Background Genome-wide association studies (GWASs) in Parkinson's disease (PD) have increased the scope of biological knowledge about the disease over the past decade. We sought to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into disease etiology. Methods We performed the largest meta-GWAS of PD to date, involving the analysis of 7.8M SNPs in 37.7K cases, 18.6K UK Biobank proxy-cases (having a first degree relative with PD), and 1.4M controls. We carried out a meta-analysis of this GWAS data to nominate novel loci. We then evaluated heritable risk estimates and predictive models using this data. We also utilized large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type and biological pathway enrichments for the identified risk factors. Additionally we examined shared genetic risk between PD and other phenotypes of interest via genetic correlations followed by Mendelian randomization. Findings We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of PD depending on prevalence. Integrating methylation and expression data within a Mendelian randomization framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested PD loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes, smoking status, and educational attainment. Mendelian randomization between cognitive performance and PD risk showed a robust association. Interpretation These data provide the most comprehensive understanding of the genetic architecture of PD to date by revealing many additional PD risk loci, providing a biological context for these risk factors, and demonstrating that a considerable genetic component of this disease remains unidentified. Funding See supplemental materials (Text S2). lead to earlier detection and refined diagnostics, which may help improve clinical trials (4). The generation of copious amounts of public summary statistics created by this effort relating to both the GWAS and subsequent analyses of gene expression and methylation patterns may be of use to investigators planning follow-up functional studies in stem cells or other cellular screens, allowing them to prioritize targets more efficiently using our data as additional evidence. We hope our findings may have some downstream clinical impact in the future such as improved patient stratification for clinical trials and genetically informed drug targets.

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Induced pluripotent stem cells (iPSC) offer an unprecedented opportunity to model human disease in relevant cell types, but it is unclear whether they could successfully model age-related diseases such as Parkinson's disease (PD). Here, we generated iPSC lines from seven patients with idiopathic PD (ID-PD), four patients with familial PD associated to the G2019S mutation in the Leucine-Rich Repeat Kinase 2 (LRRK2) gene (LRRK2-PD) and four age- and sex-matched healthy individuals (Ctrl). Over long-time culture, dopaminergic neurons (DAn) differentiated from either ID-PD- or LRRK2-PD-iPSC showed morphological alterations, including reduced numbers of neurites and neurite arborization, as well as accumulation of autophagic vacuoles, which were not evident in DAn differentiated from Ctrl-iPSC. Further induction of autophagy and/or inhibition of lysosomal proteolysis greatly exacerbated the DAn morphological alterations, indicating autophagic compromise in DAn from ID-PD- and LRRK2-PD-iPSC, which we demonstrate occurs at the level of autophagosome clearance. Our study provides an iPSC-based in vitro model that captures the patients' genetic complexity and allows investigation of the pathogenesis of both sporadic and familial PD cases in a disease-relevant cell type.