nutrigenomics

# Nutrigenomics — Personalised Nutrition from Genetic Data **Skill ID**: `nutrigenomics` **Version**: 0.3.1 **Status**: Beta **Author**: David de Lorenzo **Requires**: Python 3.11+, pandas, numpy, matplotlib, seaborn, reportlab (optional) --- ## 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`) --- ## 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` --- ## 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 | --- ## 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. --- ## 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" ``` --- ## File Structure ``` 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. --- ## 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. --- ## 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*). --- ## 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) --- ## 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. --- ## 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](https://github.com/drdaviddelorenzo)) or open an issue on GitHub.