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Generates a personalized nutrition report from consumer genetic data analyzing key SNPs to provide actionable dietary and supplementation guidance.
# NutriGx Advisor — Personalised Nutrition from Genetic Data
**Skill ID**: `nutrigx-advisor`
**Version**: 0.1.0
**Status**: MVP
**Author**: David de Lorenzo (ClawBio Community)
**Requires**: Python 3.11+, pandas, numpy, matplotlib, seaborn, reportlab (optional)
---
## What This Skill Does
The NutriGx Advisor 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 recommendations
- Radar chart of nutrient risk profile
- Gene × nutrient heatmap
- Reproducibility bundle (`commands.sh`, `environment.yml`, SHA-256 checksums)
---
## 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):
- `commands.sh` — full CLI to reproduce analysis
- `environment.yml` — pinned conda environment
- `checksums.txt` — SHA-256 checksums of input and output files
- `provenance.json` — timestamp and ClawBio version tag
---
## Usage
```bash
# From 23andMe raw data
openclaw "Generate my personalised nutrition report from genome.csv"
# From VCF
openclaw "Run NutriGx analysis on variants.vcf and flag any folate pathway risks"
# Targeted query
openclaw "What does my APOE status mean for my saturated fat intake?"
# Generate a random demo patient and run the report
python examples/generate_patient.py --run
```
---
## File Structure
```
skills/nutrigx-advisor/
├── SKILL.md ← this file (agent instructions)
├── nutrigx_advisor.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
├── .gitignore
├── data/
│ └── snp_panel.json ← curated SNP definitions
├── tests/
│ ├── synthetic_patient.csv ← fixed 23andMe-format test data (for pytest)
│ └── test_nutrigx.py ← pytest suite
└── examples/
├── generate_patient.py ← random patient generator (demo use)
├── data/ ← generated patient files land here (gitignored)
└── output/
├── nutrigx_report.md ← pre-rendered demo report
├── nutrigx_radar.png ← demo radar chart (nutrient risk profile)
└── nutrigx_heatmap.png ← demo gene × nutrient heatmap
```
> **Note**: Runtime output directories and randomly generated patient files are
> excluded from version control via `.gitignore`. Only the pre-rendered demo
> report in `examples/output/` is committed.
---
## Privacy
All computation runs **locally**. No genetic data is transmitted. Input files are
read-only; no raw genotype data appears in any output file (reports contain only
gene names, SNP IDs, and risk categories).
---
## 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)
- [ ] **v0.5**: Integration with NeoTree neonatal data for maternal nutrition risk scoring
- [ ] **v1.0**: Multi-omics integration (metabolomics + genomics + dietary recall)
---
## References
Key literature underpinning the SNP panel and scoring algorithm:
- Corbin JM & Ruczinski I (2023). Nutrigenomics: current state and future directions. *Annu Rev Nutr*.
- Fenech M et al. (2011). Nutrigenetics and nutrigenomics: viewpoints on the current status. *J Nutrigenet Nutrigenomics*.
- Stover PJ (2006). Influence of human genetic variation on nutritional requirements. *Am J Clin Nutr*.
- Phillips CM (2013). Nutrigenetics and metabolic disease: current status and implications for personalised nutrition. *Nutrients*.
- Minihane AM et al. (2015). APOE genotype, cardiovascular risk and responsiveness to dietary fat manipulation. *Proc Nutr Soc*.
- Frayling TM et al. (2007). A common variant in the FTO gene is associated with body mass index. *Science*.
- Pare G et al. (2010). MTHFR variants and cardiovascular risk. *Hum Genet*.
- Lecerf JM & de Lorgeril M (2011). Dietary cholesterol: from physiology to cardiovascular risk. *Br J Nutr*.
- Tanaka T et al. (2009). Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study. *PLoS Genet* (FADS1/2).
- Cornelis MC et al. (2006). Coffee, CYP1A2 genotype, and risk of myocardial infarction. *JAMA*.
---
## 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 tagging him in the main ClawBio repository.
don't have the plugin yet? install it then click "run inline in claude" again.