101
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Le MK, Smith OS, Akbari A, Harpak A, Reich D, Narasimhan VM. 1,000 ancient genomes uncover 10,000 years of natural selection in Europe. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.08.24.505188. [PMID: 36052370 PMCID: PMC9435429 DOI: 10.1101/2022.08.24.505188] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Ancient DNA has revolutionized our understanding of human population history. However, its potential to examine how rapid cultural evolution to new lifestyles may have driven biological adaptation has not been met, largely due to limited sample sizes. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, providing a dataset that is large enough to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods. We identified 25 genetic loci with rapid changes in frequency during these periods, a majority of which were previously undetected. Signals specific to the Neolithic transition are associated with body weight, diet, and lipid metabolism-related phenotypes. They also include immune phenotypes, most notably a locus that confers immunity to Salmonella infection at a time when ancient Salmonella genomes have been shown to adapt to human hosts, thus providing a possible example of human-pathogen co-evolution. In the Bronze Age, selection signals are enriched near genes involved in pigmentation and immune-related traits, including at a key human protein interactor of SARS-CoV-2. Only in the Historical period do the selection candidates we detect largely mirror previously-reported signals, highlighting how the statistical power of previous studies was limited to the last few millennia. The Historical period also has multiple signals associated with vitamin D binding, providing evidence that lactase persistence may have been part of an oligogenic adaptation for efficient calcium uptake and challenging the theory that its adaptive value lies only in facilitating caloric supplementation during times of scarcity. Finally, we detect selection on complex traits in all three periods, including selection favoring variants that reduce body weight in the Neolithic. In the Historical period, we detect selection favoring variants that increase risk for cardiovascular disease plausibly reflecting selection for a more active inflammatory response that would have been adaptive in the face of increased infectious disease exposure. Our results provide an evolutionary rationale for the high prevalence of these deadly diseases in modern societies today and highlight the unique power of ancient DNA in elucidating biological change that accompanied the profound cultural transformations of recent human history.
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Affiliation(s)
- Megan K Le
- Department of Computer Science, The University of Texas at Austin
| | - Olivia S Smith
- Department of Integrative Biology, The University of Texas at Austin
| | - Ali Akbari
- Department of Genetics, Harvard Medical School
- Department of Human Evolutionary Biology, Harvard University
- Broad Institute of MIT and Harvard
| | - Arbel Harpak
- Department of Integrative Biology, The University of Texas at Austin
- Department of Population Health, Dell Medical School
| | - David Reich
- Department of Genetics, Harvard Medical School
- Department of Human Evolutionary Biology, Harvard University
- Howard Hughes Medical Institute, Harvard Medical School
- Broad Institute of MIT and Harvard
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin
- Department of Statistics and Data Science, The University of Texas at Austin
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102
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Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination. PLoS Comput Biol 2022; 18:e1010422. [PMID: 35984849 PMCID: PMC9447913 DOI: 10.1371/journal.pcbi.1010422] [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: 02/15/2022] [Revised: 09/06/2022] [Accepted: 07/21/2022] [Indexed: 11/19/2022] Open
Abstract
Movement of individuals between populations or demes is often restricted, especially between geographically isolated populations. The structured coalescent provides an elegant theoretical framework for describing how movement between populations shapes the genealogical history of sampled individuals and thereby structures genetic variation within and between populations. However, in the presence of recombination an individual may inherit different regions of their genome from different parents, resulting in a mosaic of genealogical histories across the genome, which can be represented by an Ancestral Recombination Graph (ARG). In this case, different genomic regions may have different ancestral histories and so different histories of movement between populations. Recombination therefore poses an additional challenge to phylogeographic methods that aim to reconstruct the movement of individuals from genealogies, although also a potential benefit in that different loci may contain additional information about movement. Here, we introduce the Structured Coalescent with Ancestral Recombination (SCAR) model, which builds on recent approximations to the structured coalescent by incorporating recombination into the ancestry of sampled individuals. The SCAR model allows us to infer how the migration history of sampled individuals varies across the genome from ARGs, and improves estimation of key population genetic parameters such as population sizes, recombination rates and migration rates. Using the SCAR model, we explore the potential and limitations of phylogeographic inference using full ARGs. We then apply the SCAR to lineages of the recombining fungus Aspergillus flavus sampled across the United States to explore patterns of recombination and migration across the genome. Phylogeographic methods are widely used to reconstruct the historical movement of individuals between different populations. When applied to infectious pathogens, these methods are often used to reconstruct the origin or source of novel pathogen lineages. Most existing phylogeographic methods reconstruct movement based on a single phylogenetic tree, which is assumed to reflect the genetic ancestry of all sampled individuals. However in populations undergoing recombination, genetic material can be exchanged between lineages such that individuals may inherit different regions of their genome from different ancestors. In this case, phylogenetic relationships among individuals can only be captured by a reticulated network rather than any single tree. Ancestral Recombination Graphs (ARGs) provide one way of capturing these reticulate relationships and we develop new models that allow for demographic inference of historical population sizes, recombination rates and migration rates between subpopulations from ARGs. By accounting for recombination, our models not only allow for accurate demographic inference, but can take full advantage of the additional information contained in ARGs about how ancestry varies across genomes to more precisely reconstruct the movement of genetic material between populations.
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103
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Song W, Yuan K, Liu Z, Cai W, Chen J, Yu S, Zhao M, Lin GN. Locus-level antagonistic selection shaped the polygenic architecture of human complex diseases. Hum Genet 2022; 141:1935-1947. [PMID: 35943608 DOI: 10.1007/s00439-022-02471-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/11/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND We aimed to evaluate the potential role of antagonistic selection in polygenic diseases: if one variant increases the risk of one disease and decreases the risk of another disease, the signals of genetic risk elimination by natural selection will be distorted, which leads to a higher frequency of risk alleles. METHODS We applied local genetic correlations and transcriptome-wide association studies to identify genomic loci and genes adversely associated with at least two diseases. Then, we used different population genetic metrics to measure the signals of natural selection for these loci and genes. RESULTS First, we identified 2120 cases of antagonistic pleiotropy (negative local genetic correlation) among 87 diseases in 716 genomic loci (antagonistic loci). Next, by comparing with non-antagonistic loci, we observed that antagonistic loci explained an excess proportion of disease heritability (median 6%), showed enhanced signals of balancing selection, and reduced signals of directional polygenic adaptation. Then, at the gene expression level, we identified 31,991 cases of antagonistic pleiotropy among 98 diseases at 4368 genes. However, evidence of altered signals of selection pressure and heritability distribution at the gene expression level is limited. CONCLUSION We conclude that antagonistic pleiotropy is widespread among human polygenic diseases, and it has distorted the evolutionary signal and genetic architecture of diseases at the locus level.
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Affiliation(s)
- Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Kai Yuan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhe Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenxiang Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jue Chen
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shunying Yu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
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104
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Brucato N, André M, Hudjashov G, Mondal M, Cox MP, Leavesley M, Ricaut FX. Chronology of natural selection in Oceanian genomes. iScience 2022; 25:104583. [PMID: 35880026 PMCID: PMC9308150 DOI: 10.1016/j.isci.2022.104583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/11/2022] [Accepted: 06/07/2022] [Indexed: 11/30/2022] Open
Abstract
As human populations left Asia to first settle in Oceania around 50,000 years ago, they entered a territory ecologically separated from the Old World for millions of years. We analyzed genomic data of 239 modern Oceanian individuals to detect and date signals of selection specific to this region. Combining both relative and absolute dating approaches, we identified a strong selection pattern between 52,000 and 54,000 years ago in the genomes of descendants of the first settlers of Sahul. This strikingly corresponds to the dates of initial settlement as inferred from archaeological evidence. Loci under selection during this period, some showing enrichment in Denisovan ancestry, overlap genes involved in the immune response and diet, especially based on plants. Pathogens and natural resources, especially from endemic plants, therefore appear to have acted as strong selective pressures on the genomes of the first settlers of Sahul. 239 human genomes from both sides of the Wallacean ecogeographical barriers Signals of selection are dated between -54,000 to -52,000 in modern Oceanian genomes Genes related to immunity and diet were under strong selection Denisovan introgressions participated to the genetic adaptations present in Oceanians
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Affiliation(s)
- Nicolas Brucato
- Laboratoire Évolution et Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS. 118 route de Narbonne, Bat 4R1, 31062 cedex 9 Toulouse, France
| | - Mathilde André
- Laboratoire Évolution et Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS. 118 route de Narbonne, Bat 4R1, 31062 cedex 9 Toulouse, France.,Institute of Genomics, University of Tartu, Tartu, 51010 Tartumaa, Estonia
| | - Georgi Hudjashov
- Institute of Genomics, University of Tartu, Tartu, 51010 Tartumaa, Estonia
| | - Mayukh Mondal
- Institute of Genomics, University of Tartu, Tartu, 51010 Tartumaa, Estonia
| | - Murray P Cox
- School of Natural Sciences, Massey University, Palmerston North 4442, New Zealand
| | - Matthew Leavesley
- Strand of Anthropology, Sociology and Archaeology, School of Humanities and Social Sciences, University of Papua New Guinea, PO Box 320, National Capital District 134, Papua New Guinea.,College of Arts, Society and Education, James Cook University, P.O. Box 6811, Cairns, QLD 4870, Australia.,ARC Centre of Excellence for Australian Biodiversity and Heritage, University of Wollongong, Wollongong, NSW 2522, Australia
| | - François-Xavier Ricaut
- Laboratoire Évolution et Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS. 118 route de Narbonne, Bat 4R1, 31062 cedex 9 Toulouse, France
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105
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Johri P, Eyre-Walker A, Gutenkunst RN, Lohmueller KE, Jensen JD. On the prospect of achieving accurate joint estimation of selection with population history. Genome Biol Evol 2022; 14:6604401. [PMID: 35675379 PMCID: PMC9254643 DOI: 10.1093/gbe/evac088] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2022] [Indexed: 11/15/2022] Open
Abstract
As both natural selection and population history can affect genome-wide patterns of variation, disentangling the contributions of each has remained as a major challenge in population genetics. We here discuss historical and recent progress towards this goal—highlighting theoretical and computational challenges that remain to be addressed, as well as inherent difficulties in dealing with model complexity and model violations—and offer thoughts on potentially fruitful next steps.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | | | - Ryan N Gutenkunst
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Kirk E Lohmueller
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.,Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
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106
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Bergström A, Stanton DWG, Taron UH, Frantz L, Sinding MHS, Ersmark E, Pfrengle S, Cassatt-Johnstone M, Lebrasseur O, Girdland-Flink L, Fernandes DM, Ollivier M, Speidel L, Gopalakrishnan S, Westbury MV, Ramos-Madrigal J, Feuerborn TR, Reiter E, Gretzinger J, Münzel SC, Swali P, Conard NJ, Carøe C, Haile J, Linderholm A, Androsov S, Barnes I, Baumann C, Benecke N, Bocherens H, Brace S, Carden RF, Drucker DG, Fedorov S, Gasparik M, Germonpré M, Grigoriev S, Groves P, Hertwig ST, Ivanova VV, Janssens L, Jennings RP, Kasparov AK, Kirillova IV, Kurmaniyazov I, Kuzmin YV, Kosintsev PA, Lázničková-Galetová M, Leduc C, Nikolskiy P, Nussbaumer M, O'Drisceoil C, Orlando L, Outram A, Pavlova EY, Perri AR, Pilot M, Pitulko VV, Plotnikov VV, Protopopov AV, Rehazek A, Sablin M, Seguin-Orlando A, Storå J, Verjux C, Zaibert VF, Zazula G, Crombé P, Hansen AJ, Willerslev E, Leonard JA, Götherström A, Pinhasi R, Schuenemann VJ, Hofreiter M, Gilbert MTP, Shapiro B, Larson G, Krause J, Dalén L, Skoglund P. Grey wolf genomic history reveals a dual ancestry of dogs. Nature 2022; 607:313-320. [PMID: 35768506 PMCID: PMC9279150 DOI: 10.1038/s41586-022-04824-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 04/28/2022] [Indexed: 01/01/2023]
Abstract
The grey wolf (Canis lupus) was the first species to give rise to a domestic population, and they remained widespread throughout the last Ice Age when many other large mammal species went extinct. Little is known, however, about the history and possible extinction of past wolf populations or when and where the wolf progenitors of the present-day dog lineage (Canis familiaris) lived1–8. Here we analysed 72 ancient wolf genomes spanning the last 100,000 years from Europe, Siberia and North America. We found that wolf populations were highly connected throughout the Late Pleistocene, with levels of differentiation an order of magnitude lower than they are today. This population connectivity allowed us to detect natural selection across the time series, including rapid fixation of mutations in the gene IFT88 40,000–30,000 years ago. We show that dogs are overall more closely related to ancient wolves from eastern Eurasia than to those from western Eurasia, suggesting a domestication process in the east. However, we also found that dogs in the Near East and Africa derive up to half of their ancestry from a distinct population related to modern southwest Eurasian wolves, reflecting either an independent domestication process or admixture from local wolves. None of the analysed ancient wolf genomes is a direct match for either of these dog ancestries, meaning that the exact progenitor populations remain to be located. DNA from ancient wolves spanning 100,000 years sheds light on wolves’ evolutionary history and the genomic origin of dogs.
