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