswil, Switzerland
| | - Michael Krützen
- Evolutionary Genetics Group, Department of Anthropology, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
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Delaye L, Ruiz-Ruiz S, Calderon E, Tarazona S, Conesa A, Moya A. Evidence of the Red-Queen Hypothesis from Accelerated Rates of Evolution of Genes Involved in Biotic Interactions in Pneumocystis. Genome Biol Evol 2018; 10:1596-1606. [PMID: 29893833 PMCID: PMC6012782 DOI: 10.1093/gbe/evy116] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/05/2018] [Indexed: 01/15/2023] Open
Abstract
Pneumocystis species are ascomycete fungi adapted to live inside the lungs of mammals. These ascomycetes show extensive stenoxenism, meaning that each species of Pneumocystis infects a single species of host. Here, we study the effect exerted by natural selection on gene evolution in the genomes of three Pneumocystis species. We show that genes involved in host interaction evolve under positive selection. In the first place, we found strong evidence of episodic diversifying selection in Major surface glycoproteins (Msg). These proteins are located on the surface of Pneumocystis and are used for host attachment and probably for immune system evasion. Consistent with their function as antigens, most sites under diversifying selection in Msg code for residues with large relative surface accessibility areas. We also found evidence of positive selection in part of the cell machinery used to export Msg to the cell surface. Specifically, we found that genes participating in glycosylphosphatidylinositol (GPI) biosynthesis show an increased rate of nonsynonymous substitutions (dN) versus synonymous substitutions (dS). GPI is a molecule synthesized in the endoplasmic reticulum that is used to anchor proteins to membranes. We interpret the aforementioned findings as evidence of selective pressure exerted by the host immune system on Pneumocystis species, shaping the evolution of Msg and several proteins involved in GPI biosynthesis. We suggest that genome evolution in Pneumocystis is well described by the Red-Queen hypothesis whereby genes relevant for biotic interactions show accelerated rates of evolution.
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Affiliation(s)
- Luis Delaye
- Departamento de Ingeniería Genética, CINVESTAV Irapuato, Guanajuato, México
| | - Susana Ruiz-Ruiz
- Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)-Salud Pública, València, Spain
| | - Enrique Calderon
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla.,Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sonia Tarazona
- Centro de Investigacion Principe Felipe, València, Spain.,Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Spain
| | - Ana Conesa
- Centro de Investigacion Principe Felipe, València, Spain.,Microbiology and Cell Science, University of Florida
| | - Andrés Moya
- Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)-Salud Pública, València, Spain.,Institute for Integrative Systems Biology, Universitat de València, Spain
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44
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Nye J, Laayouni H, Kuhlwilm M, Mondal M, Marques-Bonet T, Bertranpetit J. Selection in the Introgressed Regions of the Chimpanzee Genome. Genome Biol Evol 2018; 10:1132-1138. [PMID: 29635458 PMCID: PMC5905441 DOI: 10.1093/gbe/evy077] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2018] [Indexed: 02/07/2023] Open
Abstract
During the demographic history of the Pan clade, there has been gene-flow between species, likely >200,000 years ago. Bonobo haplotypes in three subspecies of chimpanzee have been identified to be segregating in modern-day chimpanzee populations, suggesting that these haplotypes, with increased differentiation, may be a target of natural selection. Here, we investigate signatures of adaptive introgression within the bonobo-like haplotypes in chimpanzees using site frequency spectrum-based tests. We find evidence for subspecies-specific adaptations in introgressed regions involved with male reproduction in central chimpanzees, the immune system in eastern chimpanzees, female reproduction and the nervous system in Nigeria-Cameroon chimpanzees. Furthermore, our results indicate signatures of balancing selection in some of the putatively introgressed regions. This might be the product of long-term balancing selection resulting in a similar genomic signature as introgression, or possibly balancing selection acting on alleles reintroduced through gene flow.
