The article provides an in-depth review of methods and technologies used to prioritize genes from genome-wide association study (GWAS) loci, aiming to translate genetic findings into biological insights and medical applications. Key methods discussed include gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, enhancer-gene connection maps, and network-based prioritization. The review highlights the challenges in identifying causal genes due to the complexity of gene regulation and the presence of non-coding variants. It also outlines strategies for generating context-dependent xQTL data and their applications in gene prioritization, emphasizing the potential of these methods in drug repurposing. The article further discusses future challenges and opportunities in this field, including the integration of multi-omics data and the development of context-specific gene networks.The article provides an in-depth review of methods and technologies used to prioritize genes from genome-wide association study (GWAS) loci, aiming to translate genetic findings into biological insights and medical applications. Key methods discussed include gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, enhancer-gene connection maps, and network-based prioritization. The review highlights the challenges in identifying causal genes due to the complexity of gene regulation and the presence of non-coding variants. It also outlines strategies for generating context-dependent xQTL data and their applications in gene prioritization, emphasizing the potential of these methods in drug repurposing. The article further discusses future challenges and opportunities in this field, including the integration of multi-omics data and the development of context-specific gene networks.