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HBV infection remains a major global health challenge, characterized by diverse genotypes with distinct geographical distributions and clinical outcomes. Comprehensive analysis of HBV genomic variability and its association with disease progression is essential for improving clinical management and public health strategies. This study aimed to systematically investigate HBV genotype, subgenotype, phylogenetic clade and serotype distribution, phylogenetic clustering, and clinically relevant mutations, focusing on their potential association with diverse disease outcomes. We analyzed 1001 HBV genomic sequences with available clinical metadata from public databases. Advanced bioinformatics tools were employed for genotype/subgenotype classification, recombination detection, and phylogenetic clustering. Genome-wide statistical analyses identified previously reported clinically relevant mutations. Multivariate logistic regression models adjusted for phylogenetic confounding were applied genome-wise to identify potentially HCC-associated SNVs. HBV/C, especially subgenotype C2, predominated in Asia and was enriched in CHB and HBV-HCC cases, exhibiting a high prevalence of clinically significant mutations linked to HCC. HBV/F (subgenotypes F1 and F4) was mainly found in acute cases from the Americas, while HBV/A was globally distributed and associated with acute infection. HBV/B sequences showed higher recombination levels. Phylogenetic clustering revealed distinct disease- and geography-associated patterns, with clusters differing in mutation occurrence. Several SNVs, including A1383C, C1653T, and G1899A, were identified as potential HCC risk factors, complementing cluster-based findings. Our integrative genomic and phylogenetic analysis delineates HBV genotype-specific epidemiological patterns and mutation landscapes that influence disease outcomes. These findings highlight the value of genotype- and mutation-informed surveillance and therapeutic strategies, underscoring the need for well-characterized cohorts to validate and refine risk prediction models.
Bousali et al. (Mon,) studied this question.