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      The population genomic legacy of the second plague pandemic

      brief-report
      1 , 48 , 49 , , 2 , 3 , 48 , 1 , 48 , 4 , 5 , 2 , 6 , 7 , 1 , 8 , 1 , 8 , 2 , 2 , 2 , 2 , 9 , 10 , 11 , 11 , 12 , 13 , 14 , 15 , 15 , 20 , 15 , 1 , 1 , 1 , 21 , 22 , 23 , 8 , 8 , 1 , 1 , 24 , 25 , 26 , 27 , 28 , 8 , 1 , 29 , 1 , 30 , 1 , 1 , 31 , 2 , 3 , 1 , 32 , 1 , 32 , 33 , 34 , 35 , 34 , 35 , 36 , 37 , 38 , 38 , 39 , 40 , 41 , 40 , 42 , 43 , 44 , 1 , 45 , 10 , 15 , 16 , 17 , 18 , 15 , 19 , 1 , 46 , 2 , 47 , 2 , 3 , 1 , 8
      Current Biology
      Cell Press
      plague, second plague pandemic, Yersinia pestis, pandemic genomics, population genomics, selection, population replacement, Trondheim

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          Summary

          Human populations have been shaped by catastrophes that may have left long-lasting signatures in their genomes. One notable example is the second plague pandemic that entered Europe in ca. 1,347 CE and repeatedly returned for over 300 years, with typical village and town mortality estimated at 10%–40%. 1 It is assumed that this high mortality affected the gene pools of these populations. First, local population crashes reduced genetic diversity. Second, a change in frequency is expected for sequence variants that may have affected survival or susceptibility to the etiologic agent ( Yersinia pestis). 2 Third, mass mortality might alter the local gene pools through its impact on subsequent migration patterns. We explored these factors using the Norwegian city of Trondheim as a model, by sequencing 54 genomes spanning three time periods: (1) prior to the plague striking Trondheim in 1,349 CE, (2) the 17 th–19 th century, and (3) the present. We find that the pandemic period shaped the gene pool by reducing long distance immigration, in particular from the British Isles, and inducing a bottleneck that reduced genetic diversity. Although we also observe an excess of large F ST values at multiple loci in the genome, these are shaped by reference biases introduced by mapping our relatively low genome coverage degraded DNA to the reference genome. This implies that attempts to detect selection using ancient DNA (aDNA) datasets that vary by read length and depth of sequencing coverage may be particularly challenging until methods have been developed to account for the impact of differential reference bias on test statistics.

          Highlights

          • The second plague pandemic homogenized ancestry in Trondheim

          • Gaelic ancestry is sharply reduced in post-pandemic Trondheim

          • Pervasive reference bias taints frequency differences observed between populations

          Abstract

          Gopalakrishnan et al. investigate the genomic signatures of the second plague pandemic on the residents of Trondheim in Norway. They find that the pandemic resulted in a sharp reduction in Gaelic ancestry and also find evidence of differential reference bias among their ancient samples, which reduces the reliability of selection analyses.

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          Most cited references57