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Affiliation(s)
- Anders Bergström
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK.
| | - David W G Stanton
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.,Centre for Palaeogenetics, Stockholm, Sweden.,School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Ulrike H Taron
- Evolutionary Adaptive Genomics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Laurent Frantz
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.,Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, Munich, Germany
| | - Mikkel-Holger S Sinding
- The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland.,The Qimmeq Project, University of Greenland, Nuuk, Greenland.,Greenland Institute of Natural Resources, Nuuk, Greenland
| | - Erik Ersmark
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.,Centre for Palaeogenetics, Stockholm, Sweden
| | - Saskia Pfrengle
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany.,Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
| | - Molly Cassatt-Johnstone
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Ophélie Lebrasseur
- The Palaeogenomics & Bio-Archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK
| | - Linus Girdland-Flink
- Department of Archaeology, School of Geosciences, University of Aberdeen, Aberdeen, UK.,School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK
| | - Daniel M Fernandes
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.,CIAS, Department of Life Sciences, University of Coimbra, Coimbra, Portugal
| | - Morgane Ollivier
- University of Rennes, CNRS, ECOBIO (Ecosystèmes, biodiversité, évolution)-UMR 6553, Rennes, France
| | - Leo Speidel
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK.,Genetics Institute, University College London, London, UK
| | | | - Michael V Westbury
- Evolutionary Adaptive Genomics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.,The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | | | - Tatiana R Feuerborn
- The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,The Qimmeq Project, University of Greenland, Nuuk, Greenland.,Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany
| | - Ella Reiter
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany
| | - Joscha Gretzinger
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany.,Max Planck Institute for the Science of Human History, Jena, Germany
| | - Susanne C Münzel
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany
| | - Pooja Swali
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Nicholas J Conard
- Department of Early Prehistory and Quaternary Ecology, University of Tübingen, Tübingen, Germany.,Senckenberg Centre for Human Evolution and Palaeoenvironment, University of Tübingen, Tübingen, Germany
| | - Christian Carøe
- The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - James Haile
- The Palaeogenomics & Bio-Archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK
| | - Anna Linderholm
- Centre for Palaeogenetics, Stockholm, Sweden.,The Palaeogenomics & Bio-Archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK.,Texas A&M University, College Station, TX, USA.,Department of Geological Sciences, Stockholm University, Stockholm, Sweden
| | | | - Ian Barnes
- Department of Earth Sciences, Natural History Museum, London, UK
| | - Chris Baumann
- Senckenberg Centre for Human Evolution and Palaeoenvironment, University of Tübingen, Tübingen, Germany.,Department of Geosciences and Geography, Faculty of Science, University of Helsinki, Helsinki, Finland
| | | | - Hervé Bocherens
- Senckenberg Centre for Human Evolution and Palaeoenvironment, University of Tübingen, Tübingen, Germany.,Biogeology, Department of Geosciences, University of Tübingen, Tübingen, Germany
| | - Selina Brace
- Department of Earth Sciences, Natural History Museum, London, UK
| | - Ruth F Carden
- School of Archaeology, University College Dublin, Dublin, Ireland
| | - Dorothée G Drucker
- Senckenberg Centre for Human Evolution and Palaeoenvironment, University of Tübingen, Tübingen, Germany
| | - Sergey Fedorov
- North-Eastern Federal University, Yakutsk, Russian Federation
| | | | | | | | - Pam Groves
- University of Alaska, Fairbanks, AK, USA
| | - Stefan T Hertwig
- Naturhistorisches Museum Bern, Bern, Switzerland.,Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | | | | | - Richard P Jennings
- School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK
| | - Aleksei K Kasparov
- Institute for the History of Material Culture, Russian Academy of Sciences, St Petersburg, Russian Federation
| | - Irina V Kirillova
- Ice Age Museum, Shidlovskiy National Alliance 'Ice Age', Moscow, Russian Federation
| | - Islam Kurmaniyazov
- Department of Archaeology, Ethnology and Museology, Al-Farabi Kazakh State University, Almaty, Kazakhstan
| | - Yaroslav V Kuzmin
- Sobolev Institute of Geology and Mineralogy, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation
| | | | | | | | - Pavel Nikolskiy
- Geological Institute, Russian Academy of Sciences, Moscow, Russian Federation
| | | | - Cóilín O'Drisceoil
- National Monuments Service, Department of Housing, Local Government and Heritage, Dublin, Ireland
| | - Ludovic Orlando
- Centre d'Anthropobiologie et de Génomique de Toulouse UMR 5288, CNRS, Faculté de Médecine Purpan, Université Paul Sabatier, Toulouse, France
| | - Alan Outram
- Department of Archaeology, University of Exeter, Exeter, UK
| | - Elena Y Pavlova
- Arctic & Antarctic Research Institute, St Petersburg, Russian Federation
| | - Angela R Perri
- PaleoWest, Henderson, NV, USA.,Department of Anthropology, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Małgorzata Pilot
- Museum & Institute of Zoology, Polish Academy of Sciences, Gdańsk, Poland
| | - Vladimir V Pitulko
- Institute for the History of Material Culture, Russian Academy of Sciences, St Petersburg, Russian Federation
| | | | | | | | - Mikhail Sablin
- Zoological Institute of the Russian Academy of Sciences, St. Petersburg, Russian Federation
| | - Andaine Seguin-Orlando
- Centre d'Anthropobiologie et de Génomique de Toulouse UMR 5288, CNRS, Faculté de Médecine Purpan, Université Paul Sabatier, Toulouse, France
| | - Jan Storå
- Stockholm University, Stockholm, Sweden
| | | | - Victor F Zaibert
- Institute of Archaeology and Steppe Civilizations, Al-Farabi Kazakh National University, Almaty, Kazakhstan
| | - Grant Zazula
- Yukon Palaeontology Program, Whitehorse, Yukon Territories, Canada.,Collections and Research, Canadian Museum of Nature, Ottawa, Ontario, Canada
| | | | - Anders J Hansen
- The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Eske Willerslev
- The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,Department of Zoology, University of Cambridge, Cambridge, UK
| | | | - Anders Götherström
- Centre for Palaeogenetics, Stockholm, Sweden.,Stockholm University, Stockholm, Sweden
| | - Ron Pinhasi
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.,Human Evolution and Archaeological Sciences, University of Vienna, Vienna, Austria
| | - Verena J Schuenemann
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany.,Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland.,Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
| | - Michael Hofreiter
- Evolutionary Adaptive Genomics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - M Thomas P Gilbert
- The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,University Museum, NTNU, Trondheim, Norway
| | - Beth Shapiro
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA.,Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Greger Larson
- The Palaeogenomics & Bio-Archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK
| | - Johannes Krause
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Love Dalén
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.,Centre for Palaeogenetics, Stockholm, Sweden
| | - Pontus Skoglund
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK.
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107
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Chen DS, Clark AG, Wolfner MF. Octopaminergic/tyraminergic Tdc2 neurons regulate biased sperm usage in female Drosophila melanogaster. Genetics 2022; 221:6613932. [PMID: 35736370 DOI: 10.1093/genetics/iyac097] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/04/2022] [Indexed: 11/14/2022] Open
Abstract
In polyandrous internally fertilizing species, a multiply-mated female can use stored sperm from different males in a biased manner to fertilize her eggs. The female's ability to assess sperm quality and compatibility is essential for her reproductive success, and represents an important aspect of postcopulatory sexual selection. In Drosophila melanogaster, previous studies demonstrated that the female nervous system plays an active role in influencing progeny paternity proportion, and suggested a role for octopaminergic/tyraminergic Tdc2 neurons in this process. Here, we report that inhibiting Tdc2 neuronal activity causes females to produce a higher-than-normal proportion of first-male progeny. This difference is not due to differences in sperm storage or release, but instead is attributable to the suppression of second-male sperm usage bias that normally occurs in control females. We further show that a subset of Tdc2 neurons innervating the female reproductive tract is largely responsible for the progeny proportion phenotype that is observed when Tdc2 neurons are inhibited globally. On the contrary, overactivation of Tdc2 neurons does not further affect sperm storage and release or progeny proportion. These results suggest that octopaminergic/tyraminergic signaling allows a multiply-mated female to bias sperm usage, and identify a new role for the female nervous system in postcopulatory sexual selection.
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Affiliation(s)
- Dawn S Chen
- Department of Molecular Biology and Genetics, Cornell University, Ithaca NY 14853, USA
| | - Andrew G Clark
- Department of Molecular Biology and Genetics, Cornell University, Ithaca NY 14853, USA
| | - Mariana F Wolfner
- Department of Molecular Biology and Genetics, Cornell University, Ithaca NY 14853, USA
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108
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Jensen EL, Gaughran SJ, Fusco NA, Poulakakis N, Tapia W, Sevilla C, Málaga J, Mariani C, Gibbs JP, Caccone A. The Galapagos giant tortoise Chelonoidis phantasticus is not extinct. Commun Biol 2022; 5:546. [PMID: 35681083 PMCID: PMC9184544 DOI: 10.1038/s42003-022-03483-w] [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: 09/03/2021] [Accepted: 05/11/2022] [Indexed: 11/25/2022] Open
Abstract
The status of the Fernandina Island Galapagos giant tortoise (Chelonoidis phantasticus) has been a mystery, with the species known from a single specimen collected in 1906. The discovery in 2019 of a female tortoise living on the island provided the opportunity to determine if the species lives on. By sequencing the genomes of both individuals and comparing them to all living species of Galapagos giant tortoises, here we show that the two known Fernandina tortoises are from the same lineage and distinct from all others. The whole genome phylogeny groups the Fernandina individuals within a monophyletic group containing all species with a saddleback carapace morphology and one semi-saddleback species. This grouping of the saddleback species is contrary to mitochondrial DNA phylogenies, which place the saddleback species across several clades. These results imply the continued existence of lineage long considered extinct, with a current known population size of a single individual. Based on genomic data, the Galapagos giant tortoise species native to Fernandina Island appears to be alive and well, survived by at least one female after being considered extinct since 1906.
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Affiliation(s)
- Evelyn L Jensen
- School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne, UK.
| | - Stephen J Gaughran
- Department of Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Nicole A Fusco
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Nikos Poulakakis
- Department of Biology, School of Sciences and Engineering, University of Crete, Irakleio, Greece.,The Natural History Museum of Crete, School of Sciences and Engineering, University of Crete, Heraklion, Greece.,Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - Washington Tapia
- Galapagos Conservancy, Fairfax, VA, USA.,University of Málaga, Campus Teatinos, Apdo, 59.29080, Málaga, Spain
| | - Christian Sevilla
- Conservation and Restoration of Insular Ecosystems Department, Galapagos National Park Directorate, Puerto Ayora, Galapagos, Ecuador
| | - Jeffreys Málaga
- Conservation and Restoration of Insular Ecosystems Department, Galapagos National Park Directorate, Puerto Ayora, Galapagos, Ecuador
| | - Carol Mariani
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.,Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
| | - James P Gibbs
- Galapagos Conservancy, Fairfax, VA, USA.,Department of Environmental Biology, College of Environmental Science and Forestry, State University of New York, Syracuse, NY, USA
| | - Adalgisa Caccone
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
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109
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Focosi D, Maggi F. Recombination in Coronaviruses, with a Focus on SARS-CoV-2. Viruses 2022; 14:1239. [PMID: 35746710 PMCID: PMC9228924 DOI: 10.3390/v14061239] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 02/07/2023] Open
Abstract
Recombination is a common evolutionary tool for RNA viruses, and coronaviruses are no exception. We review here the evidence for recombination in SARS-CoV-2 and reconcile nomenclature for recombinants, discuss their origin and fitness, and speculate how recombinants could make a difference in the future of the COVID-19 pandemics.
