Nutrigenomics

Generate a personalised nutrition report from your genetic data (23andMe, AncestryDNA, or VCF). Analyses 40+ genes affecting nutrient metabolism, absorption, and food sensitivities. All processing is local — your genetic data never leaves your device.

Nutrigenomics — Personalised Nutrition from Genetic Data

Skill ID: nutrigenomics Version: 0.3.2 Status: Beta Author: David de Lorenzo Requires: Python 3.11+, pandas, numpy, matplotlib, seaborn, reportlab (optional)

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What This Skill Does

The Nutrigenomics generates a personalised nutrition report from consumer genetic data (23andMe, AncestryDNA raw files or VCF). It interrogates a curated set of nutritionally-relevant SNPs drawn from GWAS Catalog, ClinVar, and peer-reviewed nutrigenomics literature, then translates genotype calls into actionable dietary and supplementation guidance — all computed locally.

Key outputs

• Markdown nutrition report with risk scores and per-SNP genotype calls
• Radar chart of nutrient risk profile
• Gene × nutrient heatmap
• Reproducibility bundle (README_reproducibility.txt, environment.yml, checksums.txt, provenance.json)

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Trigger Phrases

The Bio Orchestrator should route to this skill when the user says anything like:

• "personalised nutrition", "nutrigenomics", "diet genetics"
• "what should I eat based on my DNA"
• "nutrient metabolism", "vitamin absorption genetics"
• "MTHFR", "APOE", "FTO", "BCMO1", "VDR", "FADS1/2"
• "folate", "omega-3", "vitamin D", "caffeine metabolism", "lactose", "gluten"
• Input files: .txt or .csv (23andMe), .csv (AncestryDNA), .vcf

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Curated SNP Panel

Macronutrient Metabolism

Gene	SNP	Nutrient Impact	Evidence
FTO	rs9939609	Energy balance, fat mass, carb sensitivity	Strong (GWAS)
PPARG	rs1801282	Fat metabolism, insulin sensitivity	Moderate
APOA5	rs662799	Triglyceride response to dietary fat	Strong
TCF7L2	rs7903146	Carbohydrate metabolism, T2D risk	Strong
ADRB2	rs1042713	Fat oxidation, exercise × diet interaction	Moderate

Micronutrient Metabolism

Gene	SNP	Nutrient	Effect of risk allele
MTHFR	rs1801133	Folate / B12	↓ 5-MTHF conversion (~70%)
MTHFR	rs1801131	Folate / B12	↓ enzyme activity (~30%)
MTR	rs1805087	B12 / homocysteine	↑ homocysteine risk
BCMO1	rs7501331	Beta-carotene → Vitamin A	↓ conversion (~50%)
BCMO1	rs12934922	Beta-carotene → Vitamin A	↓ conversion (compound het)
VDR	rs2228570	Vitamin D absorption	↓ VDR function
VDR	rs731236	Vitamin D	↓ bone mineral density response
GC	rs4588	Vitamin D binding	↑ deficiency risk
SLC23A1	rs33972313	Vitamin C transport	↓ renal reabsorption
ALPL	rs1256335	Vitamin B6	↓ alkaline phosphatase activity

Omega-3 / Fatty Acid Metabolism

Gene	SNP	Nutrient	Effect
FADS1	rs174546	LC-PUFA synthesis	↑/↓ EPA/DHA from ALA
FADS2	rs1535	LC-PUFA synthesis	Modulates omega-6:omega-3 ratio
ELOVL2	rs953413	DHA synthesis	↓ elongation of EPA→DHA
APOE	rs429358	Saturated fat response	ε4 → ↑ LDL-C on high SFA diet
APOE	rs7412	Saturated fat response	Combined with rs429358 for ε typing

Caffeine & Alcohol

Gene	SNP	Compound	Effect
CYP1A2	rs762551	Caffeine	Slow/Fast metaboliser
AHR	rs4410790	Caffeine	Modulates CYP1A2 induction
ADH1B	rs1229984	Alcohol	Acetaldehyde accumulation risk
ALDH2	rs671	Alcohol	Asian flush / toxicity risk

