Personal Genomics Skill v4.4.0

Description

# Personal Genomics Skill v4.4.0 Comprehensive local DNA analysis with **1600+ markers** across **30 categories**. Privacy-first genetic analysis for AI agents. **🆕 v4.4.0: 1000 GENOMES POPULATION COMPARISON & ANCIENT DNA** - Transparent population comparison (not black-box percentages) - Ancient DNA signal detection (WHG, ANF, Yamnaya, Neanderthal) - Interactive dashboard with population frequency visualizations - Complete methodology documentation **⚠️ v4.3.0 focuses on ACCURACY AND HONESTY** - improved uncertainty handling, PMIDs for all claims, and explicit limitations. ## Quick Start ```bash python comprehensive_analysis.py /path/to/dna_file.txt ``` ## ⚠️ Important Limitations 1. **Haplogroups are LOW CONFIDENCE** - Consumer arrays cannot reliably call haplogroups. Recommend dedicated Y-DNA/mtDNA testing (FTDNA, YFull) for accuracy. 2. **Ancestry shows ANCIENT SIGNALS, not modern ethnicity** - Modern ethnicity percentages are unreliable. Instead we detect signals from well-characterized ancient populations (WHG, Neolithic Farmers, Steppe, Neanderthal, Denisovan). 3. **PRS scores show RANGES, not point estimates** - Polygenic risk scores have wide confidence intervals. Most conditions are 50-80% non-genetic. 4. **Every marker has PMIDs** - All claims are backed by literature citations linked to PubMed. ## Triggers Activate this skill when user mentions: - DNA analysis, genetic analysis, genome analysis - 23andMe, AncestryDNA, MyHeritage results - Pharmacogenomics, drug-gene interactions - Medication interactions, drug safety - Genetic risk, disease risk, health risk - Carrier status, carrier testing - VCF file analysis - APOE, MTHFR, CYP2D6, BRCA, or other gene names - Polygenic risk scores - Haplogroups, maternal lineage, paternal lineage - Ancestry composition, ethnicity - Hereditary cancer, Lynch syndrome - Autoimmune genetics, HLA, celiac - Pain sensitivity, opioid response - Sleep optimization, chronotype, caffeine metabolism - Dietary genetics, lactose intolerance, celiac - Athletic genetics, sports performance - UV sensitivity, skin type, melanoma risk - Telomere length, longevity genetics ## Supported Files - 23andMe, AncestryDNA, MyHeritage, FTDNA - VCF files (whole genome/exome, .vcf or .vcf.gz) - Any tab-delimited rsid format ## Output Location `~/dna-analysis/reports/` - `agent_summary.json` - AI-optimized, priority-sorted - `full_analysis.json` - Complete data - `report.txt` - Human-readable - `genetic_report.pdf` - Professional PDF report - `dashboard.html` - Interactive visualization ## New v4.3.0 Features (Accuracy Update) ### Honest Haplogroup Reporting - **LOW CONFIDENCE** labels on all haplogroup calls - Explicit disclaimer that consumer arrays can't reliably call haplogroups - Recommendations for dedicated Y-DNA/mtDNA testing services - PMIDs for haplogroup marker sources ### Ancient Ancestral Signals (Replaces Modern Ethnicity) - **Western Hunter-Gatherers (WHG)** - Mesolithic Europeans (~15,000-8,000 BP) - **Early European Farmers (EEF)** - Neolithic Anatolians (~10,000-5,000 BP) - **Steppe Pastoralists** - Yamnaya/Bronze Age (~5,000-4,000 BP) - **Neanderthal Introgression** - Archaic human (~50,000-40,000 BP) - **Denisovan Introgression** - Archaic human (high-altitude adaptation) - Shows "Signals Detected" not percentages - Includes time periods and trait contributions - Based on ancient DNA studies with PMIDs ### PRS with Uncertainty Ranges - Percentile RANGES instead of point estimates - Confidence intervals based on marker coverage - Explicit interpretation guidance ("likely average", "uncertain", etc.) ### PMIDs Throughout - Every marker has at least one literature citation - Clickable PubMed links in dashboard - Methodology & Limitations section in dashboard ## Legacy v4.0-4.2 