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MubasherMohammed
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fine-mapping

Identify credible sets of GWAS loci with SuSiE and Approximate Bayes Factors.

Updated May 10, 2026
Search Tags:#pip#gwas#fine-mapping#abf#credible sets#susie
One-click install
npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill fine-mapping
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-mapping
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/fine-mapping
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill fine-mapping

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, pandas, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables the statistical fine-mapping of GWAS loci, identifying credible sets and posterior inclusion probabilities for causal variant discovery.

Core Features & Use Cases

  • Fine-Mapping: Statistical fine-mapping of GWAS loci using SuSiE, SuSiE-inf, and Approximate Bayes Factors (ABF).
  • Credible Sets: Identifies credible sets of likely causal variants and computes posterior inclusion probabilities (PIPs).
  • Visualisation: Generates locus PIP plots, regional association plots, and credible set summary tables.
  • Use Case: Researchers can use this Skill to fine-map GWAS loci and identify potential causal variants for further investigation.

Quick Start

Run the fine-mapping skill on your GWAS summary stats and LD matrix. For example:

python skills/fine-mapping/fine_mapping.py --sumstats gwas_sumstats.tsv --ld ld_matrix.npy --output /tmp/finemapping

Frequently Asked Questions about fine-mapping

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform statistical fine-mapping of GWAS loci to identify causal variants?▼

Statistical fine-mapping of GWAS loci identifies credible sets of likely causal variants by processing GWAS summary statistics and an LD matrix with SuSiE and Approximate Bayes Factors to compute posterior inclusion probabilities.

How do I calculate posterior inclusion probabilities and credible sets from GWAS summary statistics?▼

Calculate posterior inclusion probabilities (PIPs) and credible sets by running the fine-mapping script with your GWAS summary stats and LD matrix to group likely causal variants using SuSiE and ABF algorithms.

What input data do I need to run fine-mapping with SuSiE and ABF?▼

Fine-mapping with SuSiE and ABF requires GWAS summary statistics and a linkage disequilibrium (LD) matrix as inputs, along with numpy, scipy, pandas, and matplotlib installed for computation and visualization.

Can I visualize credible sets and PIPs for my GWAS analysis?▼

You can visualize GWAS analysis results by generating locus PIP plots, regional association plots, and credible set summary tables directly from the fine-mapping outputs using matplotlib.

What is the difference between SuSiE and Approximate Bayes Factors for fine-mapping?▼

SuSiE and Approximate Bayes Factors (ABF) are statistical methods used in fine-mapping to identify credible sets and compute posterior inclusion probabilities, with SuSiE handling multiple causal variants and ABF providing Bayesian evidence for single variants.

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