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Multi-ancestry Population Attributable Risk Assessment of Common Genetic Variation in Alzheimer’s and Parkinson’s Diseases

GP2 ❤️ Open Science 😍

DOI

Last Updated: September 2024

Summary

This is the online repository for the manuscript titled "Multi-ancestry Population Attributable Risk Assessment of Common Genetic Variation in Alzheimer’s and Parkinson’s Diseases".

This study aims to assess the population attributable risk (PAR) for Alzheimer’s disease (AD) and Parkinson's disease (PD) across diverse ancestries, thereby identifying genetic disparities in risk factors and their implications for precision medicine. Using genome-wide association data from multiple ethnicities, our analysis revealed that genetic susceptibilities vary significantly across populations, with several loci showing unique associations in non-European ancestries. These findings highlight the critical need for developing therapeutic strategies that are personalized to genetic backgrounds, ensuring effective and equitable treatment across all population groups.

Data Statement

Our reference datasets consisted of summary statistics from previously published studies. 23andMe GWAS summary statistics (available via collaboration with 23andMe).

  • Parkinson's disease - summary statistics for the following studies are available via release 8 (DOI 10.5281/zenodo.13755496) GP2 Tier 1 access:
    • European GWAS meta-analysis; Nalls et al 2019
    • African and African admixed GWAS meta-analysis; Rizig et al 2023
    • East Asian GWAS meta-analysis; Foo et al 2020
    • Latino GWAS meta-analysis; Loesch et al 2021

• Alzheimer's disease

  • FinnGen Release 6; see here
  • African American GWAS meta-analysis; Kunkle 2021; see here (please contact the authors for summary statistics)
  • East Asian GWAS meta-analysis; Shigemizu et al 2021; see here (please contact the authors for summary statistics)
  • Latino GWAS meta-analysis; Lake et al 2023; see here

Helpful Links

Repository Orientation

  • The analyses/ directory includes all analyses discussed in the manuscript
  • The tables/ directory includes all the supplementary tables referenced in the manuscript
analyses/
├── 00_clean_and_prep_PD.ipynb
├── 01_PAR_calculations_PD.ipynb
├── 02_clean_and_prep_AD.ipynb
├── 03_PAR_calculations_AD.ipynb
├── 04_data_visualization_all.R
└── 05_data_visualization_known_variants.R

tables/
└── PAR_Supplementary_Tables.xlsx

Analysis Notebooks

  • Languages: Python, bash, and R
Notebooks Description
00_clean_and_prep_PD.ipynb Load the list of PD risk loci, import ancestry-specific GWAS summary statistics, select top hits for each ancestry, and identify known and population-specific risk variants for analysis.
01_PAR_calculations_PD.ipynb Calculate population attributable risk for each target and generate table with summary statistics and PAR
02_clean_and_prep_AD.ipynb Load the list of AD risk loci, import ancestry-specific GWAS summary statistics, select top hits for each ancestry, and identify known and population-specific risk variants for analysis.
03_PAR_calculations_AD.ipynb Calculate population attributable risk for each target and generate table with summary statistics and PAR
04_data_visualization_all.R Rscript to visualize PAR in each ancestry
05_data_visualization_known_variants.R Rscript to visualize known disease variants within genes of interest

Software

Software Version(s) Resource URL RRID Notes
Python Programming Language 3.9 http://www.python.org/ RRID:SCR_008394 pandas; numpy; seaborn; matplotlib; statsmodel; used for general data wrangling/plotting/analyses
R Project for Statistical Computing 4.2 http://www.r-project.org/ RRID:SCR_001905 tidyverse; dplyr; tidyr; ggplot; data.table; used for general data wrangling/plotting/analyses
ANNOVAR 2020-06-08 http://www.openbioinformatics.org/annovar/ RRID:SCR_012821 Genetic annotation software