GWAS-Summary-Report-Generator

This project is designed to read and analyze GWAS (Genome-Wide Association Study) summary statistics from a .gz file. It performs various statistical calculations and generates several plots to visualize the data.

Table of Contents

• Structure
• Usage
• Dependencies
• Output
• Functions
• License

Structure

• main.py: Main script to read the .gz file, perform calculations, and generate plots.
• plot.py: Contains functions to generate QQ plot, Manhattan plot, p-value histogram, effect size distribution plot, volcano plot, and population stratification assessment.
• calculations.py: Contains functions for LD score regression, displaying top SNPs, calculating power, column statistics, minor allele frequency, genomic inflation factor, heritability, and effective sample size.

Usage

1. Ensure you have Python 3 installed.

2. Install the necessary dependencies (see below).

3. Place your GWAS summary statistics .gz file in the project directory.

4. Modify the file_path variable in main.py to point to your .gz file.

5. Run the main script:

   python main.py

The .gz file must contain the following columns for the script to work correctly:

• CHR: Chromosome number
• BP: Base-pair position
• ID: SNP identifier
• A1: First allele
• A2: Second allele
• BETA: Effect size estimate
• SE: Standard error of the effect size estimate
• P: P-value for the association
• INFO: Imputation information score
• NCAS: Number of cases
• NCON: Number of controls

If any of these columns are missing, the script will log an error and stop execution.

Dependencies

Create and activate a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate   # On Windows use `venv\Scripts\activate`

Install the required packages:

pip install -r requirements.txt
requirements.txt
Copia codice
numpy
pandas
matplotlib
scipy
statsmodels
scikit-learn

Output

The script will generate the following output files in a directory named after the input file (without the .gz extension):

• log.txt: Contains logs and summaries of the calculations.
• qq_plot.png: QQ plot of p-values.
• manhattan_plot.png: Manhattan plot of p-values.
• pvalue_histogram.png: Histogram of p-values.
• effect_size_distribution.png: Histogram of effect sizes.
• volcano_plot.png: Volcano plot of effect sizes vs. p-values.
• population_stratification.png: PCA plot for population stratification assessment.

Functions

1. main.py
   • read_gz_file(file_path, n_lines=10): Reads the .gz file, performs various calculations, generates plots, and logs the results.
2. plot.py
   • plot_qq(p_values, file_path): Generates a QQ plot of p-values.
   • plot_manhattan(df, file_path): Generates a Manhattan plot of p-values.
   • plot_pvalue_histogram(p_values, file_path): Generates a histogram of p-values.
   • plot_effect_size_distribution(effect_sizes, file_path): Generates a histogram of effect sizes.
   • plot_volcano(df, file_path): Generates a volcano plot of effect sizes vs. p-values.
   • assess_population_stratification(df, file_path, n_components=2): Assesses population stratification using PCA.
3. calculations.py
   • ld_score_regression(df): Performs LD score regression.
   • display_top_snps(df, top_n=10): Displays the top SNPs based on p-values.
   • calculate_power(df, alpha=0.05, effect_size=0.2): Calculates the statistical power of the study.
   • calculate_column_statistics(df, numeric_cols): Calculates mean and standard deviation of numeric columns.
   • calculate_minor_allele_frequency(df): Calculates the minor allele frequency (MAF).
   • calculate_genomic_inflation_factor(df): Calculates the genomic inflation factor (λ GC).
   • calculate_heritability(df): Calculates narrow-sense heritability (h²).
   • calculate_effective_sample_size(df): Calculates the effective sample size (NEFF).

License

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