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)
- 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
1---2name: personal-genomics3description: Personal Genomics Skill v4.2.04---5# Personal Genomics Skill v4.2.067Comprehensive local DNA analysis with **1600+ markers** across **30 categories**. Privacy-first genetic analysis for AI agents.89## Quick Start1011```bash12python comprehensive_analysis.py /path/to/dna_file.txt13```1415## Triggers1617Activate this skill when user mentions:18- DNA analysis, genetic analysis, genome analysis19- 23andMe, AncestryDNA, MyHeritage results20- Pharmacogenomics, drug-gene interactions21- Medication interactions, drug safety22- Genetic risk, disease risk, health risk23- Carrier status, carrier testing24- VCF file analysis25- APOE, MTHFR, CYP2D6, BRCA, or other gene names26- Polygenic risk scores27- Haplogroups, maternal lineage, paternal lineage28- Ancestry composition, ethnicity29- Hereditary cancer, Lynch syndrome30- Autoimmune genetics, HLA, celiac31- Pain sensitivity, opioid response32- Sleep optimization, chronotype, caffeine metabolism33- Dietary genetics, lactose intolerance, celiac34- Athletic genetics, sports performance35- UV sensitivity, skin type, melanoma risk36- Telomere length, longevity genetics3738## Supported Files3940- 23andMe, AncestryDNA, MyHeritage, FTDNA41- VCF files (whole genome/exome, .vcf or .vcf.gz)42- Any tab-delimited rsid format4344## Output Location4546`~/dna-analysis/reports/`4748- `agent_summary.json` - AI-optimized, priority-sorted49- `full_analysis.json` - Complete data50- `report.txt` - Human-readable51- `genetic_report.pdf` - Professional PDF report5253## New v4.0 Features5455### Haplogroup Analysis56- Mitochondrial DNA (mtDNA) - maternal lineage57- Y-chromosome - paternal lineage (males only)58- Migration history context59- PhyloTree/ISOGG standards6061### Ancestry Composition62- Population comparisons (EUR, AFR, EAS, SAS, AMR)63- Admixture detection64- Ancestry informative markers6566### Hereditary Cancer Panel67- BRCA1/BRCA2 comprehensive68- Lynch syndrome (MLH1, MSH2, MSH6, PMS2)69- Other genes (APC, TP53, CHEK2, PALB2, ATM)70- ACMG-style classification7172### Autoimmune HLA73- Celiac (DQ2/DQ8) - can rule out if negative74- Type 1 Diabetes75- Ankylosing spondylitis (HLA-B27)76- Rheumatoid arthritis, lupus, MS7778### Pain Sensitivity79- COMT Val158Met80- OPRM1 opioid receptor81- SCN9A pain signaling82- TRPV1 capsaicin sensitivity83- Migraine susceptibility8485### PDF Reports86- Professional format87- Physician-shareable88- Executive summary89- Detailed findings90- Disclaimers included9192## New v4.1.0 Features9394### Medication Interaction Checker95```python96from markers.medication_interactions import check_medication_interactions9798result = check_medication_interactions(99 medications=["warfarin", "clopidogrel", "omeprazole"],100 genotypes=user_genotypes101)102# Returns critical/serious/moderate interactions with alternatives103```104- Accepts brand or generic names105- CPIC guidelines integrated106- PubMed citations included107- FDA warning flags108109### Sleep Optimization Profile110```python111from markers.sleep_optimization import generate_sleep_profile112113profile = generate_sleep_profile(genotypes)114# Returns ideal wake/sleep times, coffee cutoff, etc.115```116- Chronotype (morning/evening preference)117- Caffeine metabolism speed118- Personalized timing recommendations119120### Dietary Interaction Matrix121```python122from markers.dietary_interactions import analyze_dietary_interactions123124diet = analyze_dietary_interactions(genotypes)125# Returns food-specific guidance126```127- Caffeine, alcohol, saturated fat, lactose, gluten128- APOE-specific diet recommendations129- Bitter taste perception130131### Athletic Performance Profile132```python133from markers.athletic_profile import calculate_athletic_profile134135profile = calculate_athletic_profile(genotypes)136# Returns power/endurance type, recovery profile, injury risk137```138- Sport suitability scoring139- Training recommendations140- Injury prevention guidance141142### UV Sensitivity Calculator143```python144from markers.uv_sensitivity import generate_uv_sensitivity_report145146uv = generate_uv_sensitivity_report(genotypes)147# Returns skin type, SPF recommendation, melanoma risk148```149- Fitzpatrick skin type estimation150- Vitamin D synthesis capacity151- Melanoma risk factors152153### Natural Language Explanations154```python155from markers.explanations import generate_plain_english_explanation156157explanation = generate_plain_english_explanation(158 rsid="rs3892097", gene="CYP2D6", genotype="GA",159 trait="Drug metabolism", finding="Poor metabolizer carrier"160)161```162- Plain-English summaries163- Research variant flagging164- PubMed links165166### Telomere & Longevity167```python168from markers.advanced_genetics import