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Curr Biol
                Curr Biol
                Current Biology
                Cell Press
                0960-9822
                1879-0445
                07 November 2022
                07 November 2022
                : 32
                : 21
                : 4743-4751.e6
                Affiliations
                [1 ]The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Øster Farimagsgade 5A, 1353 Copenhagen, Denmark
                [2 ]deCODE Genetics, AMGEN Inc., Sturlugata 8, 102 Reykjavík, Iceland
                [3 ]Department of Anthropology, School of Social Sciences, University of Iceland, Gimli, Sæmundargata, 102 Reykjavík, Iceland
                [4 ]National Yunlin University of Science & Technology, 123 University Road, Section 3, 64002 Douliu, Yun-Lin County, Taiwan
                [5 ]Department of Archaeology and Anthropology, National Museum of Natural Science, 1 Guanqian Road, North District Taichung City 404023, Taiwan
                [6 ]Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba, Córdoba, Argentina
                [7 ]Microbial Paleogenomics Unit, Institut Pasteur, 25-28 Rue du Dr Roux, 75015 Paris, France
                [8 ]NTNU University Museum, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
                [9 ]Department of Archaeological Sciences, Faculty of Archaeology, Leiden University, Leiden, the Netherlands
                [10 ]UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK
                [11 ]Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
                [12 ]International Laboratory for Human Genome Research, Laboratorio Internacional de Investigación sobre el Genoma Humano (LIIGH), Universidad Nacional Autónoma de México (UNAM), 3001 Boulevard Juriquilla, 76230 Querétaro, Mexico
                [13 ]Illumina Artificial Intelligence Laboratory, Illumina Inc., San Diego, CA, USA
                [14 ]Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3, 2200 Copenhagen, Denmark
                [15 ]Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain
                [16 ]Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010 Barcelona, Spain
                [17 ]CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain
                [18 ]Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
                [19 ]Museu de Ciències Naturals de Barcelona, 08019 Barcelona, Spain
                [20 ]Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
                [21 ]Max Planck Institute for the Science of Human History, Kahlaische Strasse 10, 07745 Jena, Germany
                [22 ]Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany
                [23 ]Museum of Archaeology, University of Stavanger, Stavanger, Norway
                [24 ]China National GeneBank, BGI-Shenzhen, Shenzhen 518083, China
                [25 ]Evolutionsbiologisk Centrum EBC, Norbyv. 18A, 752 36 Uppsala, Sweden
                [26 ]KU Leuven, Herestraat 49, 3000 Leuven, Belgium
                [27 ]Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
                [28 ]Department of Business, History and Social Sciences, University of South-Eastern Norway, Notodden, Norway
                [29 ]EA – Eco-anthropologie (UMR 7206), Muséum National d’Histoire Naturelle, CNRS, Université Paris Diderot, Paris, France
                [30 ]Department of Archaeology, Kings Manor and Principals House, University of York, Exhibition Square, York YO1 7EP, UK
                [31 ]CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, Matosinhos, Portugal
                [32 ]Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Sciences, Asian Institute of Medicine, Science and Technology (AIMST), 08100 Bedong, Kedah, Malaysia
                [33 ]Biobank1, St. Olavs Hospital HF, Trondheim, Norway
                [34 ]School of Pharmacy and Biomolecular Sciences, RCSI, Dublin, Ireland
                [35 ]FutureNeuro SFI Research Centre, RCSI, Dublin, Ireland
                [36 ]Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
                [37 ]Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
                [38 ]Center for Molecular Medicine, Department of Clinical Neuroscience, Neuroimmunology Unit, Karolinska Institutet, Stockholm, Sweden
                [39 ]Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
                [40 ]Institute of Biological Psychiatry, Copenhagen Mental Health Services, Copenhagen, Denmark
                [41 ]Danish Headache Center, Department of Neurology, Copenhagen University Hospital, 2600 Glostrup, Denmark
                [42 ]Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
                [43 ]The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Copenhagen, Denmark
                [44 ]The Globe Institute, Lundbeck Foundation Center for Geogenetics, Øster Voldgade 5-7, 1350 Copenhagen K, Denmark
                [45 ]Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
                [46 ]Department of Integrative Biology, University of California, Berkeley, 3060 Valley Life Sciences Bldg #3140, Berkeley, CA 94720-3140, USA
                [47 ]Faculty of Medicine, University of Iceland, Reykjavík, Iceland
                Author notes
                []Corresponding author shyam.gopalakrishnan@ 123456sund.ku.dk
                [48]

                These authors contributed equally

                [49]

                Lead contact

                Article
                S0960-9822(22)01467-1
                10.1016/j.cub.2022.09.023
                9671091
                36182700
                168d7736-031c-4907-8c2b-69c647340f87
                © 2022 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 7 June 2022
                : 15 June 2022
                : 9 September 2022
                Categories
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                Life sciences
                plague,second plague pandemic,yersinia pestis,pandemic genomics,population genomics,selection,population replacement,trondheim

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