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-sortedfull_analysis.json- Complete datareport.txt- Human-readablegenetic_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)
Pharmacogenomics (159) - Drug metabolism
Polygenic Risk Scores (277) - Disease risk
Carrier Status (181) - Recessive carriers
Health Risks (233) - Disease susceptibility
Traits (163) - Physical/behavioral
Haplogroups (44) - Lineage markers
Ancestry (124) - Population informative
Hereditary Cancer (41) - BRCA, Lynch, etc.
Autoimmune HLA (31) - HLA associations
Pain Sensitivity (20) - Pain/opioid response
Rare Diseases (29) - Rare conditions
Mental Health (25) - Psychiatric genetics
Dermatology (37) - Skin and hair
Vision & Hearing (33) - Sensory genetics
Fertility (31) - Reproductive health
Nutrition (34) - Nutrigenomics
Fitness (30) - Athletic performance
Neurogenetics (28) - Cognition/behavior
Longevity (30) - Aging markers
Immunity (43) - HLA and immune
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