Black and African American Connections to Parkinson’s Disease Study: Addressing Missing Diversity in Parkinson’s Disease Genetics
By onOur current understanding of Parkinson's disease and atypical parkinsonism-related syndromes is disproportionately based on studying populations of European ancestry, leading to a significant gap of knowledge concerning clinical features, genetics, and pathophysiology underlying disease etiology in underrepresented populations, including Black and African American individuals.
Multi-modality machine learning predicting Parkinson’s disease
By onPersonalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
Underrepresented Populations in Parkinson’s Genetics Research: Current Landscape and Future Directions
By onThis 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.
Genome-wide association identified novel etiological insights associated with Parkinson’s disease in African and African admixed populations
By onUnderstanding the genetic mechanisms underlying diseases in ancestrally diverse populations is a critical step towards the realization of the global application of precision medicine. Here we perform a comprehensive genome-wide assessment of Parkinson’s disease (PD) in African and African admixed ancestry, characterizing population-specific risk, differential haplotype structure and admixture, coding and structural genetic variation and polygenic risk profiling. We identified a novel common risk factor for PD and age at onset at the GBA1 locus, that was found to be rare in non-African/African admixed populations.
Global Parkinson’s Genetics Program Data Release 5
By onThis release includes 7,462 additional new complex disease participants and 487 new monogenic disease participants, adding to the previous releases from the Complex and Monogenic Networks. The complex disease data (genotypes) now consists of a total of 24,935 genotyped participants (12,728 PD cases, 10,533 Controls, and 1,674 ‘Other’ phenotypes). The monogenic disease data (whole genome sequences) now consists of a total of 722 sequenced participants. For any publications using data from this release, please reference the DOI number and the following statement: "Data (DOI 10.5281/zenodo.7904832, release 5) used in the preparation of this article were obtained from the Global Parkinson’s Genetics Program (GP2)."
Global Parkinson’s Genetics Program – Code
By onA centralized github repository for all code associated with the GP2 initative. This will be continually updated with new code as it gets developed.
Multi-ancestry genome-wide meta-analysis in Parkinson’s disease
By onAlthough over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, all studies have been performed in just one population at the time. Here we performed the first large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases, and 2,458,063 controls including individuals of European, East Asian, Latin American, and African ancestry. In a single joint meta-analysis, we identified 78 independent genome-wide significant loci including 12 potentially novel loci (MTF2, RP11-360P21.2, ADD1, SYBU, IRS2, USP8:RP11-562A8.5, PIGL, FASN, MYLK2, AJ006998.2, Y_RNA, PPP6R2) and finemapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 23 genes near these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations.
The IPDGC/GP2 Hackathon – an open science event for training in data science, genomics, and collaboration using Parkinson’s disease data
By onOpen science and collaboration are necessary to facilitate the advancement of Parkinson’s disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities, thus we coordinated a virtual 3-day hackathon event, during which 49 early-career scientists from 12 countries built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools. Each team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools. Hackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early-career researchers. The resources generated can be used to accelerate research on the genetics of PD.
The non-coding GBA1 rs3115534 variant is associated with REM sleep behavior disorder in Nigerians
By onBackground Damaging coding variants in GBA1 are a genetic risk factor for rapid eye movement sleep behavior disorder (RBD), which is a known early feature of synucleinopathies. Recently, a population-specific non-coding variant (rs3115534) was found to be associated with PD risk and earlier disease onset in individuals of African ancestry. Objectives To investigate whether the GBA1 rs3115534 PD risk variant is associated with RBD. Methods We studied 709 persons with PD and 776 neurologically healthy controls from Nigeria. The GBA1 rs3115534 risk variant status was imputed from previous genotyping for all. Symptoms of RBD were assessed with the RBD screening questionnaire (RBDSQ). Results The non-coding GBA1 rs3115534 risk variant is associated with possible RBD in individuals of Nigerian origin (Beta = 0.3640, SE = 0.103, P =4.093e-04), as well as after adjusting for PD status (Beta = 0.2542, SE = 0.108, P = 0.019) suggesting that this variant may have the same downstream consequences as GBA1 coding variants. Conclusions We show that the non-coding GBA1 rs3115534 risk variant is associated with increased RBD symptomatology in Nigerians with PD. Further research is required to assess association with polysomnography-defined RBD.
NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations
By onGenome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson′s Genetics Program (GP2) and the Center for Alzheimer′s and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson ′s (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson′s disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale.
Evaluating the performance of polygenic risk profiling across diverse ancestry populations in Parkinson’s disease
By onObjective This study aims to address disparities in risk prediction by evaluating the performance of polygenic risk score (PRS) models using the 90 risk variants across 78 independent loci previously linked to Parkinson’s disease (PD) risk across seven diverse ancestry populations.
Global Parkinson’s Genetics Program Data Release 6
By onThis release includes >20,000 additional participants adding to the previous releases from the Complex and Monogenic Networks. The complex disease data (genotypes), including locally-restricted samples, now consists of a total of 44,831 genotyped participants (24,709 PD cases, 17,246 Controls, and 2,876 ‘Other’ phenotypes) The monogenic disease data (whole genome sequences) now consists of a total of 2,324 sequenced participants (1,854 PD cases, 314 Controls, and 156 ‘Other’ phenotypes) For any publications using data from this release, please reference the DOI number and the following statement: "Data (DOI 10.5281/zenodo.7904832, release 5) used in the preparation of this article were obtained from the Global Parkinson’s Genetics Program (GP2)."
iSCORE-PD: an isogenic stem cell collection to research Parkinson Disease
By onA collection of 55 cell lines genetically engineered to harbor mutations in genes associated with monogenic PD were generated using CRISPR/Cas9 and prime editing. This collection offers a valuable platform for studying Parkinson's disease.
Polygenic Parkinson’s Disease Genetic Risk Score as Risk Modifier of Parkinsonism in Gaucher Disease
By onBiallelic pathogenic variants in GBA1 are the cause of Gaucher disease (GD) type 1 (GD1), a lysosomal storage disorder resulting from deficient glucocerebrosidase. Heterozygous GBA1 variants are also a common genetic risk factor for Parkinson's disease (PD). GD manifests with considerable clinical heterogeneity and is also associated with an increased risk for PD. The objective of this study was to investigate the contribution of PD risk variants to risk for PD in patients with GD1.
RAB32 Ser71Arg in autosomal dominant Parkinson’s disease: linkage, association, and functional analyses
By onRAB GTPases are regulators and substrates of LRRK2, and variants in the LRRK2 gene are important risk factors for Parkinson's disease. Here, the authors explore genetic variability in RAB GTPases within cases of familial Parkinson's disease.
2024: A Year in Review
ASAP is excited to share how we pushed the Parkinson’s disease research field forward. Together, our supported programs worked to funnel new ideas into Parkinson’s disease R&D, facilitate the rapid exchange of ideas, ensure researchers can build upon ASAP-funded work, and establish a diverse pipeline for the next generation of researchers.
The Components of GP2’s Eighth Data Release
In September 2024, GP2 announced the eighth data release on the Terra and the Verily® Workbench platforms in collaboration with AMP® PD. This release includes 5,481 additional whole genome sequences and 10,454 clinical exome sequences. Additional genotyping will be provided in the following release.
The Components of GP2’s Seventh Data Release
In April 2024, GP2 announced the seventh data release on the Terra and the Verily® Workbench platforms in collaboration with AMP® PD. This release includes >9,000 additional genotyped participants.
The Components of GP2’s Sixth Data Release
In December 2023, GP2 announced the sixth data release on the Terra and the Verily® Workbench platforms in collaboration with AMP® PD. This release includes >20,000 additional participants, adding to the previous releases from the Complex and Monogenic Networks.
2023: A Year in Review
ASAP reflects on the results our initiative, network, and supported programs have had on providing new insights into Parkinson’s disease in 2023. Together, we have uncovered novel discoveries and made progress toward our vision of advancing collaborative, transparent research processes, and environments that deliver faster and better outcomes in PD research.