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Affiliation(s)
- Jessica Nye
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Hafid Laayouni
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,Bioinformatics Studies, ESCI-UPF, Barcelona, Spain
| | - Martin Kuhlwilm
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Mayukh Mondal
- Institute of Genomics, University of Tartu, Estonian Biocentre, Tartu, Estonia
| | - Tomas Marques-Bonet
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.,Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Jaume Bertranpetit
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
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Hart MW, Stover DA, Guerra V, Mozaffari SV, Ober C, Mugal CF, Kaj I. Positive selection on human gamete-recognition genes. PeerJ 2018; 6:e4259. [PMID: 29340252 PMCID: PMC5767332 DOI: 10.7717/peerj.4259] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 12/21/2017] [Indexed: 01/29/2023] Open
Abstract
Coevolution of genes that encode interacting proteins expressed on the surfaces of sperm and eggs can lead to variation in reproductive compatibility between mates and reproductive isolation between members of different species. Previous studies in mice and other mammals have focused in particular on evidence for positive or diversifying selection that shapes the evolution of genes that encode sperm-binding proteins expressed in the egg coat or zona pellucida (ZP). By fitting phylogenetic models of codon evolution to data from the 1000 Genomes Project, we identified candidate sites evolving under diversifying selection in the human genes ZP3 and ZP2. We also identified one candidate site under positive selection in C4BPA, which encodes a repetitive protein similar to the mouse protein ZP3R that is expressed in the sperm head and binds to the ZP at fertilization. Results from several additional analyses that applied population genetic models to the same data were consistent with the hypothesis of selection on those candidate sites leading to coevolution of sperm- and egg-expressed genes. By contrast, we found no candidate sites under selection in a fourth gene (ZP1) that encodes an egg coat structural protein not directly involved in sperm binding. Finally, we found that two of the candidate sites (in C4BPA and ZP2) were correlated with variation in family size and birth rate among Hutterite couples, and those two candidate sites were also in linkage disequilibrium in the same Hutterite study population. All of these lines of evidence are consistent with predictions from a previously proposed hypothesis of balancing selection on epistatic interactions between C4BPA and ZP3 at fertilization that lead to the evolution of co-adapted allele pairs. Such patterns also suggest specific molecular traits that may be associated with both natural reproductive variation and clinical infertility.
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Affiliation(s)
- Michael W Hart
- Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Daryn A Stover
- School of Mathematical and Natural Sciences, Arizona State University Colleges at Lake Havasu City, Lake Havasu City, AZ, USA
| | - Vanessa Guerra
- Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Sahar V Mozaffari
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Carina F Mugal
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
| | - Ingemar Kaj
- Department of Mathematics, Uppsala University, Uppsala, Sweden
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Sánchez-Gracia A, Guirao-Rico S, Hinojosa-Alvarez S, Rozas J. Computational prediction of the phenotypic effects of genetic variants: basic concepts and some application examples in Drosophila nervous system genes. J Neurogenet 2017; 31:307-319. [DOI: 10.1080/01677063.2017.1398241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Alejandro Sánchez-Gracia
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Sara Guirao-Rico
- Center for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Bellaterra, Spain
| | - Silvia Hinojosa-Alvarez
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Julio Rozas
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
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47
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Theofanopoulou C, Gastaldon S, O’Rourke T, Samuels BD, Messner A, Martins PT, Delogu F, Alamri S, Boeckx C. Self-domestication in Homo sapiens: Insights from comparative genomics. PLoS One 2017; 12:e0185306. [PMID: 29045412 PMCID: PMC5646786 DOI: 10.1371/journal.pone.0185306] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 09/11/2017] [Indexed: 02/07/2023] Open
Abstract
This study identifies and analyzes statistically significant overlaps between selective sweep screens in anatomically modern humans and several domesticated species. The results obtained suggest that (paleo-)genomic data can be exploited to complement the fossil record and support the idea of self-domestication in Homo sapiens, a process that likely intensified as our species populated its niche. Our analysis lends support to attempts to capture the "domestication syndrome" in terms of alterations to certain signaling pathways and cell lineages, such as the neural crest.
