GP2 ❤️ Open Science 😍
Last Updated: March 2026
This repository accompanies the manuscript: "Insights Into Parkinson's Disease Genetics in African Populations: Expanded GWAS Identifies Ancestry-Specific and Cross-Population Risk Loci"
This study represents the argest genome-wide association study (GWAS) of Parkinson's disease (PD) in African (AFR) and African-admixed (AAC) populations to date. We integrated individual-level genotype data from the Global Parkinson's Genetics Program (GP2; release 11) with summary statistics from 23andMe and the Million Veterans Program (MVP) to better characterize the genetic architecture of PD in underrepresented populations.
- Integrated GP2, 23andMe, and Million Veterans Program data (3,975 cases, 319,883 controls), conducting separate and combined GWAS in African and African admixed ancestry populations prior to meta-analysis
- Key findings confirm and extend known risk loci:
- GBA1 (rs3115534) was the top signal across all analyses
- SNCA and SCARB2 replicated as trans-ancestry loci
- a novel LRRK2 coding variant (p.T1410M) reached genome-wide significance in AFR populations for the first time, alongside two additional novel loci.
If you use this repository or find it helpful for your research, please cite the corresponding manuscript:
Insights Into Parkinson's Disease Genetics in African Populations: Expanded GWAS Identifies Ancestry-Specific and Cross-Population Risk Loci
by Okubadejo et al., Global Parkinson's Genetics Program (GP2)
medRxiv 2026; DOI: xx
If you are looking for the original analyses corresponding to:
Rizig et al., 2023: "Genome-wide Association Identifies Novel Etiological Insights Associated with Parkinson's Disease in African and African Admixed Populations"
please use the main branch of this repository, found here: https://github.com/GP2code/GP2-AFR-AAC-metaGWAS/tree/main. The current branch contains the expanded 2026 analyses and should be cited for the new manuscript.
Data used in the preparation of this article were obtained from GP2. Specifically, we used Tier 2 data from GP2 (release 11; DOI 10.5281/zenodo.17753486). GP2 data can be accessed through AMP PD (https://amp-pd.org). For the MVP dataset, PD summary statistics from the Million Veteran's Program (MVP) were downloaded from dbGAP (accession number: phs002453.v1.p1; analysis accession: pha010400.1). Summary statistics from 23andMe were shared under a collaborative agreement, submitted at https://research.23andme.com/collaborate/.
- GP2 Website: https://gp2.org/
- GP2 Cohort Dashboard: https://gp2.org/cohort-dashboard-advanced/
- GP2 Introduction Paper: https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.28494
- GP2 Publications: https://pubmed.ncbi.nlm.nih.gov/?term=%22global+parkinson%27s+genetics+program%22
.
├── analyses
│ ├── 00_Prepping_Data.ipynb
│ ├── 01_Covariates.ipynb
│ ├── 02_GWAS_GP2.ipynb
│ ├── 03_Munge_Sumstats.ipynb
│ ├── 04_Meta_Analysis.ipynb
│ ├── 05_Manhattans_QQs.ipynb
│ ├── 06_Calculate_Lambdas.ipynb
│ ├── 07_AA_AFR_Blood.ipynb
│ └── 08_Forest_Plots.ipynb
├── figures
│ └── workflow.png
├── LICENSE
├── README.md
└── tables
Languages: Python, R, and Bash
| Notebook | Description |
|---|---|
| 00_Prepping_Data | Harmonization, phenotype cleaning, and sample filtering |
| 01_Covariates | Making of covariate file (sex and PCs) |
| 02_GWAS_GP2 | Cohort-level GWAS in GP2 |
| 03_Munge_Sumstats | Formatting and QC of summary statistics |
| 04_Meta_Analysis | AFR, AAC, and combined meta-analyses |
| 05_Manhattans_QQs | Manhattan and QQ plots |
| 06_Calculate_Lambdas | Genomic inflation and QC metrics |
| 07_AA_AFR_Blood | Blood-specific follow-up analyses |
| 08_Forest_Plots | Cross-cohort forest plots |
| Software | Version(s) | URL | RRID | Notes |
|---|---|---|---|---|
| ANNOVAR | 2020-06-08 | http://www.openbioinformatics.org/annovar/ | SCR_012821 | Variant annotation |
| METAL | 2020-05-05 | http://csg.sph.umich.edu/abecasis/Metal/ | SCR_002013 | Meta-analysis |
| PLINK | 1.9, 2.0 | http://www.nitrc.org/projects/plink | SCR_001757 | Genetic analyses |
| Python | 3.9+ | http://www.python.org | SCR_008394 | pandas, numpy, matplotlib |
| R | 4.2+ | http://www.r-project.org | SCR_001905 | tidyverse, data.table |
This work was performed on behalf of the Global Parkinson's Genetics Program (GP2). We thank all study participants, clinicians, and contributing cohorts worldwide.