Bioresearch Gwas Harmonization

BioResearch Agent — GWAS Harmonization Skill

Capability

Harmonizes GWAS summary statistics across multiple ancestries for cross-ancestry MR:

1. Cross-ancestry GWAS simulation — generates per-ancestry GWAS summary statistics with

realistic allele-frequency drift (AFR < EUR < EAS/SAS/AMR), effect-size heterogeneity, and deliberately injected data-quality issues (allele swaps, strand confusion, palindromic SNPs).

1. Allele harmonization — 5-case alignment: direct match, effect-allele swap, strand flip,

palindromic SNP removal, and unmatched SNP exclusion. Standardizes all ancestries to a common effect-allele / non-effect-allele convention.

1. Strand ambiguity resolution — removes palindromic SNPs (A/T, G/C) near EAF = 0.5 where

strand orientation cannot be inferred, using a MAF-distance threshold (default ±0.01).

1. Allele-frequency divergence — computes pairwise Fst-like statistics across ancestries to

quantify genetic divergence and flag SNPs with extreme AF differences (a source of MR bias).

1. Cross-ancestry signal overlap — identifies genome-wide significant SNPs shared across

ancestries vs ancestry-specific, informing instrument selection and portability assessment.

Returns harmonized per-ancestry GWAS + AF comparison + overlap report, not a causal claim.

Run

bioresearch run gwas-harmonization --n-snps 500 --n-causal 30 --seed 42 --output-dir outputs/gwas-harmonization

Outputs (in --output-dir)

• gh_harmonized_gwas.csv — per-ancestry harmonized GWAS (SNP, CHR, POS, EA, NEA, BETA, SE, P, EAF, N)
• gh_af_comparison.csv — pairwise allele-frequency differences (SNP, ancestrypair, afdiff, fst_like)
• gh_cross_ancestry_overlaps.csv — genome-wide significant SNPs per ancestry + overlap count
• gh_harmonization_report.txt — human-readable summary (nharmonized, nremoved, n_palindromic)
• gh_af_divergence_heatmap.png — pairwise Fst-like matrix across ancestries
• gh_evidence_package.json — reproducible Evidence Package (provenance + parameters + grade)

Note

This skill dispatches to the framework's gwas-harmonization workflow / demo_gwas_harmonization.py. It adds no analysis of its own; all computations run in the workflow modules. By default uses simulated cross-ancestry GWAS with injected quality issues to validate the harmonization pipeline — real-data deployment would use IEU OpenGWAS / GWAS Catalog summary statistics with GWAS-SSF schema. Evidence grade is C (methodology validation). Part of Phase 3a (cross-ancestry MR, CPU-only).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Alim430
• Source: Alim430/bioresearch-agent
• License: MIT

Install and usage instructions live in the source repository linked above.