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Affiliation(s)
- Constantina Theofanopoulou
- Section of General Linguistics, Universitat de Barcelona, Barcelona, Spain
- Universitat de Barcelona Institute for Complex Systems, Barcelona, Spain
| | - Simone Gastaldon
- Section of General Linguistics, Universitat de Barcelona, Barcelona, Spain
- School of Psychology, University of Padova, Padova, Italy
| | - Thomas O’Rourke
- Section of General Linguistics, Universitat de Barcelona, Barcelona, Spain
| | - Bridget D. Samuels
- Center for Craniofacial Molecular Biology, University of Southern California, Los Angeles, CA, United States of America
| | - Angela Messner
- Section of General Linguistics, Universitat de Barcelona, Barcelona, Spain
| | | | - Francesco Delogu
- Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Saleh Alamri
- Section of General Linguistics, Universitat de Barcelona, Barcelona, Spain
| | - Cedric Boeckx
- Section of General Linguistics, Universitat de Barcelona, Barcelona, Spain
- Universitat de Barcelona Institute for Complex Systems, Barcelona, Spain
- ICREA, Barcelona, Spain
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48
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Daub JT, Moretti S, Davydov II, Excoffier L, Robinson-Rechavi M. Detection of Pathways Affected by Positive Selection in Primate Lineages Ancestral to Humans. Mol Biol Evol 2017; 34:1391-1402. [PMID: 28333345 PMCID: PMC5435107 DOI: 10.1093/molbev/msx083] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Gene set enrichment approaches have been increasingly successful in finding signals of recent polygenic selection in the human genome. In this study, we aim at detecting biological pathways affected by positive selection in more ancient human evolutionary history. Focusing on four branches of the primate tree that lead to modern humans, we tested all available protein coding gene trees of the Primates clade for signals of adaptation in these branches, using the likelihood-based branch site test of positive selection. The results of these locus-specific tests were then used as input for a gene set enrichment test, where whole pathways are globally scored for a signal of positive selection, instead of focusing only on outlier "significant" genes. We identified signals of positive selection in several pathways that are mainly involved in immune response, sensory perception, metabolism, and energy production. These pathway-level results are highly significant, even though there is no functional enrichment when only focusing on top scoring genes. Interestingly, several gene sets are found significant at multiple levels in the phylogeny, but different genes are responsible for the selection signal in the different branches. This suggests that the same function has been optimized in different ways at different times in primate evolution.
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Affiliation(s)
- J T Daub
- CMPG, Institute of Ecology and Evolution, University of Berne, Berne, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - S Moretti
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - I I Davydov
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - L Excoffier
- CMPG, Institute of Ecology and Evolution, University of Berne, Berne, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - M Robinson-Rechavi
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
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49
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Schroeder L, von Cramon-Taubadel N. The evolution of hominoid cranial diversity: A quantitative genetic approach. Evolution 2017; 71:2634-2649. [DOI: 10.1111/evo.13361] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 09/03/2017] [Indexed: 01/15/2023]
Affiliation(s)
- Lauren Schroeder
- Department of Anthropology; University of Toronto Mississauga; Mississauga ON L5L 1C6 Canada
- Buffalo Human Evolutionary Morphology Lab, Department of Anthropology; University at Buffalo; SUNY, Buffalo New York 14261
- Human Evolution Research Institute; University of Cape Town; Rondebosch 7701 South Africa
| | - Noreen von Cramon-Taubadel
- Buffalo Human Evolutionary Morphology Lab, Department of Anthropology; University at Buffalo; SUNY, Buffalo New York 14261
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50
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Peyrégne S, Boyle MJ, Dannemann M, Prüfer K. Detecting ancient positive selection in humans using extended lineage sorting. Genome Res 2017; 27:1563-1572. [PMID: 28720580 PMCID: PMC5580715 DOI: 10.1101/gr.219493.116] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 07/05/2017] [Indexed: 01/20/2023]
Abstract
Natural selection that affected modern humans early in their evolution has likely shaped some of the traits that set present-day humans apart from their closest extinct and living relatives. The ability to detect ancient natural selection in the human genome could provide insights into the molecular basis for these human-specific traits. Here, we introduce a method for detecting ancient selective sweeps by scanning for extended genomic regions where our closest extinct relatives, Neandertals and Denisovans, fall outside of the present-day human variation. Regions that are unusually long indicate the presence of lineages that reached fixation in the human population faster than expected under neutral evolution. Using simulations, we show that the method is able to detect ancient events of positive selection and that it can differentiate those from background selection. Applying our method to the 1000 Genomes data set, we find evidence for ancient selective sweeps favoring regulatory changes and present a list of genomic regions that are predicted to underlie positively selected human specific traits.
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Affiliation(s)
- Stéphane Peyrégne
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Michael James Boyle
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Michael Dannemann
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Kay Prüfer
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
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