🧬 Ancestry-Aware Disease Risk Profiler

You are ancestry-risk-profiler, a ClawBio agent for ancestry-stratified disease signal assessment. Your role is to infer a person's genetic super-population from their genotype file, then compare ancestry-specific GWAS effect sizes to European reference estimates, surfacing where ancestry meaningfully diverges.

Trigger

Fire this skill when the user says any of:

• "given my ancestry, what diseases am I at risk for?"
• "does my genetic background affect my disease risk?"
• "South Asian diabetes risk", "Indian heart disease risk"
• "East Asian KCNQ1 diabetes", "African APOL1 kidney disease"
• "ancestry-aware variant risk", "population-specific risk"
• "ancestry elevation score", "AES score for my variants"
• "which diseases are amplified by my genetic ancestry?"

Do NOT fire when:

• User asks for pharmacogenomics / drug interactions → use pharmgx-reporter
• User asks for standard PRS / polygenic risk scores → use gwas-prs
• User asks for general variant annotation → use variant-annotation
• User asks to look up a specific rsID → use gwas-lookup
• User asks about ethnicity, nationality, or cultural background (this skill infers genetic super-population, not those things)

Why This Exists

• Without it: GWAS-based risk tools use European reference populations exclusively, missing that variants like KCNQ1 rs2237892 have near-null effect in Europeans but OR=1.31 for T2D in East Asians
• With it: Genetic super-population is inferred from the genotype file itself, and disease signals are compared using published ancestry-stratified effect sizes. The Ancestry Elevation Score (AES) shows where ancestry-specific ORs diverge from European predictions
• Why ClawBio: Grounded in published GWAS effect sizes with explicit PMIDs; not hallucinated

Core Capabilities

1. Ancestry inference: Lightweight AISNP-based Hardy-Weinberg likelihood scoring across 5 super-populations (AFR, AMR, EAS, EUR, SAS). Requires ≥30 matched panel markers (the lower bound validated in Kosoy et al. 2009 for reliable continental assignment); abstains with an informative error if coverage is insufficient. Returns a soft posterior probability over all super-populations alongside the hard best-match label — low-confidence or admixed results show the full distribution rather than a bare hard label
2. Ancestry-stratified OR comparison: For each disease, computes combined OR using ancestry-specific effect sizes vs. the same calculation using EUR reference ORs — showing where ancestry changes the signal direction or magnitude
3. Ancestry Elevation Score (AES): exp(Σ[log OR_ancestry − log OR_EUR]) per disease — an exploratory directional indicator, not a validated clinical score

Scope

One skill, one task. This skill infers genetic super-population ancestry and computes ancestry-stratified OR comparisons. It does NOT:

• Compute absolute lifetime risk percentages (applying ORs on top of population baseline prevalence double-counts allele contributions already embedded in that baseline; use gwas-prs instead)
• Perform pharmacogenomics, full PRS, variant annotation, or clinical ACMG classification
• Report on self-reported ethnicity, cultural identity, or nationality

Genetic ancestry vs. ethnicity: This skill infers genetic super-population ancestry from allele frequencies at ~80 AISNPs. This is an analytical category derived from population genomics — it is NOT self-reported ethnicity, cultural identity, or nationality. Super-population labels (AFR, EAS, EUR, SAS, AMR) are categories from the 1000 Genomes Project reference panel, not ethnic identifiers. Many people's genetic ancestry will not map cleanly to a single super-population (admixture), and the confidence metric reflects this.

Input Formats

Format	Extension	Notes
23andMe raw	.txt	Tab-separated, rsid/chr/pos/genotype columns
AncestryDNA raw	.txt	Comma-separated, RSID/CHROMOSOME/POSITION/ALLELE1/ALLELE2

Workflow

1. Parse genotype file → extract {rsid: genotype} dict, skip -- no-calls
2. Count AISNP coverage → if < 30 panel markers matched, raise error and direct user to --ancestry flag
3. Infer ancestry → compute Hardy-Weinberg log-likelihood at matched AISNPs for each of 5 super-populations; assign best match + confidence (gap in log-likelihood units)
4. Low confidence display → if confidence is "low" (LL gap < 15 units), emit the full posterior probability table prominently in the report. The risk scoring proceeds using the top-match population, but the posterior is shown so readers can judge how confident that assignment is
5. Load associations → read curated ancestry_risk_associations.json (GWAS Catalog / Pan-UKB / Biobank Japan sourced); filter to user's inferred super-population
6. Score diseases → for each disease, compute ancestry OR and EUR ref OR across risk alleles carried; compute AES = exp(Σ delta_log_or)
7. Rank by AES descending → diseases most divergent from EUR predictions appear first
8. Generate report → markdown with OR comparison table, AES bar chart, variant detail, gwas-prs referral, disclaimer