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Affiliation(s)
- Daniele Focosi
- North-Western Tuscany Blood Bank, Pisa University Hospital, 56124 Pisa, Italy
| | - Fabrizio Maggi
- Department of Medicine and Surgery, University of Insubria, 21100 Varese, Italy
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110
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Yang D, Jones MG, Naranjo S, Rideout WM, Min KHJ, Ho R, Wu W, Replogle JM, Page JL, Quinn JJ, Horns F, Qiu X, Chen MZ, Freed-Pastor WA, McGinnis CS, Patterson DM, Gartner ZJ, Chow ED, Bivona TG, Chan MM, Yosef N, Jacks T, Weissman JS. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 2022; 185:1905-1923.e25. [PMID: 35523183 DOI: 10.1016/j.cell.2022.04.015] [Citation(s) in RCA: 103] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 02/09/2022] [Accepted: 04/08/2022] [Indexed: 12/19/2022]
Abstract
Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.
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Affiliation(s)
- Dian Yang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Matthew G Jones
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA; Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Santiago Naranjo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - William M Rideout
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Kyung Hoi Joseph Min
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Raymond Ho
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joseph M Replogle
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94158, USA; Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jennifer L Page
- Cell and Genome Engineering Core, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jeffrey J Quinn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Felix Horns
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Xiaojie Qiu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Michael Z Chen
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Medical Scientist Training Program, Harvard Medical School, Boston, MA 02115, USA
| | - William A Freed-Pastor
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Christopher S McGinnis
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David M Patterson
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg BioHub Investigator, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Cellular Construction, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Eric D Chow
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Advanced Technology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Michelle M Chan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Chan Zuckerberg BioHub Investigator, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA 94720, USA; Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA.
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
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111
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Tergemina E, Elfarargi AF, Flis P, Fulgione A, Göktay M, Neto C, Scholle M, Flood PJ, Xerri SA, Zicola J, Döring N, Dinis H, Krämer U, Salt DE, Hancock AM. A two-step adaptive walk rewires nutrient transport in a challenging edaphic environment. SCIENCE ADVANCES 2022; 8:eabm9385. [PMID: 35584228 PMCID: PMC9116884 DOI: 10.1126/sciadv.abm9385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
Most well-characterized cases of adaptation involve single genetic loci. Theory suggests that multilocus adaptive walks should be common, but these are challenging to identify in natural populations. Here, we combine trait mapping with population genetic modeling to show that a two-step process rewired nutrient homeostasis in a population of Arabidopsis as it colonized the base of an active stratovolcano characterized by extremely low soil manganese (Mn). First, a variant that disrupted the primary iron (Fe) uptake transporter gene (IRT1) swept quickly to fixation in a hard selective sweep, increasing Mn but limiting Fe in the leaves. Second, multiple independent tandem duplications occurred at NRAMP1 and together rose to near fixation in the island population, compensating the loss of IRT1 by improving Fe homeostasis. This study provides a clear case of a multilocus adaptive walk and reveals how genetic variants reshaped a phenotype and spread over space and time.
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Affiliation(s)
- Emmanuel Tergemina
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Ahmed F. Elfarargi
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Paulina Flis
- Future Food Beacon of Excellence and the School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD Nottingham, UK
| | - Andrea Fulgione
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Mehmet Göktay
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Célia Neto
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Marleen Scholle
- Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| | - Pádraic J. Flood
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Sophie-Asako Xerri
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Johan Zicola
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Nina Döring
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Herculano Dinis
- Parque Natural do Fogo, Direção Nacional do Ambiente, 115 Chã d’Areia, Praia, Santiago, Cabo Verde, Africa
- Associação Projecto Vitó, 8234, Xaguate, Cidade de São Filipe, Fogo, Cabo Verde, Africa
| | - Ute Krämer
- Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| | - David E. Salt
- Future Food Beacon of Excellence and the School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD Nottingham, UK
| | - Angela M. Hancock
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
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112
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Fan C, Mancuso N, Chiang CW. A genealogical estimate of genetic relationships. Am J Hum Genet 2022; 109:812-824. [PMID: 35417677 DOI: 10.1016/j.ajhg.2022.03.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/25/2022] [Indexed: 12/23/2022] Open
Abstract
The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM treats linked markers as independent and does not explicitly model the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework, namely the expected GRM (eGRM), to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations, we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed with ARG inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to SNP array genotypes from a population sample from Northern and Eastern Finland, we find that clustering analysis with the eGRM reveals population structure driven by subpopulations that would not be apparent via the canonical GRM and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.
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113
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Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJ, Horikoshi M, Mercader JM, Taliun D, Moon S, Kwak SH, Robertson NR, Rayner NW, Loh M, Kim BJ, Chiou J, Miguel-Escalada I, Della Briotta Parolo P, Lin K, Bragg F, Preuss MH, Takeuchi F, Nano J, Guo X, Lamri A, Nakatochi M, Scott RA, Lee JJ, Huerta-Chagoya A, Graff M, Chai JF, Parra EJ, Yao J, Bielak LF, Tabara Y, Hai Y, Steinthorsdottir V, Cook JP, Kals M, Grarup N, Schmidt EM, Pan I, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Long J, Sun M, Tong L, Chen WM, Ahmad M, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Lecoeur C, Prins BP, Nicolas A, Yanek LR, Chen G, Jensen RA, Tajuddin S, Kabagambe EK, An P, Xiang AH, Choi HS, Cade BE, Tan J, Flanagan J, Abaitua F, Adair LS, Adeyemo A, Aguilar-Salinas CA, Akiyama M, Anand SS, Bertoni A, Bian Z, Bork-Jensen J, Brandslund I, Brody JA, Brummett CM, Buchanan TA, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Fornage M, Franco OH, Frayling TM, Freedman BI, Fuchsberger C, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Goodarzi MO, Gordon-Larsen P, Gorkin D, Gross M, Guo Y, Hackinger S, Han S, Hattersley AT, Herder C, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen ME, Jørgensen T, Kamatani Y, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kohara K, Kriebel J, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lyssenko V, Mamakou V, Mani KR, Meitinger T, Metspalu A, Morris AD, Nadkarni GN, Nadler JL, Nalls MA, Nayak U, Nongmaithem SS, Ntalla I, Okada Y, Orozco L, Patel SR, Pereira MA, Peters A, Pirie FJ, Porneala B, Prasad G, Preissl S, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sander M, Sandow K, Sattar N, Schönherr S, Schurmann C, Shahriar M, Shi J, Shin DM, Shriner D, Smith JA, So WY, Stančáková A, Stilp AM, Strauch K, Suzuki K, Takahashi A, Taylor KD, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tomlinson B, Torres JM, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Vujkovic M, Wacher-Rodarte N, Wheeler E, Whitsel EA, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamauchi T, Yengo L, Yoon K, Yu C, Yuan JM, Yusuf S, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Hanis CL, Peyser PA, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Zeggini E, Yokota M, Rich SS, Kooperberg C, Pankow JS, Engert JC, Chen YDI, Froguel P, Wilson JG, Sheu WHH, Kardia SLR, Wu JY, Hayes MG, Ma RCW, Wong TY, Groop L, Mook-Kanamori DO, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, McKean-Cowdin R, Grallert H, Cheng CY, Bottinger EP, Dehghan A, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Palmer CNA, Liu S, Abecasis G, Kooner JS, Loos RJF, North KE, Haiman CA, Florez JC, Saleheen D, Hansen T, Pedersen O, Mägi R, Langenberg C, Wareham NJ, Maeda S, Kadowaki T, Lee J, Millwood IY, Walters RG, Stefansson K, Myers SR, Ferrer J, Gaulton KJ, Meigs JB, Mohlke KL, Gloyn AL, Bowden DW, Below JE, Chambers JC, Sim X, Boehnke M, Rotter JI, McCarthy MI, Morris AP. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 2022; 54:560-572. [PMID: 35551307 PMCID: PMC9179018 DOI: 10.1038/s41588-022-01058-3] [Citation(s) in RCA: 224] [Impact Index Per Article: 112.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 03/23/2022] [Indexed: 02/02/2023]
Abstract
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.
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Affiliation(s)
- Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Genentech, South San Francisco, CA, USA.
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Epidemiology and Biostatistics, University of Massachusetts Amherst, Amherst, MA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
| | - Maggie C Y Ng
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hidetoshi Kitajima
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, Sendai, Japan
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Cancer Center, Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Grace Z Yu
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Sina Rüeger
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Leo Speidel
- Genetics Institute, University College London, London, UK
- Francis Crick Institute, London, UK
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Sanghoon Moon
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Soo-Heon Kwak
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Neil R Robertson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nigel W Rayner
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR) and National University of Singapore (NUS), Singapore, Singapore
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Joshua Chiou
- Biomedical Sciences Graduate Studies Program, University of California San Diego, La Jolla, CA, USA
- Internal Medicine Research Unit, Pfizer Worldwide Research, Cambridge, MA, USA
| | - Irene Miguel-Escalada
- Regulatory Genomics and Diabetes, Centre for Genomic Regulation, the Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Madrid, Spain
| | | | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Alicia Huerta-Chagoya
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxicologí, a AmbientalInstituto de Investigaciones Biomédicas, UNAM, Ciudad de Mexico, Mexico, Mexico
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ellen M Schmidt
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloe Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Human Genetics, and Environmental Sciences, the University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA
| | - Christian Gieger
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Meraj Ahmad
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Victor J Y Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Institute of Data Science, Korea University, Seoul, South Korea
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cécile Lecoeur
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Salman Tajuddin
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Edmond K Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Anny H Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E Cade
- Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jack Flanagan
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Fernando Abaitua
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Vertex Pharmaceuticals Ltd, Oxford, UK
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Masato Akiyama
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Beijing, China
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Chad M Brummett
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Thomas A Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, the Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, the Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Wellcome Sanger Institute, Hinxton, UK
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - Swapan K Das
- Section on Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David Gorkin
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
| | - Jost B Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Marit E Jørgensen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- National Institute of Public Health, Southern Denmark University, Copenhagen, Denmark
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Fouad R Kandeel
- Department of Clinical Diabetes, Endocrinology & Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jacob M Keaton
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G Kibriya
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Katsuhiko Kohara
- Department of Regional Resource Management, Ehime University Faculty of Collaborative Regional Innovation, Ehime, Japan
- Ibusuki Kozenkai Hospital, Ibusuki, Japan
| | - Jennifer Kriebel
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Myung-Shik Lee
- Severance Biomedical Science Institute and Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Symen Ligthart
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Andrea O Luk
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing, China
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - K Radha Mani
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andrew D Morris
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Digital Engineering Faculty of Hasso Plattner Institue and University Potsdam, Potsdam, Germany
- The Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Baltimore, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Fraser J Pirie
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Centre Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Maike Sander
- Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Digital Engineering Faculty of Hasso Plattner Institue and University Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mohammad Shahriar
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, the University of Chicago, Chicago, IL, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, the Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Ken Suzuki
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Atsushi Takahashi
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genomic Medicine, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Reykjavik, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Jason M Torres
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Department of Health, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Neuroscience and Preventive Medicine, Danube University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología, Ambiental Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S Udler
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA, USA
| | - Marijana Vujkovic
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Loïc Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Craig L Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | | | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | | | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C Engert
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Wayne H H Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, the Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, the Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Leif Groop
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Centre Campus, Ghaziabad, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Michèle M Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Habibul Ahsan
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Digital Engineering Faculty of Hasso Plattner Institue and University Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Colin N A Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Goncalo Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christopher A Haiman
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Danish Saleheen
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité Universitätsmedizin, Berlin, Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Reykjavik, Reykjavik, Iceland
| | - Simon R Myers
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jorge Ferrer
- Regulatory Genomics and Diabetes, Centre for Genomic Regulation, the Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Madrid, Spain
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Kyle J Gaulton
- Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - James B Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
- Genentech, South San Francisco, CA, USA.
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Department of Health Data Science, University of Liverpool, Liverpool, UK.
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK.
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK.
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Porubsky D, Höps W, Ashraf H, Hsieh P, Rodriguez-Martin B, Yilmaz F, Ebler J, Hallast P, Maria Maggiolini FA, Harvey WT, Henning B, Audano PA, Gordon DS, Ebert P, Hasenfeld P, Benito E, Zhu Q, Lee C, Antonacci F, Steinrücken M, Beck CR, Sanders AD, Marschall T, Eichler EE, Korbel JO. Recurrent inversion polymorphisms in humans associate with genetic instability and genomic disorders. Cell 2022; 185:1986-2005.e26. [PMID: 35525246 PMCID: PMC9563103 DOI: 10.1016/j.cell.2022.04.017] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/14/2022] [Accepted: 04/08/2022] [Indexed: 12/13/2022]
Abstract
Unlike copy number variants (CNVs), inversions remain an underexplored genetic variation class. By integrating multiple genomic technologies, we discover 729 inversions in 41 human genomes. Approximately 85% of inversions <2 kbp form by twin-priming during L1 retrotransposition; 80% of the larger inversions are balanced and affect twice as many nucleotides as CNVs. Balanced inversions show an excess of common variants, and 72% are flanked by segmental duplications (SDs) or retrotransposons. Since flanking repeats promote non-allelic homologous recombination, we developed complementary approaches to identify recurrent inversion formation. We describe 40 recurrent inversions encompassing 0.6% of the genome, showing inversion rates up to 2.7 × 10-4 per locus per generation. Recurrent inversions exhibit a sex-chromosomal bias and co-localize with genomic disorder critical regions. We propose that inversion recurrence results in an elevated number of heterozygous carriers and structural SD diversity, which increases mutability in the population and predisposes specific haplotypes to disease-causing CNVs.
