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:

Identifique y priorice variantes causales en loci GWAS mediante mapeo estadístico fino y predicciones de locus a gen. Calcula probabilidades posteriores de variantes causales, vincula variantes con genes mediante predicciones L2G, anota consecuencias funcionales y sugiere estrategias de validación. Úselo cuando se le solicite mapear loci GWAS, priorizar variantes causales, identificar conjuntos creíbles o vincular señales GWAS con genes causales. Fuente: mims-harvard/tooluniverse.