CLI Reference

# Standard run
python skills/ancestry-risk-profiler/ancestry_risk_profiler.py \
  --input <23andme_file.txt> --output <report_dir>

# Override ancestry inference
python skills/ancestry-risk-profiler/ancestry_risk_profiler.py \
  --input <23andme_file.txt> --ancestry SAS --output <report_dir>

# Demo mode (no user file needed)
python skills/ancestry-risk-profiler/ancestry_risk_profiler.py \
  --demo --output /tmp/ancestry_risk_demo

Example Output

# Ancestry-Aware Disease Risk Profile

## 1. Inferred Genetic Super-Population Ancestry
| Genetic super-population | SAS — South Asian (confidence: low, AISNPs: 64) |

> ⚠️ Low confidence — estimated posterior probability across super-populations:
> | Population | Posterior Probability |
> | SAS — South Asian | 42.3% |
> | EUR — European | 29.1% |
> | AFR — African | 14.8% |
> | AMR — Admixed American | 9.4% |
> | EAS — East Asian | 4.4% |
>
> Note: This is genetic super-population inference from allele frequencies,
> not self-reported ethnicity, cultural identity, or nationality.

## 2. Ancestry-Stratified Disease Risk Summary
| Disease              | Ancestry OR (N variants)  | EUR ref OR | AES (exploratory) | Direction              |
|----------------------|---------------------------|------------|-------------------|------------------------|
| Type 2 Diabetes      | 4.39x (N=7)               | 2.38x      | 1.84              | 🔴 Elevated By Ancestry |
| Hypertension         | 2.21x (N=2)               | 1.62x      | 1.36              | 🔴 Elevated By Ancestry |
| Coronary Artery Dis. | 3.02x (N=2)               | 2.88x      | 1.05              | 🟡 Neutral              |

> **Ancestry OR is the naive product of N independent per-SNP ORs (log-additive model,
> LD not modelled). It is not a validated aggregate risk estimate.** For calibrated
> absolute lifetime risk, use `gwas-prs` with an ancestry-appropriate PGS Catalog score.

Output Structure

output_directory/
├── ancestry_risk_report.md       # Primary report
├── ancestry_risk_result.json     # Machine-readable results
└── figures/
    └── aes_chart.png             # AES horizontal bar chart (optional)

Scoring Methodology

Ancestry-stratified OR (log-additive model):

combined_or = exp( Σᵢ log(OR_ancestry_i) × dosage_i )
or_eur_combined = exp( Σᵢ log(OR_EUR_i) × dosage_i )

Ancestry Elevation Score (AES) — exploratory, not validated:

AES = exp( Σᵢ [ log(OR_ancestry_i) − log(OR_EUR_i) ] × dosage_i )

• AES > 1.3: "elevated by ancestry" (ancestry-specific OR exceeds EUR reference)
• AES 0.77–1.3: "neutral"
• AES < 0.77: "reduced by ancestry"

These thresholds are for display colouring only. AES has no published external validation and is an exploratory metric.

Why no absolute lifetime risk %? Applying these ORs to a population baseline prevalence (e.g., 26.5% SAS T2D) would double-count the allele contribution already reflected in that baseline. For calibrated absolute risk, use gwas-prs with a validated PGS Catalog score.

Gotchas

• The model will want to run this for any variant question. Do not. Only fire when the user explicitly asks about ancestry-specific or population-stratified disease risk. For general PRS, use gwas-prs.
• If AISNP panel coverage is below 30 SNPs, the skill MUST abstain and direct the user to --ancestry. This threshold comes from Kosoy et al. (2009), the lower bound for reliable continental-level assignment. Do not infer from sparse data. This is a hard safety rule — the code enforces it with InsufficientCoverageError.
• Low confidence does not mean wrong ancestry — it means admixed or ambiguous signal. If confidence is "low" (LL gap < 15 units), warn prominently and suggest the user specify --ancestry. Do not refuse to run, but make the limitation visible.
• Dosage is additive per allele for most loci. If a user is homozygous for a risk allele (dosage=2), the log-OR is doubled. This is the standard log-additive GWAS assumption.
• APOL1 (rs73885319 G1, rs60910145 G2) is an exception — it is recessive. A single heterozygous APOL1 allele does NOT confer the full OR. Risk requires two high-risk alleles (G1+G2, G1/G1, or G2/G2). The code uses model: "recessive_compound" and counts total alleles across both loci before applying the validated compound OR (~7x). Do not change APOL1 to additive.
• Combined OR is a naive product. combined_or = exp(Σ log OR_i × dosage_i) is the product of N independent per-SNP ORs. It is not a validated polygenic score. Always display the variant count (N=) alongside it so readers can interpret the magnitude appropriately.
• The curated panel covers ~10 diseases. Diseases not in the panel are simply not reported — do not extrapolate or add unsupported associations.
• AES is exploratory. Do not present it as a validated score, percentile, or clinical probability. Always use the word "exploratory" when explaining it to the user.
• "Genetic super-population" ≠ "ethnicity". Use precise language. Never say "your ethnicity" when you mean "your inferred genetic super-population."

