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      A dynastic elite in monumental Neolithic society

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          Abstract

          The nature and distribution of political power in Europe during the Neolithic era remains poorly understood 1 . During this period, many societies began to invest heavily in building monuments, which suggests an increase in social organization. The scale and sophistication of megalithic architecture along the Atlantic seaboard, culminating in the great passage tomb complexes, is particularly impressive 2 . Although co-operative ideology has often been emphasised as a driver of megalith construction1, the human expenditure required to erect the largest monuments has led some researchers to emphasize hierarchy 3 —of which the most extreme case is a small elite marshalling the labour of the masses. Here we present evidence that a social stratum of this type was established during the Neolithic period in Ireland. We sampled 44 whole genomes, among which we identify the adult son of a first-degree incestuous union from remains that were discovered within the most elaborate recess of the Newgrange passage tomb. Socially sanctioned matings of this nature are very rare, and are documented almost exclusively among politico-religious elites 4 —specifically within polygynous and patrilineal royal families that are headed by god-kings 5, 6 . We identify relatives of this individual within two other major complexes of passage tombs 150 km to the west of Newgrange, as well as dietary differences and fine-scale haplotypic structure (which is unprecedented in resolution for a prehistoric population) between passage tomb samples and the larger dataset, which together imply hierarchy. This elite emerged against a backdrop of rapid maritime colonization that displaced a unique Mesolithic isolate population, although we also detected rare Irish hunter-gatherer introgression within the Neolithic population.

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          Is Open Access

          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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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Journal
                0410462
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                17 February 2021
                01 June 2020
                17 June 2020
                08 March 2021
                : 582
                : 7812
                : 384-388
                Affiliations
                [1 ]Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland
                [2 ]The Irish Fieldschool of Prehistoric Archaeology, National University of Ireland, Galway, Ireland
                [3 ]Archaeological Management Solutions, Consultancy, Kilrush, Clare, Ireland
                [4 ]UCL Arts and Sciences, University College London, London, WC1E 6BT, UK
                [5 ]Former Chief Archaeologist, National Monuments Service, Department of Culture, Heritage and the Gaeltacht, Custom House, Dublin
                [6 ]School of Geography and Archaeology, National University of Ireland Galway, Galway, Ireland
                [7 ]Department of Archaeology, University College Cork, Cork, Ireland
                [8 ]Archaeology & Palaeoecology, School of Natural & Built Environment, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland
                [9 ]National Museums NI, Cultra, Holywood BT18 0EU, Northern Ireland
                [10 ]CERIS, School of Science, Institute of Technology Sligo, Sligo, Ireland
                [11 ]Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
                Author notes
                Materials & Correspondence Requests: Please direct to Lara M. Cassidy or Daniel G. Bradley
                [†]

                Deceased

                Article
                EMS114623
                10.1038/s41586-020-2378-6
                7116870
                32555485
                5787f7e9-9fb7-4254-8397-e995c3367c60

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