Features ### Haplogroup Analysis (indicative only) - Mitochondrial DNA (mtDNA) - maternal lineage - Y-chromosome - paternal lineage (males only) - Migration history context - PhyloTree/ISOGG standards ### Ancient Ancestry (scientifically grounded) - Detection of ancient population signals - Based on well-characterized ancient DNA - Includes archaic introgression (Neanderthal/Denisovan) ### Hereditary Cancer Panel - BRCA1/BRCA2 comprehensive - Lynch syndrome (MLH1, MSH2, MSH6, PMS2) - Other genes (APC, TP53, CHEK2, PALB2, ATM) - ACMG-style classification ### Autoimmune HLA - Celiac (DQ2/DQ8) - can rule out if negative - Type 1 Diabetes - Ankylosing spondylitis (HLA-B27) - Rheumatoid arthritis, lupus, MS ### Pain Sensitivity - COMT Val158Met - OPRM1 opioid receptor - SCN9A pain signaling - TRPV1 capsaicin sensitivity - Migraine susceptibility ### PDF Reports - Professional format - Physician-shareable - Executive summary - Detailed findings - Disclaimers included ## New v4.1.0 Features ### Medication Interaction Checker ```python from markers.medication_interactions import check_medication_interactions result = check_medication_interactions( medications=["warfarin", "clopidogrel", "omeprazole"], genotypes=user_genotypes ) # Returns critical/serious/moderate interactions with alternatives ``` - Accepts brand or generic names - CPIC guidelines integrated - PubMed citations included - FDA warning flags ### Sleep Optimization Profile ```python from markers.sleep_optimization import generate_sleep_profile profile = generate_sleep_profile(genotypes) # Returns ideal wake/sleep times, coffee cutoff, etc. ``` - Chronotype (morning/evening preference) - Caffeine metabolism speed - Personalized timing recommendations ### Dietary Interaction Matrix ```python from markers.dietary_interactions import analyze_dietary_interactions diet = analyze_dietary_interactions(genotypes) # Returns food-specific guidance ``` - Caffeine, alcohol, saturated fat, lactose, gluten - APOE-specific diet recommendations - Bitter taste perception ### Athletic Performance Profile ```python from markers.athletic_profile import calculate_athletic_profile profile = calculate_athletic_profile(genotypes) # Returns power/endurance type, recovery profile, injury risk ``` - Sport suitability scoring - Training recommendations - Injury prevention guidance ### UV Sensitivity Calculator ```python from markers.uv_sensitivity import generate_uv_sensitivity_report uv = generate_uv_sensitivity_report(genotypes) # Returns skin type, SPF recommendation, melanoma risk ``` - Fitzpatrick skin type estimation - Vitamin D synthesis capacity - Melanoma risk factors ### Natural Language Explanations ```python from markers.explanations import generate_plain_english_explanation explanation = generate_plain_english_explanation( rsid="rs3892097", gene="CYP2D6", genotype="GA", trait="Drug metabolism", finding="Poor metabolizer carrier" ) ``` - Plain-English summaries - Research variant flagging - PubMed links ### Telomere & Longevity ```python from markers.advanced_genetics import estimate_telomere_length telomere = estimate_telomere_length(genotypes) # Returns relative estimate with appropriate caveats ``` - TERT, TERC, OBFC1 variants - Longevity associations (FOXO3, APOE) ### Data Quality - Call rate analysis - Platform detection - Confidence scoring - Quality warnings ### Export Formats - Genetic counselor clinical export - Apple Health compatible - API-ready JSON - Integration hooks ## Marker Categories (21 total) 1. **Pharmacogenomics** (159) - Drug metabolism 2. **Polygenic Risk Scores** (277) - Disease risk 3. **Carrier Status** (181) - Recessive carriers 4. **Health Risks** (233) - Disease susceptibility 5. **Traits** (163) - Physical/behavioral 6. **Haplogroups** (44) - Lineage markers 7. **Ancestry** (124) - Population