This review discusses the challenges and methods for identifying causal genes from genome-wide association study (GWAS) loci. GWAS has identified numerous genetic variants associated with human traits and diseases, but pinpointing the causal genes remains a challenge. The review outlines various methods for gene prioritization, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer–gene connection maps, and network-based prioritization. It also highlights the potential of gene prioritization in drug repurposing and discusses future challenges and opportunities in this field. GWAS has led to the identification of numerous genetic variants associated with over 5000 human traits. However, the mechanisms underlying most GWAS loci remain elusive due to the difficulty in determining the causal variants and genes responsible for these associations. One major hurdle is the vast number of variants in linkage disequilibrium (LD) with the lead variants, making it challenging to discern the causal variants driving the observed associations. Additionally, the fact that most GWAS signals are in non-coding regions of the genome adds another layer of complexity to establishing a definitive link between the genetic association signals and genes. The review discusses various methods for gene prioritization, including gene-based association tests, integrative analysis of GWAS and xQTL data, and network-based prioritization. It also highlights the potential of gene prioritization in drug repurposing and discusses future challenges and opportunities in this field. The review also discusses the integration of GWAS with cellular xQTL data, which can help identify gene-trait associations that may remain concealed in analyses conducted using bulk tissue data. The review concludes with a discussion of the challenges and opportunities in this field, emphasizing the need for further research and development of new methods and technologies.This review discusses the challenges and methods for identifying causal genes from genome-wide association study (GWAS) loci. GWAS has identified numerous genetic variants associated with human traits and diseases, but pinpointing the causal genes remains a challenge. The review outlines various methods for gene prioritization, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer–gene connection maps, and network-based prioritization. It also highlights the potential of gene prioritization in drug repurposing and discusses future challenges and opportunities in this field. GWAS has led to the identification of numerous genetic variants associated with over 5000 human traits. However, the mechanisms underlying most GWAS loci remain elusive due to the difficulty in determining the causal variants and genes responsible for these associations. One major hurdle is the vast number of variants in linkage disequilibrium (LD) with the lead variants, making it challenging to discern the causal variants driving the observed associations. Additionally, the fact that most GWAS signals are in non-coding regions of the genome adds another layer of complexity to establishing a definitive link between the genetic association signals and genes. The review discusses various methods for gene prioritization, including gene-based association tests, integrative analysis of GWAS and xQTL data, and network-based prioritization. It also highlights the potential of gene prioritization in drug repurposing and discusses future challenges and opportunities in this field. The review also discusses the integration of GWAS with cellular xQTL data, which can help identify gene-trait associations that may remain concealed in analyses conducted using bulk tissue data. The review concludes with a discussion of the challenges and opportunities in this field, emphasizing the need for further research and development of new methods and technologies.