Updated 8 September 2009

1. Introduction

In the past decade, literally hundreds of reports have been published claiming or refuting genetic association between putative PD genes and disease risk, onset age variation, or other phenotypic variables. Currently, 40 to 80 PD genetic association studies are being published annually from research groups worldwide. For the public but also for the PD genetics research community this wealth of information is becoming increasingly more difficult to follow, evaluate, and much less to interpret.

2. Database Organization and Methods

Overview

The goal of the PDGene database is to serve as a comprehensive, unbiased, publicly available and regularly updated collection of published genetic association studies performed on PD. Eligible publications are identified following systematic searches of scientific literature databases, as well as the table of contents of journals in genetics, neurology, and psychiatry. Data selected for display summarize key characteristics of the investigated study cohorts (e.g., gene overview), as well as genotype distributions in cases and controls (e.g., polymorphism details). For polymorphisms with genotype data in at least four case-control samples, continuously updated random-effects meta-analyses are presented (see meta-analysis methods). Note that data obtained from family-based studies are not included in the meta-analyses, as crude odds ratios cannot be readily calculated from overall genotype distributions. However, these studies and their qualitative results are still listed on the gene-summary pages of the PDGene website (see Table 2 for example).

To ensure the highest degree of scientific objectivity, only studies published in peer-reviewed journals available in English are considered for inclusion into the database. In particular, this precludes the inclusion of data presented only in abstracted form, e.g. at scientific meetings. We encourage authors of original reports fulfilling the above criteria to submit their data as soon as their work is accepted for publication.

Meta-Analysis Methods

For all polymorphisms with minor allele frequencies in healthy controls >1%, and for which case-control genotype data are available in four or more independent samples, crude odds ratios (ORs) and 95 percent confidence intervals (CIs) are calculated from the reported allele distributions for each study. Summary ORs and 95 percent CIs are calculated using the DerSimonian and Laird (1986) random-effects model, which utilizes weights that incorporate both within-study and between-study variance. This procedure is done including all studies irrespective of ethnicity (denoted by "All studies" on the meta-analysis figures), and repeated after exclusion of the initial study ("All excl initial"), after exclusion of studies in which a deviation of Hardy-Weinberg Equilibrium (HWE) was detected in controls ("All excl HWE deviations"). In addition, ethnicity-specific meta-analyses are performed whenever genotype data is available from at least three independent case-control populations. Visually, the results of these meta-analyses are displayed for each polymorphism in form of forest plots. Overlapping samples (of which usually only the largest is included), studies with missing data, or control samples deviating from HWE are indicated on these graphs. Note that when only a few studies are included in the meta-analyses (i.e. less than ~10), the random effects model may yield summary ORs and confidence bounds that are slightly anti-conservative.

Along with the forest plots, which depict summary ORs only for the most current set of available data, the results of cumulative meta-analyses are displayed for each polymorphism with data available in at least four independent case-control datasets. These graphs display summary ORs (for the "All studies" paradigm using the same allele-based random-effects analyses as outlined above) recalculated after each study. Thus, these graphs allow an evaluation of the estimated summary effects over time. Note that the most current summary OR (listed at the very top of the cumulative meta-analysis plots) is identical to the "All studies" results depicted on the forest plots (see above).

Inclusion of Genome-wide Association Studies (GWAS)

The systematic inclusion of data from GWAS and other large-scale studies represents a conceptual and computational challenge for any genetic database. We have devised the following step-wise protocol, which we believe allows us to capture the most relevant genetic information without the need to include every data-point from these studies. Note that this feature of PDGene is new and still under development. Please visit this page to see a summary of all published large-scale studies currently included in PDGene.

Stage I: Represents the inclusion of genes and polymorphisms “featured” or highlighted by the authors of the GWAS or large-scale study, usually because they show some degree of genetic association after completion of all analyses, e.g. testing multiple independent samples. These genes and polymorphisms probably represent the most important findings of each large-scale analysis and are therefore included here with highest priority. Genomic loci that do not map within any known gene are represented by a surrogate name specifying the cytogenetic location (e.g. “GWA_1q25.12”). This stage has already been implemented in the current version of PDGene (e.g. for the SEMA5A gene featured in the GWAS by Maraganore et al. [2005]).

