GWAS Analysis

Perform Genome-Wide Association Studies on your genetic data with Zygotrix's high-performance C++ engine.

What is GWAS?

GWAS (Genome-Wide Association Study) identifies genetic variants (SNPs) associated with traits or diseases by testing each variant for statistical association with a phenotype.

Supported File Formats

Format	Extension	Description
VCF	.vcf	Variant Call Format
PLINK	.bed/.bim/.fam	Binary genotype format
23andMe	.txt	Direct-to-consumer format

Quick Start

1. Upload Your Dataset

Through the API:

curl -X POST http://localhost:8000/api/gwas/datasets/upload \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -F "file=@your_data.vcf"

Or through Zygotrix AI:

[Upload file] "Analyze this VCF file"

2. Run Analysis

curl -X POST http://localhost:8000/api/gwas/analyze \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "dataset_id": "your-dataset-id",
    "analysis_type": "linear",
    "maf_threshold": 0.01
  }'

3. Get Results

curl http://localhost:8000/api/gwas/results/YOUR_JOB_ID \
  -H "Authorization: Bearer YOUR_TOKEN"

Analysis Types

Linear Regression

For continuous phenotypes (height, blood pressure, etc.):

Y = β₀ + β₁G + ε

Where:

• Y = phenotype value
• G = genotype (0, 1, or 2)
• β₁ = effect size

Best for: Quantitative traits

Logistic Regression

For binary phenotypes (case/control, disease/healthy):

log(p/(1-p)) = β₀ + β₁G

Best for: Disease association studies

Chi-Square Test

For categorical associations:

χ² = Σ(O-E)²/E

Best for: Simple allele frequency comparisons

Parameters

Parameter	Default	Description
maf_threshold	0.01	Minimum minor allele frequency
num_threads	4	Parallel processing threads
analysis_type	linear	Type of statistical test

Understanding Results

Result Fields

{
  "rsid": "rs1234567",
  "chromosome": 1,
  "position": 123456,
  "ref_allele": "A",
  "alt_allele": "G",
  "beta": 0.25,
  "se": 0.05,
  "p_value": 1.5e-8,
  "maf": 0.15
}

Field	Description
rsid	SNP identifier
beta	Effect size (per allele)
se	Standard error
p_value	Statistical significance
maf	Minor allele frequency

Significance Thresholds

Threshold	Value	Interpretation
Genome-wide significant	p < 5×10⁻⁸	Strong evidence
Suggestive	p < 1×10⁻⁵	Worth investigating
Nominal	p < 0.05	Weak evidence

Performance

Powered by the C++ GWAS engine with Eigen library:

Dataset Size	Analysis Time
1,000 SNPs, 100 samples	Under 1 second
10,000 SNPs, 500 samples	~5 seconds
100,000 SNPs, 1,000 samples	~30 seconds
1M SNPs, 1,000 samples	~5 minutes

Quality Control

MAF Filtering

SNPs with MAF < threshold are excluded (default: 1%).

Missing Data

Genotypes marked as -9 or ./. are treated as missing.

Example Workflow

# 1. Upload dataset
response = requests.post(
    f"{API_URL}/gwas/datasets/upload",
    headers={"Authorization": f"Bearer {token}"},
    files={"file": open("data.vcf", "rb")}
)
dataset_id = response.json()["dataset_id"]

# 2. Start analysis
response = requests.post(
    f"{API_URL}/gwas/analyze",
    headers={"Authorization": f"Bearer {token}"},
    json={
        "dataset_id": dataset_id,
        "analysis_type": "linear",
        "maf_threshold": 0.05
    }
)
job_id = response.json()["job_id"]

# 3. Poll for results
while True:
    status = requests.get(f"{API_URL}/gwas/jobs/{job_id}/status")
    if status.json()["status"] == "completed":
        break
    time.sleep(5)

# 4. Get results
results = requests.get(f"{API_URL}/gwas/results/{job_id}")
significant_snps = [r for r in results.json()["results"] if r["p_value"] < 5e-8]

Troubleshooting

"C++ GWAS CLI not found"

Build the C++ engine:

cd zygotrix_engine_cpp
sudo apt install libeigen3-dev
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build

Analysis timeout

For large datasets, increase the timeout or reduce dataset size.

Low-quality results

Check:

• Sample size (need 50+ for reliable results)
• MAF threshold (too low = noisy results)
• Phenotype distribution