Causal genomics

Causal Genomics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially summary-statistics and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for fine-mapping, colocalization, mediation, pleiotropy analysis, and Mendelian randomization.

When To Use This Skill

• use when the task is causal variant, trait-to-gene, or mediation-style genomic inference
• use when GWAS and QTL summary data must be integrated
• use when the user needs statistical evidence about shared signals or directionality assumptions

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• GWAS summary statistics
• QTL or molecular trait summary statistics
• LD reference

Expected Outputs

• colocalization results
• credible sets
• causal evidence summaries

Preferred Tools

• summary-statistics workflows
• pandas
• numpy

Starter Pattern

Preferred starting point: summary-statistics
Inputs: GWAS summary statistics, QTL or molecular trait summary statistics, LD reference
Outputs: colocalization results, credible sets, causal evidence summaries

Workflow

1. Harmonize summary statistics

Align alleles, genome builds, and variant IDs before combining datasets.

2. Pick the causal framework

Use fine-mapping, colocalization, mediation, or MR according to the question.

3. Test and compare signals

Quantify shared or potentially causal effects with the required assumptions stated clearly.

4. Review sensitivity

Inspect heterogeneity, pleiotropy, and LD-related caveats before interpretation.

5. Export assumption-aware results

Save summary tables with methods, assumptions, and confidence measures.

Output Artifacts

• Recommended output layout:
  • results/ for final tables and serialized objects
  • figures/ for plots and static visual exports
  • qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• colocalization results
• credible sets
• causal evidence summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Verify that modalities, samples, and model assumptions align before integration or inference.
• Export factors, scores, or model outputs together with interpretation context.

Anti-Patterns

• treating statistical colocalization as definitive causal proof
• ignoring allele harmonization issues
• running MR without checking instrument quality and pleiotropy

Related Skills

• Multi-Omics Integration
• Pathway Analysis
• Systems Biology
• Machine Learning For Omics

Optional Supplements

• None required for the first pass.