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)

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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), INLINECODE8

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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 INLINECODE12
• 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. 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)
• INLINECODE20 — heterozygous
• INLINECODE21 — 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
• INLINECODE25 — pinned conda environment
• INLINECODE26 — SHA-256 checksums of the SNP panel and output report (input file intentionally excluded to avoid persisting a fingerprint of genetic data)
• INLINECODE27 — 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:

CODEBLOCK0

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

CODEBLOCK1

INLINECODE28 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

CODEBLOCK2

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File Structure

CODEBLOCK3

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. 1. Not a medical device. This skill provides educational, research-oriented

nutrigenomics analysis. It does not constitute medical advice.

1. 2. Common variants only. The panel covers SNPs with MAF > 1% in at least one

major population. Rare pathogenic variants are out of scope.

1. 3. Population context. Effect sizes are predominantly derived from European

GWAS cohorts. Risk estimates may not generalise equally across all ancestries.

1. 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.

1. 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.