Nutrigx

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

Micronutrient Metabolism

Omega-3 / Fatty Acid Metabolism

Caffeine & Alcohol

Food Sensitivities

Antioxidant & Detoxification

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

# 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
├── 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) or open an issue tagging him in the main ClawBio repository.