tooluniverse-gwas-finemapping

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

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.

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:

使用統計精細映射和基因座到基因預測來識別 GWAS 位點的因果變異並確定其優先順序。計算因果變異的後驗機率，透過 L2G 預測將變異與基因聯繫起來，註釋功能後果，並建議驗證策略。當需要精細繪製 GWAS 位點、優先考慮因果變異、識別可信集或將 GWAS 訊號與因果基因聯繫起來時使用。 來源：mims-harvard/tooluniverse。