The Global Parkinson's Genetics Program (GP2): Advancing genetic discovery and capacity building worldwide.

Identification of genetic risk loci and causal insights associated with Parkinson's disease in African and African admixed populations: a genome-wide association study

We read with great interest the article by Bademosi and colleagues,1 where they investigated the role of SH3GL2 p.G276V on neuron dysfunction in Parkinson's disease (PD). The SH3GL2 gene encodes the endophilin-A1 (EndoA1) protein, crucial for synaptic vesicle endocytosis and blood–brain barrier permeability regulation.1 Two SH3GL2 independent signals have been identified to potentially increase PD risk in the latest European genome-wide association studies (GWAS) meta-analysis: rs13294100 and rs10756907.2 Exome sequencing on a German cohort suggested the p.G276V variant as an independent PD risk factor. Bademosi et al1 recently demonstrated that p.G276V impairs Ca2+ influx-induced synaptic autophagy without destabilizing EndoA1. The authors found that the human p.G276V protein was stable but showed a significant decrease in the number of autophagosomes compared with control neurons. To clarify the association between SH3GL2 and PD, we leveraged whole-genome sequencing (WGS) data from the Accelerating Medicines Partnership-Parkinson's Disease (AMP-PD; https://amp-pd.org/) release 3.0, consisting of 3,105 cases and 3,670 controls from European descent, and large-scale genotyping imputed data from the Global Parkinson's Genetics Program (GP2; https://gp2.org/) release 5.0, consisting of 12,728 cases and 10,533 controls from 10 different ancestries. Quality control analyses are described elsewhere (https://github.com/vitale199/GenoTools/). Variants were annotated using ANNOVAR, and Fisher's exact test was applied using PLINK 1.9. Summary statistics from the latest PD risk GWAS meta-analyses were assessed.2-5 We leveraged data from the omicSynth data resource looking at quantitative trait loci (QTL). Gene-based burden analyses were performed by using RVTESTS. We identified 14,590 SH3GL2 variants in AMP-PD (Supplementary Table S1). Likewise, 30,719 variants were identified in GP2 (Supplementary Table S1). The p.G276V variant was found in Europeans in both AMP-PD (one case, two controls) and GP2 (three cases, one control); however, no association was found with PD risk (AMP-PD: P = 0.394, odds ratio [OR] = 1.296; GP2: P = 0.869, OR = 1.034) (Table 1). This variant was also identified in GP2 in three African American (AAC) controls and one Ashkenazi Jew (AJ) PD patient. No linkage disequilibrium (LD) was observed between p.G276V and the European GWAS lead single nucleotide polymorphisms (SNPs). Conditional analyses suggested that these variants were most likely independent signals. No significant association between SH3GL2 common genetic variation and PD risk was identified in the Latino nor Asian populations.4, 5 The analysis of a multi-ancestry population3 identified the intronic variant rs910316833 as the most significant SNP (Supplementary Fig. S1). The QTL analysis revealed six potential functional impacts for SH3GL2 (top SNPs: rs2145659, rs3758217, rs10756899, and rs2383044). Finally, no cumulative effect of multiple genetic variants within SH3GL2 on PD risk was found after conducting rare variant burden meta-analyses. Using the largest case–control genetic cohorts publicly available to date in the PD field, the p.G276V variant was found in both AMP-PD and GP2 in Europeans; however, this variant was not associated with PD risk and consequently does not causally explain the PD GWAS significant association at the SH3GL2 locus. M.T.P. and S.B.C. conceived and designed the study. A.L.F., A.J.H.M., D.B.R.P., A.N.C. and M.B.M. performed the analysis of the results. A.L.F., A.J.H.M. and D.B.R.P. wrote the manuscript with support from A.N.C., M.B.M., S.B.C. and M.T.P. All authors contributed to the editing of the final manuscript. This work was carried out with the support and guidance of the “GP2 Trainee Network” that is part of the Global Parkinson's Genetics Program and funded by the Aligning Science Across Parkinson's (ASAP) initiative. Data used in the preparation of this article were obtained from Global Parkinson's Genetics Program (GP2). For a complete list of GP2 members, see https://gp2.org. Data used in the preparation of this article were obtained from the Accelerating Medicines Partnership® (AMP®) Parkinson's Disease (AMP® PD) Knowledge Platform. For up-to-date information on the study, visit https://www.amp-pd.org. ACCELERATING MEDICINES PARTNERSHIP and AMP are registered service marks of the United States Department of Health and Human Services. All GP2 data is hosted in collaboration with the AMP PD, and is available via application on the website (https://amp-pd.org/register-for-amp-pd; https://doi.org/10.5281/zenodo.7904832). Genotyping imputation, quality control, ancestry prediction, and processing was performed using GenoTools v1.0, publicly available on GitHub (https://github.com/GP2code/GenoTools). All scripts for analyses are publicly available on GitHub (https://github.com/GP2-TNC-WG/GP2_TRAINEES-SH3GL2/; Zenodo DOI: https://doi.org/10.5281/zenodo.10257319). Data S1. Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Limited proteolysis-coupled mass spectrometry (LiP-MS) is a recently developed approach for the global identification of structural proteome alterations affecting protein functions, including protein misfolding, conformational changes, binding of small molecules, and protein–protein interactions.1 Through identifying protein structural changes, this technique can provide insights into pathological conditions such as Parkinson's disease (PD) over conventional protein abundance measures.1 PD represents an ideal model for testing the performance of LiP-MS due to the fact that its pathophysiology is closely linked to protein aberrations.2 Mackmull and colleagues have recently investigated the potential of LiP-MS for detecting structural proteome alterations in PD.3 Using cerebrospinal fluid (CSF) samples from a cohort of 52 PD patients and 51 age- and sex-matched healthy subjects, the researchers identified a total of 76 structurally altered proteins in PD patients compared to controls. These proteins were enriched in processes misregulated in PD, including synapse-related functions, indicating a potential link between such proteins and disease pathology. This was further supported by the finding that 16 out of the 76 proteins identified in CSF PD samples were also structurally altered in PD brain samples compared to controls. Interestingly, three of these proteins (AAK1, CYRIB, and APOE) were previously linked to PD risk or progression in GWAS studies. Investigating the reliability of the 76 structurally altered proteins as disease biomarkers, they demonstrated higher efficiency in discriminating PD patients from controls than protein abundance measures. The performance of the 76 proteins was found comparable and complementary to the oligomeric/total α-synuclein ratio. The combination of the LiP model with the ratio of oligomeric/total α-synuclein classified individuals with PD with better performance (91% accuracy) than either measure alone (75% accuracy).3 Overall, this study highlighted the potential of global proteome structural alterations as PD biomarkers with high sensitivity and specificity. LiP-MS represents a promising technique for analysis of proteome conformational changes directly in biofluids of participants in clinical cohorts, following a simple protocol that is applicable in a standard biochemistry laboratory.1 However, despite the remarkable findings of this work, further studies are needed to validate the utility of the identified structurally altered proteins as disease biomarkers and decipher their implication in PD pathogenesis. This work was supported in part by the Global Parkinson's Genetics Program (GP2). GP2 is funded by the Aligning Science Against Parkinson's (ASAP) initiative and implemented by The Michael J. Fox Foundation for Parkinson's Research (https://gp2.org). For a complete list of GP2 members see https://gp2.org. (1) Research project: A. Conception, B. Organization, C. Execution; (2) Statistical analysis: A. Design, B. Execution, C. Review and critique; (3) Manuscript preparation: A. Writing of the first draft, B. Review and critique. M.A.H.: 1A, 1B, 2A, 2B I.E.: 1A, 1B, 2A, 2B Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

