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 | Service | File Pattern | SNP Count | |---------|--------------|-----------| | **AncestryDNA** | `AncestryDNA.txt` | ~700K | | **23andMe** | `genome_*.txt` | ~600K | | **MyHeritage** | CSV export | ~700K | | **FamilyTreeDNA** | CSV/TSV export | ~700K | | **LivingDNA** | CSV export | ~600K | ## Quick Start ```bash # Set your DNA file path export DNA_FILE="/path/to/your/raw_data.txt" # Run comprehensive analysis python3 {baseDir}/analyze_dna.py "$DNA_FILE" ``` ## Analysis Capabilities ### 🏥 Health Markers - **Cardiovascular:** APOE, LPA, PCSK9, F5 (Factor V Leiden), 9p21 - **Cancer predisposition:** BRCA1/2 indicators, TP53, MLH1, APC - **Metabolic:** MTHFR, HFE (hemochromatosis), TCF7L2 (diabetes) - **Autoimmune:** HLA variants, celiac markers - **Neurological:** APOE ε4 (Alzheimer's risk), Parkinson's markers - **Eye health:** CFH, ARMS2 (macular degeneration) ### 💊 Pharmacogenomics - **Drug metabolism:** CYP2D6, CYP2C19, CYP3A4, CYP2C9 - **Warfarin:** VKORC1 sensitivity, CYP2C9 variants - **Statins:** SLCO1B1 myopathy risk - **Opioids:** OPRM1 response variants - **Antidepressants:** SLC6A4, HTR2A response - **Caffeine:** CYP1A2 metabolism speed ### 🧬 Traits & Characteristics - Eye/hair/skin color prediction - Muscle fiber composition (ACTN3) - Caffeine metabolism speed - Lactose tolerance (MCM6) - Circadian rhythm (CLOCK genes) - Alcohol flush reaction (ALDH2) - Bitter taste perception (TAS2R38) ### 🌍 Ancestry & Ancient DNA - Y-DNA haplogroup prediction - mtDNA haplogroup prediction - Ancient population comparisons (AADR database) - Neanderthal variant analysis - Population-specific markers - Parental ancestry inference ### 📊 Advanced Data Science - Polygenic risk score calculations - Principal component analysis (PCA) - Population clustering - ROH (runs of homozygosity) analysis - Rare variant identification ## Output Reports are generated in `~/dna-analysis/reports/`: | File | Contents | |------|----------| | `health_report.json` | Health marker analysis | | `pharma_report.json` | Pharmacogenomics | | `traits_report.json` | Physical traits | | `ancestry_report.json` | Haplogroups & ancestry | | `ancient_dna_report.json` | Ancient population matches | | `supplement_protocol.md` | Personalized supplement suggestions | | `full_report.md` | Human-readable comprehensive summary | ## Advanced Analysis Scripts ### Health & Pharmacogenomics ```bash python3 {baseDir}/analyze_dna.py "$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 ```bash 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 "$DNA_FILE" # Maternal/paternal split ``` ### Utilities ```bash python3 {baseDir}/convert_to_plink.py "$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: ```python # 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 ```python # 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 ```bash # 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](https://www.snpedia.com) — SNP encyclopedia - [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar/) — Clinical variants - [PharmGKB](https://www.pharmgkb.org) — Pharmacogenomics - [AADR](https://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-data) — Ancient DNA - [gnomAD](https://gnomad.broadinstitute.org/) — 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.* 🧬