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      Genome-Wide Association Study reveals genetic risk underlying Parkinson’s disease

      1 , 2 , 3 , 1 , 4 , 3 , 1 , 5 , 3 , 5 , 6 , 1 , 5 , 1 , 5 , 3 , 1 , 7 , 8 , 8 , 9 , 1 , 10 , 10 , 5 , 11 , 12 , 1 , 12 , 12 , 1 , 13 , 1 , 1 , 14 , 1 , 15 , 16 , 16 , 16 , 17 , 18 , 19 , 5 , 20 , 20 , 18 , 9 , 4 , 1 , 3

      Nature genetics

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          We performed a genome-wide association study (GWAS) in 1,713 Caucasian patients with Parkinson’s disease (PD) and 3,978 controls. After replication in 3,361 cases and 4,573 controls, two strong association signals were observed: in the α-synuclein gene( SNCA) (rs2736990, OR=1.23, p=2.24×10 −16) and at the MAPT locus (rs393152, OR=0.77, p=1.95×10 −16). We exchanged data with colleagues performing a GWAS in Asian PD cases. Association at SNCA was replicated in the Asian GWAS 1, confirming this as a major risk locus across populations. We were able to replicate the effect of a novel locus detected in the Asian cohort ( PARK16, rs823128, OR=0.66, p=7.29×10 −8) and provide evidence supporting the role of common variability around LRRK2 in modulating risk for PD (rs1491923, OR=1.14, p=1.55×10 −5). These data demonstrate an unequivocal role for common genetic variability in the etiology of typical PD and suggest population specific genetic heterogeneity in this disease.

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          Most cited references 22

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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.
            • Record: found
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            Haploview: analysis and visualization of LD and haplotype maps.

            Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.
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              The structure of haplotype blocks in the human genome.

              Haplotype-based methods offer a powerful approach to disease gene mapping, based on the association between causal mutations and the ancestral haplotypes on which they arose. As part of The SNP Consortium Allele Frequency Projects, we characterized haplotype patterns across 51 autosomal regions (spanning 13 megabases of the human genome) in samples from Africa, Europe, and Asia. We show that the human genome can be parsed objectively into haplotype blocks: sizable regions over which there is little evidence for historical recombination and within which only a few common haplotypes are observed. The boundaries of blocks and specific haplotypes they contain are highly correlated across populations. We demonstrate that such haplotype frameworks provide substantial statistical power in association studies of common genetic variation across each region. Our results provide a foundation for the construction of a haplotype map of the human genome, facilitating comprehensive genetic association studies of human disease.

                Author and article information

                [1 ]Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
                [2 ]Department of Clinical Genetics Section of Medical Genomics VU Medical Center De Boelelaan 1085 1081 Amsterdam, the Netherlands
                [3 ]Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tubingen, and German Center for Neurodegenerative Diseases, Tubingen, Germany
                [4 ]Center for Neurosciences and Cell Biology, University of Coimbra, Coimbra, Portugal
                [5 ]Department of Molecular Neuroscience and Reta Lila Weston Laboratories, Institute of Neurology, University College London, London, United Kingdom
                [6 ]Institute of Human Genetics, Helmholtz Zentrum Munchen, German Research Centre for Environmental Health, Neuherberg, Germany
                [7 ]Section of Clinical and Molecular Neurogenetics at the Department of Neurology, University of Luebeck, Germany
                [8 ]Department of Psychiatry; Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
                [9 ]Department of Medical Genetics, Institute of Human Genetics, University of Tubingen, Tubingen, Germany
                [10 ]Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Centre for Environmental Health, Neuherberg, Germany
                [11 ]Institute of Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
                [12 ]Movement Disorders Center, University of Florida, Gainesville, Florida, USA
                [13 ]Institut fur Klinische Molekularbiologie, Christian-Albrechts-Universitat Kiel, Germany
                [14 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Texas, USA
                [15 ]Parkinson’s disease clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
                [16 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
                [17 ]AARP, Washington DC, USA
                [18 ]Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, North Carolina, USA
                [19 ]Departments of Neurology, Radiology, Neurosurgery, Pharmacology, Kinesiology & Bioengineering, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
                [20 ]Klinik fur Neurologie, UK-SH, Campus Kiel, Christian-Albrechts-Universitat Kiel, Kiel, Germany
                Author notes
                [CA ]Please address correspondence to Dr. Thomas Gasser ( thomas.gasser@ ) and Dr. Andrew Singleton ( singleta@ )

                these authors contributed equally

                Nat Genet
                Nature genetics
                27 October 2009
                15 November 2009
                December 2009
                1 June 2010
                : 41
                : 12
                : 1308-1312

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