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Nutrigx
Personalised nutrition report from consumer genetic data (23andMe, AncestryDNA, VCF) — interrogates nutritionally-relevant SNPs and generates actionable dietary guidance, all computed locally.
Archived 2026-10-23 · Source updated 2026-10-04 · License: MIT. Check the original repository for the latest version.
SKILL.md instructions
# NutriGx Advisor — Personalised Nutrition from Genetic Data
**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 | A allele: lower EPA→DHA conversion (association, not a requirement) |
| 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 | Risk allele G (GRCh38 plus / -13910C) is non-persistence; persistence allele A (-13910T) is dominant |
| 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)
**Palindromic SNPs.** Three panel SNPs are palindromic, so strand cannot be told from the genotype: `rs9939609` (FTO, T/A), `rs12934922` (BCMO1, A/T) and `rs1801282` (PPARG, C/G). Their calls are read as reported on the plus strand, which is how 23andMe and AncestryDNA export them. A file on the minus strand would score these three the wrong way round without warning.
### 3. Risk Scoring (`score_variants.py`)
Each SNP is scored on a **0 / 0.5 / 1.0** scale by default:
- `0.0` — homozygous reference (lowest risk)
- `0.5` — heterozygous
- `1.0` — homozygous risk allele
Lactose (`rs4988235`) uses `inheritance: dominant_protective`. One copy of the
persistence allele is enough, so AA and AG score as persistent (0.0) and only
GG scores as non-persistence (1.0).
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 (`nutrigx_repro_bundle.py`)
Delegates to the shared `clawbio.common.reproducibility` layer and exports to
`<output_dir>/reproducibility/` (not committed to the repo):
- `commands.sh` — full CLI to reproduce analysis
- `environment.yml` — pinned conda environment
- `checksums.sha256` — SHA-256 checksums of output files, labelled relative to
the output directory (verify with `cd <output_dir> && sha256sum -c
reproducibility/checksums.sha256`); files that fail to generate abort the
bundle rather than being silently omitted
- `provenance.json` — timestamp, ClawBio version tag, and SHA-256 checksums of
the input file and SNP panel
---
## 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/
├── SKILL.md ← this file (agent instructions)
├── nutrigx.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
├── nutrigx_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
│ └── test_repro_bundle.py ← reproducibility bundle tests
└── 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
**Symbolic links in the output path are refused.** Report, figure and reproducibility files will not be written through a symlink, including a deliberately symlinked output directory; point `--output` at a real directory. On platforms without `O_NOFOLLOW` and `dir_fd` support (such as Windows) the same refusal is made with a less race-proof check.
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; ELOVL2 `rs953413`, minor A allele with lower DHA). PMID 19148276.
- rs953413 regulates polyunsaturated fatty acid metabolism by modulating ELOVL2 expression (2020). *iScience* (G allele gives higher ELOVL2 enhancer activity than A). PMID 31928966.
- Cornelis MC et al. (2006). Coffee, CYP1A2 genotype, and risk of myocardial infarction. *JAMA*.
- Enattah NS et al. (2002). Identification of a variant associated with adult-type hypolactasia. *Nat Genet* 30:233–237. PMID 11788828.
---
## 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.