tooluniverse-gwas-finemapping

GWAS Fine-Mapping & Causal Variant Prioritization

Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions.

Overview

Genome-wide association studies (GWAS) identify genomic regions associated with traits, but linkage disequilibrium (LD) makes it difficult to pinpoint the causal variant. Fine-mapping uses Bayesian statistical methods to compute the posterior probability that each variant is causal, given the GWAS summary statistics.

This skill provides tools to:

• Prioritize causal variants using fine-mapping posterior probabilities
• Link variants to genes using locus-to-gene (L2G) predictions
• Annotate variants with functional consequences
• Suggest validation strategies based on fine-mapping results

Key Concepts

Credible Sets

A credible set is a minimal set of variants that contains the causal variant with high confidence (typically 95% or 99%). Each variant in the set has a posterior probability of being causal, computed using methods like:

• SuSiE (Sum of Single Effects)
• FINEMAP (Bayesian fine-mapping)
• PAINTOR (Probabilistic Annotation INtegraTOR)

Posterior Probability

The probability that a specific variant is causal, given the GWAS data and LD structure. Higher posterior probability = more likely to be causal.

Locus-to-Gene (L2G) Predictions

L2G scores integrate multiple data types to predict which gene is affected by a variant:

• Distance to gene (closer = higher score)
• eQTL evidence (expression changes)
• Chromatin interactions (Hi-C, promoter capture)
• Functional annotations (coding variants, regulatory regions)

L2G scores range from 0 to 1, with higher scores indicating stronger gene-variant links.

Use Cases

1. Prioritize Variants at a Known Locus

Question: "Which variant at the TCF7L2 locus is likely causal for type 2 diabetes?"

from python_implementation import prioritize_causal_variants

# Prioritize variants in TCF7L2 for diabetes
result = prioritize_causal_variants("TCF7L2", "type 2 diabetes")
print(result.get_summary())

# Output shows:
# - Credible sets containing TCF7L2 variants
# - Posterior probabilities (via fine-mapping methods)
# - Top L2G genes (which genes are likely affected)
# - Associated traits