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)
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