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claw-ancestry-pca

Maintainer FreedomIntelligence · Last updated April 1, 2026

Ancestry decomposition PCA against the Simons Genome Diversity Project.

OpenClawNanoClawAnalysisReproductionclaw-ancestry-pca⚙️ clawbio pipelinesgenomics, ancestry & pharmacogenomicsancestry

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/claw-ancestry-pca

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Place your study cohort in global genetic context by computing a joint PCA against the Simons Genome Diversity Project (SGDP) — 345 samples from 164 populations spanning every inhabited continent.
Panel A: PC1 vs PC2 — main population structure of your cohort.
Panel B: PC3 vs PC2 with regional groupings and confidence ellipses.
Panel C: PC3 vs PC1 with language/cultural groupings.
Panel D: Global context — your samples (circles) vs SGDP (triangles).

Source Doc

Excerpt From SKILL.md

Why this exists

If you ask ChatGPT to "run a PCA against a global reference panel," it will:

Not know which reference panel to use
Hallucinate PLINK flags for merging datasets with different variant sets
Skip IBD removal (related individuals distort PCA)
Not normalise contig names between your VCF and the reference
Produce a single scatter plot with no population labels

This skill encodes the correct methodological decisions:

Uses SGDP (the gold-standard reference for global diversity)
Handles contig normalisation (chr1 vs 1)
Filters to common biallelic SNPs shared between datasets
Removes related individuals via IBD checks
Produces publication-quality multi-panel figures with confidence ellipses
Differentiates your samples (circles) from reference (triangles)

Reference Panel

The skill bundles the SGDP v4 dataset (Mallick et al., 2016, Nature):

345 samples from 164 populations
Whole-genome sequencing at high coverage
MAF > 0.1% filter applied
Populations span: Africa, Americas, Central/South Asia, East Asia, Europe, Middle East, Oceania

Demo (works out of the box)

The demo uses pre-computed PCA results from the Peruvian Genome Project (736 samples, 28 populations) and generates the full 4-panel figure instantly.