We conducted a meta-analysis of Parkinson's disease genome-wide association study summary statistics, stratified by source (clinically-recruited case-control cohorts versus population biobanks) and by general European versus European isolate ancestries. This study included 63,555 cases, 17,700 proxy cases with a family history of Parkinson's disease, and 1,746,386 controls, making it the largest investigation of Parkinson's disease genetic risk to date. Meta-analyses were performed using standard fixed and random effect models for the European sub-populations, the case-control studies, and the population biobanks separately. Finally, all of the European ancestries for all study types as well as proxy cases were combined in our final cross-European meta-analysis. We estimated heritable risk across ancestry groups, investigated tissue and cell-type enrichment, and prioritized risk genes using public data to facilitate functional follow-up efforts. The final combined cross-European meta-analysis identified 134 risk loci (59 novel), with a total of 157 independent signals, significantly expanding our understanding of Parkinson's disease risk. Multi-omic data integration revealed that expression of the nominated risk genes are highly enriched in brain tissues, particularly in neuronal and astrocyte cell types. Additionally, we prioritized 33 high-confidence genes across these 134 loci for future follow-up studies. By integrating diverse European populations and leveraging harmonized data from the Global Parkinson's Genetics Program (GP2), we reveal new insight into the genetic architecture of Parkinson's disease. We identified a total of 134 risk loci, expanding the number of known loci associated with PD by approximately 24%. We also provided an initial layer of biological context to these results through follow-up analyses in an effort to facilitate follow-up studies and precision medicine efforts with the goal of advancing Parkinson's disease research.

