bio-workflows-gwas-pipeline

Version Compatibility

Reference examples tested with: ggplot2 3.5+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

GWAS Pipeline

"Run a GWAS from my genotype data" → Orchestrate sample/variant QC (PLINK2), population stratification (PCA), association testing (linear/logistic regression), multiple testing correction, and Manhattan/QQ plot visualization.

Complete workflow for genome-wide association studies from genotype data to significant associations.

Workflow Overview

VCF/PLINK files
    |
    v
[1. QC Filtering] ------> Sample and variant QC
    |
    v
[2. LD Pruning] --------> Independent variants for PCA
    |
    v
[3. Population Structure] --> PCA for covariates
    |
    v
[4. Association Testing] --> Logistic/linear regression
    |
    v
[5. Results] -----------> Manhattan plot, QQ plot
    |
    v
Significant associations

Step 1: Data Import and QC

Convert VCF to PLINK

# VCF to PLINK binary format
plink2 --vcf input.vcf.gz \
    --make-bed \
    --out study

# Or with phenotype/covariate files
plink2 --vcf input.vcf.gz \
    --pheno phenotypes.txt \
    --make-bed \
    --out study

Sample QC

# Calculate sample statistics
plink2 --bfile study \
    --missing \
    --out study_stats

# Remove samples with high missing rate (>5%)
plink2 --bfile study \
    --mind 0.05 \
    --make-bed \
    --out study_sample_qc

# Check for sex discrepancies (if sex chromosome data available)
plink2 --bfile study_sample_qc \
    --check-sex \
    --out study_sex_check

# Remove related individuals (optional, requires IBD)
plink2 --bfile study_sample_qc \
    --king-cutoff 0.0884 \
    --make-bed \
    --out study_unrelated