Tooluniverse Gwas Finemapping

What it does

This skill enables AI agents to perform fine-mapping analysis on Genome-Wide Association Study (GWAS) summary statistics. It helps identify specific causal genetic variants within broad genomic regions associated with a trait or disease by leveraging statistical methods and reference panels.

When to use it

• You have GWAS summary data and need to narrow down associations from hundreds of thousands of SNPs to a credible set of causal variants.
• You are conducting genetic research where pinpointing the exact biological mechanism behind a phenotypic association is critical.
• You require integration of functional annotations or reference panels (like 1000 Genomes) to prioritize variants in specific populations.
• Your workflow involves downstream tasks like polygenic risk score refinement or Mendelian randomization that depend on high-resolution variant localization.

Key capabilities

• Processes GWAS summary statistics files directly within the agent environment.
• Implements fine-mapping algorithms to calculate posterior inclusion probabilities (PIP) for variants.
• Integrates with external reference panels to account for linkage disequilibrium patterns.
• Outputs prioritized lists of candidate causal variants with statistical confidence metrics.

Example prompts

• "Run a fine-mapping analysis on my uploaded GWAS summary statistics using the default reference panel and return the top 10 credible set variants."
• "Perform conditional fine-mapping to distinguish independent signals within the identified locus for height, adjusting for population stratification."
• "Execute a Bayesian fine-mapping workflow incorporating functional annotation weights to prioritize non-coding regulatory variants in the FTO region."

Tips & gotchas

Ensure your input GWAS summary statistics include standard columns (e.g., SNP ID, effect allele, non-effect allele, effect size, p-value) to avoid parsing errors. Fine-mapping accuracy heavily depends on the quality and ancestry match of the reference panel used; mismatched populations can lead to inflated false positives or missed causal signals.