Developing therapies for Parkinson's disease

A cornerstone initiative for progress in Parkinson's disease

The pathogenesis of Parkinson's disease (PD) remains incompletely understood; large-scale studies are needed to further elucidate its genetic and environmental underpinnings. The Australian Parkinson's Genetics Study (APGS) is an ongoing nationwide, population-based initiative established to advance understanding of PD determinants and progression. We present a cross-sectional characterisation of 10,929 participants with self-reported PD recruited across Australia through assisted mail-outs, media outreach, and digital engagement. Participants complete comprehensive questionnaires capturing sociodemographic, clinical, environmental, lifestyle, and behavioural data, and provide saliva samples for genetic analysis. A control cohort is currently being recruited and not reported here. The cohort is 63% male, with a mean age of 71 years and symptom onset at 64 years; 79% report diagnosis by a neurologist and 25% report a family history. Non-motor symptoms and neuropsychiatric comorbidities are common, including sleep disturbances, memory changes, and depression, alongside risk factors such as pesticide exposure (36%), traumatic brain injury (16%), and high-risk occupations (33%). Sex-based differences are evident: females more frequently report unilateral onset (81% vs 75%), falls (45% vs 41%), and pain (70% vs 63%), whereas males report higher rates of memory changes (67% vs 61%), pesticide exposure (42% vs 28%), high-risk occupations (44% vs 16%), and impulsive control behaviour, such as sexual behaviour (56% vs 19%). Leveraging APGS, the largest active PD cohort globally, our findings highlight the clinical and risk heterogeneity of PD and the importance of sex-specific research and care. The study's successful recruitment demonstrates the feasibility of large-scale remote enrolment, while its comprehensive design and ongoing expansion, including genomic profiling and digital phenotyping, position APGS as a transformative platform for advancing PD risk prediction, biomarker discovery, and therapeutic development. APGS is supported by the Global Parkinson's Genetics Program (GP2), Shake It Up Australia Foundation, and Michael J. Fox Foundation for Parkinson's Research (MJFF-021952).

Background and ObjectivesKnown pathogenic variants (PVs) in Parkinson disease (PD) contribute to disease development but have yet to be fully explored by arrays on a large scale. This study evaluated genotyping success of the NeuroBooster array (NBA) and determined the frequencies of PVs across ancestries.MethodsWe analyzed the presence and allele frequency of PVs in 28,710 PD cases, 9,614 other neurodegenerative disorder cases, and 15,821 controls across 11 ancestries within the Global Parkinson's Genetics Program (GP2) data set. Cluster plots were used to assess the quality of PVs genotyped on NBA.ResultsGenes previously predicted to have high or very high confidence of causing PD tend to have more PVs and are present across ancestry groups. Of 34 known PD gene PVs assessed, 25 were typed by NBA and classified as “good” (n = 12), “medium” (n = 4), or “bad” (n = 9) quality variants.DiscussionOur results confirm the likelihood that established PD genes are pathogenic and highlight the importance of ancestrally diverse research in PD. We also show the usefulness of the NBA as a reliable tool for the genotyping of rare variants of PD.

