# **Genome-Wide Association Study (GWAS) of Sickle Cell Disease in Tanzania Using Regenie**
## **1. Introduction**
Genome-wide association studies (GWAS) are used to identify genetic variants associated with complex traits and diseases. This study focuses on **Sickle Cell Disease (SCD) in Tanzania**, aiming to detect **genetic variants significantly associated with the phenotype of interest** using **Regenie**, a two-step approach suitable for large-scale GWAS.
**Key Details:**
- **Total Samples:** 3,210 individuals.
- **Original SNPs:** 8,457,145 (before quality control).
- **Post-QC SNPs:** 1,466,733 (after applying quality control).
- **Phenotype Type:** Continuous variable.
- **Software Used:** `PLINK` for quality control, `Regenie` for association testing.
- **Analysis Type:** GWAS with **stepwise regression modeling in Regenie**.
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## **2. Quality Control (QC) Using PLINK**
Before conducting a GWAS, it is **crucial to perform QC** to ensure **reliable** and **valid** results by removing:
- **Low-quality SNPs** (e.g., missing data, low minor allele frequency).
- **Samples with excess heterozygosity** (potential genotyping errors).
- **Population structure issues** (through pruning of correlated SNPs).
### **Step 1: SNP and Sample Filtering**
We apply the following **PLINK filters**:
```bash
plink --bfile "$BFILE" \
--geno 0.02 \ # Remove SNPs with >2% missing genotypes
--mind 0.02 \ # Remove individuals with >2% missing genotypes
--maf 0.01 \ # Exclude SNPs with Minor Allele Frequency (MAF) 0.05 || $6 "$OUTDIR/remove_het_samples.txt"
plink --bfile "$OUTDIR/step2_ld_pruned" \
--remove "$OUTDIR/remove_het_samples.txt" \
--make-bed --out "$OUTDIR/QC_passed"
```
**Why?**
- Samples with **excess heterozygosity (|F coefficient| > 0.05)** may be **misgenotyped or contaminated**.
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## **3. GWAS Analysis Using Regenie**
### **Why Regenie?**
- Efficient for **large-scale genetic data**.
- Uses **stepwise ridge regression**, reducing confounding by p …