Gwas Pipeline

---
name: bio-workflows-gwas-pipeline
description: End-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies.
tool_type: mixed
primary_tool: PLINK2
workflow: true
depends_on:
  - population-genetics/plink-basics
  - population-genetics/population-structure
  - population-genetics/association-testing
  - population-genetics/linkage-disequilibrium
qc_checkpoints:
  - after_qc: "Sample/variant call rates >95%, HWE p>1e-6"
  - after_structure: "No population stratification bias"
  - after_association: "Lambda ~1.0, expected QQ plot"
---

## 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

```bash
# 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

```bash
# 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
```

### Variant QC

```bash
# Apply standard variant filters
plink2 --bfile study_sample_qc \
    --geno 0.05 \
    --maf 0.01 \
    --hwe 1e-6 \
    --make-bed \
    --out study_qc

# Summary
plink2 --bfile study_qc --freq --out study_qc
```

**QC Checkpoint:**
- Sample call rate >95%
- Variant call rate >95%
- MAF >1%
- HWE p-value >1e-6 (controls only for case-control)

## Step 2: LD Pruning for PCA

```bash
# Identify independent variants
plink2 --bfile study_qc \
    --indep-pairwise 50 5 0.2 \
    --out pruned

# Extract pruned variants
plink2 --bfile study_qc \
    --extract pruned.prune.in \
    --make-bed \
    --out study_pruned
```

## Step 3: Population Structure (PCA)

```bash
# Calculate principal components
plink2 --bfile study_pruned \
    --pca 10 \
    --out study_pca

# The eigenvec file contains PCs for use as covariates
```

### Visualize PCA

```r
library(ggplot2)

# Load PCA results
pca <- read.table('study_pca.eigenvec', header = FALSE)
colnames(pca) <- c('FID', 'IID', paste0('PC', 1:10))

# Load phenotype for coloring
pheno <- read.table('phenotypes.txt', header = TRUE)
pca <- merge(pca, pheno, by = c('FID', 'IID'))

# Plot
ggplot(pca, aes(x = PC1, y = PC2, color = as.factor(PHENO))) +
    geom_point(alpha = 0.5) +
    labs(title = 'PCA of Study Samples', color = 'Phenotype') +
    theme_minimal()
ggsave('pca_plot.pdf', width = 8, height = 6)
```

## Step 4: Association Testing

### Case-Control (Binary Trait)

```bash
# Logistic regression with PCA covariates
plink2 --bfile study_qc \
    --pheno phenotypes.txt \
    --covar study_pca.eigenvec \
    --covar-col-nums 3-12 \
    --glm hide-covar \
    --out gwas_results

# Results in gwas_results.PHENO.glm.logistic
```

### Quantitative Trait

```bash
# Linear regression
plink2 --bfile study_qc \
    --pheno phenotypes.txt \
    --pheno-name BMI \
    --covar study_pca.eigenvec \
    --covar-col-nums 3-12 \
    --glm hide-covar \
    --out gwas_bmi

# Results in gwas_bmi.BMI.glm.linear
```

### With Additional Covariates

```bash
# Include age, sex, and PCs
plink2 --bfile study_qc \
    --pheno phenotypes.txt \
    --covar covariates.txt \
    --covar-name AGE,SEX,PC1-PC10 \
    --glm hide-covar \
    --out gwas_adjusted
```

## Step 5: Results Visualization

### Manhattan Plot

```r
library(qqman)

# Load results
results <- read.table('gwas_results.PHENO.glm.logistic', header = TRUE)
results <- results[!is.na(results$P),]

# Manhattan plot
png('manhattan.png', width = 1200, height = 600)
manhattan(results, chr = 'X.CHROM', bp = 'POS', snp = 'ID', p = 'P',
          suggestiveline = -log10(1e-5), genomewideline = -log10(5e-8))
dev.off()

# QQ plot
png('qq_plot.png', width = 600, height = 600)
qq(results$P)
dev.off()
```

### Calculate Genomic Inflation

```r
# Lambda (genomic inflation factor)
chisq <- qchisq(1 - results$P, 1)
lambda <- median(chisq) / qchisq(0.5, 1)
cat('Lambda:', round(lambda, 3), '\n')
# Lambda should be close to 1.0 (1.0-1.1 acceptable)
```

### Extract Significant Hits

```bash
# Genome-wide significant (p < 5e-8)
awk '$12 < 5e-8' gwas_results.PHENO.glm.logistic > significant_hits.txt

# Suggestive (p < 1e-5)
awk '$12 < 1e-5' gwas_results.PHENO.glm.logistic > suggestive_hits.txt
```

## Parameter Recommendations

| Step | Parameter | Value |
|------|-----------|-------|
| Sample QC | --mind | 0.05 |
| Variant QC | --geno | 0.05 |
| Variant QC | --maf | 0.01 |
| Variant QC | --hwe | 1e-6 |
| LD pruning | --indep-pairwise | 50 5 0.2 |
| PCA | --pca | 10 |
| Significance | p-value | 5e-8 |

## Troubleshooting

| Issue | Likely Cause | Solution |
|-------|--------------|----------|
| High lambda (>1.1) | Population stratification | Add more PCs, check ancestry |
| No significant hits | Low power | Increase sample size, meta-analysis |
| Deflated lambda (<1) | Over-correction | Reduce PC covariates |
| QQ deviation at low end | Batch effects | Check for technical artifacts |

## Complete Pipeline Script

```bash
#!/bin/bash
set -e

INPUT_VCF="genotypes.vcf.gz"
PHENO="phenotypes.txt"
OUTDIR="gwas_results"

mkdir -p ${OUTDIR}

# Step 1: Convert and QC
plink2 --vcf ${INPUT_VCF} --make-bed --out ${OUTDIR}/raw
plink2 --bfile ${OUTDIR}/raw --mind 0.05 --geno 0.05 --maf 0.01 --hwe 1e-6 \
    --make-bed --out ${OUTDIR}/qc

# Step 2: LD pruning
plink2 --bfile ${OUTDIR}/qc --indep-pairwise 50 5 0.2 --out ${OUTDIR}/pruned
plink2 --bfile ${OUTDIR}/qc --extract ${OUTDIR}/pruned.prune.in \
    --make-bed --out ${OUTDIR}/pruned

# Step 3: PCA
plink2 --bfile ${OUTDIR}/pruned --pca 10 --out ${OUTDIR}/pca

# Step 4: Association
plink2 --bfile ${OUTDIR}/qc --pheno ${PHENO} \
    --covar ${OUTDIR}/pca.eigenvec --covar-col-nums 3-12 \
    --glm hide-covar --out ${OUTDIR}/gwas

echo "=== GWAS Complete ==="
echo "Results: ${OUTDIR}/gwas.*.glm.*"
```

## Related Skills

- database-access/ensembl-rest - VEP annotation for top GWAS variants (per-variant); local VEP for >1K
- database-access/biomart-queries - Bulk SNP-to-gene mapping via BioMart
- population-genetics/plink-basics - PLINK file formats and commands
- population-genetics/population-structure - PCA and admixture
- population-genetics/association-testing - Statistical models
- population-genetics/linkage-disequilibrium - LD concepts