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115
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Robledo-Ruiz DA, Gan HM, Kaur P, Dudchenko O, Weisz D, Khan R, Lieberman Aiden E, Osipova E, Hiller M, Morales HE, Magrath MJL, Clarke RH, Sunnucks P, Pavlova A. Chromosome-length genome assembly and linkage map of a critically endangered Australian bird: the helmeted honeyeater. Gigascience 2022; 11:6554768. [PMID: 35348671 PMCID: PMC8963300 DOI: 10.1093/gigascience/giac025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/13/2022] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
Background The helmeted honeyeater (Lichenostomus melanops cassidix) is a Critically Endangered bird endemic to Victoria, Australia. To aid its conservation, the population is the subject of genetic rescue. To understand, monitor, and modulate the effects of genetic rescue on the helmeted honeyeater genome, a chromosome-length genome and a high-density linkage map are required. Results We used a combination of Illumina, Oxford Nanopore, and Hi-C sequencing technologies to assemble a chromosome-length genome of the helmeted honeyeater, comprising 906 scaffolds, with length of 1.1 Gb and scaffold N50 of 63.8 Mb. Annotation comprised 57,181 gene models. Using a pedigree of 257 birds and 53,111 single-nucleotide polymorphisms, we obtained high-density linkage and recombination maps for 25 autosomes and Z chromosome. The total sex-averaged linkage map was 1,347 cM long, with the male map being 6.7% longer than the female map. Recombination maps revealed sexually dimorphic recombination rates (overall higher in males), with average recombination rate of 1.8 cM/Mb. Comparative analyses revealed high synteny of the helmeted honeyeater genome with that of 3 passerine species (e.g., 32 Hi-C scaffolds mapped to 30 zebra finch autosomes and Z chromosome). The genome assembly and linkage map suggest that the helmeted honeyeater exhibits a fission of chromosome 1A into 2 chromosomes relative to zebra finch. PSMC analysis showed a ∼15-fold decline in effective population size to ∼60,000 from mid- to late Pleistocene. Conclusions The annotated chromosome-length genome and high-density linkage map provide rich resources for evolutionary studies and will be fundamental in guiding conservation efforts for the helmeted honeyeater.
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Affiliation(s)
| | - Han Ming Gan
- Deakin Genomics Centre, Deakin University, Geelong, VIC 3220, Australia.,GeneSEQ Sdn Bhd, 48300 Rawang, Selangor, Malaysia
| | - Parwinder Kaur
- UWA School of Agriculture and Environment, The University of Western Australia, Perth WA 6009,Australia
| | - Olga Dudchenko
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.,Center for Theoretical Biological Physics and Department of Computer Science, Rice University, Houston, TX 77030, USA
| | - David Weisz
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ruqayya Khan
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Erez Lieberman Aiden
- UWA School of Agriculture and Environment, The University of Western Australia, Perth WA 6009,Australia.,The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.,Center for Theoretical Biological Physics and Department of Computer Science, Rice University, Houston, TX 77030, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA.,Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech, Pudong 201210, China
| | - Ekaterina Osipova
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr 108, 101307 Dresden, Germany.,LOEWE Centre for Translational Biodiversity Genomics, Senckenberganlage 25, 60325 Frankfurt, Germany.,Senckenberg Research Institute, Senckenberganlage 25, 60325 Frankfurt, Germany.,Goethe-University, Faculty of Biosciences, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany
| | - Michael Hiller
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr 108, 101307 Dresden, Germany.,LOEWE Centre for Translational Biodiversity Genomics, Senckenberganlage 25, 60325 Frankfurt, Germany.,Senckenberg Research Institute, Senckenberganlage 25, 60325 Frankfurt, Germany.,Goethe-University, Faculty of Biosciences, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany
| | - Hernán E Morales
- Section for Evolutionary Genomics, GLOBE Institute, University of Copenhagen, Denmark
| | - Michael J L Magrath
- Department of Wildlife Conservation and Science, Zoos Victoria, Parkville, VIC 3052, Australia
| | - Rohan H Clarke
- School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Paul Sunnucks
- School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Alexandra Pavlova
- School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
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Fulgione A, Neto C, Elfarargi AF, Tergemina E, Ansari S, Göktay M, Dinis H, Döring N, Flood PJ, Rodriguez-Pacheco S, Walden N, Koch MA, Roux F, Hermisson J, Hancock AM. Parallel reduction in flowering time from de novo mutations enable evolutionary rescue in colonizing lineages. Nat Commun 2022; 13:1461. [PMID: 35304466 PMCID: PMC8933414 DOI: 10.1038/s41467-022-28800-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 02/07/2022] [Indexed: 12/11/2022] Open
Abstract
Understanding how populations adapt to abrupt environmental change is necessary to predict responses to future challenges, but identifying specific adaptive variants, quantifying their responses to selection and reconstructing their detailed histories is challenging in natural populations. Here, we use Arabidopsis from the Cape Verde Islands as a model to investigate the mechanisms of adaptation after a sudden shift to a more arid climate. We find genome-wide evidence of adaptation after a multivariate change in selection pressures. In particular, time to flowering is reduced in parallel across islands, substantially increasing fitness. This change is mediated by convergent de novo loss of function of two core flowering time genes: FRI on one island and FLC on the other. Evolutionary reconstructions reveal a case where expansion of the new populations coincided with the emergence and proliferation of these variants, consistent with models of rapid adaptation and evolutionary rescue. Detailing how populations adapted to environmental change is needed to predict future responses, but identifying adaptive variants and detailing their fitness effects is rare. Here, the authors show that parallel loss of FRI and FLC function reduces time to flowering and drives adaptation in a drought prone environment.
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Affiliation(s)
- Andrea Fulgione
- Max Planck Institute for Plant Breeding Research, Cologne, Germany.,Mathematics and Bioscience, Department of Mathematics and Max F. Perutz Labs, University of Vienna, Vienna, Austria.,Vienna Graduate School for Population Genetics, Vienna, Austria
| | - Célia Neto
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | | | - Shifa Ansari
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Mehmet Göktay
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Herculano Dinis
- Parque Natural do Fogo, Direção Nacional do Ambiente, Praia, Santiago, Cabo Verde.,Associação Projecto Vitó, São Filipe, Fogo, Cabo Verde
| | - Nina Döring
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Pádraic J Flood
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | - Nora Walden
- Centre for Organismal Studies (COS) Heidelberg, Biodiversity and Plant Systematics, Heidelberg University, Heidelberg, Germany.,Biosystematics, Wageningen University, Wageningen, The Netherlands
| | - Marcus A Koch
- Centre for Organismal Studies (COS) Heidelberg, Biodiversity and Plant Systematics, Heidelberg University, Heidelberg, Germany
| | - Fabrice Roux
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - Joachim Hermisson
- Mathematics and Bioscience, Department of Mathematics and Max F. Perutz Labs, University of Vienna, Vienna, Austria
| | - Angela M Hancock
- Max Planck Institute for Plant Breeding Research, Cologne, Germany. .,Mathematics and Bioscience, Department of Mathematics and Max F. Perutz Labs, University of Vienna, Vienna, Austria.
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117
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Schaschl H, Göllner T, Morris DL. Positive selection acts on regulatory genetic variants in populations of European ancestry that affect ALDH2 gene expression. Sci Rep 2022; 12:4563. [PMID: 35296751 PMCID: PMC8927298 DOI: 10.1038/s41598-022-08588-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 03/09/2022] [Indexed: 11/09/2022] Open
Abstract
ALDH2 is a key enzyme in alcohol metabolism that protects cells from acetaldehyde toxicity. Using iHS, iSAFE and FST statistics, we identified regulatory acting variants affecting ALDH2 gene expression under positive selection in populations of European ancestry. Several SNPs (rs3184504, rs4766578, rs10774625, rs597808, rs653178, rs847892, rs2013002) that function as eQTLs for ALDH2 in various tissues showed evidence of strong positive selection. Very large pairwise FST values indicated high genetic differentiation at these loci between populations of European ancestry and populations of other global ancestries. Estimating the timing of positive selection on the beneficial alleles suggests that these variants were recently adapted approximately 3000-3700 years ago. The derived beneficial alleles are in complete linkage disequilibrium with the derived ALDH2 promoter variant rs886205, which is associated with higher transcriptional activity. The SNPs rs4766578 and rs847892 are located in binding sequences for the transcription factor HNF4A, which is an important regulatory element of ALDH2 gene expression. In contrast to the missense variant ALDH2 rs671 (ALDH2*2), which is common only in East Asian populations and is associated with greatly reduced enzyme activity and alcohol intolerance, the beneficial alleles of the regulatory variants identified in this study are associated with increased expression of ALDH2. This suggests adaptation of Europeans to higher alcohol consumption.
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Affiliation(s)
- Helmut Schaschl
- Department of Evolutionary Anthropology, Faculty of Life Sciences, University of Vienna, Djerassiplatz 1, 1030, Vienna, Austria.
| | - Tobias Göllner
- Department of Evolutionary Anthropology, Faculty of Life Sciences, University of Vienna, Djerassiplatz 1, 1030, Vienna, Austria
| | - David L Morris
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, Great Maze Pond, London, SE1 9RT, UK
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118
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Kwon YM, Vranken N, Hoge C, Lichak MR, Norovich AL, Francis KX, Camacho-Garcia J, Bista I, Wood J, McCarthy S, Chow W, Tan HH, Howe K, Bandara S, von Lintig J, Rüber L, Durbin R, Svardal H, Bendesky A. Genomic consequences of domestication of the Siamese fighting fish. SCIENCE ADVANCES 2022; 8:eabm4950. [PMID: 35263139 PMCID: PMC8906746 DOI: 10.1126/sciadv.abm4950] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/13/2022] [Indexed: 05/08/2023]
Abstract
Siamese fighting (betta) fish are among the most popular and morphologically diverse pet fish, but the genetic bases of their domestication and phenotypic diversification are largely unknown. We assembled de novo the genome of a wild Betta splendens and whole-genome sequenced 98 individuals across five closely related species. We find evidence of bidirectional hybridization between domesticated ornamental betta and other wild Betta species. We discover dmrt1 as the main sex determination gene in ornamental betta and that it has lower penetrance in wild B. splendens. Furthermore, we find genes with signatures of recent, strong selection that have large effects on color in specific parts of the body or on the shape of individual fins and that most are unlinked. Our results demonstrate how simple genetic architectures paired with anatomical modularity can lead to vast phenotypic diversity generated during animal domestication and launch betta as a powerful new system for evolutionary genetics.
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Affiliation(s)
- Young Mi Kwon
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Nathan Vranken
- Department of Biology, University of Antwerp, 2020 Antwerp, Belgium
- Department of Biology, KU Leuven, 3000 Leuven, Belgium
| | - Carla Hoge
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Madison R. Lichak
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Amy L. Norovich
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Kerel X. Francis
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | | | - Iliana Bista
- Wellcome Sanger Institute, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | | | - Shane McCarthy
- Wellcome Sanger Institute, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | | | - Heok Hui Tan
- Lee Kong Chian Natural History Museum, National University of Singapore, Singapore, Singapore
| | | | - Sepalika Bandara
- Department of Pharmacology, Case Western Reserve University, Cleveland, OH, USA
| | - Johannes von Lintig
- Department of Pharmacology, Case Western Reserve University, Cleveland, OH, USA
| | - Lukas Rüber
- Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern 3012, Switzerland
- Naturhistorisches Museum Bern, Bern 3005, Switzerland
| | - Richard Durbin
- Wellcome Sanger Institute, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Hannes Svardal
- Department of Biology, University of Antwerp, 2020 Antwerp, Belgium
- Naturalis Biodiversity Center, 2333 Leiden, Netherlands
| | - Andres Bendesky
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
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119
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Mahmoudi A, Koskela J, Kelleher J, Chan YB, Balding D. Bayesian inference of ancestral recombination graphs. PLoS Comput Biol 2022; 18:e1009960. [PMID: 35263345 PMCID: PMC8936483 DOI: 10.1371/journal.pcbi.1009960] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 03/21/2022] [Accepted: 02/23/2022] [Indexed: 11/18/2022] Open
Abstract
We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.