For GWAS/large-scale studies that have made their genotype data publicly available, we will also make use of “non-featured” genotype distributions, i.e. of polymorphisms not believed to be associated with PD in the original publications:

Stage II: Will add GWAS genotype data for polymorphisms already available in PDGene, i.e. usually derived from candidate gene studies. GWAS data for such overlapping polymorphisms will be added to the gene-specific entries and, if genotype data is then available in a total of at least four independent case-control samples, included and displayed in the meta-analyses. This stage adds valuable information to the existing PDGene meta-analyses as it is derived from assessments that are largely unbiased with respect to gene function, in contrast to most conventional candidate gene studies. See the entries for LINGO1 as an example of such "Stage II" analyses. Here, the original data published by Vilarino-Guell, 2009 on two independent cohorts would have precluded meta-analysis. However, one (rs9652490) of the two tested polymorphisms was also included in the GWAS by Fung, 2006 and Pankratz, 2008, which were added to the gene entry and then allowed meta-analysis of this SNP across all four datasets.

Stage III: Focuses on published meta-analyses of the existing GWAS datasets. Genes and loci resulting from these analyses are treated equivalently to the "featured genes" of Stage I (above). Genotype data on the gene-specific pages will be extracted from the primary GWAS studies (if their data is publicly available) and displayed alongside a "GWAS meta-analysis" entry. For an example of this stage, please consult the entry for TPRG1. This locus was originally identified in the GWAS meta-analysis by Evangelou, 2007. The genotype data used for this GWAS meta-analysis can be found in the respective entries from the original GWAS, i.e. Maraganore, 2005 (family-based and case-control), and Fung, 2006. This stage also entails the inclusion of more complex genetic analyses, e.g. those jointly analyzing large numbers of polymorphisms at different loci based on assumptions regarding the functional interconnection of these loci, e.g. in forms of "pathways". To the degree that it can be achieved in this context, these pathway-based results are labeled as such and stored in separate "unmapped" section of the database (see this entry as example).

Association studies on mitochondrial genes

Studies assessing a potential association between PD and genetic variants in the mitochondrial (mt) genome are subject to the same inclusion criteria as studies investigating markers from the nuclear genome, and are displayed on a separate "chromosome graph" (which is adapted from imagery on the "Mito Map" website [http://www.mitomap.org/]). Owing to the specific characteristics of human mt-inheritance (e.g. its multicopy nature and the high frequency of somatic mutation events) and the innate heterogeneity of mt-association studies, however, genotype data from these studies are not included on PDGene and therefore not subject to meta-analysis.

For more details on inclusion criteria, literature searches, data-management procedures, statistical analyses, and online database structure, please see Bertram et al. (2007).

3. Summary of Meta-analysis Highlights: The "Top Results" List

In an effort to facilitate the identification of the most promising meta-analysis results available in PDGene, a continuously updated list displaying the most strongly associated genes ("Top Results") has been added to the homepage. The list is ranked by effect size, and only includes genes that contain at least one variant showing a nominally significant summary OR in the analysis of all ethnic groups (“All”), or ethnicity-specific meta-analyses with at least three available datasets (see above). While we believe that this list represents an up-to-date summary of particularly promising PD candidate genes that warrant follow-up with high priority, we note that many of these may represent false-positive findings, in particular those based on small (<10) sample sizes.

4. Mendelian PD Genes

Note that the PDGene database will not sample and catalogue reports of rare causal mutations leading to PD or parkinsonism of classic Mendelian inheritance (such as disease-causing mutations in a-synuclein or parkin), but will only focus on genetic association studies using polymorphisms with a minor-allele frequency of 1% or greater in at least one of the studied control populations. For a collection of Mendelian PD genes, please consult the Parkinson's disease Mutation Database curated by the Parkinson's Institute from Leiden University Medical Center, or the Mutation Database for Parkinson's Disease curated by the Institute for Infocomm Research in Singapore. Note that despite the exclusion of disease-causing mutations, data from bona fide association studies using common polymorphisms in any of the Mendelian PD genes will be included and meta-analyzed for PDGene (e.g. see the entries for SNCA or LRRK2).

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