informative 8. **Hereditary Cancer** (41) - BRCA, Lynch, etc. 9. **Autoimmune HLA** (31) - HLA associations 10. **Pain Sensitivity** (20) - Pain/opioid response 11. **Rare Diseases** (29) - Rare conditions 12. **Mental Health** (25) - Psychiatric genetics 13. **Dermatology** (37) - Skin and hair 14. **Vision & Hearing** (33) - Sensory genetics 15. **Fertility** (31) - Reproductive health 16. **Nutrition** (34) - Nutrigenomics 17. **Fitness** (30) - Athletic performance 18. **Neurogenetics** (28) - Cognition/behavior 19. **Longevity** (30) - Aging markers 20. **Immunity** (43) - HLA and immune 21. **Ancestry AIMs** (24) - Admixture markers ## Agent Integration The `agent_summary.json` provides: ```json { "critical_alerts": [], "high_priority": [], "medium_priority": [], "pharmacogenomics_alerts": [], "apoe_status": {}, "polygenic_risk_scores": {}, "haplogroups": { "mtDNA": {"haplogroup": "H", "lineage": "maternal"}, "Y_DNA": {"haplogroup": "R1b", "lineage": "paternal"} }, "ancestry": { "composition": {}, "admixture": {} }, "hereditary_cancer": {}, "autoimmune_risk": {}, "pain_sensitivity": {}, "lifestyle_recommendations": { "diet": [], "exercise": [], "supplements": [], "avoid": [] }, "drug_interaction_matrix": {}, "data_quality": {} } ``` ## Critical Findings (Always Alert User) ### Pharmacogenomics - **DPYD** variants - 5-FU/capecitabine FATAL toxicity risk - **HLA-B*5701** - Abacavir hypersensitivity - **HLA-B*1502** - Carbamazepine SJS (certain populations) - **MT-RNR1** - Aminoglycoside-induced deafness ### Hereditary Cancer - **BRCA1/BRCA2** pathogenic - Breast/ovarian cancer syndrome - **Lynch syndrome** genes - Colorectal/endometrial cancer - **TP53** pathogenic - Li-Fraumeni syndrome (multi-cancer) ### Disease Risk - **APOE ε4/ε4** - ~12x Alzheimer's risk - **Factor V Leiden** - Thrombosis risk, contraceptive implications - **HLA-B27** - Ankylosing spondylitis susceptibility (OR ~70) ### Carrier Status - **CFTR** - Cystic fibrosis (1 in 25 Europeans) - **HBB** - Sickle cell (1 in 12 African Americans) - **HEXA** - Tay-Sachs (1 in 30 Ashkenazi Jews) ## Usage Examples ### Basic Analysis ```python from comprehensive_analysis import main main() # Uses command line args ``` ### Haplogroup Analysis ```python from markers.haplogroups import analyze_haplogroups result = analyze_haplogroups(genotypes) print(result["mtDNA"]["haplogroup"]) # e.g., "H" ``` ### Ancestry ```python from markers.ancestry_composition import get_ancestry_summary ancestry = get_ancestry_summary(genotypes) ``` ### Cancer Panel ```python from markers.cancer_panel import analyze_cancer_panel cancer = analyze_cancer_panel(genotypes) if cancer["pathogenic_variants"]: print("ALERT: Pathogenic variants detected") ``` ### Generate PDF ```python from pdf_report import generate_pdf_report pdf_path = generate_pdf_report(analysis_results) ``` ### Export for Genetic Counselor ```python from exports import generate_genetic_counselor_export clinical = generate_genetic_counselor_export(results, "clinical.json") ``` ## Privacy - All analysis runs locally - Zero network requests - No data leaves the machine ## Limitations - Consumer arrays miss rare variants (~0.1% of genome) - Results are probabilistic, not deterministic - Not a medical diagnosis - Most conditions 50-80% non-genetic - Consult healthcare providers for medical decisions - Negative hereditary cancer result does NOT rule out cancer syndrome - Haplogroup resolution limited without WGS ## When to Recommend Genetic Counseling - Any pathogenic hereditary cancer variant - APOE ε4/ε4 genotype - Multiple critical pharmacogenomic findings - Carrier status with reproduction implications - High-risk autoimmune HLA types with symptoms - Results causing significant user distress

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