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causal-genomics

Integrate GWAS and QTL summary statistics to identify shared causal signals.

25|5|Updated Mar 22, 2026
Search Tags:#gwas#mediation#mendelian-randomization#colocalization#causal-genomics#qtl#finemapping
One-click install
npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill causal-genomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: causal-genomics
Source: https://github.com/zongtingwei/Bioclaw_Skills_Hub/tree/main/skills/multi-omics-and-systems/causal-genomics
Command: npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill causal-genomics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Identify causal relationships between genetic variants and molecular/phenotypic traits by integrating GWAS and QTL data, enabling robust inference beyond association.

Core Features & Use Cases

  • Fine-mapping: Pin down candidate causal variants within associated loci.
  • Colocalization & Mediation: Assess shared signals across traits and dissect causal pathways.
  • Mendelian Randomization: Infer directionality and causal effects using summary statistics.
  • Use Case: A researcher wants to know whether a GWAS signal for a disease shares a causal variant with expression QTLs to prioritize genes for follow-up.

Quick Start

Start by providing GWAS and QTL summary statistics along with an LD reference, then run the causal-genomics workflow to obtain colocalization results, credible sets, and causal evidence summaries.

Frequently Asked Questions about causal-genomics

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

FAQPage Schema
How do I integrate GWAS and QTL summary statistics for colocalization analysis?▼

To perform colocalization analysis, provide harmonized GWAS and QTL summary statistics along with an LD reference to assess shared causal variants across traits. The workflow requires clear assumptions and thorough QC steps to produce robust results.

What is fine-mapping in GWAS and how does it identify causal variants?▼

Fine-mapping in GWAS pinpoints candidate causal variants within associated loci by producing credible sets. It requires harmonized summary statistics and LD references to narrow associations to specific genetic variants.

Can I use Mendelian randomization with summary statistics to infer causal effects?▼

Yes, Mendelian randomization infers directionality and causal effects using diverse summary-statistics inputs. It requires harmonized data and LD references to generate causal evidence summaries beyond simple association.

Do I need to harmonize data before running mediation and pleiotropy analyses?▼

Yes, mediation and pleiotropy analyses require harmonized data, clear assumptions, and thorough QC steps. Proper harmonization ensures the workflow can accurately dissect causal pathways.

What's the best way to prioritize genes from a GWAS signal using QTL data?▼

Integrate GWAS and QTL summary statistics to test whether a disease signal shares a causal variant with expression QTLs. Colocalization results and credible sets help prioritize genes for follow-up.

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