estimate_telomere_length169170telomere = estimate_telomere_length(genotypes)171# Returns relative estimate with appropriate caveats172```173- TERT, TERC, OBFC1 variants174- Longevity associations (FOXO3, APOE)175176### Data Quality177- Call rate analysis178- Platform detection179- Confidence scoring180- Quality warnings181182### Export Formats183- Genetic counselor clinical export184- Apple Health compatible185- API-ready JSON186- Integration hooks187188## Marker Categories (21 total)1891901. **Pharmacogenomics** (159) - Drug metabolism1912. **Polygenic Risk Scores** (277) - Disease risk1923. **Carrier Status** (181) - Recessive carriers1934. **Health Risks** (233) - Disease susceptibility1945. **Traits** (163) - Physical/behavioral1956. **Haplogroups** (44) - Lineage markers1967. **Ancestry** (124) - Population informative1978. **Hereditary Cancer** (41) - BRCA, Lynch, etc.1989. **Autoimmune HLA** (31) - HLA associations19910. **Pain Sensitivity** (20) - Pain/opioid response20011. **Rare Diseases** (29) - Rare conditions20112. **Mental Health** (25) - Psychiatric genetics20213. **Dermatology** (37) - Skin and hair20314. **Vision & Hearing** (33) - Sensory genetics20415. **Fertility** (31) - Reproductive health20516. **Nutrition** (34) - Nutrigenomics20617. **Fitness** (30) - Athletic performance20718. **Neurogenetics** (28) - Cognition/behavior20819. **Longevity** (30) - Aging markers20920. **Immunity** (43) - HLA and immune21021. **Ancestry AIMs** (24) - Admixture markers211212## Agent Integration213214The `agent_summary.json` provides:215216```json217{218 "critical_alerts": [],219 "high_priority": [],220 "medium_priority": [],221 "pharmacogenomics_alerts": [],222 "apoe_status": {},223 "polygenic_risk_scores": {},224 "haplogroups": {225 "mtDNA": {"haplogroup": "H", "lineage": "maternal"},226 "Y_DNA": {"haplogroup": "R1b", "lineage": "paternal"}227 },228 "ancestry": {229 "composition": {},230 "admixture": {}231 },232 "hereditary_cancer": {},233 "autoimmune_risk": {},234 "pain_sensitivity": {},235 "lifestyle_recommendations": {236 "diet": [],237 "exercise": [],238 "supplements": [],239 "avoid": []240 },241 "drug_interaction_matrix": {},242 "data_quality": {}243}244```245246## Critical Findings (Always Alert User)247248### Pharmacogenomics249- **DPYD** variants - 5-FU/capecitabine FATAL toxicity risk250- **HLA-B*5701** - Abacavir hypersensitivity251- **HLA-B*1502** - Carbamazepine SJS (certain populations)252- **MT-RNR1** - Aminoglycoside-induced deafness253254### Hereditary Cancer255- **BRCA1/BRCA2** pathogenic - Breast/ovarian cancer syndrome256- **Lynch syndrome** genes - Colorectal/endometrial cancer257- **TP53** pathogenic - Li-Fraumeni syndrome (multi-cancer)258259### Disease Risk260- **APOE ε4/ε4** - ~12x Alzheimer's risk261- **Factor V Leiden** - Thrombosis risk, contraceptive implications262- **HLA-B27** - Ankylosing spondylitis susceptibility (OR ~70)263264### Carrier Status265- **CFTR** - Cystic fibrosis (1 in 25 Europeans)266- **HBB** - Sickle cell (1 in 12 African Americans)267- **HEXA** - Tay-Sachs (1 in 30 Ashkenazi Jews)268269## Usage Examples270271### Basic Analysis272```python273from comprehensive_analysis import main274main() # Uses command line args275```276277### Haplogroup Analysis278```python279from markers.haplogroups import analyze_haplogroups280result = analyze_haplogroups(genotypes)281print(result["mtDNA"]["haplogroup"]) # e.g., "H"282```283284### Ancestry285```python286from markers.ancestry_composition import get_ancestry_summary287ancestry = get_ancestry_summary(genotypes)288```289290### Cancer Panel291```python292from markers.cancer_panel import analyze_cancer_panel293cancer = analyze_cancer_panel(genotypes)294if cancer["pathogenic_variants"]:295 print("ALERT: Pathogenic variants detected")296```297298### Generate PDF299```python300from pdf_report import generate_pdf_report301pdf_path = generate_pdf_report(analysis_results)302```303304### Export for Genetic Counselor305```python306from exports import generate_genetic_counselor_export307clinical = generate_genetic_counselor_export(results, "clinical.json")308```309310## Privacy311312- All analysis runs locally313- Zero network requests314- No data leaves the machine315316## Limitations317318- Consumer arrays miss rare variants (~0.1% of genome)319- Results are probabilistic, not deterministic320- Not a medical diagnosis321- Most conditions 50-80% non-genetic322- Consult healthcare providers for medical decisions323- Negative hereditary cancer result does NOT rule out cancer syndrome324- Haplogroup resolution limited without WGS325326## When to Recommend Genetic Counseling327328- Any pathogenic hereditary cancer variant329- APOE ε4/ε4 genotype330- Multiple critical pharmacogenomic findings331- Carrier status with reproduction implications332- High-risk autoimmune HLA types with symptoms333- Results causing significant user distress