In the Global Parkinson's Genetics Program (GP2) we aim to advance precision medicine by integrating large-scale clinico-genetic data from diverse populations worldwide. We investigated potentially trial-eligible carriers of pathogenic and high-risk GBA1 and LRRK2 variants and conducted a global precision-medicine survey across GP2 sites. Among 65,509 individuals with Parkinson's disease, we identified 9,019 (13.8%) potentially trial-eligible genetic variant carriers, including 6,789 GBA1, 2,084 LRRK2, and 146 dual GBA1-LRRK2 carriers. Individuals were distributed across multiple global regions, many of which currently lack active gene-targeted trials, highlighting a global disparity between relevant variant carriers and the availability of disease modifying treatment trials. GP2's unified framework supports equitable recruitment for gene-targeted therapeutic studies and helps address critical gaps in Parkinson's disease genetics and future therapeutic development.

The genetic architecture of Parkinson's disease (PD) comprises five autosomal dominantly inherited forms with a clinical picture overall resembling idiopathic disease (PARK-SNCA, PARK-LRRK2, PARK-VPS35, PARK-CHCHD2, and PARK-RAB32) and three recessive types (PARK-PRKN, PARK-PINK1, and PARK-PARK7), several monogenic forms causing atypical parkinsonism, as well as a plethora of known genetic risk factors, most notably SNCA and GBA1 including a recently discovered risk variant unique to individuals of African descent, as well as polygenic scores. The Movement Disorder Society Genetic mutation database (MDSGene) (www.mdsgene.org) provides PD genotype-phenotype relationships, whereas global PD genetics networks, such as the Global Parkinson's Genetics Program (www.gp2.org) elucidate PD genetic factors at an unprecedented scale. Two large studies in relatively unselected, multicenter PD samples estimate the frequency of genetic forms, including PARK-GBA1, at ∼15%. PD genetics are becoming increasingly actionable, with the first gene-targeted clinical trials underway. Furthermore, PD genetics has recently been incorporated into a new biological classification of PD.

The need for more diversity in research is a widely recognized problem, especially in the genetics and genomics fields. While resolving this problem seems straightforward by recruiting and sequencing research participants from underrepresented populations, implementing an effort like this is complex operationally. Key considerations include ensuring equity, building capacity, and creating a sustainable research collective that works collaboratively to address local and global questions in research. Here, we provide a roadmap detailing how the Global Parkinson's Genetics Program (GP2) is tackling the lack of diversity in Parkinson disease (PD) genetics research and also reflect on 5 years of progress. GP2 aims to be a global hub facilitating subject recruitment, sample collection, data generation, harmonization, and sharing. It also acts as a centralized target discovery hub for PD genetics worldwide. The underlying tenets of GP2 center on transparency, the democratization of data and discovery, training and career support, providing (or generating) actionable results, and creating a functional collective of PD researchers worldwide. GP2 is working with 275 research groups worldwide. There are data and samples from 265,000 subjects currently committed to the program as of May 2025. We discuss the lessons learned in this process and highlight what we view as the emerging opportunities that the program will aim to target over the next period.