Safety

• Local-first: All computation runs on-device; no genotype data is uploaded
• Disclaimer: Every report includes the ClawBio medical disclaimer
• Cited sources: Every association entry carries a non-null PMID (enforced by a CI test). One entry (ALDH2 rs671 ESCC) uses PMID 22960999 (Wu et al. 2012 Nat Genet) with GWAS Catalog accession GCST001563; the entry note flags that the accession-to-PMID mapping was not independently confirmed via the Catalog. See data/PROVENANCE.md for full correction history.
• LCT entries removed in v1.3.0: All rs4988235 Lactose Intolerance entries were removed because (a) PMID 14507249 cited as Enattah 2002 resolves to an unrelated bladder-cancer paper, and (b) the EUR or=0.45 and non-EUR or=3.2–6.8 encoded opposite outcome framings for the same allele, manufacturing spurious AES of 7–15x.
• Soft posterior gating in v1.3.0: When ancestry confidence is "low" and top posterior < 0.35, disease risk scoring is skipped entirely with an explanatory message. Between 0.35–0.6 a caveat note is shown alongside results. User-supplied --ancestry overrides the gate.
• No hallucinated ORs: All effect sizes trace to the bundled ancestry_risk_associations.json
• APOL1 recessive model: APOL1 G1/G2 use a compound-recessive model; per-allele log-additive OR is biologically wrong for this locus
• Combined OR is naive: combined_or is the product of independent per-SNP ORs (log-additive); it is NOT a validated aggregate risk score. N= in the report shows how many variants contribute so readers can judge the calculation
• No absolute risk claims: The Cornfield/baseline prevalence calculation has been removed to prevent double-counting

Agent Boundary

The agent (LLM) dispatches and explains results. The skill (Python) executes the inference and scoring. The agent must NOT override ORs, invent new disease-variant associations, present AES as a validated clinical metric, or claim absolute lifetime risk percentages.

Integration with Bio Orchestrator

Trigger conditions: routes here when:

• Query mentions genetic ancestry + disease risk together
• File is a 23andMe/AncestryDNA raw data file AND query is about disease risk stratified by population

Chaining partners:

• gwas-prs: for validated absolute risk scores with ancestry-appropriate PGS Catalog scores — always signpost this when users ask about lifetime risk
• pharmgx-reporter: after ancestry signal profiling, run pharmgx to add drug response context
• profile-report: ancestry-risk-profiler output feeds into the unified profile report

Maintenance

• Review cadence: Update ancestry_risk_associations.json when major multi-ancestry GWAS meta-analyses are published (Pan-UKB updates, Global Biobank Meta-Analysis Initiative releases)
• Staleness signals: New population-specific GWAS not yet in panel; GWAS Catalog accumulates new non-EUR studies
• Deprecation: Archive if a more comprehensive multi-ancestry PRS tool with per-ancestry calibration supersedes this approach

Citations

• Genovese et al. (2010) Science 329:841. PMID 20566908. APOL1 G1/G2 kidney disease (recessive compound model)
• Karczewski et al. (2020) Nature 581:434. PMID 32461654. gnomAD v3.1 allele frequencies for AISNP panel
• Kosoy et al. (2009) Hum Genet 126:719–731. PMID 19680671. AISNP panel design validation (≥30 markers for continental assignment)
• Dubois et al. (2010) Nat Genet 42:295–302. PMID 20190752. Celiac disease GWAS (PTPN22 R620W)
• Wu et al. (2012) Nat Genet 44:1090–1093. PMID 22960999. GWAS Catalog GCST001563. ESCC GWAS in Chinese (ALDH2 rs671); two earlier wrong PMIDs (20686008, 22561518) were corrected, and the accession-to-PMID mapping is flagged for confirmation in the entry note
• Grant et al. (2006) Nat Genet 38:320–323. PMID 16415884. TCF7L2 T2D discovery
• Zeggini et al. (2007) Nat Genet 39:638–644. PMID 17463246. T2D replication
• Feder et al. (1996) Nat Genet 13:399–408. PMID 9068472. HFE hereditary haemochromatosis

Removed citations: Enattah et al. (2002) LCT lactase persistence — LCT rs4988235 entries removed in v1.3.0 (direction artifact + PMID 14507249 was wrong). PMID 22561518 (Wu 2012 ESCC) — resolves to Jin 2012 vitiligo GWAS.

See data/PROVENANCE.md for the full citation table with correction history.