GWAS Analysis of Inflammatory Bowel Disease (IBD)

A genome-wide association study analyzing genetic variants associated with Inflammatory Bowel Disease, developed for the "Statistical Models in Human Genetics" module in the Genomic Sciences program at UNAM Juriquilla.

Overview

This project implements a complete GWAS pipeline including:

• Data preprocessing and quality control
• Sample and SNP filtering
• Population stratification correction via PCA
• Association testing using logistic regression
• Results visualization (Manhattan and QQ plots)

Data

The analysis uses the PennCath cohort dataset:

• Genotype data: ~500,000 SNPs across 1,401 individuals (PLINK format: .bed, .bim, .fam)
• Clinical data: Clinical_IBD.csv containing phenotype (IBD case/control status), age, sex, and lipid measurements

Pipeline

Script	Description
MyGWAS_Lab1.R	Data import, initial SNP QC (call rate ≥95%, MAF >1%)
MyGWAS_Lab2.R	Sample QC: heterozygosity, call rate, HWE, IBD/relatedness filtering, PCA
MyGWAS_Lab3.R	GWAS execution using parallelized GLM
MyGWAS_LabFunctions.R	Visualization functions (Manhattan plot, QQ plot, λGC calculation)
GWAS_IBD_RodriguezAxel.R	Complete integrated pipeline

Quality Control Thresholds

• SNP call rate: ≥95%
• Minor allele frequency: >1%
• Sample call rate: ≥90%
• Inbreeding coefficient |F|: ≤0.055
• Hardy-Weinberg equilibrium: p > 10⁻⁶ (controls only)
• Kinship coefficient: <0.05

Dependencies

# Bioconductor
BiocManager::install(c("snpStats", "SNPRelate"))

# CRAN
install.packages(c("dplyr", "doParallel", "qqman", "gdsfmt"))

Usage

Run scripts sequentially (Lab1 → Lab2 → Lab3) or use the integrated script:

source("GWAS_IBD_RodriguezAxel.R")

Note: Update file paths in scripts to match your local directory structure.

Output

• GWAA.txt: Association results (SNP, estimate, SE, t-value, p-value)
• Manhattan and QQ plots for visualization
• Genomic inflation factor (λGC) for assessing population stratification

Author

Axel Rodriguez Perez
Genomic Sciences, UNAM · November 2023