Food Sensitivities

Gene	SNP	Sensitivity	Effect
MCM6	rs4988235	Lactose intolerance	Non-persistence of lactase
HLA-DQ2	Proxy SNPs	Coeliac / gluten	HLA-DQA1/DQB1 risk haplotypes

Antioxidant & Detoxification

Gene	SNP	Pathway	Effect
SOD2	rs4880	Manganese SOD	↓ mitochondrial antioxidant
GPX1	rs1050450	Selenium / GSH-Px	↓ glutathione peroxidase
GSTT1	Deletion	Glutathione-S-trans	Null genotype → ↑ oxidative risk
NQO1	rs1800566	Coenzyme Q10	↓ CoQ10 regeneration
COMT	rs4680	Catechol / B vitamins	Met/Val → methylation load

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Algorithm

1. Input Parsing (parse_input.py)

Accepts:

• 23andMe .txt or .csv (tab-separated: rsid, chromosome, position, genotype)
• AncestryDNA .csv
• Standard VCF (extracts GT field)

Auto-detects format from header lines. Normalises alleles to forward strand using a hard-coded reference table (avoids requiring external databases).

2. Genotype Extraction (extract_genotypes.py)

For each SNP in the panel:

1. Look up rsid in parsed data
2. Return genotype string (e.g. "AT", "TT", "AA")
3. Flag as "NOT_TESTED" if absent (common for chip-to-chip variation)

3. Risk Scoring (score_variants.py)

Each SNP is scored on a 0 / 0.5 / 1.0 scale:

• 0.0 — homozygous reference (lowest risk)
• 0.5 — heterozygous
• 1.0 — homozygous risk allele

Composite Nutrient Risk Scores (0–10) are computed per nutrient domain by summing weighted SNP scores. Weights are derived from reported effect sizes (beta coefficients or OR) in the primary literature.

Risk categories:

• 0–3: Low risk — standard dietary advice applies
• 3–6: Moderate risk — dietary optimisation recommended
• 6–10: Elevated risk — consider testing and targeted supplementation

Important caveat: These are polygenic risk indicators based on common variants. They are not diagnostic. Rare pathogenic variants (e.g. MTHFR compound heterozygosity with high homocysteine) require clinical confirmation.

4. Report Generation (generate_report.py)

Outputs a structured Markdown report with:

• Executive summary (top 3 personalised findings)
• Per-nutrient sections: genotype table → interpretation → recommendation
• Radar chart (matplotlib) of nutrient risk scores
• Gene × nutrient heatmap (seaborn)
• Supplement interactions table
• Disclaimer section
• Reproducibility block

5. Reproducibility Bundle (repro_bundle.py)

Exports to the output directory (not committed to the repo):

• README_reproducibility.txt — step-by-step instructions to reproduce the analysis manually
• environment.yml — pinned conda environment
• checksums.txt — SHA-256 checksums of the SNP panel and output report (input file intentionally excluded to avoid persisting a fingerprint of genetic data)
• provenance.json — timestamp, version, and format arguments (input filename intentionally omitted)

Note: No executable scripts are generated. The reproducibility bundle contains only text files for documentation and integrity verification.

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Execution

To run the analysis on a user-provided genetic file, execute this command directly:

bash

python {baseDir}/openclaw_adapter.py --input <path_to_genetic_file> --format auto

To run a demo without real genetic data (synthetic patient file included with the skill):

bash

python {baseDir}/openclaw_adapter.py --input {baseDir}/tests/synthetic_patient.csv --format 23andme

{baseDir} is replaced by OpenClaw at runtime with the absolute path to this skill's folder. Do not substitute it manually. Output is written to a timestamped directory (nutrigenomics_output_YYYYMMDD_HHMMSS/) in the current working directory and persists until manually deleted.

Supported --format values: auto (default), 23andme, ancestry, vcf.

