(A) Genome-wide association studies (GWAS) identify common genetic variants associated with phenotypes. Significant associations, visualized as peaks in a Manhattan plot, highlight genomic loci linked to disease susceptibility but often encompass multiple correlated variants within linkage disequilibrium (LD) blocks. (B) Fine mapping integrates LD structure and effect sizes to calculate the posterior probability that each variant is causal. Conditional analysis iteratively accounts for the most important variants within a locus to identify independent association signals. (C) Colocalization analysis tests whether GWAS and molecular quantitative trait locus (QTL) signals share a common causal variant. Expression QTL (eQTL) mapping associates genetic variants with gene expression levels across tissues or cell types, helping prioritize effector genes and relevant biological contexts. (D) Aggregating signals across genes or pathways enables the identification of biological processes disproportionately affected by disease-associated variants. Gene set enrichment, tissue-specific expression profiling, and network-based methods reveal convergent mechanisms. (E) Transcriptome-wide association studies (TWAS) integrate GWAS summary statistics with reference transcriptomic data to infer associations between genetically predicted gene expression and disease. Integration with drug–gene interaction databases facilitates drug repurposing by identifying compounds that counteract disease-associated expression profiles or target implicated pathways. (F) High-throughput reporter-based assays, such as massively parallel reporter assays (MPRAs), test the regulatory activity of thousands of variants simultaneously, while deep mutational scanning (DMS) assesses the functional impact of coding variants on protein stability or activity. (G) Cellular and organismal models provide experimental validation of causal mechanisms. Patient-derived cells, patient-derived organoids, and genetically engineered animal models enable functional interrogation of candidate variants and pathways in disease-relevant systems. (H) Insights from GWAS and post-GWAS analyses inform therapeutic development and precision medicine. Polygenic risk scores integrate the cumulative effects of multiple variants to stratify disease risk and predict progression, while genetic insights guide drug discovery and repurposing efforts targeting causal genes or pathways.