Genome-Wide Association Studies in Hepatocellular Carcinoma: Aetiology-Specific Susceptibility, Functional Interpretation, and Clinical Translation.

Hepatocellular carcinoma (HCC) arises through heterogeneous pathways involving chronic hepatitis B virus infection, hepatitis C virus infection, alcohol-related liver disease, metabolic dysfunction-associated steatotic liver disease, fibrosis, cirrhosis, and environmental exposures. Genome-wide association studies (GWASs) have identified host germline loci associated with HCC susceptibility, but interpretation is complicated by aetiology, ancestry, liver disease stage, and the definition of controls. This narrative review examines current GWAS evidence for HCC, with emphasis on aetiology-specific susceptibility, functional interpretation, cross-disorder genetic effects, and clinical translation. Studies were identified through iterative searches of PubMed/PMC, publisher pages, academic search tools, and citation tracking, supplemented by targeted searches for major HCC-associated loci. Sources were chosen based on relevance to GWAS discovery, replication, meta-analysis, functional interpretation, polygenic risk modelling, or HCC risk stratification, rather than by a formal systematic review protocol. Viral HCC studies most often implicate immune regulation and antigen presentation, including MICA, HLA-DQ, HLA-DQB1, HLA class I, HCP5, STAT4, DEPDC5, and FAM114A1. Alcohol-related, metabolic, and non-viral HCC studies more often implicate hepatic lipid metabolism, telomere biology, iron metabolism, steatosis, and cirrhosis-related pathways, including PNPLA3, TM6SF2, TERT, HSD17B13, APOE, HFE, and MTARC1. Recent studies increasingly combine GWASs with fine-mapping, functional annotation, transcriptomic analyses, and risk modelling. HCC genetic susceptibility is highly aetiology-specific and overlaps with other liver and metabolic disorders, but discoveries from genetic studies have not yet been translated into routine clinical practice. Future work should prioritise multi-ancestry cohorts, disease-stage-aware controls, functional validation, and prospectively tested genetic risk models.