Usage

bash

# From 23andMe raw data
openclaw "Generate my personalised nutrition report from genome.csv"

# From VCF
openclaw "Run Nutrigenomics analysis on variants.vcf and flag any folate pathway risks"

# Targeted query
openclaw "What does my APOE status mean for my saturated fat intake?"

# Run the demo report (no real genetic data needed)
openclaw "Run a demo nutrigenomics report using the synthetic patient file"

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File Structure

text

skills/nutrigenomics/
├── SKILL.md                      ← this file (agent instructions)
├── nutrigenomics.py            ← main entry point
├── parse_input.py                ← multi-format parser
├── extract_genotypes.py          ← SNP lookup engine
├── score_variants.py             ← risk scoring algorithm
├── generate_report.py            ← Markdown + figures
├── repro_bundle.py               ← reproducibility export
├── data/
│   └── snp_panel.json            ← curated SNP definitions
├── tests/
│   ├── synthetic_patient.csv     ← fixed 23andMe-format test data (for pytest)
│   └── test_nutrigenomics.py           ← pytest suite
└── examples/
    ├── generate_patient.py       ← random patient generator (demo use)
    ├── data/                     ← generated patient files land here (gitignored)
    └── output/
        ├── nutrigenomics_report.md     ← pre-rendered demo report
        ├── nutrigenomics_radar.png     ← demo radar chart (nutrient risk profile)
        └── nutrigenomics_heatmap.png   ← demo gene × nutrient heatmap

Note: Runtime output directories and randomly generated patient files are excluded from version control. Only the pre-rendered demo report in examples/output/ is committed.

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Privacy

All computation runs locally — no genetic data is ever transmitted to external servers or third-party services.

What the report contains: The Markdown report includes per-SNP genotype calls (e.g. AT, TT) for each of the 58 panel SNPs analysed. This is intentional: knowing your specific genotype at each nutrition-related locus is what makes the report actionable. Full raw genome data from the input file is not reproduced in the report; only the 58 panel SNPs are included.

File persistence: Output files (report, figures, reproducibility bundle) are written to a timestamped nutrigenomics_output_YYYYMMDD_HHMMSS/ directory under the working directory and persist on disk until manually deleted. The input file is read-only and is never copied into the output directory.

If you are running this skill on behalf of others or in a shared environment, delete the output directory once the user has downloaded their results.

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Limitations & Disclaimer

1. Not a medical device. This skill provides educational, research-oriented nutrigenomics analysis. It does not constitute medical advice.
2. Common variants only. The panel covers SNPs with MAF > 1% in at least one major population. Rare pathogenic variants are out of scope.
3. Population context. Effect sizes are predominantly derived from European GWAS cohorts. Risk estimates may not generalise equally across all ancestries.
4. Gene–environment interaction. Genetic risk scores interact with baseline diet, lifestyle, microbiome, and epigenetic state. A "high risk" score does not mean a nutrient deficiency is present — it means the individual may benefit from monitoring.
5. Simpson's Paradox note. Population-level associations used to derive weights may not reflect individual trajectories (see Corpas 2025, Nutrigenomics and the Ecological Fallacy).

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Roadmap

• v0.2: Microbiome × genotype interaction module (16S rRNA input)
• v0.3: Longitudinal tracking — compare reports across time
• v0.4: HLA typing for immune-mediated food reactions (coeliac, gluten sensitivity)
• v1.0: Multi-omics integration (metabolomics + genomics + dietary recall)

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References

This skill's SNP panel and methodology are informed by peer-reviewed nutrigenomics research. For verification and additional details, consult:

• PubMed MEDLINE: https://pubmed.ncbi.nlm.nih.gov/
• GWAS Catalog: https://www.ebi.ac.uk/gwas/ (published genome-wide association studies)
• ClinVar: https://www.ncbi.nlm.nih.gov/clinvar/ (variant interpretations)

Users are encouraged to verify specific claims through these authoritative sources and with qualified healthcare providers.

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Contributing

The SNP panel (data/snp_panel.json) is maintained by the skill author. To suggest additions or corrections, contact David de Lorenzo directly via GitHub (@drdaviddelorenzo) or open an issue on GitHub.