Personal Genomics

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

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

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 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

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 ε4/ε4 genotype
• Multiple critical pharmacogenomic findings
• Carrier status with reproduction implications
• High-risk autoimmune HLA types with symptoms
• Results causing significant user distress