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      Upper Palaeolithic genomes reveal deep roots of modern Eurasians

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          We extend the scope of European palaeogenomics by sequencing the genomes of Late Upper Palaeolithic (13,300 years old, 1.4-fold coverage) and Mesolithic (9,700 years old, 15.4-fold) males from western Georgia in the Caucasus and a Late Upper Palaeolithic (13,700 years old, 9.5-fold) male from Switzerland. While we detect Late Palaeolithic–Mesolithic genomic continuity in both regions, we find that Caucasus hunter-gatherers (CHG) belong to a distinct ancient clade that split from western hunter-gatherers ∼45 kya, shortly after the expansion of anatomically modern humans into Europe and from the ancestors of Neolithic farmers ∼25 kya, around the Last Glacial Maximum. CHG genomes significantly contributed to the Yamnaya steppe herders who migrated into Europe ∼3,000 BC, supporting a formative Caucasus influence on this important Early Bronze age culture. CHG left their imprint on modern populations from the Caucasus and also central and south Asia possibly marking the arrival of Indo-Aryan languages.


          Upper Palaeolithic and Mesolithic genomes from western Europe and the Caucasus reveal a previously undescribed strand of Eurasian ancestry with a deep divergence from other hunter-gatherer genomes. This had a profound impact on ancient and modern populations from the Atlantic to Central Asia.

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          Most cited references 47

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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: Contact:
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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: Contact:
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

                Author and article information

                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                16 November 2015
                : 6
                [1 ]Smurfit Institute of Genetics, Trinity College Dublin , Dublin, Dublin 2, Ireland
                [2 ]Department of Mathematics and Natural Sciences, Institute of Biochemistry and Biology, University of Potsdam , Karl-Liebknecht-Straße 24–25, Potsdam 14476, Germany
                [3 ]Department of Biology and Evolution, University of Ferrara , Via L. Borsari 46, Ferrara I-44100, Italy
                [4 ]School of Archaeology and Earth Institute, University College Dublin , Belfield, Dublin 4, Ireland
                [5 ]Department of Zoology, University of Cambridge , Cambridge, CB2 3EJ, UK
                [6 ]Integrative Systems Biology Laboratory, Division of Biological and Environmental Sciences & Engineering, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955-6900, Kingdom of Saudi Arabia
                [7 ]Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen , Øster Voldgade 5–7, Copenhagen 1350, Denmark
                [8 ]Georgian National Museum , 3 Rustaveli Avenue, Tbilisi 0105, Georgia
                [9 ]Department of Anthropology, Peabody Museum, Harvard University , Cambridge, Massachusetts 02138, USA
                [10 ]Laboratoire d'archéozoologie, Université de Neuchâtel , Neuchâtel 2000, Switzerland
                [11 ]Office du patrimoine et de l'archéologie de Neuchâtel, Section archéologie, LATÉNIUM , Hauterive 2068, Switzerland
                [12 ]Institute of Archaeology, Hebrew University , Jerusalem 91905, Israel
                [13 ]Israel Antiquities Authority , PO Box 586, Jerusalem 91004, Israel
                [14 ]Oxford Radiocarbon Accelerator Unit, Research Laboratory for Archaeology & the History of Art, University of Oxford , Oxford OX1 3QY, UK
                [15 ]Laboratory of Anthropology, Genetics and Peopling History (AGP), Department of Genetics and Evolution - Anthropology Unit, University of Geneva , Geneva 1227, Switzerland
                Author notes

                These authors contributed equally to this work.

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