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BytesAgain β€Ί Skills β€Ί Personal Genomics
πŸ¦€ ClawHub

Personal Genomics

by @wkyleg

Analyze raw DNA data from consumer genetics services (23andMe, AncestryDNA, etc.). Extract health markers, pharmacogenomics, traits, ancestry composition, ancient DNA comparisons, and generate comprehensive reports. Uses open-source bioinformatics tools locally β€” no data leaves your machine.

Versionv4.2.0
Downloads3,067
Installs3
Stars⭐ 3
#legal#health
TERMINAL
clawhub install personal-genomics

πŸ“– About This Skill

Personal Genomics Skill v4.2.0

Comprehensive local DNA analysis with 1600+ markers across 30 categories. Privacy-first genetic analysis for AI agents.

Quick Start

python comprehensive_analysis.py /path/to/dna_file.txt

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

    • New v4.0 Features

    Haplogroup Analysis

  • Mitochondrial DNA (mtDNA) - maternal lineage
  • Y-chromosome - paternal lineage (males only)
  • Migration history context
  • PhyloTree/ISOGG standards

    • Ancestry Composition

  • Population comparisons (EUR, AFR, EAS, SAS, AMR)
  • Admixture detection
  • Ancestry informative markers

    • 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

    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

    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

    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

    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

    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

    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

    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:

    {
  "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 Ξ΅4/Ξ΅4 - ~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

    from comprehensive_analysis import main
main()  # Uses command line args

    Haplogroup Analysis

    from markers.haplogroups import analyze_haplogroups
result = analyze_haplogroups(genotypes)
print(result["mtDNA"]["haplogroup"])  # e.g., "H"

    Ancestry

    from markers.ancestry_composition import get_ancestry_summary
ancestry = get_ancestry_summary(genotypes)

    Cancer Panel

    from markers.cancer_panel import analyze_cancer_panel
cancer = analyze_cancer_panel(genotypes)
if cancer["pathogenic_variants"]:
    print("ALERT: Pathogenic variants detected")

    Generate PDF

    from pdf_report import generate_pdf_report
pdf_path = generate_pdf_report(analysis_results)

    Export for Genetic Counselor

    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 Ξ΅4/Ξ΅4 genotype
  • Multiple critical pharmacogenomic findings
  • Carrier status with reproduction implications
  • High-risk autoimmune HLA types with symptoms
  • Results causing significant user distress

    • πŸ’‘ Examples

    python comprehensive_analysis.py /path/to/dna_file.txt