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      The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease

      research-article
      1 , 2 , 3 , 4 , 31 , 5 , 6 , 31 , 4 , 31 , 7 , 1 , 2 , 3 , 5 , 5 , 5 , 5 , 1 , 2 , 8 , 1 , 2 , 1 , 9 , 1 , 2 , 1 , 5 , 5 , 10 , 11 , 12 , 1 , 2 , 13 , 14 , 15 , 1 , 2 , 1 , 2 , 1 , 2 , 11 , 13 , 10 , 10 , 4 , 10 , 10 , 16 , 17 , 10 , 1 , 2 , 13 , 1 , 2 , 13 , 16 , 17 , 1 , 2 , 10 , 18 , 4 , 1 , 13 , 10 , 5 , 10 , 5 , 8 , 5 , 8 , 4 , 1 , 2 , 4 , 10 , 4 , 6 , 19 , 4 , 6 , 20 , 21 , 22 , 22 , 22 , 23 , 22 , 1 , 5 , 24 , 5 , 24 , 25 , 26 , 5 , 5 , 25 , 26 , 27 , 28 , 4 , 28 , 24 , 24 , 24 , 1 , 2 , 19 , 4 , 5 , 6 , 12 , 19 , , 29 , 30 , ∗∗ , 1 , 2 , 5 , 6 , 19 , ∗∗∗ , 4 , 6 , 19 , ∗∗∗∗ , 1 , 5 , 6 , 19 , 32 , ∗∗∗∗∗
      Cell
      Cell Press
      blood, genetics, hematology, epigenetics, hematopoiesis, Mendelian randomization, complex disease, autoimmune diseases, cardiovascular diseases

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          Summary

          Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

          Graphical Abstract

          Highlights

          • Genome-wide association study interrogates 36 traits across the hematopoietic system

          • A total of 2,706 associated variants, including 130 rare and 230 low frequency

          • Describes allelic spectrum and heritability of coding and regulatory variants

          • Unravels causal contributions to cardiovascular, immune, and psychiatric disease

          Abstract

          As part of the IHEC Consortium, this study probes the allelic architecture and regulatory landscape of cellular complex traits with power to identify causal pathways and links to diseases such as schizophrenia. Explore the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC.

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          Most cited references98

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            Second-generation PLINK: rising to the challenge of larger and richer datasets

            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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              UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

              Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                17 November 2016
                17 November 2016
                : 167
                : 5
                : 1415-1429.e19
                Affiliations
                [1 ]Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
                [2 ]National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
                [3 ]Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge CB2 0SR, UK
                [4 ]MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort’s Causeway, Cambridge CB1 8RN, UK
                [5 ]Department of Human Genetics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK
                [6 ]The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort’s Causeway, Cambridge CB1 8RN, UK
                [7 ]Blood Research Group, NHS Blood and Transplant, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9BQ, UK
                [8 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
                [9 ]Department of Haematology, Barts Health NHS Trust, The Royal London Hospital, Whitechapel Road, London, London E1 1BB, UK
                [10 ]Department of Molecular Biology, Radboud University, Faculty of Science, Nijmegen 6525GA, the Netherlands
                [11 ]Department of Medicine, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0QQ, UK
                [12 ]National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge University Hospitals, Cambridge CB2 0QQ, UK
                [13 ]NIHR BioResource-Rare Diseases, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
                [14 ]Département de Génétique et Développement (GEDEV), University of Geneva, 1211 Geneve 4, Switzerland
                [15 ]Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, University of Leuven, 3000 Leuven, Belgium
                [16 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK
                [17 ]Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
                [18 ]UK Biobank Ltd., 1-4 Spectrum Way, Adswood, Stockport SK3 0SA, UK
                [19 ]British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
                [20 ]Emma Children’s Hospital, Academic Medical Center (AMC), University of Amsterdam, Location H7-230, Meibergdreef 9, Amsterdam 1105AZ, the Netherlands
                [21 ]Blood Cell Research, Sanquin Research and Landsteiner Laboratory, Plesmanlaan 125, Amsterdam, 1066CX, the Netherlands
                [22 ]Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029 Madrid, Spain
                [23 ]Institute of Cellular Medicine, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
                [24 ]Human Genetics, McGill University, 740 Dr. Penfield, Montreal, QC H3A 0G1, Canada
                [25 ]Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Carrer del Dr. Aiguader, 88, Barcelona 8003, Spain
                [26 ]Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10- 12, Barcelona 8002, Spain
                [27 ]Computational Genomics, Institut Hospital del Mar d’Investigacions Mediques (IMIM), Carrer del Dr. Aiguader, 88, Barcelona 8003, Spain
                [28 ]UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
                [29 ]Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, UK
                [30 ]Department of Haematology, Churchill Hospital, Headington, Oxford OX3 7LE, UK
                Author notes
                []Corresponding author jd292@ 123456medschl.cam.ac.uk
                [∗∗ ]Corresponding author david.roberts@ 123456ndcls.ox.ac.uk
                [∗∗∗ ]Corresponding author who1000@ 123456cam.ac.uk
                [∗∗∗∗ ]Corresponding author asb38@ 123456medschl.cam.ac.uk
                [∗∗∗∗∗ ]Corresponding author ns6@ 123456sanger.ac.uk
                [31]

                Co-first author

                [32]

                Lead Contact

                Article
                S0092-8674(16)31463-5
                10.1016/j.cell.2016.10.042
                5300907
                27863252
                657e1c27-0299-4964-b644-c49955701b93
                © 2016 The Authors. Published by Elsevier Inc.

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

                History
                : 1 February 2016
                : 13 September 2016
                : 21 October 2016
                Categories
                Resource

                Cell biology
                blood,genetics,hematology,epigenetics,hematopoiesis,mendelian randomization,complex disease,autoimmune diseases,cardiovascular diseases

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