DNA Analysis

Local analysis of AncestryDNA raw data. No data leaves your machine.

Quick Start

This project uses uv for Python dependency management.

Simple Command-Line Interface

# Full analysis (all modules)
uv run python dna_cli.py ancestryDNA.txt

# Athletic Performance Score
uv run python dna_cli.py --athletic ancestryDNA.txt

# Y-chromosome Haplogroup (males only)
uv run python dna_cli.py --haplogroup ancestryDNA.txt

# Help and options
uv run python dna_cli.py --help

The CLI includes:

• File validation and helpful error messages
• File size checking to ensure valid DNA files
• Support for AncestryDNA, 23andMe, and other formats
• Automatic format detection

Advanced Usage

1. Place your AncestryDNA.txt file in this directory
2. Run the analysis:

# Full DNA analysis
uv run python analyze_dna.py AncestryDNA.txt

# Athletic performance score
uv run python athletic_score.py AncestryDNA.txt

Modules

analyze_dna.py - Main Analysis

• Pharmacogenomics: Drug metabolism variants (CYP2C9, CYP2C19, CYP2D6, etc.)
• Disease Risk: APOE (Alzheimer), Factor V Leiden, hemochromatosis, diabetes (TCF7L2), obesity (FTO)
• Mental Health: Serotonin (HTR1A/2A, SLC6A4), dopamine (COMT, DRD2), BDNF, bipolar/schizophrenia markers
• Physical Traits: Eye/hair/skin color, muscle type (ACTN3), lactose tolerance, caffeine metabolism
• Y-Haplogroup: Paternal lineage prediction

athletic_score.py - Athletic Performance Analysis

Analyzes athletic performance genetics based on validated SNPs:

• Muscle/energy systems (ACTN3, ACE, PPARGC1A, AMPD1)
• Adrenergic response (ADRB2, ADRB3)
• Oxygen/hypoxia (HIF1A, EPAS1)
• Inflammation/recovery (IL6, TNF, COL5A1)
• Fuel metabolism (PPARA, UCP2, SLC16A1)
• Note: Polygenic scoring is under revision for improved accuracy

Data Sources

• FDA Pharmacogenetic Associations
• ACMG Secondary Findings v3.2
• CPIC Guidelines
• Replicated GWAS findings
• SNPedia references for all markers

Requirements

This project uses uv for dependency management. Install uv first:

curl -LsSf https://astral.sh/uv/install.sh | sh

Dependencies are managed via pyproject.toml and will be automatically installed when you run:

uv run analyze_dna.py

Manual installation (if needed):

uv pip install snps pandas numpy

Output

• ~300+ SNPs analyzed across clinical and research categories
• Color-coded risk assessment
• Direct SNPedia links for each variant
• Athletic performance analysis
• Y-chromosome haplogroup (for males)