BackgroundHuman genetics research lacks diversity; over 80% of genome‐wide association studies have been conducted on individuals of European ancestry. In addition to limiting insights regarding disease mechanisms, disproportionate representation can create disparities preventing equitable implementation of personalized medicine.ObjectiveThis systematic review provides an overview of research involving Parkinson's disease (PD) genetics in underrepresented populations (URP) and sets a baseline to measure the future impact of current efforts in those populations.MethodsWe searched PubMed and EMBASE until October 2021 using search strings for “PD,” “genetics,” the main “URP,” and and the countries in Latin America, Caribbean, Africa, Asia, and Oceania (excluding Australia and New Zealand). Inclusion criteria were original studies, written in English, reporting genetic results on PD from non‐European populations. Two levels of independent reviewers identified and extracted information.ResultsWe observed imbalances in PD genetic studies among URPs. Asian participants from Greater China were described in the majority of the articles published (57%), but other populations were less well studied; for example, Blacks were represented in just 4.0% of the publications. Also, although idiopathic PD was more studied than monogenic forms of the disease, most studies analyzed a limited number of genetic variants. We identified just nine studies using a genome‐wide approach published up to 2021, including URPs.ConclusionThis review provides insight into the significant lack of population diversity in PD research highlighting the immediate need for better representation. The Global Parkinson's Genetics Program (GP2) and similar initiatives aim to impact research in URPs, and the early metrics presented here can be used to measure progress in the field of PD genetics in the future. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Known pathogenic variants in Parkinson's disease (PD) contribute to disease development but have yet to be fully explored by arrays at scale. This study evaluated genotyping success of the NeuroBooster array (NBA) and determined the frequencies of pathogenic variants across ancestries. We analyzed the presence and allele frequency of 34 pathogenic variants in 28,710 PD cases, 9,614 other neurodegenerative disorder cases, and 15,821 controls across 11 ancestries within the Global Parkinson's Genetics Program dataset. Of these, 25 were genotyped on NBA and cluster plots were used to assess their quality. Genes previously predicted to have high or very high confidence of causing PD tend to have more pathogenic variants and are present across ancestry groups. Twenty-five of the 34 pathogenic variants were typed by the NBA array and classified "good" (n=12), "medium" (n=4), and "bad" (n=9) variants. Our results confirm the likelihood that established PD genes are pathogenic and highlight the importance of ancestrally diverse research in PD. We also show the usefulness of the NBA as a reliable tool for genotyping of rare variants for PD.

BackgroundRecessive genetic variation and extended runs of homozygosity (ROHs) may contribute to the unexplained heritability of Parkinson's disease (PD), particularly in diverse and understudied populations.ObjectiveWe conducted the first large‐scale, multi‐ancestral investigation of PD to examine the impact of genome‐wide homozygosity on disease risk and age at onset (AAO). Using genotyping, imputed, and whole‐genome sequencing data from 36,127 PD cases and 19,475 controls across nine ancestral populations from the Global Parkinson's Genetics Program, we aimed to identify novel regions of homozygosity contributing to PD heritability.MethodsWe analyzed ROHs for total length (SROH), number (NROH), average length (AVROH), and genomic inbreeding coefficient (FROH). ROHs were intersected with known PD, pallido‐pyramidal syndrome, and atypical parkinsonism gene regions and risk loci to assess pleomorphic or pleiotropic contributions. Homozygosity mapping identified ROH overlaps in families, consanguineous individuals, and early‐onset PD (EOPD) cases.ResultsSignificant differences in SROH, AVROH, NROH, and FROH were observed between case status across ancestries, persisting after excluding known PD‐associated recessive genes. Our analysis revealed distinct patterns of ROH enrichment associated with AAO, suggesting recessive genetic modifiers of PD. Homozygosity mapping was used to prioritize 52 variants either segregating in families or present in individuals with consanguinity. In total, 1,559 ROHs in consanguineous individuals and EOPD overlapped known PD gene regions and risk loci.ConclusionsROH regions contribute to PD heritability across ancestries, partly reflecting recessive genetic architecture. Larger and more diverse whole‐genome sequencing studies are needed to identify rare recessive variants influencing PD risk. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control, and genome-wide association studies capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias and the Global Parkinson's Genetics Program, GenoTools was used to process and analyze the UK Biobank and major Alzheimer's disease and Parkinson's disease datasets with over 400,000 genotypes from arrays and 5,000 whole genome sequencing samples and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous quality control, and conducted genetic ancestry-specific genome-wide association studies to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotyping and sequencing (whole genome sequencing and exome) data with reproducible and scalable ancestry, quality control, and genome-wide association studies pipelines.