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# Insights Into Parkinson's Disease Genetics in African Populations
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## Expanded GWAS Identifies Ancestry-Specific and Cross-Population Risk Loci
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`GP2 ❤️ Open Science 😍`
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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[![DOI](https://zenodo.org/badge/635483971.svg)](https://zenodo.org/badge/latestdoi/635483971)
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**Last Updated:** March 2026
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------------------------------------------------------------------------
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## Overview
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This repository accompanies the manuscript: **"Insights Into Parkinson's Disease Genetics in African Populations: Expanded GWAS Identifies Ancestry-Specific and Cross-Population Risk Loci"**
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This study represents the argest genome-wide association study (GWAS)
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of Parkinson's disease (PD) in African (AFR) and African-admixed (AAC)
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populations to date. We integrated individual-level genotype data from the Global
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Parkinson's Genetics Program (GP2; release 11) with summary statistics from
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23andMe and the Million Veterans Program (MVP) to better
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characterize the genetic architecture of PD in underrepresented
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populations.
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### Summary
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* 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
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* Key findings confirm and extend known risk loci:
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* GBA1 (rs3115534) was the top signal across all analyses
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* SNCA and SCARB2 replicated as trans-ancestry loci
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* a novel LRRK2 coding variant (p.T1410M) reached genome-wide significance in AFR populations for the first time, alongside two additional novel loci.
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### Citation
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If you use this repository or find it helpful for your research, please cite the corresponding manuscript:
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> **Insights Into Parkinson's Disease Genetics in African Populations: Expanded GWAS Identifies Ancestry-Specific and Cross-Population Risk Loci**
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>
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> by Okubadejo et al., Global Parkinson's Genetics Program (GP2)
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>
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> medRxiv 2026; DOI: xx
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------------------------------------------------------------------------
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## Important Note About the 2023 Paper
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If you are looking for the original analyses corresponding to:
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> *Rizig et al., 2023: "Genome-wide Association Identifies Novel Etiological Insights Associated with Parkinson's Disease in African and African Admixed Populations"*
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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.
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## Data Statement
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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/.
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## Helpful Links
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- GP2 Website: https://gp2.org/
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- GP2 Cohort Dashboard: https://gp2.org/cohort-dashboard-advanced/
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- GP2 Introduction Paper:
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https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.28494
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- GP2 Publications:
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https://pubmed.ncbi.nlm.nih.gov/?term=%22global+parkinson%27s+genetics+program%22
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------------------------------------------------------------------------
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## Workflow Overview
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![Workflow
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Diagram](https://github.com/GP2code/GP2-AFR-AAC-metaGWAS/blob/2026/figures/workflow.png)
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------------------------------------------------------------------------
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## Repository Structure
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```
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.
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├── analyses
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│   ├── 00_Prepping_Data.ipynb
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│   ├── 01_Covariates.ipynb
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│   ├── 02_GWAS_GP2.ipynb
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│   ├── 03_Munge_Sumstats.ipynb
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│   ├── 04_Meta_Analysis.ipynb
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│   ├── 05_Manhattans_QQs.ipynb
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│   ├── 06_Calculate_Lambdas.ipynb
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│   ├── 07_AA_AFR_Blood.ipynb
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│   └── 08_Forest_Plots.ipynb
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├── figures
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│   └── workflow.png
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├── LICENSE
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├── README.md
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└── tables
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```
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------------------------------------------------------------------------
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## Analysis Notebooks
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Languages: Python, R, and Bash
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| Notebook | Description |
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| -------------------- | ------------------------------------------------------- |
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| 00_Prepping_Data | Harmonization, phenotype cleaning, and sample filtering |
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| 01_Covariates | Making of covariate file (sex and PCs) |
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| 02_GWAS_GP2 | Cohort-level GWAS in GP2 |
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| 03_Munge_Sumstats | Formatting and QC of summary statistics |
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| 04_Meta_Analysis | AFR, AAC, and combined meta-analyses |
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| 05_Manhattans_QQs | Manhattan and QQ plots |
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| 06_Calculate_Lambdas | Genomic inflation and QC metrics |
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| 07_AA_AFR_Blood | Blood-specific follow-up analyses |
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| 08_Forest_Plots | Cross-cohort forest plots |
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## Software
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| Software | Version(s) | URL | RRID | Notes |
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| --------------------- | ---------- | ---------------------------------------------------------------------------------------------- | ---------- | ------------------------- |
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| ANNOVAR | 2020-06-08 | [http://www.openbioinformatics.org/annovar/](http://www.openbioinformatics.org/annovar/) | SCR_012821 | Variant annotation |
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| METAL | 2020-05-05 | [http://csg.sph.umich.edu/abecasis/Metal/](http://csg.sph.umich.edu/abecasis/Metal/) | SCR_002013 | Meta-analysis |
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| PLINK | 1.9, 2.0 | [http://www.nitrc.org/projects/plink](http://www.nitrc.org/projects/plink) | SCR_001757 | Genetic analyses |
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| Python | 3.9+ | [http://www.python.org](http://www.python.org) | SCR_008394 | pandas, numpy, matplotlib |
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| R | 4.2+ | [http://www.r-project.org](http://www.r-project.org) | SCR_001905 | tidyverse, data.table |
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------------------------------------------------------------------------
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## Acknowledgments
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This work was performed on behalf of the Global Parkinson's Genetics
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Program (GP2). We thank all study participants, clinicians, and contributing cohorts
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worldwide.

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