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Affiliation(s)
- Ali Mahmoudi
- Melbourne Integrative Genomics / School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Jere Koskela
- Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Jerome Kelleher
- Big Data Institute, The University of Oxford, Oxford, United Kingdom
| | - Yao-ban Chan
- Melbourne Integrative Genomics / School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - David Balding
- Melbourne Integrative Genomics / School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- School of BioSciences, The University of Melbourne, Melbourne, Australia
- * E-mail:
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120
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Sanderson MJ, Búrquez A, Copetti D, McMahon MM, Zeng Y, Wojciechowski MF. Origin and diversification of the saguaro cactus (Carnegiea gigantea): a within-species phylogenomic analysis. Syst Biol 2022; 71:1178-1194. [PMID: 35244183 DOI: 10.1093/sysbio/syac017] [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: 03/25/2020] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 11/14/2022] Open
Abstract
Reconstructing accurate historical relationships within a species poses numerous challenges, not least in many plant groups in which gene flow is high enough to extend well beyond species boundaries. Nonetheless, the extent of tree-like history within a species is an empirical question on which it is now possible to bring large amounts of genome sequence to bear. We assess phylogenetic structure across the geographic range of the saguaro cactus, an emblematic member of Cactaceae, a clade known for extensive hybridization and porous species boundaries. Using 200 Gb of whole genome resequencing data from 20 individuals sampled from 10 localities, we assembled two data sets comprising 150,000 biallelic single nucleotide polymorphisms (SNPs) from protein coding sequences. From these we inferred within-species trees and evaluated their significance and robustness using five qualitatively different inference methods. Despite the low sequence diversity, large census population sizes, and presence of wide-ranging pollen and seed dispersal agents, phylogenetic trees were well resolved and highly consistent across both data sets and all methods. We inferred that the most likely root, based on marginal likelihood comparisons, is to the east and south of the region of highest genetic diversity, which lies along the coast of the Gulf of California in Sonora, Mexico. Together with striking decreases in marginal likelihood found to the north, this supports hypotheses that saguaro's current range reflects post-glacial expansion from the refugia in the south of its range. We conclude with observations about practical and theoretical issues raised by phylogenomic data sets within species, in which SNP-based methods must be used rather than gene tree methods that are widely used when sequence divergence is higher. These include computational scalability, inference of gene flow, and proper assessment of statistical support in the presence of linkage effects.
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Affiliation(s)
- Michael J Sanderson
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
| | - Alberto Búrquez
- Instituto de Ecología, Unidad Hermosillo, Universidad Nacional Autónoma de México, Hermosillo, Sonora, Mexico
| | - Dario Copetti
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, 85721 USA
| | | | - Yichao Zeng
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
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121
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Rees J, Andrés A. Inferring human evolutionary history. Science 2022; 375:817-818. [PMID: 35201893 DOI: 10.1126/science.abo0498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Unified genetic genealogy improves our understanding of how humans evolved.
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Affiliation(s)
- Jasmin Rees
- UCL Genetics Institute, Department of Genetics, Evolution and Environnment, University College London, London, UK.,Genetics and Genomic Medicine Programme, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Aida Andrés
- UCL Genetics Institute, Department of Genetics, Evolution and Environnment, University College London, London, UK.,Genetics and Genomic Medicine Programme, Great Ormond Street Institute of Child Health, University College London, London, UK
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122
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Wohns AW, Wong Y, Jeffery B, Akbari A, Mallick S, Pinhasi R, Patterson N, Reich D, Kelleher J, McVean G. A unified genealogy of modern and ancient genomes. Science 2022; 375:eabi8264. [PMID: 35201891 PMCID: PMC10027547 DOI: 10.1126/science.abi8264] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The sequencing of modern and ancient genomes from around the world has revolutionized our understanding of human history and evolution. However, the problem of how best to characterize ancestral relationships from the totality of human genomic variation remains unsolved. Here, we address this challenge with nonparametric methods that enable us to infer a unified genealogy of modern and ancient humans. This compact representation of multiple datasets explores the challenges of missing and erroneous data and uses ancient samples to constrain and date relationships. We demonstrate the power of the method to recover relationships between individuals and populations as well as to identify descendants of ancient samples. Finally, we introduce a simple nonparametric estimator of the geographical location of ancestors that recapitulates key events in human history.
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Affiliation(s)
- Anthony Wilder Wohns
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford; Oxford OX3 7LF, UK
| | - Yan Wong
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford; Oxford OX3 7LF, UK
| | - Ben Jeffery
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford; Oxford OX3 7LF, UK
| | - Ali Akbari
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Department of Human Evolutionary Biology, Harvard University; Cambridge, MA 02138, USA
- Department of Genetics, Harvard Medical School; Boston, MA 02115, USA
| | - Swapan Mallick
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Howard Hughes Medical Institute; Boston, MA 02115, USA
| | - Ron Pinhasi
- Department of Evolutionary Anthropology, University of Vienna; 1090 Vienna, Austria
| | - Nick Patterson
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Department of Human Evolutionary Biology, Harvard University; Cambridge, MA 02138, USA
- Howard Hughes Medical Institute; Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School; Boston, MA 02115, USA
| | - David Reich
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Department of Human Evolutionary Biology, Harvard University; Cambridge, MA 02138, USA
- Howard Hughes Medical Institute; Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School; Boston, MA 02115, USA
| | - Jerome Kelleher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford; Oxford OX3 7LF, UK
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford; Oxford OX3 7LF, UK
- Corresponding author.
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Matschiner M, Barth JMI, Tørresen OK, Star B, Baalsrud HT, Brieuc MSO, Pampoulie C, Bradbury I, Jakobsen KS, Jentoft S. Supergene origin and maintenance in Atlantic cod. Nat Ecol Evol 2022; 6:469-481. [PMID: 35177802 PMCID: PMC8986531 DOI: 10.1038/s41559-022-01661-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 01/10/2022] [Indexed: 12/19/2022]
Abstract
Supergenes are sets of genes that are inherited as a single marker and encode complex phenotypes through their joint action. They are identified in an increasing number of organisms, yet their origins and evolution remain enigmatic. In Atlantic cod, four megabase-scale supergenes have been identified and linked to migratory lifestyle and environmental adaptations. Here we investigate the origin and maintenance of these four supergenes through analysis of whole-genome-sequencing data, including a new long-read-based genome assembly for a non-migratory Atlantic cod individual. We corroborate the finding that chromosomal inversions underlie all four supergenes, and we show that they originated at different times between 0.40 and 1.66 million years ago. We reveal gene flux between supergene haplotypes where migratory and stationary Atlantic cod co-occur and conclude that this gene flux is driven by gene conversion, on the basis of an increase in GC content in exchanged sites. Additionally, we find evidence for double crossover between supergene haplotypes, leading to the exchange of an ~275 kilobase fragment with genes potentially involved in adaptation to low salinity in the Baltic Sea. Our results suggest that supergenes can be maintained over long timescales in the same way as hybridizing species, through the selective purging of introduced genetic variation. Atlantic cod carries four supergenes linked to migratory lifestyle and environmental adaptations. Using whole-genome sequencing, the authors show that the genome inversions that underlie the supergenes originated at different times and show gene flux between supergene haplotypes.
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Affiliation(s)
- Michael Matschiner
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway. .,Department of Palaeontology and Museum, University of Zurich, Zurich, Switzerland. .,Natural History Museum, University of Oslo, Oslo, Norway.
| | - Julia Maria Isis Barth
- Zoological Institute, Department of Environmental Sciences, University of Basel, Basel, Switzerland
| | - Ole Kristian Tørresen
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway
| | - Bastiaan Star
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway
| | - Helle Tessand Baalsrud
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway
| | - Marine Servane Ono Brieuc
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway
| | | | - Ian Bradbury
- Fisheries and Oceans Canada, St John's, Newfoundland and Labrador, Canada
| | - Kjetill Sigurd Jakobsen
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway
| | - Sissel Jentoft
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway.
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124
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Benchmarking phasing software with a whole-genome sequenced cattle pedigree. BMC Genomics 2022; 23:130. [PMID: 35164677 PMCID: PMC8845340 DOI: 10.1186/s12864-022-08354-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/24/2022] [Indexed: 12/30/2022] Open
Abstract
Background Accurate haplotype reconstruction is required in many applications in quantitative and population genomics. Different phasing methods are available but their accuracy must be evaluated for samples with different properties (population structure, marker density, etc.). We herein took advantage of whole-genome sequence data available for a Holstein cattle pedigree containing 264 individuals, including 98 trios, to evaluate several population-based phasing methods. This data represents a typical example of a livestock population, with low effective population size, high levels of relatedness and long-range linkage disequilibrium. Results After stringent filtering of our sequence data, we evaluated several population-based phasing programs including one or more versions of AlphaPhase, ShapeIT, Beagle, Eagle and FImpute. To that end we used 98 individuals having both parents sequenced for validation. Their haplotypes reconstructed based on Mendelian segregation rules were considered the gold standard to assess the performance of population-based methods in two scenarios. In the first one, only these 98 individuals were phased, while in the second one, all the 264 sequenced individuals were phased simultaneously, ignoring the pedigree relationships. We assessed phasing accuracy based on switch error counts (SEC) and rates (SER), lengths of correctly phased haplotypes and the probability that there is no phasing error between a pair of SNPs as a function of their distance. For most evaluated metrics or scenarios, the best software was either ShapeIT4.1 or Beagle5.2, both methods resulting in particularly high phasing accuracies. For instance, ShapeIT4.1 achieved a median SEC of 50 per individual and a mean haplotype block length of 24.1 Mb (scenario 2). These statistics are remarkable since the methods were evaluated with a map of 8,400,000 SNPs, and this corresponds to only one switch error every 40,000 phased informative markers. When more relatives were included in the data (scenario 2), FImpute3.0 reconstructed extremely long segments without errors. Conclusions We report extremely high phasing accuracies in a typical livestock sample. ShapeIT4.1 and Beagle5.2 proved to be the most accurate, particularly for phasing long segments and in the first scenario. Nevertheless, most tools achieved high accuracy at short distances and would be suitable for applications requiring only local haplotypes. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08354-6.
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125
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Ignatieva A, Hein J, Jenkins PA. Ongoing Recombination in SARS-CoV-2 Revealed Through Genealogical Reconstruction. Mol Biol Evol 2022; 39:6519867. [PMID: 35106601 PMCID: PMC8841603 DOI: 10.1093/molbev/msac028] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The evolutionary process of genetic recombination has the potential to rapidly change the properties of a viral pathogen, and its presence is a crucial factor to consider in the development of treatments and vaccines. It can also significantly affect the results of phylogenetic analyses and the inference of evolutionary rates. The detection of recombination from samples of sequencing data is a very challenging problem, and is further complicated for SARS-CoV-2 by its relatively slow accumulation of genetic diversity. The extent to which recombination is ongoing for SARS-CoV-2 is not yet resolved. To address this, we use a parsimony-based method to reconstruct possible genealogical histories for samples of SARS-CoV-2 sequences, which enables us to pinpoint specific recombination events that could have generated the data. We propose a statistical framework for disentangling the effects of recurrent mutation from recombination in the history of a sample, and hence provide a way of estimating the probability that ongoing recombination is present. We apply this to samples of sequencing data collected in England and South Africa, and find evidence of ongoing recombination.
