Parkinson's disease (PD) is the second most common neurodegenerative disease with a prevalence of ∼1% over 60 years of age ^[1]. Approximately 5–10% of the patients show an autosomal dominant or recessive mode of inheritance, and several causative genes have been identified, e.g. SNCA, LRRK2, PARK2, and PINK1 (for review see ref. ^[2]). Recently, two other novel autosomal dominant PD genes, VPS35 and EIF4G1^[3]–^[5], have been identified, the former via application of next-generation sequencing techniques. It can be anticipated that causal mutations in additional genes will emerge within the next years. However, the vast majority of patients suffer from non-Mendelian forms of PD, which are likely caused by the combined effects of genetic and environmental factors. In order to decipher the genetic architecture underlying PD susceptibility, more than 800 genetic association studies have been performed over the past 20 years. While early candidate gene studies and subsequent meta-analyses provided conclusive evidence showing that polymorphisms in SNCA^[6] (encoding alpha-synuclein), LRRK2^[7] (leucine-rich repeat kinase 2), MAPT^[8] (microtubule-associated protein tau), and GBA^[9] (acid beta-glucosidase) significantly impact PD susceptibility, most association studies in the field provided inconclusive or even conflicting results.

During the last few years, genome-wide association studies (GWAS) ^[10]–^[19] have postulated additional PD loci. While the early GWAS and a GWAS-meta-analysis ^[20] were of limited sample sizes and yielded mostly inconsistent results, more recent studies have identified a number of loci that were independently confirmed in follow-up studies (e.g. GAK, BST1, and PARK16, see Table 1 for all proposed GWAS findings across GWAS publications). Very recently, a GWAS meta-analysis ^[21] implicated several other new putative PD loci which currently await further validation. Despite this progress, approximately 40% or more of the population-attributable risk probably remains unexplained by today's most promising PD loci ^[21]. To this end, genetic association studies remain one of the mainstays of PD genetics research. However, GWAS and other large-scale association studies typically only highlight the most promising results and often do not provide data on variants showing suggestive evidence for association, or previously implied variants that could not be confirmed in the GWAS setting. As a result, the cumulative genetic evidence in favor of or against association with certain variants in the PD field is becoming increasingly difficult to follow, evaluate and interpret. To address this problem, we have comprehensively collected, catalogued and systematically meta-analyzed the data from all genetic association studies published in the field of non-Mendelian PD, including GWAS, and made all results publicly available on a regularly updated online database, “PDGene” (http://www.pdgene.org).

The PDGene database represents a comprehensive, regularly updated and freely available online research synopsis of genetic association studies in PD. Detailed summaries of the most compelling findings are provided within an easy-to-use, dedicated online framework, displaying forest plots, cumulative meta-analyses, and an up-to-date ranking of “Top Results”. To allow comparison of PDGene results with association findings from other complex diseases and to facilitate their interpretation with respect to functional genetics data, all meta-analysis results have been ported as a customized track onto the UCSC Genome Browser. This will also allow for a integration and visualization ^[28] of association results from large-scale resequencing data (e.g. from whole-exome or whole-genome studies) into PDGene once these become available.

To the best of our knowledge, our study represents the most comprehensive research synopsis in the field of PD genetics. In addition, it represents the first disease-specific genetic database that allows a systematic and exhaustive inclusion of GWAS data, and may serve as a model for similar databases in other complex genetic diseases. Owing to our multi-pronged data retrieval and analysis protocol we were able to perform meta-analyses on the vast majority of PD risk-gene candidates, including those “featured” as top association results in all published GWAS. In particular, this includes the five novel loci recently featured in the recent GWAS meta-analysis ^[21]. Through collaboration with other PD genetics laboratories we obtained independent summary data for these and 142 additional SNPs, substantially extending the hitherto available evidence. Taken together, our analyses provide unequivocal evidence that BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, ITGA8, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, SYT11/RAB25 represent genuine PD risk loci, while the role of several other loci (e.g. ACMSD/TMEM163, and the HLA locus) remains to be determined. The unpublished data aggregated here from various PD genetics groups for selected candidate genes represents the first step towards a systematic meta-analysis across the full GWAS datasets from the same populations. Once completed, the results of this “mega” meta-analysis will be posted on the PDGene database, allowing users to browse the complete results via the customized genome browser track already in place.

