Personal Genomics

Personal Genomics Analysis 🧬

⚠️ IMPORTANT DISCLAIMERS

This tool is for educational and research purposes only.

• NOT a medical diagnostic tool — Results are not clinically validated
• Consult healthcare professionals — Do not make medical decisions based solely on these results
• Privacy responsibility — You are responsible for securing your genetic data
• No warranties — Results may contain errors; verify with clinical testing
• Genetic data is sensitive — Once exposed, it cannot be "unexposed"

Analyze your raw DNA data locally and privately. No data is uploaded anywhere.

Security & Privacy

🔒 All analysis runs 100% locally on your machine

• Zero network requests during analysis
• Your genetic data never leaves your computer
• Results stored only in your local filesystem
• No telemetry, no analytics, no external calls
• You control your data completely

Recommendations:

• Store DNA files in encrypted volumes
• Don't share raw genetic data publicly
• Be cautious about sharing detailed health results
• Consider implications for family members (shared genetics)

Supported Input Formats

Quick Start

# Set your DNA file path
export DNA_FILE="/path/to/your/raw_data.txt"

# Run comprehensive analysis
python3 {baseDir}/analyze_dna.py "$DNA_FILE"

Analysis Capabilities

🏥 Health Markers

• Cardiovascular: APOE, LPA, PCSK9, F5 (Factor V Leiden), 9p21
• Cancer predisposition: BRCA1/2 indicators, TP53, MLH1, APC
• Metabolic: MTHFR, HFE (hemochromatosis), TCF7L2 (diabetes)
• Autoimmune: HLA variants, celiac markers
• Neurological: APOE ε4 (Alzheimer's risk), Parkinson's markers
• Eye health: CFH, ARMS2 (macular degeneration)

💊 Pharmacogenomics

• Drug metabolism: CYP2D6, CYP2C19, CYP3A4, CYP2C9
• Warfarin: VKORC1 sensitivity, CYP2C9 variants
• Statins: SLCO1B1 myopathy risk
• Opioids: OPRM1 response variants
• Antidepressants: SLC6A4, HTR2A response
• Caffeine: CYP1A2 metabolism speed

🧬 Traits & Characteristics

• Eye/hair/skin color prediction
• Muscle fiber composition (ACTN3)
• Caffeine metabolism speed
• Lactose tolerance (MCM6)
• Circadian rhythm (CLOCK genes)
• Alcohol flush reaction (ALDH2)
• Bitter taste perception (TAS2R38)

🌍 Ancestry & Ancient DNA

• Y-DNA haplogroup prediction
• mtDNA haplogroup prediction
• Ancient population comparisons (AADR database)
• Neanderthal variant analysis
• Population-specific markers
• Parental ancestry inference

📊 Advanced Data Science

• Polygenic risk score calculations
• Principal component analysis (PCA)
• Population clustering
• ROH (runs of homozygosity) analysis
• Rare variant identification

Output

Reports are generated in ~/dna-analysis/reports/:

Advanced Analysis Scripts

Health & Pharmacogenomics

python3 {baseDir}/analyze_dna.py "$DNA_FILE"           # Core analysis
python3 {baseDir}/extended_analysis.py "$DNA_FILE"     # 600+ markers
python3 {baseDir}/advanced_analysis.py "$DNA_FILE"     # PRS & clustering

Ancestry & Ancient DNA

python3 {baseDir}/ethnicity_analysis.py "$DNA_FILE"    # Population composition
python3 {baseDir}/ancient_dna.py "$DNA_FILE"           # Ancient markers
python3 {baseDir}/ancient_comparison.py "$DNA_FILE"    # AADR comparison
python3 {baseDir}/neanderthal_analysis.py "$DNA_FILE"  # Archaic variants
python3 {baseDir}/parental_inference.py "$DNA_FILE"    # Maternal/paternal split

Utilities

python3 {baseDir}/convert_to_plink.py "$DNA_FILE" out  # PLINK format
python3 {baseDir}/supplement_protocol.py "$DNA_FILE"   # Supplement suggestions

For AI Agents

Structured Output for Agent Consumption

All JSON reports use consistent schemas for easy parsing:

# Example: Reading health results
import json
with open("~/dna-analysis/reports/health_report.json") as f:
    health = json.load(f)
    
# Access specific markers
apoe_status = health["alzheimers"]["apoe_status"]
cardiovascular_risk = health["cardiovascular"]["risk_level"]

Agent-Actionable Insights

The reports include actionable fields:

• priority: high/medium/low
• action_type: monitor/discuss_with_doctor/lifestyle/supplement
• evidence_level: strong/moderate/preliminary
• references: PubMed IDs for verification

Integration Example

# Your agent can use this pattern:
if health["mthfr"]["status"] == "compound_heterozygous":
    if health["mthfr"]["actionable"]["priority"] == "high":
        suggest_supplement("methylfolate", "400-800mcg")

Requirements

• Python 3.10+
• ~500MB disk space for analysis
• 4GB RAM recommended
• Optional: plink2 for advanced analysis

Installation

# Via ClawHub
clawhub install personal-genomics

# Or manually
git clone https://github.com/wkyleg/personal-genomics
cd personal-genomics
pip install pandas numpy scipy rich scikit-learn

Limitations

⚠️ Important limitations to understand:

1. Consumer chips miss rare variants — Only ~0.02% of genome covered
2. Imputation has errors — Some reported variants are statistical guesses
3. Population bias — Most research is on European populations
4. Penetrance varies — Having a risk variant ≠ getting the condition
5. Environment matters — Genetics is only part of health outcomes
6. Science evolves — Today's understanding may change

Ethical Considerations

• Family implications: Your DNA reveals information about relatives
• Insurance: Some jurisdictions allow genetic discrimination
• Employment: Consider privacy before sharing results
• Law enforcement: DNA databases have been used in investigations
• Future unknowns: We don't know all future uses of genetic data

References

• SNPedia — SNP encyclopedia
• ClinVar — Clinical variants
• PharmGKB — Pharmacogenomics
• AADR — Ancient DNA
• gnomAD — Population frequencies

License

MIT License — Use freely, but you accept all responsibility for how you use results.

Built for personal exploration of your own genetics. Use wisely. 🧬