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Affiliation(s)
| | - Jotun Hein
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- The Alan Turing Institute, British Library, London, United Kingdom
| | - Paul A Jenkins
- Department of Statistics, University of Warwick, Coventry, United Kingdom
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Corresponding author: E-mail:
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126
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Marnetto D, Pankratov V, Mondal M, Montinaro F, Pärna K, Vallini L, Molinaro L, Saag L, Loog L, Montagnese S, Costa R, Metspalu M, Eriksson A, Pagani L. Ancestral genomic contributions to complex traits in contemporary Europeans. Curr Biol 2022; 32:1412-1419.e3. [DOI: 10.1016/j.cub.2022.01.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/11/2021] [Accepted: 01/18/2022] [Indexed: 10/19/2022]
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127
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Novo I, Santiago E, Caballero A. The estimates of effective population size based on linkage disequilibrium are virtually unaffected by natural selection. PLoS Genet 2022; 18:e1009764. [PMID: 35077457 PMCID: PMC8815936 DOI: 10.1371/journal.pgen.1009764] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 02/04/2022] [Accepted: 12/21/2021] [Indexed: 11/19/2022] Open
Abstract
The effective population size (Ne) is a key parameter to quantify the magnitude of genetic drift and inbreeding, with important implications in human evolution. The increasing availability of high-density genetic markers allows the estimation of historical changes in Ne across time using measures of genome diversity or linkage disequilibrium between markers. Directional selection is expected to reduce diversity and Ne, and this reduction is modulated by the heterogeneity of the genome in terms of recombination rate. Here we investigate by computer simulations the consequences of selection (both positive and negative) and recombination rate heterogeneity in the estimation of historical Ne. We also investigate the relationship between diversity parameters and Ne across the different regions of the genome using human marker data. We show that the estimates of historical Ne obtained from linkage disequilibrium between markers (NeLD) are virtually unaffected by selection. In contrast, those estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, which could have important consequences for the estimation of human demography. The simulation results are supported by the analysis of human data. The estimates of NeLD obtained for particular genomic regions do not correlate, or they do it very weakly, with recombination rate, nucleotide diversity, proportion of polymorphic sites, background selection statistic, minor allele frequency of SNPs, loss of function and missense variants and gene density. This suggests that NeLD measures mainly reflect demographic changes in population size across generations. The inference of the demographic history of populations is of great relevance in evolutionary biology. This inference can be made from genomic data using coalescence methods or linkage disequilibrium methods. However, the assessment of these methods is usually made assuming neutrality (absence of selection). Here we show by computer simulations and analyses of human data that the estimates of historical effective population size obtained from linkage disequilibrium between markers are virtually unaffected by natural selection, either positive or negative. In contrast, estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, which could have important consequences for recent estimations of human demography.
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Affiliation(s)
- Irene Novo
- Centro de Investigación Mariña, Universidade de Vigo, Facultade de Bioloxía, Vigo, Spain
- * E-mail:
| | - Enrique Santiago
- Departamento de Biología Funcional, Facultad de Biología, Universidad de Oviedo, Oviedo, Spain
| | - Armando Caballero
- Centro de Investigación Mariña, Universidade de Vigo, Facultade de Bioloxía, Vigo, Spain
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128
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Dilber E, Terhorst J. Robust detection of natural selection using a probabilistic model of tree imbalance. Genetics 2022; 220:6511494. [PMID: 35100408 PMCID: PMC8893258 DOI: 10.1093/genetics/iyac009] [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: 10/06/2021] [Accepted: 12/16/2021] [Indexed: 01/21/2023] Open
Abstract
Neutrality tests such as Tajima's D and Fay and Wu's H are standard implements in the population genetics toolbox. One of their most common uses is to scan the genome for signals of natural selection. However, it is well understood that D and H are confounded by other evolutionary forces-in particular, population expansion-that may be unrelated to selection. Because they are not model-based, it is not clear how to deconfound these tests in a principled way. In this article, we derive new likelihood-based methods for detecting natural selection, which are robust to fluctuations in effective population size. At the core of our method is a novel probabilistic model of tree imbalance, which generalizes Kingman's coalescent to allow certain aberrant tree topologies to arise more frequently than is expected under neutrality. We derive a frequency spectrum-based estimator that can be used in place of D, and also extend to the case where genealogies are first estimated. We benchmark our methods on real and simulated data, and provide an open source software implementation.
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Affiliation(s)
- Enes Dilber
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jonathan Terhorst
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA,Corresponding author: Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.
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129
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Kreiner JM, Sandler G, Stern AJ, Tranel PJ, Weigel D, Stinchcombe J, Wright SI. Repeated origins, widespread gene flow, and allelic interactions of target-site herbicide resistance mutations. eLife 2022; 11:70242. [PMID: 35037853 PMCID: PMC8798060 DOI: 10.7554/elife.70242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 01/16/2022] [Indexed: 11/13/2022] Open
Abstract
Causal mutations and their frequency in agricultural fields are well-characterized for herbicide resistance. However, we still lack understanding of their evolutionary history: the extent of parallelism in the origins of target-site resistance (TSR), how long these mutations persist, how quickly they spread, and allelic interactions that mediate their selective advantage. We addressed these questions with genomic data from 19 agricultural populations of common waterhemp (Amaranthus tuberculatus), which we show to have undergone a massive expansion over the past century, with a contemporary effective population size estimate of 8 x 107. We found variation at seven characterized TSR loci, two of which had multiple amino acid substitutions, and three of which were common. These three common resistance variants show extreme parallelism in their mutational origins, with gene flow having shaped their distribution across the landscape. Allele age estimates supported a strong role of adaptation from de novo mutations, with a median age of 30 suggesting that most resistance alleles arose soon after the onset of herbicide use. However, resistant lineages varied in both their age and evidence for selection over two different timescales, implying considerable heterogeneity in the forces that govern their persistence. Two such forces are intra- and inter-locus allelic interactions; we report a signal of extended haplotype competition between two common TSR alleles, and extreme linkage with genome-wide alleles with known functions in resistance adaptation. Together, this work reveals a remarkable example of spatial parallel evolution in a metapopulation, with important implications for the management of herbicide resistance.
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Affiliation(s)
- Julia M Kreiner
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
| | - George Sandler
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
| | - Aaron J Stern
- Graduate Group in Computational Biology, University of California, Berkeley, Berkeley, United States
| | - Patrick J Tranel
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, United States
| | - Detlef Weigel
- Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - John Stinchcombe
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
| | - Stephen Isaac Wright
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
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130
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Cheng JY, Stern AJ, Racimo F, Nielsen R. Detecting Selection in Multiple Populations by Modeling Ancestral Admixture Components. Mol Biol Evol 2022; 39:msab294. [PMID: 34626111 PMCID: PMC8763095 DOI: 10.1093/molbev/msab294] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
One of the most powerful and commonly used approaches for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this article, we present a new maximum likelihood method for finding regions under positive selection. It is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. Using simulated data, we compare our method to related approaches, and show that it is orders of magnitude faster than the state-of-the-art, while retaining similar or higher power for most simulation scenarios. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin, and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, such as immunity, fat distribution, food intake, vision, and hair development.
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Affiliation(s)
- Jade Yu Cheng
- Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Aaron J Stern
- Graduate Group in Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Fernando Racimo
- Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Nielsen
- Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
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131
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Hejase HA, Mo Z, Campagna L, Siepel A. A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph. Mol Biol Evol 2022; 39:msab332. [PMID: 34888675 PMCID: PMC8789311 DOI: 10.1093/molbev/msab332] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.
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Affiliation(s)
- Hussein A Hejase
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ziyi Mo
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Leonardo Campagna
- Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Ithaca, NY, USA
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
| | - Adam Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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132
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Colomer-Vilaplana A, Murga-Moreno J, Canalda-Baltrons A, Inserte C, Soto D, Coronado-Zamora M, Barbadilla A, Casillas S. PopHumanVar: an interactive application for the functional characterization and prioritization of adaptive genomic variants in humans. Nucleic Acids Res 2022; 50:D1069-D1076. [PMID: 34664660 PMCID: PMC8728255 DOI: 10.1093/nar/gkab925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 09/17/2021] [Accepted: 09/28/2021] [Indexed: 12/22/2022] Open
Abstract
Adaptive challenges that humans faced as they expanded across the globe left specific molecular footprints that can be decoded in our today's genomes. Different sets of metrics are used to identify genomic regions that have undergone selection. However, there are fewer methods capable of pinpointing the allele ultimately responsible for this selection. Here, we present PopHumanVar, an interactive online application that is designed to facilitate the exploration and thorough analysis of candidate genomic regions by integrating both functional and population genomics data currently available. PopHumanVar generates useful summary reports of prioritized variants that are putatively causal of recent selective sweeps. It compiles data and graphically represents different layers of information, including natural selection statistics, as well as functional annotations and genealogical estimations of variant age, for biallelic single nucleotide variants (SNVs) of the 1000 Genomes Project phase 3. Specifically, PopHumanVar amasses SNV-based information from GEVA, SnpEFF, GWAS Catalog, ClinVar, RegulomeDB and DisGeNET databases, as well as accurate estimations of iHS, nSL and iSAFE statistics. Notably, PopHumanVar can successfully identify known causal variants of frequently reported candidate selection regions, including EDAR in East-Asians, ACKR1 (DARC) in Africans and LCT/MCM6 in Europeans. PopHumanVar is open and freely available at https://pophumanvar.uab.cat.
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Affiliation(s)
- Aina Colomer-Vilaplana
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Jesús Murga-Moreno
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Aleix Canalda-Baltrons
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Clara Inserte
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Daniel Soto
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Marta Coronado-Zamora
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Antonio Barbadilla
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Sònia Casillas
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
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133
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Hayward LK, Sella G. Polygenic adaptation after a sudden change in environment. eLife 2022; 11:66697. [PMID: 36155653 PMCID: PMC9683794 DOI: 10.7554/elife.66697] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Polygenic adaptation is thought to be ubiquitous, yet remains poorly understood. Here, we model this process analytically, in the plausible setting of a highly polygenic, quantitative trait that experiences a sudden shift in the fitness optimum. We show how the mean phenotype changes over time, depending on the effect sizes of loci that contribute to variance in the trait, and characterize the allele dynamics at these loci. Notably, we describe the two phases of the allele dynamics: The first is a rapid phase, in which directional selection introduces small frequency differences between alleles whose effects are aligned with or opposed to the shift, ultimately leading to small differences in their probability of fixation during a second, longer phase, governed by stabilizing selection. As we discuss, key results should hold in more general settings and have important implications for efforts to identify the genetic basis of adaptation in humans and other species.
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Affiliation(s)
- Laura Katharine Hayward
- Department of Mathematics, Columbia UniversityNew YorkUnited States,Institute of Science and TechnologyMaria GuggingAustria
| | - Guy Sella
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States,Program for Mathematical Genomics, Columbia UniversityNew YorkUnited States
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134
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Rohland N, Mallick S, Mah M, Maier R, Patterson N, Reich D. Three assays for in-solution enrichment of ancient human DNA at more than a million SNPs. Genome Res 2022; 32:2068-2078. [PMID: 36517229 PMCID: PMC9808625 DOI: 10.1101/gr.276728.122] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022]
Abstract
The strategy of in-solution enrichment for hundreds of thousands of single-nucleotide polymorphisms (SNPs) has been used to analyze >70% of individuals with genome-scale ancient DNA published to date. This approach makes it economical to study ancient samples with low proportions of human DNA and increases the rate of conversion of sampled remains into interpretable data. So far, nearly all such data have been generated using a set of bait sequences targeting about 1.24 million SNPs (the "1240k reagent"), but synthesis of the reagent has been cost-effective for only a few laboratories. In 2021, two companies, Daicel Arbor Biosciences and Twist Bioscience, made available assays that target the same core set of SNPs along with supplementary content. We test all three assays on a common set of 27 ancient DNA libraries and show that all three are effective at enriching many hundreds of thousands of SNPs. For all assays, one round of enrichment produces data that are as useful as two. In our testing, the "Twist Ancient DNA" assay produces the highest coverages, greatest uniformity on targeted positions, and almost no bias toward enriching one allele more than another relative to shotgun sequencing. We also identify hundreds of thousands of targeted SNPs for which there is minimal allelic bias when comparing 1240k data to either shotgun or Twist data. This facilitates coanalysis of the large data sets that have been generated using 1240k and Twist capture, as well as shotgun sequencing approaches.
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Affiliation(s)
- Nadin Rohland
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA;,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Swapan Mallick
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA;,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;,Howard Hughes Medical Institute, Boston, Massachusetts 02115, USA
| | - Matthew Mah
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA;,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;,Howard Hughes Medical Institute, Boston, Massachusetts 02115, USA
| | - Robert Maier
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA;,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;,Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Nick Patterson
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;,Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - David Reich
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA;,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;,Howard Hughes Medical Institute, Boston, Massachusetts 02115, USA;,Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA
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135
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Baumdicker F, Bisschop G, Goldstein D, Gower G, Ragsdale AP, Tsambos G, Zhu S, Eldon B, Ellerman EC, Galloway JG, Gladstein AL, Gorjanc G, Guo B, Jeffery B, Kretzschmar WW, Lohse K, Matschiner M, Nelson D, Pope NS, Quinto-Cortés CD, Rodrigues MF, Saunack K, Sellinger T, Thornton K, van Kemenade H, Wohns AW, Wong Y, Gravel S, Kern AD, Koskela J, Ralph PL, Kelleher J. Efficient ancestry and mutation simulation with msprime 1.0. Genetics 2021; 220:6460344. [PMID: 34897427 PMCID: PMC9176297 DOI: 10.1093/genetics/iyab229] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.
