ancestry-risk-profiler

Files (8)

• SKILL.md
• ancestry_risk_profiler.py
• PROVENANCE.md · data/
• aisnp_panel.csv · data/
• ancestry_risk_associations.json · data/
• demo_patient_south_asian.txt · data/
• test_ancestry_reproducibility.py · tests/
• test_ancestry_risk_profiler.py · tests/

SKILL.md (first lines)

# 🧬 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 when the input has enough high-Fst AIM coverage, then compare ancestry-specific GWAS effect sizes to European reference estimates. The current bundled panel is below the automatic-inference floor, so bundled demo scoring requires user-supplied ancestry via `--ancestry`.

## 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 can be inferred from the genotype file itself when high-Fst AIM coverage is sufficient; otherwise the skill abstains and accepts documented user-supplied ancestry. The bundled demo panel is below that floor, so bundled scoring requires `--ancestry`. 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). Matched panel SNPs contribute to the likelihood, but automatic inference is allowed only when at least 30 matched SNPs have Wright Fst ≥ 0.3. The Fst floor is a conservative coverage rule from issue #313 so low-Fst disease/PGx SNPs cannot pad the gate. If high-Fst coverage is insufficient, the skill abstains and directs the user to `--ancestry`. 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 estimates genetic super-population from allele-frequency likelihoods across matched panel SNPs, with Wright Fst ≥ 0.3 used as the coverage gate for automatic inference. 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 informative AISNP coverage** → compute Wright Fst for each matched panel SNP; count only Fst ≥ 0.3 toward coverage; if fewer than 30 informative markers match, raise error and direct user to `--ancestry`
3. **Infer ancestry** → compute Hardy-Weinberg log-likelihood from matched panel SNPs 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
9. **Write reproducibility bundle** → for successful scoring runs, write `reproducibility/commands.sh`, `environment.yml`, `checksums.sha256`, and `inputs.json` using the shared `ReproCommand` / `ReproPath` helpers. Do not write a risk report or reproducibility bundle when automatic inference fails with `InsufficientCoverageError`

## CLI Reference

```bash
# 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 with user-supplied ancestry; the bundled demo has only five high-Fst AIMs,
# so automatic ancestry inference abstains until the panel is expanded.
python skills/ancestry-risk-profiler/ancestry_risk_profiler.py \
  --demo --ancestry SAS --output /tmp/ancestry_risk_demo
```

Successful runs with `--demo --ancestry SAS` or `--input <file> --ancestry EUR|SAS|...`
also write a reproducibility bundle. `commands.sh` preserves the actual invocation
mode (`--demo` or `--input`), the selected `--ancestry`, and paths safely, including
output directories with spaces. Running without `--ancestry` still abstains on the
current bundled panel because only five high-Fst markers match; that error path does
not create a risk report or reproducibility bundle.

## Example Output

```markdown
# Ancestry-Aware Disease Risk Profile

## 1. Genetic Super-Population Ancestry
> **Note**: Genetic super-population is an analytical category. It is **not**
> self-reported ethnicity, cultural identity, or nationality. Labels (AFR, EAS, EUR,
> SAS, AMR) are analytical categories from the 1000 Genomes Project — not ethnic identifiers.

| Field | Value |
|---|---|
| Genetic super-population | **SAS** — South Asian *(user-supplied)* |
| Confidence | user-supplied |
| Informative AISNPs matched (Fst >= 0.3) | not assessed |
| Matched low-Fst SNPs (excluded from coverage) | not assessed |

Ancestry was supplied with `--ancestry`. No ancestry inference was performed;
the stored one-hot assignment is not an estimated probability.

## 2. Ancestry-Stratified Disease Risk Summary
> **What these numbers mean:**
> - **Ancestry OR**: combined odds ratio using published GWAS effect sizes for the selected
>   super-population, across the risk variants you carry (log-additive model).
> - **EUR ref OR**: the same calculation using European reference effect sizes for those
>   same variants — allows direct comparison.
> - **AES** (Ancestry Elevation Score): exp(Σ[log OR_ancestry − log OR_EUR]) — an
>   **exploratory directional indicator** showing where ancestry-specific effect sizes
>   diverge from European estimates. AES > 1 = ancestry amplifies the signal; AES < 1 =
>   ancestry attenuates it. **AES has not been externally validated and is not a clinical
>   risk score.** Use it as a signal to explore further, not a probability.
>
> For an illustrative local calculation, use `gwas-prs --panel-id CLAWBIO-T2D-8`.

From ClawBio/ClawBio, MIT.

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