Simple-genetic-correlation

Simple-genetic-correlation is a simple tool designed for calculating and visualizing genomic trait correlations using summary statistics from GWAS (Genome-Wide Association Studies).

Table of Contents

• Installation
• Usage
• Input Files
• Output Files
• Example
• License

Installation

Prerequisites

• R (with GenomicSEM, utils, and Matrix packages)
• Python 3 (with pandas and matplotlib packages)

Steps

1. Clone the repository:

   git clone git@github.com:gpmerola/Simple-genetic-correlation.git
   cd GenomicCorrelator

2. Install R packages:

   install.packages(c("GenomicSEM", "utils", "Matrix"))

3. Install Python packages:

   pip install pandas matplotlib

Usage

Step 1: Calculate Correlations

Run the main.R script to compute the trait correlations and generate a results table.

Rscript main.R

Step 2: Generate Correlation Plot

Run the corrplot.py script to create a visual representation of the trait correlations.

python corrplot.py

Input Files

• Correlation_input.csv: A CSV file containing the traits name, traits file name, sample prevalence, and population prevalence.

trait,code,sampleprev,popprev
Trait1.gz,trait1,0.1,0.01
Trait2.gz,trait2,0.2,0.02
Trait3.gz,trait3,0.15,0.015

• Update these paths in the script according to your data storage locations:

  • Summary Statistics Path (paths_corr): Set to the directory holding the GWAS summary statistics.

  • LD Score Path (ld): Set to the directory with the LD score files used for analysis.

Output Files

• results_table.csv: A CSV file containing the calculated correlations and standard errors.
• correlation_plot.png: A PNG file visualizing the correlations with error bars.

License

This project is licensed under the MIT License - see the LICENSE file for details.