Of particular interest are loci with unusually large effect sizes. While most loci in PDGene have only small effects on PD risk (with ORs ranging from 1.10 to 1.35, which are typical for complex diseases), for some loci much larger ORs were estimated (i.e. GBA [OR 3.51 in Caucasians], LRRK2 [OR 2.23 in Asians], and SYT11/RAB25 [OR 1.73 in Caucasians], see Table 2). The risk-allele frequencies at these polymorphisms are typically rather small (i.e. below 0.05), resulting in low population attributable risks for these loci (for the above mentioned loci individually less than 2%).

Interestingly, the meta-analysis results of GBA N370S as well as the LRRK2 rs34778348 are solely based on candidate-gene approaches since these SNPs are not on any of the current GWAS arrays or imputation reference panels. Thus, even in the “GWAS era” smaller-scale, non-GWAS but “focused” genetic studies, will likely continue to play an important role. This is also true when it comes to providing independent replication of proposed disease associations and/or when validating imputation-derived results by direct genotyping in sufficiently sized datasets. PDGene systematically concatenates all these different types of data into one database framework, vastly facilitating an assessment of the overall evidence for any given SNP or locus.

The strength of our approach is further exemplified by the identification of genome-wide significant association between disease risk and a SNP in ITGA8, which was not featured as a relevant PD gene in any previous study. ITGA8 (encoding integrin alpha 8, a type-I transmembrane protein) is functionally interesting as it is expressed in brain ^[29], mediates cell-cell interactions and regulates neurite outgrowth of sensory and motor neurons ^[30]. Additional studies are needed to further assess the potential role of this gene in PD pathogenesis. Furthermore, PDGene shows that two additional loci, not highlighted by the recent GWAS meta-analysis ^[21], yield genome-wide signficiant results in the PDGene meta-analyses, i.e. PARK16, originally implicated as a PD susceptibility locus in an Asian GWAS ^[14] but not highlighted in the recent GWAS meta-analysis on Caucasian samples ^[21] and GBA, a gene that was found soley by candidate-gene approaches. Another strength of our study is that it combines genetic data from currently more than 50 different countries allowing a systematic assessment of genetic associations across populations of different ethnic descent. For instance, these analyses suggest that variants in BST1, LRRK2, the PARK16 locus, and SNCA show genome-wide significant association with PD risk in both Caucasian and Asian-descent samples. Furthermore, the recently described Caucasian GWAS loci CCDC62/HIP1R, MCC1, and STK39^[21] also show similar effect size estimates in populations of Asian-descent ^[23]. PD association data originating from other ethnic groups are still relatively scarce. However, they could easily be added to the already existing data on the respective polymorphisms available on PDGene.

In summary, we have created a continuously updated online resource for genetic association studies in the field of PD. Synthesizing essentially all available data in the field led to the identification of ITGA8 as a novel potential PD risk locus. Our quantitative approach to data integration across a multitude of different study designs can be readily scaled to include large-scale resequencing efforts that will emerge over the coming years, making the complex field of PD genetics accessible to a broad range of investigators.

Note that the following section only provides a brief summary of the methods applied to our study. A much more detailed description can be found in Text S1.

