+1 Recommend
1 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database


      1 , 2 , 3 , 4 , 1 , 5 , 6 , 7 , 8 , 9 , 2 , 1 , 1 , 1 , 1 , 2 , 1 , 10 , 1 , 5 , 11 , 12 , 13 , 14 , 11 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 21 , 22 , 23 , 24 , 13 , 14 , 25 , 26 , 27 , 25 , 28 , 19 , 29 , 12 , 30 , 31 , 19 , 32 , 24 , 25 , 33 , 12 , 13 , 14 , 32 , 34 , 33 , 21 , 12 , 35 , 36 , 37 , 23andMe, The Genetic Epidemiology of Parkinson's Disease (GEO-PD) Consortium, The International Parkinson's Disease Genomics Consortium (IPDGC), The Parkinson's Disease GWAS Consortium, The Wellcome Trust Case Control Consortium 2 (WTCCC2), 4 , 2 , 38 , 3 , 1 , 7 , 39 , 40 , 41 , 1 , 2 , *

      PLoS Genetics

      Public Library of Science

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant ( P<5×10 −8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10 −8). All meta-analysis results are freely available on a dedicated online database ( www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

          Author Summary

          The genetic basis of Parkinson's disease is complex, i.e. it is determined by a number of different disease-causing and disease-predisposing genes. Especially the latter have proven difficult to find, evidenced by more than 800 published genetic association studies, typically showing discrepant results. To facilitate the interpretation of this large and continuously increasing body of data, we have created a freely available online database (“PDGene”: http://www.pdgene.org) which provides an exhaustive account of all published genetic association studies in PD. One particularly useful feature is the calculation and display of up-to-date summary statistics of published data for overlapping DNA sequence variants (polymorphisms). These meta-analyses revealed eleven gene loci that showed a statistically very significant ( P<5×10 −8; a.k.a. genome-wide significance) association with risk for PD: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, SYT11/RAB25. In addition and purely by data-mining, we identified one novel PD susceptibility locus in a gene called ITGA8 (rs7077361, P = 1.3×10 −8). We note that our continuously updated database represents the most comprehensive research synopsis of genetic association studies in PD to date. In addition to vastly facilitating the work of other PD geneticists, our approach may serve as a valuable example for other complex diseases.

          Related collections

          Most cited references 33

          • Record: found
          • Abstract: found
          • Article: not found

          Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database.

          The past decade has witnessed hundreds of reports declaring or refuting genetic association with putative Alzheimer disease susceptibility genes. This wealth of information has become increasingly difficult to follow, much less interpret. We have created a publicly available, continuously updated database that comprehensively catalogs all genetic association studies in the field of Alzheimer disease (http://www.alzgene.org). We performed systematic meta-analyses for each polymorphism with available genotype data in at least three case-control samples. In addition to identifying the epsilon4 allele of APOE and related effects, we pinpointed over a dozen potential Alzheimer disease susceptibility genes (ACE, CHRNB2, CST3, ESR1, GAPDHS, IDE, MTHFR, NCSTN, PRNP, PSEN1, TF, TFAM and TNF) with statistically significant allelic summary odds ratios (ranging from 1.11-1.38 for risk alleles and 0.92-0.67 for protective alleles). Our database provides a powerful tool for deciphering the genetics of Alzheimer disease, and it serves as a potential model for tracking the most viable gene candidates in other genetically complex diseases.
            • Record: found
            • Abstract: found
            • Article: not found

            Meta-analysis in clinical trials.

            This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              BigWig and BigBed: enabling browsing of large distributed datasets

              Summary: BigWig and BigBed files are compressed binary indexed files containing data at several resolutions that allow the high-performance display of next-generation sequencing experiment results in the UCSC Genome Browser. The visualization is implemented using a multi-layered software approach that takes advantage of specific capabilities of web-based protocols and Linux and UNIX operating systems files, R trees and various indexing and compression tricks. As a result, only the data needed to support the current browser view is transmitted rather than the entire file, enabling fast remote access to large distributed data sets. Availability and implementation: Binaries for the BigWig and BigBed creation and parsing utilities may be downloaded at http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/. Source code for the creation and visualization software is freely available for non-commercial use at http://hgdownload.cse.ucsc.edu/admin/jksrc.zip, implemented in C and supported on Linux. The UCSC Genome Browser is available at http://genome.ucsc.edu Contact: ann@soe.ucsc.edu Supplementary information: Supplementary byte-level details of the BigWig and BigBed file formats are available at Bioinformatics online. For an in-depth description of UCSC data file formats and custom tracks, see http://genome.ucsc.edu/FAQ/FAQformat.html and http://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html