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Affiliation(s)
- Franz Baumdicker
- Cluster of Excellence "Controlling Microbes to Fight Infections", Mathematical and Computational Population Genetics, University of Tübingen, 72076 Tübingen, Germany
| | - Gertjan Bisschop
- Institute of Evolutionary Biology,The University of Edinburgh, EH9 3FL, UK
| | - Daniel Goldstein
- Khoury College of Computer Sciences, Northeastern University, MA 02115, USA.,No affiliation
| | - Graham Gower
- Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, 1350 Copenhagen K, Denmark
| | - Aaron P Ragsdale
- Department of Integrative Biology, University of Wisconsin-Madison, WI 53706, USA
| | - Georgia Tsambos
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Victoria, 3010, Australia
| | - Sha Zhu
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
| | - Bjarki Eldon
- Leibniz Institute for Evolution and Biodiversity Science,Museum für Naturkunde Berlin, 10115, Germany
| | | | - Jared G Galloway
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA.,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98102, USA
| | - Ariella L Gladstein
- Department of Genetics, University of North Carolina at Chapel Hill, NC 27599-7264, USA.,Embark Veterinary, Inc., Boston, MA 02111, USA
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, EH25 9RG, UK
| | - Bing Guo
- Institute for Genome Sciences,University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ben Jeffery
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
| | - Warren W Kretzschmar
- Center for Hematology and Regenerative Medicine, Karolinska Institute, 141 83 Huddinge, Sweden
| | - Konrad Lohse
- Institute of Evolutionary Biology,The University of Edinburgh, EH9 3FL, UK
| | | | - Dominic Nelson
- Department of Human Genetics, McGill University, Montréal, QC H3A 0C7, Canada
| | - Nathaniel S Pope
- Department of Entomology, Pennsylvania State University, PA 16802, USA
| | - Consuelo D Quinto-Cortés
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Unit of Advanced Genomics, CINVESTAV, Irapuato, Mexico
| | - Murillo F Rodrigues
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA
| | - Kumar Saunack
- IIT Bombay, Powai, Mumbai 400 076, Maharashtra, India
| | - Thibaut Sellinger
- Professorship for Population Genetics, Department of Life Science Systems, Technical University of Munich, 85354 Freising, Germany
| | - Kevin Thornton
- Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA
| | | | - Anthony W Wohns
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yan Wong
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
| | - Simon Gravel
- Department of Human Genetics, McGill University, Montréal, QC H3A 0C7, Canada
| | - Andrew D Kern
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA
| | - Jere Koskela
- Department of Statistics, University of Warwick, CV4 7AL, UK
| | - Peter L Ralph
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA.,Department of Mathematics, University of Oregon, OR 97403-5289 USA
| | - Jerome Kelleher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
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136
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Hartfield M, Poulsen NA, Guldbrandtsen B, Bataillon T. Using singleton densities to detect recent selection in Bos taurus. Evol Lett 2021; 5:595-606. [PMID: 34917399 PMCID: PMC8645200 DOI: 10.1002/evl3.263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 11/05/2022] Open
Abstract
Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the "Singleton Density Score" (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans-milk protein content, milk fat content, and stature-have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies.
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Affiliation(s)
- Matthew Hartfield
- Bioinformatics Research CentreAarhus UniversityAarhusDK‐8000Denmark
- Institute of Evolutionary BiologyUniversity of EdinburghEdinburghEH9 3FLUnited Kingdom
| | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and GeneticsAarhus UniversityTjeleDK‐8830Denmark
- Rheinische Friedrich‐Wilhelms‐Universität BonnInstitut für TierwissenschaftenBonnDE‐53115Germany
- Department of Veterinary SciencesCopenhagen UniversityFrederiksberg CDK‐1870Denmark
| | - Thomas Bataillon
- Bioinformatics Research CentreAarhus UniversityAarhusDK‐8000Denmark
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137
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A selection pressure landscape for 870 human polygenic traits. Nat Hum Behav 2021; 5:1731-1743. [PMID: 34782732 DOI: 10.1038/s41562-021-01231-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 09/28/2021] [Indexed: 02/05/2023]
Abstract
Characterizing the natural selection of complex traits is important for understanding human evolution and both biological and pathological mechanisms. We leveraged genome-wide summary statistics for 870 polygenic traits and attempted to quantify signals of selection on traits of different forms in European ancestry across four periods in human history and evolution. We found that 88% of these traits underwent polygenic change in the past 2,000-3,000 years. Recent selection was associated with ancient selection signals in the same trait. Traits related to pigmentation, body measurement and nutritional intake exhibited strong selection signals across different time scales. Our findings are limited by our use of exclusively European data and the use of genome-wide association study data, which identify associations between genetic variants and phenotypes that may not be causal. In sum, we provide an overview of signals of selection on human polygenic traits and their characteristics across human evolution, based on a European subset of human genetic diversity. These findings could serve as a foundation for further populational and medical genetic studies.
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138
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Agarwal I, Przeworski M. Mutation saturation for fitness effects at human CpG sites. eLife 2021; 10:e71513. [PMID: 34806592 PMCID: PMC8683084 DOI: 10.7554/elife.71513] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 11/21/2021] [Indexed: 01/06/2023] Open
Abstract
Whole exome sequences have now been collected for millions of humans, with the related goals of identifying pathogenic mutations in patients and establishing reference repositories of data from unaffected individuals. As a result, we are approaching an important limit, in which datasets are large enough that, in the absence of natural selection, every highly mutable site will have experienced at least one mutation in the genealogical history of the sample. Here, we focus on CpG sites that are methylated in the germline and experience mutations to T at an elevated rate of ~10-7 per site per generation; considering synonymous mutations in a sample of 390,000 individuals, ~ 99 % of such CpG sites harbor a C/T polymorphism. Methylated CpG sites provide a natural mutation saturation experiment for fitness effects: as we show, at nt sample sizes, not seeing a non-synonymous polymorphism is indicative of strong selection against that mutation. We rely on this idea in order to directly identify a subset of CpG transitions that are likely to be highly deleterious, including ~27 % of possible loss-of-function mutations, and up to 20 % of possible missense mutations, depending on the type of functional site in which they occur. Unlike methylated CpGs, most mutation types, with rates on the order of 10-8 or 10-9, remain very far from saturation. We discuss what these findings imply for interpreting the potential clinical relevance of mutations from their presence or absence in reference databases and for inferences about the fitness effects of new mutations.
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Affiliation(s)
- Ipsita Agarwal
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
| | - Molly Przeworski
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
- Department of Systems Biology, Columbia UniversityNew YorkUnited States
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139
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Montinaro F, Pankratov V, Yelmen B, Pagani L, Mondal M. Revisiting the out of Africa event with a deep-learning approach. Am J Hum Genet 2021; 108:2037-2051. [PMID: 34626535 PMCID: PMC8595897 DOI: 10.1016/j.ajhg.2021.09.006] [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: 03/29/2021] [Accepted: 09/09/2021] [Indexed: 10/20/2022] Open
Abstract
Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, several studies argued in favor of a single out of Africa event for modern humans on the basis of whole-genome sequence analyses. However, the single out of Africa model is in contrast with some of the findings from fossil records, which support two out of Africa events, and uniparental data, which propose a back to Africa movement. Here, we used a deep-learning approach coupled with approximate Bayesian computation and sequential Monte Carlo to revisit these hypotheses from the whole-genome sequence perspective. Our results support the back to Africa model over other alternatives. We estimated that there are two sequential separations between Africa and out of African populations happening around 60-90 thousand years ago and separated by 13-15 thousand years. One of the populations resulting from the more recent split has replaced the older West African population to a large extent, while the other one has founded the out of Africa populations.
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Affiliation(s)
- Francesco Montinaro
- Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Biology-Genetics, University of Bari, Bari 70124, Italy
| | - Vasili Pankratov
- Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Burak Yelmen
- Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
| | - Luca Pagani
- Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Biology, University of Padova, Padova 35121, Italy
| | - Mayukh Mondal
- Institute of Genomics, University of Tartu, Tartu 51010, Estonia.
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140
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Blischak PD, Barker MS, Gutenkunst RN. Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks. Mol Ecol Resour 2021; 21:2676-2688. [PMID: 33682305 PMCID: PMC8675098 DOI: 10.1111/1755-0998.13355] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 01/26/2021] [Accepted: 02/05/2021] [Indexed: 11/30/2022]
Abstract
Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their nonindependence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here, we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P1 , P2 ), P3 ), Out) and a matrix of pairwise nucleotide divergence (dXY ) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe-CNN, was able to accurately perform model selection for hybridization scenarios across a wide breath of parameter space. We then used HyDe-CNN to test models of admixture in Heliconius butterflies, as well as comparing it to phylogeny-based introgression statistics. Given the flexibility of our approach, the dropping cost of long-read sequencing and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.
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Affiliation(s)
- Paul D. Blischak
- Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
- Department of Molecular & Cellular Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Michael S. Barker
- Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Ryan N. Gutenkunst
- Department of Molecular & Cellular Biology, University of Arizona, Tucson, AZ, 85721, USA
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141
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Nadachowska‐Brzyska K, Konczal M, Babik W. Navigating the temporal continuum of effective population size. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13740] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
| | | | - Wieslaw Babik
- Jagiellonian University in Kraków Faculty of Biology Institute of Environmental Sciences Kraków Poland
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142
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Chiang CWK. The Opportunities and Challenges of Integrating Population Histories Into Genetic Studies for Diverse Populations: A Motivating Example From Native Hawaiians. Front Genet 2021; 12:643883. [PMID: 34646295 PMCID: PMC8503554 DOI: 10.3389/fgene.2021.643883] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 08/19/2021] [Indexed: 11/25/2022] Open
Abstract
There is a well-recognized need to include diverse populations in genetic studies, but several obstacles continue to be prohibitive, including (but are not limited to) the difficulty of recruiting individuals from diverse populations in large numbers and the lack of representation in available genomic references. These obstacles notwithstanding, studying multiple diverse populations would provide informative, population-specific insights. Using Native Hawaiians as an example of an understudied population with a unique evolutionary history, I will argue that by developing key genomic resources and integrating evolutionary thinking into genetic epidemiology, we will have the opportunity to efficiently advance our knowledge of the genetic risk factors, ameliorate health disparity, and improve healthcare in this underserved population.
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Affiliation(s)
- Charleston W K Chiang
- Department of Population and Public Health Sciences, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.,Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, United States
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143
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Di C, Murga Moreno J, Salazar-Tortosa DF, Lauterbur ME, Enard D. Decreased recent adaptation at human mendelian disease genes as a possible consequence of interference between advantageous and deleterious variants. eLife 2021; 10:69026. [PMID: 34636724 PMCID: PMC8526059 DOI: 10.7554/elife.69026] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/02/2021] [Indexed: 11/27/2022] Open
Abstract
Advances in genome sequencing have improved our understanding of the genetic basis of human diseases, and thousands of human genes have been associated with different diseases. Recent genomic adaptation at disease genes has not been well characterized. Here, we compare the rate of strong recent adaptation in the form of selective sweeps between mendelian, non-infectious disease genes and non-disease genes across distinct human populations from the 1000 Genomes Project. We find that mendelian disease genes have experienced far less selective sweeps compared to non-disease genes especially in Africa. Investigating further the possible causes of the sweep deficit at disease genes, we find that this deficit is very strong at disease genes with both low recombination rates and with high numbers of associated disease variants, but is almost non-existent at disease genes with higher recombination rates or lower numbers of associated disease variants. Because segregating recessive deleterious variants have the ability to interfere with adaptive ones, these observations strongly suggest that adaptation has been slowed down by the presence of interfering recessive deleterious variants at disease genes. These results suggest that disease genes suffer from a transient inability to adapt as fast as the rest of the genome.
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Affiliation(s)
- Chenlu Di
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, United States
| | - Jesus Murga Moreno
- Institut de Biotecnologia i de Biomedicina and Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - M Elise Lauterbur
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, United States
| | - David Enard
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, United States
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144
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Ongaro L, Molinaro L, Flores R, Marnetto D, Capodiferro MR, Alarcón-Riquelme ME, Moreno-Estrada A, Mabunda N, Ventura M, Tambets K, Achilli A, Capelli C, Metspalu M, Pagani L, Montinaro F. Evaluating the Impact of Sex-Biased Genetic Admixture in the Americas through the Analysis of Haplotype Data. Genes (Basel) 2021; 12:genes12101580. [PMID: 34680976 PMCID: PMC8535939 DOI: 10.3390/genes12101580] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 01/30/2023] Open
Abstract
A general imbalance in the proportion of disembarked males and females in the Americas has been documented during the Trans-Atlantic Slave Trade and the Colonial Era and, although less prominent, more recently. This imbalance may have left a signature on the genomes of modern-day populations characterised by high levels of admixture. The analysis of the uniparental systems and the evaluation of continental proportion ratio of autosomal and X chromosomes revealed a general sex imbalance towards males for European and females for African and Indigenous American ancestries. However, the consistency and degree of this imbalance are variable, suggesting that other factors, such as cultural and social practices, may have played a role in shaping it. Moreover, very few investigations have evaluated the sex imbalance using haplotype data, containing more critical information than genotypes. Here, we analysed genome-wide data for more than 5000 admixed American individuals to assess the presence, direction and magnitude of sex-biased admixture in the Americas. For this purpose, we applied two haplotype-based approaches, ELAI and NNLS, and we compared them with a genotype-based method, ADMIXTURE. In doing so, besides a general agreement between methods, we unravelled that the post-colonial admixture dynamics show higher complexity than previously described.
