Clinical and Genetic Insights of Parkinson's Disease in a Mexican Cohort: Highlighting Latino's Diversity

GP2 ❤️ Open Science 😍

Last Updated: April 2025

Summary

This repository contains the code and data analysis pipelines used in our manuscript "Clinical and Genetic Insights of Parkinson's Disease in a Mexican Cohort: Highlighting Latino's Diversity". Our study provides a comprehensive look at both clinical and genetic factors contributing to Parkinson's Disease (PD) in a diverse Latino population

Citation

If you use this repository or find it helpful for your research, please cite the corresponding manuscript:

Clinical and Genetic Insights of Parkinson's Disease in a Mexican Cohort: Highlighting Latino's Diversity Lázaro-Figueroa A., Reyes-Pérez P., Morelos-Figaredo E., Guerra-Galicia C.M., Estrada-Bellmanne I., Salinas-Barboza K., Matuk-Pérez Y.,Gandarilla-Martínez N.A., Oropeza D., Caballero-Sánchez U.,Montés-Alcántara P., López-Pintor A., Angulo-Arrieta A.P., Flores-Ocampo V., Espinosa-Méndez I.M., Zayas-Del Moral A., Gaspar-Martínez E., Vazquez-Guevara D., Rodríguez-Violante M., Waldor E., Leal T., Inca-Martinez M., Mata I., Alcauter S.*, Renteríal M.E., Medina-Rivera A., Ruiz-Contrerasa A.E. and on behalf of the Mexican Parkinson’s Research Network (MEX-PD) and the Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD)

Manuscript DOI: coming soon

GitHub DOI: DOI coming soon

Data Statement

• All GP2 data are hosted in collaboration with the Accelerating Medicines Partnership in Parkinson's Disease (AMP-PD) and are available via application on the website. The GP2 PD case and control data are available via the GP2 analysis platform (https://gp2.org; release 9: 10.5281/zenodo.14510099).
• Genotyping imputation, quality control, ancestry prediction, and processing were performed using GenoTools (v1.0.0), publicly available on GitHub

Workflow

FOR MEX-PD DATA

1. Quality control (QC) pipeline: https://github.com/MataLabCCF/GWASQC

• 1.1 Sample Missing Data: Samples with more than 5% missing data was excluded
• 1.2 Variant Missing Data: Variants with more than 5% missing data were excluded
• 1.3 Heterozygosity Deviation: Samples deviating from the expected heterozygosity by more than 3 standard
• 1.4 HWE in Controls: Variants in control samples with p-values below 1e-6 for HWE were excluded
• 1.5 HWE in Cases: Variants in case samples with p-values below 1e-10 for HWE will be excluded.
• 1.6 Relationship Control: Related samples with a kinship coefficient > 0.0884 (equivalent to second-degree relatives) were removed.
• 1.7 Sex-Check:
  • Females should have F-statistic < 0.5
  • Males should have F-statistic > 0.8
• 1.8 Removal of Redundant Variants: Variants that are A/T or C/G were removed due to the risk of strand bias
• 1.9 Duplicate Variants: Duplicate variants are identified, and the one with the least missing data is retained.

2. Ancestry Estimation Pipeline: https://github.com/MataLabCCF/AdmixtureWithReference

• 2.1 LD Pruning: Linkage disequilibrium pruning was performed using PLINK with a window size of 200 SNPs, step size of 50 SNPs, and an r² threshold of 0.2.
• 2.2 Merging with Reference Dataset: Study data were merged with a subset of non-admixed individuals from the 1000 Genomes Project (Byrska-Bishop et al. 2022), including African (AFR), East Asian (EAS), South Asian (SAS), European (EUR), and Native American (NAT) meta-populations (Shriner et al. 2023).
• 2.3 PCA: Principal Component Analysis (PCA) was performed on the merged dataset using PLINK 2.
• 2.4 Admixture Analysis: ADMIXTURE 1.3 (https://dalexander.github.io/admixture/) was run in supervised mode to estimate ancestry proportions.

3. Variant Frequency Calculation and Association Analysis

• 3.1 Variant Selection: A curated variant list was compiled based and stored as input file.
• 3.2 Minor Allele Frequency Calculation: MAF for the selected variants was calculated separately in PD cases and controls using PLINK 1.9.
• 3.3 Association Analysis: GLM testing was conducted using PLINK 2, adjusting for age, sex, and the first five principal components.

FOR GP2 DATA

1. GP2 Variant Frequency Calculation and Association Analysis

• 1.1 Environment Setup: Python libraries and functions, set paths
• 1.2 Installing Packages: PLINK
• 1.3 Data Preparation: copying over files, and formatting covariate file, remove related samples
• 1.4 Case/Control MAF Calculation: MAF for the selected variants was calculated separately in PD cases and controls using PLINK 1.9.
• 1.5 Association Analysis: GLM testing was conducted using PLINK 2, adjusting for age, sex, and the first five principal components..

Repository Orientation

• The analyses/ directory includes all analyses discussed in the manuscript

analyses/
├── 00_Lazaro2024etal_GP2examplenotebook.ipynb
├── 01_Lazaro2024_create_freq_plots.R
├── 02_Lazaro2024etal_Table1.R
├── 03_Lazaro2024etal_Table2.R
├── 04_Lazaro2024etal_Figure1.R
└── 05_Lazaro2024etal_Figure2.R

---

Analysis Notebooks

• Languages: Python, bash, and R

Notebooks / Scripts	Description
00_Lazaro2024etal_GP2examplenotebook.ipynb	Jupyter Notebook demonstrating the genetic analysis of the AMR population from GP2 data (also applies to other ancestries and the MEX-PD cohort)
01_Lazaro2024_create_freq_plots.R	Creates bar plots showing the frequency of selected SNPs across different cohorts and populations
02_Lazaro2024etal_Table1.R	Contains data manipulation, as well as descriptive and inferential analysis for Table 1
03_Lazaro2024etal_Table2.R	Contains data manipulation, as well as descriptive and inferential analysis for Table 2
04_Lazaro2024etal_Figure1.R	Manipulates data and creates maps of Mexico showing registry frequencies
05_Lazaro2024etal_Figure2.R	Manipulates data and creates figures for clinical assessments

---

Software

Software	Version(s)	Resource URL	RRID	Notes
R Project for Statistical Computing	4.3.0	http://www.r-project.org/	RRID:SCR_001905	Packages used: - tidyverse; dplyr; tidyr; ggplot; sf (for maps) - stringr (general data) - scales (bar plots)
PLINK	1.9 and 2.0	http://www.nitrc.org/projects/plink	RRID:SCR_001757	Used for genetic analyses