                Author and article information

                Role: Editor
                PLoS Genet
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                March 2012
                March 2012
                15 March 2012
                : 8
                : 3
                [1 ]Neuropsychiatric Genetics Group, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
                [2 ]Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
                [3 ]Department of Neurology, Medical Center of the Johannes Gutenberg-University, Mainz, Germany
                [4 ]Department of Neurology, University Hospital, Münster, Germany
                [5 ]Department of Mathematics and Computer Science, Free University, Berlin, Germany
                [6 ]Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, United States of America
                [7 ]Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
                [8 ]Centre for Diabetes and Endocrinology, Royal Berkshire Hospital, Reading, United Kingdom
                [9 ]Oxford Centre for Diabetes, Endocrinology, and Metabolism, Churchill Hospital, University of Oxford, Oxford, United Kingdom
                [10 ]Max Planck Institute for Human Development, Berlin, Germany
                [11 ]Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
                [12 ]John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
                [13 ]Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
                [14 ]DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
                [15 ]INSERM, UMR_S975, Paris, France
                [16 ]Université Pierre et Marie Curie-Paris, Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière, UMR-S975, Paris, France
                [17 ]CNRS, UMR 7225, Paris, France
                [18 ]AP-HP, Pitié-Salpêtrière Hospital, Department of Genetics and Cytogenetics, Paris, France
                [19 ]Department of Neurology, Boston University School of Medicine, Boston University, Boston, Massachusetts, United States of America
                [20 ]Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
                [21 ]23andMe, Mountain View, California, United States of America
                [22 ]Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, United States of America
                [23 ]Department of Medical Genetics, University of British Columbia, Vancouver, Canada
                [24 ]Indiana University School of Medicine, Indianapolis, Indiana, United States of America
                [25 ]New York State Department of Health Wadsworth Center, Albany, New York, United States of America
                [26 ]Department of Molecular Neuroscience, UCL Institute of Neurology, University College London, London, United Kingdom
                [27 ]Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, The Netherlands
                [28 ]Section of Clinical and Molecular Neurogenetics, Department of Neurology, University of Lübeck, Lübeck, Germany
                [29 ]Department of Neurology, NorthShore University Health System, Evanston, Illinois, United States of America
                [30 ]INSERM UMR 1043, CPTP, Toulouse, France
                [31 ]Paul Sabatier University, Toulouse, France
                [32 ]Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
                [33 ]Division of Neurology/Molecular Brain Science, Kobe University Graduate School of Medicine, Kobe, Japan
                [34 ]deCODE genetics, Reykjavik, Iceland
                [35 ]UCL Genetics Institute, University College London, London, United Kingdom
                [36 ]Department of Molecular Neuroscience, UCL Institute of Neurology, University College London, London, United Kingdom
                [37 ]VA Puget Sound Health Care System and Department of Neurology, University of Washington, Seattle, Washington, United States of America
                [38 ]Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
                [39 ]Biomedical Research Institute, Foundation for Research and Technology–Hellas, Ioannina, Greece
                [40 ]Center for Genetic Epidemiology and Modeling and Tufts Clinical and Translational Science Institute, Tufts University School of Medicine, Boston, Massachusetts, United States of America
                [41 ]Stanford Prevention Research Center, Department of Medicine and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, United States of America
                University of Miami, United States of America
                Author notes

                Conceived and designed the experiments: CM Lill, MB McQueen, JPA Ioannidis, L Bertram. Performed the experiments: CM Lill, JT Roehr, S Bagade, B-M Schjeide, E Meissner, U Zauft, NC Allen, KJ Anderson, G Beecham, D Berg, JM Biernacka, A Brice, AL DeStefano, CB Do, N Eriksson, SA Factor, MJ Farrer, T Foroud, T Gasser, T Hamza, JA Hardy, P Heutink, C Klein, JC Latourelle, DM Maraganore, ER Martin, M Martinez, RH Myers, H Payami, WK Scott, M Sharma, AB Singleton, K Stefansson, T Toda, JY Tung, J Vance, NW Wood, CP Zabetian, 23andMe, GEO-PD, IPDGC, Parkinson's Disease GWAS, WTCC2. Analyzed the data: CM Lill, JT Roehr, MB McQueen, FK Kavvoura, L Bertram. Wrote the paper: CM Lill, JPA Ioannidis, L Bertram. Helped write the manuscript: E Meissner, MJ Farrer, T Foroud, T Gasser, C Klein, DM Maraganore, H Payami, AB Singleton, M Sharma, F Zipp, H Lehrach. Helped analyze the data: S Bagade, T Liu, M Schilling, CB Do, N Eriksson, T Hamza, EM Hill-Burns, MA Nalls, N Pankratz, W Satake, M Sharma. Interpretation of results: CM Lill, JPA Ioannidis, L Bertram. Study coordination: CM Lill, T Foroud, JA Hardy, H Payami, AB Singleton, P Young, RE Tanzi, MJ Khoury, F Zipp, H Lehrach, JPA Ioannidis, L Bertram. Literature searches and data entry: CM Lill, S Bagade, B-M Schjeide, E Meissner, U Zauft, N Allen.

                ¶ Memberships of the consortia are provided in Text S1.

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
                Page count
                Pages: 10
                Research Article



                Comment on this article