CB Do, N Eriksson, and JY Tung are employed by 23andMe and own stock options in the company. MJ Farrer and Mayo Foundation received royalties from H.Lundbeck A/S and Isis Pharmaceuticals. In addition, MJ Farrer has received an honorarium for a seminar at Genzyme. T Gasser has received consultancy fees from Cephalon and Merck-Serono, grants from Novartis, payments for lectures including service on speakers' bureaus from Boehringer Ingelheim, Merck-Serono, UCB, and Valean, and holds patents NGFN2 and KASPP. JA Hardy has received consulting fees or honoraria from Eisai and his institute has received consulting fees or honoraria from Merck-Serono. DM Maraganore has received extramural research funding support from the National Institutes of Health (2R01 ES10751), the Michael J. Fox Foundation (Linked Efforts to Accelerate Parkinson Solutions Award, Edmond J. Safra Global Genetics Consortia Award), and from Alnylam Pharmaceuticals and Medtronic (observational studies of Parkinson's disease). DM Maraganore has also received intramural research funding support from the Mayo Clinic and from NorthShore University Health System. DM Maraganore filed a provisional patent for a method to predict Parkinson's disease. This provisional patent is unlicensed. He also filed a provisional patent for a method to treat neurodegenerative disorders. That provisional patent has been licensed to Alnylam Pharmaceuticals and DM Maraganore has received royalty payments in total of less than $20,000. K Stefansson has received grants from deCODE.

The main funding for this study was provided by the Michael J. Fox Foundation for Parkinson's Disease (MJFF) with additional financial support by the Cure Alzheimer's Fund (CAF), the National Alliance for Research on Schizophrenia and Depression (NARSAD), Prize4Life, and EMD Serono (all to L Bertram). CM Lill was supported by a fellowship from the Deutscher Akademischer Austauschdienst (DAAD) and Fidelity Biosciences Research Initiative (FBRI). L Bertram is also supported by the German Ministry for Education and Research (BMBF). JPA Ioannidis was supported through the Tufts Clinical and Translational Science Institute (Tufts CTSI) under funding from the National Institute of Health/National Center for Research Resources (UL1 RR025752). Points of view or opinions in this paper are those of the authors and do not necessarily represent the official position or policies of the Tufts CTSI. M Sharma was supported by the Michael J. Fox Foundation. The NeuroGenetics Research Consortium GWAS [15] was funded by the Edmond J. Safra Michael J. Fox Foundation Global Genetics Consortium Initiative and NIH R01 NS 036960. The work of the International Parkinson's Disease Genomics Consortium (IPDGC) was supported in part by the Intramural Research Programs of the National Institute on Aging, National Institute of Neurological Disorders and Stroke, National Institute of Environmental Health Sciences, National Human Genome Research Institute, National Institutes of Health, Department of Health and Human Services: project numbers Z01 AG000949-02 and Z01-ES101986. In addition the work of the IPDGC was supported by the U.S. Department of Defense, award number W81XWH-09-2-0128. Portions of the work of the IPDGC utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, Md. (http://biowulf.nih.gov). T Foroud received funds from the National Institutes of Health (R01CA141668 and R01NS37167). C Klein is the recipient of a career development award from the Volkswagen Foundation and from the Hermann and Lilly Schilling Foundation. DM Maraganore acknowledges active funding support from the National Institutes of Health (2R01 ES10751), Alnylam Pharmaceuticals, Medtronic, and NorthShore University Health System. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

23andMe acknowledges Elizabeth Dorfman, Amy K. Kiefer, Emily M. Drabant, Uta Francke, Joanna L. Mountain, David Hinds, and Anne Wojcicki from 23andMe, as well as Samuel M. Goldman, Caroline M. Tanner, and J. William Langston from the Parkinson's Institute, Sunnyvale, CA, USA. We also acknowledge the contribution of Mitsutoshi Yamamoto, Nobutaka Hattori, and Miho Murata for sample collection in the Japanese GWAS 1.0 ^[14]. We are grateful to the Alzheimer Research Forum—in particular to June Kinoshita, Colin Knep, Paula Noyes, and Gabrielle Ströbel—for hosting PDGene on their website. We also thank the many PD researchers who have kindly provided us with genotype data and helpful information beyond those included in the original publications. Finally, we would like to thank the many PD patients and control subjects who volunteered to participate in the individual studies.