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      Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease

      research-article
      1 , 2 , 3 , 4 , 5 , 1 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 13 , 14 , 15 , 15 , 1 , 1 , 1 , 1 , 16 , 17 , 1 , 18 , 18 , International Parkinson’s Disease Genomics Consortium (IPDGC) 19 , Parkinson’s Study Group (PSG) Parkinson’s Research: The Organized GENetics Initiative (PROGENI) 19 , 23andMe 19 , Gene PD 19 , NeuroGenetics Research Consortium (NGRC) 19 , Hussman Institute of Human Genomics (HIHG) 19 , The Ashkenazi Jewish Dataset Investigator 19 , Cohorts for Health and Aging Research in Genetic Epidemiology (CHARGE) 19 , North American Brain Expression Consortium (NABEC) 19 , United Kingdom Brain Expression Consortium (UKBEC) 19 , Greek Parkinson’s Disease Consortium 19 , Alzheimer Genetic Analysis Group 19 , 20 , 21 , 22 , 23 , 24 , 25 , 8 , 9 , 26 , 27 , 28 , 26 , 28 , 29 , 30 , 18 , 31 , 32 , 33 , 34 , 35 , 24 , 36 , 10 , 17 , 18 , 16 , 6 , 37 , 38 , 13 , 13 , 39 , 41 , 42 , 43 , 15 , 3 , 5 , 44 , 1
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          Abstract

          We conducted a meta analysis of Parkinson’s disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as genome-wide significant; these and six additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 novel loci. Conditional analyses within loci show four loci including GBA, GAK/DGKQ, SNCA, and HLA contain a secondary independent risk variant. In total we identified and replicated 28 independent risk variants for Parkinson disease across 24 loci. While the effect of each individual locus is small, a risk profile analysis revealed a substantial cummulative risk in a comparison highest versus lowest quintiles of genetic risk (OR=3.31, 95% CI: 2.55, 4.30; p-value = 2×10 −16). We also show 6 risk loci associated with proximal gene expression or DNA methylation.

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          Most cited references37

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          Is Open Access

          METAL: fast and efficient meta-analysis of genomewide association scans

          Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
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            Is Open Access

            A map of human genome variation from population-scale sequencing.

            The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.
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              Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.

              The 1000 Genomes Project and disease-specific sequencing efforts are producing large collections of haplotypes that can be used as reference panels for genotype imputation in genome-wide association studies (GWAS). However, imputing from large reference panels with existing methods imposes a high computational burden. We introduce a strategy called 'pre-phasing' that maintains the accuracy of leading methods while reducing computational costs. We first statistically estimate the haplotypes for each individual within the GWAS sample (pre-phasing) and then impute missing genotypes into these estimated haplotypes. This reduces the computational cost because (i) the GWAS samples must be phased only once, whereas standard methods would implicitly repeat phasing with each reference panel update, and (ii) it is much faster to match a phased GWAS haplotype to one reference haplotype than to match two unphased GWAS genotypes to a pair of reference haplotypes. We implemented our approach in the MaCH and IMPUTE2 frameworks, and we tested it on data sets from the Wellcome Trust Case Control Consortium 2 (WTCCC2), the Genetic Association Information Network (GAIN), the Women's Health Initiative (WHI) and the 1000 Genomes Project. This strategy will be particularly valuable for repeated imputation as reference panels evolve.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                1 July 2014
                27 July 2014
                September 2014
                01 March 2015
                : 46
                : 9
                : 989-993
                Affiliations
                [1 ]Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892
                [2 ]Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455
                [3 ]Neuropsychiatric Genetics Group, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
                [4 ]Department of Neurology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
                [5 ]23andMe, Inc., Mountain View, California, USA
                [6 ]Reta Lila Weston Institute, University College London Institute of Neurology, Queen Square, London, United Kingdom, WC1N 3BG
                [7 ]Department of Biostatistics, University of Washington, Seattle, WA 8195-9460
                [8 ]Institut National de la Sante et de la Recherche Medicale, UMR 1043, Centre de Physiopathologie de Toulouse-Purpan, Toulouse, France
                [9 ]Paul Sabatier University, Toulouse, France
                [10 ]Department of Neurology, Boston University School of Medicine, Boston, MA, USA
                [11 ]Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
                [12 ]NHLBI’s Framingham Heart Study, Framingham, MA, USA
                [13 ]Department of Molecular Neuroscience, Institute of Neurology, University College London, London, United Kingdom, WC1N 3BG
                [14 ]Institute for Clinical Epidemiology and Applied Biometry, University of Tubingen, Tubingen, Germany
                [15 ]Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
                [16 ]deCODE genetics, Sturlugata 8, IS-101, Reykjavík, Iceland
                [17 ]Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032
                [18 ]The Taub Institute for Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, new York, NY 10032
                [19 ]A full list of investigators is in the Supplementary Note
                [20 ]Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
                [21 ]Department of Radiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
                [22 ]Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
                [23 ]Stanford Prevention Research Center, Stanford University, Stanford, USA
                [24 ]Neuroscience Unit, Department of Neurology, Faculty of Medicine, University of Thessaly, Greece
                [25 ]Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
                [26 ]Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, MO 63110
                [27 ]Department of Radiology, Washington University School of Medicine, St Louis, MO 63110
                [28 ]Department of Neurology, Washington University School of Medicine, St Louis, MO 63110
                [29 ]Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110
                [30 ]Department of Genetics, Washington University School of Medicine, St Louis, MO 63110
                [31 ]Gertrude H. Sergievsky Center, Columbia University Medical Center, New York, NY 10032
                [32 ]Department of Neurology, Columbia University Medical Center. New York
                [33 ]Department of Psychiatry, Columbia University Medical Center. New York
                [34 ]The Michael J. Fox Foundation for Parkinson’s Research, New York, NY
                [35 ]Neuroscience Center, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892
                [36 ]Department of Neurology, Papageorgiou Hospital, Thessaloniki, Greece
                [37 ]Genome Biology for Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
                [38 ]Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, North Carolina
                [39 ]New York State Department of Health Wadsworth Center, Albany, NY, USA
                [40 ]Université Pierre et Marie Curie-Paris, Centre de Recherche de l’Institut du Cerveau et de la Moelle épinière, UMR-S975, Paris, France
                [41 ]Centre National de la Recherche Scientifique, UMR 7225, Paris, France
                [42 ]AP-HP, Pitié-Salpêtrière Hospital, Department of Genetics and Cytogenetics, Paris, France
                [43 ]Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville TN, USA
                [44 ]Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202
                Author notes
                [* ]Address for correspondence: singleta@ 123456mail.nih.gov
                [#]

                Denotes shared authorship.

                Article
                NIHMS610183
                10.1038/ng.3043
                4146673
                25064009
                b5fd8218-a1cb-4ab6-a53b-bba34bfa24a6
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                Genetics
                Genetics

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