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Affiliation(s)
- Linda Ongaro
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
- Correspondence:
| | - Ludovica Molinaro
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
| | - Rodrigo Flores
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
| | - Davide Marnetto
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
| | - Marco R. Capodiferro
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (M.R.C.); (A.A.)
| | - Marta E. Alarcón-Riquelme
- Department of Medical Genomics, GENYO, Centro Pfizer—Universidad de Granada—Junta de Andalucía de Genómica e Investigación Oncológica, Av de la Ilustración 114, Parque Tecnológico de la Salud (PTS), 18016 Granada, Spain;
| | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Guanajuato 36821, Mexico;
| | - Nedio Mabunda
- Instituto Nacional de Saúde, Distrito de Marracuene, Estrada Nacional N°1, Província de Maputo, Maputo 1120, Mozambique;
| | - Mario Ventura
- Department of Biology-Genetics, University of Bari, 70126 Bari, Italy;
| | - Kristiina Tambets
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
| | - Alessandro Achilli
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (M.R.C.); (A.A.)
| | - Cristian Capelli
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK;
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy
| | - Mait Metspalu
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
| | - Luca Pagani
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
- Department of Biology, University of Padua, 35131 Padua, Italy
| | - Francesco Montinaro
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia; (L.M.); (R.F.); (D.M.); (K.T.); (M.M.); (L.P.); (F.M.)
- Department of Biology-Genetics, University of Bari, 70126 Bari, Italy;
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145
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Bisschop G, Lohse K, Setter D. Sweeps in time: leveraging the joint distribution of branch lengths. Genetics 2021; 219:iyab119. [PMID: 34849880 PMCID: PMC8633083 DOI: 10.1093/genetics/iyab119] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/10/2021] [Indexed: 11/14/2022] Open
Abstract
Current methods of identifying positively selected regions in the genome are limited in two key ways: the underlying models cannot account for the timing of adaptive events and the comparison between models of selective sweeps and sequence data is generally made via simple summaries of genetic diversity. Here, we develop a tractable method of describing the effect of positive selection on the genealogical histories in the surrounding genome, explicitly modeling both the timing and context of an adaptive event. In addition, our framework allows us to go beyond analyzing polymorphism data via the site frequency spectrum or summaries thereof and instead leverage information contained in patterns of linked variants. Tests on both simulations and a human data example, as well as a comparison to SweepFinder2, show that even with very small sample sizes, our analytic framework has higher power to identify old selective sweeps and to correctly infer both the time and strength of selection. Finally, we derived the marginal distribution of genealogical branch lengths at a locus affected by selection acting at a linked site. This provides a much-needed link between our analytic understanding of the effects of sweeps on sequence variation and recent advances in simulation and heuristic inference procedures that allow researchers to examine the sequence of genealogical histories along the genome.
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Affiliation(s)
- Gertjan Bisschop
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Konrad Lohse
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Derek Setter
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
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146
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Saitou M, Resendez S, Pradhan AJ, Wu F, Lie NC, Hall NJ, Zhu Q, Reinholdt L, Satta Y, Speidel L, Nakagome S, Hanchard NA, Churchill G, Lee C, Atilla-Gokcumen GE, Mu X, Gokcumen O. Sex-specific phenotypic effects and evolutionary history of an ancient polymorphic deletion of the human growth hormone receptor. SCIENCE ADVANCES 2021; 7:eabi4476. [PMID: 34559564 PMCID: PMC8462886 DOI: 10.1126/sciadv.abi4476] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 08/04/2021] [Indexed: 06/13/2023]
Abstract
The common deletion of the third exon of the growth hormone receptor gene (GHRd3) in humans is associated with birth weight, growth after birth, and time of puberty. However, its evolutionary history and the molecular mechanisms through which it affects phenotypes remain unresolved. We present evidence that this deletion was nearly fixed in the ancestral population of anatomically modern humans and Neanderthals but underwent a recent adaptive reduction in frequency in East Asia. We documented that GHRd3 is associated with protection from severe malnutrition. Using a novel mouse model, we found that, under calorie restriction, Ghrd3 leads to the female-like gene expression in male livers and the disappearance of sexual dimorphism in weight. The sex- and diet-dependent effects of GHRd3 in our mouse model are consistent with a model in which the allele frequency of GHRd3 varies throughout human evolution as a response to fluctuations in resource availability.
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Affiliation(s)
- Marie Saitou
- Department of Biological Sciences, University at Buffalo, Buffalo, NY, USA
| | - Skyler Resendez
- Department of Biological Sciences, University at Buffalo, Buffalo, NY, USA
| | | | - Fuguo Wu
- Department of Ophthalmology, Ross Eye Institute, Jacobs School of Medicine and Biological Sciences, University at Buffalo, Buffalo, NY, USA
| | - Natasha C. Lie
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Nancy J. Hall
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Qihui Zhu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Yoko Satta
- Department of Evolutionary Studies of Biosystems, SOKENDAI (Graduate University for Advanced Studies), Kanagawa Prefecture, Japan
| | - Leo Speidel
- University College London, Genetics Institute, London, UK
- The Francis Crick Institute, London, UK
| | | | - Neil A. Hanchard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Precision Medicine Center, The First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi, People’s Republic of China
| | | | - Xiuqian Mu
- Department of Ophthalmology, Ross Eye Institute, Jacobs School of Medicine and Biological Sciences, University at Buffalo, Buffalo, NY, USA
| | - Omer Gokcumen
- Department of Biological Sciences, University at Buffalo, Buffalo, NY, USA
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147
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Visscher PM, Yengo L, Cox NJ, Wray NR. Discovery and implications of polygenicity of common diseases. Science 2021; 373:1468-1473. [PMID: 34554790 PMCID: PMC9945947 DOI: 10.1126/science.abi8206] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The sequencing of the human genome has allowed the study of the genetic architecture of common diseases: the number of genomic variants that contribute to risk of disease and their joint frequency and effect size distribution. Common diseases are polygenic, with many loci contributing to phenotype, and the cumulative burden of risk alleles determines individual risk in conjunction with environmental factors. Most risk loci occur in noncoding regions of the genome regulating cell- and context-specific gene expression. Although the effect sizes of most risk alleles are small, their cumulative effects in individuals, quantified as a polygenic (risk) score, can identify people at increased risk of disease, thereby facilitating prevention or early intervention.
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Affiliation(s)
- Peter M. Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia,Corresponding author.
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Nancy J. Cox
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia,Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
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148
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Parental relatedness through time revealed by runs of homozygosity in ancient DNA. Nat Commun 2021; 12:5425. [PMID: 34521843 PMCID: PMC8440622 DOI: 10.1038/s41467-021-25289-w] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 07/21/2021] [Indexed: 02/08/2023] Open
Abstract
Parental relatedness of present-day humans varies substantially across the globe, but little is known about the past. Here we analyze ancient DNA, leveraging that parental relatedness leaves genomic traces in the form of runs of homozygosity. We present an approach to identify such runs in low-coverage ancient DNA data aided by haplotype information from a modern phased reference panel. Simulation and experiments show that this method robustly detects runs of homozygosity longer than 4 centimorgan for ancient individuals with at least 0.3 × coverage. Analyzing genomic data from 1,785 ancient humans who lived in the last 45,000 years, we detect low rates of first cousin or closer unions across most ancient populations. Moreover, we find a marked decay in background parental relatedness co-occurring with or shortly after the advent of sedentary agriculture. We observe this signal, likely linked to increasing local population sizes, across several geographic transects worldwide.
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149
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Speidel L, Cassidy L, Davies RW, Hellenthal G, Skoglund P, Myers SR. Inferring Population Histories for Ancient Genomes Using Genome-Wide Genealogies. Mol Biol Evol 2021; 38:3497-3511. [PMID: 34129037 PMCID: PMC8383901 DOI: 10.1093/molbev/msab174] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Ancient genomes anchor genealogies in directly observed historical genetic variation and contextualize ancestral lineages with archaeological insights into their geography and cultural associations. However, the majority of ancient genomes are of lower coverage and cannot be directly built into genealogies. Here, we present a fast and scalable method, Colate, the first approach for inferring ancestral relationships through time between low-coverage genomes without requiring phasing or imputation. Our approach leverages sharing patterns of mutations dated using a genealogy to infer coalescence rates. For deeply sequenced ancient genomes, we additionally introduce an extension of the Relate algorithm for joint inference of genealogies incorporating such genomes. Application to 278 present-day and 430 ancient DNA samples of >0.5x mean coverage allows us to identify dynamic population structure and directional gene flow between early farmer and European hunter-gatherer groups. We further show that the previously reported, but still unexplained, increase in the TCC/TTC mutation rate, which is strongest in West Eurasia today, was already present at similar strength and widespread in the Late Glacial Period ~10k-15k years ago, but is not observed in samples >30k years old. It is strongest in Neolithic farmers, and highly correlated with recent coalescence rates between other genomes and a 10,000-year-old Anatolian hunter-gatherer. This suggests gene-flow among ancient peoples postdating the last glacial maximum as widespread and localizes the driver of this mutational signal in both time and geography in that region. Our approach should be widely applicable in future for addressing other evolutionary questions, and in other species.
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Affiliation(s)
- Leo Speidel
- Francis Crick Institute, London, United Kingdom
- Genetics Institute, University College London, London, United Kingdom
| | - Lara Cassidy
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Republic of Ireland
| | - Robert W Davies
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | | | | | - Simon R Myers
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
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Souilmi Y, Lauterbur ME, Tobler R, Huber CD, Johar AS, Moradi SV, Johnston WA, Krogan NJ, Alexandrov K, Enard D. An ancient viral epidemic involving host coronavirus interacting genes more than 20,000 years ago in East Asia. Curr Biol 2021; 31:3504-3514.e9. [PMID: 34171302 PMCID: PMC8223470 DOI: 10.1016/j.cub.2021.05.067] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/22/2021] [Accepted: 05/28/2021] [Indexed: 11/30/2022]
Abstract
The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the vulnerability of human populations to novel viral pressures, despite the vast array of epidemiological and biomedical tools now available. Notably, modern human genomes contain evolutionary information tracing back tens of thousands of years, which may help identify the viruses that have impacted our ancestors-pointing to which viruses have future pandemic potential. Here, we apply evolutionary analyses to human genomic datasets to recover selection events involving tens of human genes that interact with coronaviruses, including SARS-CoV-2, that likely started more than 20,000 years ago. These adaptive events were limited to the population ancestral to East Asian populations. Multiple lines of functional evidence support an ancient viral selective pressure, and East Asia is the geographical origin of several modern coronavirus epidemics. An arms race with an ancient coronavirus, or with a different virus that happened to use similar interactions as coronaviruses with human hosts, may thus have taken place in ancestral East Asian populations. By learning more about our ancient viral foes, our study highlights the promise of evolutionary information to better predict the pandemics of the future. Importantly, adaptation to ancient viral epidemics in specific human populations does not necessarily imply any difference in genetic susceptibility between different human populations, and the current evidence points toward an overwhelming impact of socioeconomic factors in the case of coronavirus disease 2019 (COVID-19).
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Affiliation(s)
- Yassine Souilmi
- Australian Centre for Ancient DNA, School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia; National Centre for Indigenous Genomics, Australian National University, Canberra, ACT 0200, Australia
| | - M Elise Lauterbur
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, AZ, USA
| | - Ray Tobler
- Australian Centre for Ancient DNA, School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia
| | - Christian D Huber
- Australian Centre for Ancient DNA, School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia
| | - Angad S Johar
- Australian Centre for Ancient DNA, School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia
| | - Shayli Varasteh Moradi
- CSIRO-QUT Synthetic Biology Alliance, Centre for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Wayne A Johnston
- CSIRO-QUT Synthetic Biology Alliance, Centre for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Nevan J Krogan
- QBI COVID-19 Research Group (QCRG), San Francisco, CA, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, USA; J. David Gladstone Institutes, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kirill Alexandrov
- CSIRO-QUT Synthetic Biology Alliance, Centre for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane, QLD 4001, Australia.
| | - David Enard
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, AZ, USA.
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