Knowledge Mapping of Human Genetic Variation in Multiomics Biomarker Translation: A Bibliometric Analysis

Abstract

Human genetic variation and multiomics biomarker translation has emerged as a rapidly developing research field at the intersection of molecular medicine, bioinformatics, and clinical decision-making. This study used bibliometric methods to map its global structure, intellectual base, and thematic evolution. The eligibility framework was deliberately restricted to studies in which inherited or somatic human genetic variation was substantively linked to omics-integrated biomarker discovery, functional interpretation, clinical stratification, or therapeutic response; generic omics studies without a variation-linked translational endpoint were excluded. Publications indexed in Scopus, Web of Science, and PubMed from 2003 to 2025 were retrieved, screened, converted into Web of Science format, and analyzed using CiteSpace, VOSviewer, and the R package bibliometrix. A total of 673 publications were included. The annual output showed sustained acceleration, indicating a transition from exploratory molecular association studies to a more mature translational framework. Research activity was concentrated in a limited number of countries and institutions, led by the United States, with China contributing substantial output but lower citation impact relative to publication volume. The institutional network was dominated by large academic medical centers, national research systems, and oncology-oriented consortia, reflecting the infrastructural demands of biomarker translation. Journal analysis showed a dual structure in which specialized translational and omics journals accounted for much of the current output, whereas the intellectual foundations remained anchored in high-impact journals in genetics, oncology, and clinical medicine. Keyword and overlay analyses revealed a clear thematic shift from pharmacogenetics and single-marker discovery toward genomics-guided, multiomics-integrated, and clinically actionable biomarker models, with recent emphasis on liquid biopsy, heterogeneity, artificial intelligence, machine learning, and the tumor microenvironment. At the variant level, the major translational bottleneck is increasingly the functional connection between DNA-level variation and downstream RNA, protein, pathway, and clinical phenotypes rather than variant detection alone. The literature is therefore increasingly oriented toward externally validated and computationally interpretable biomarker systems, whereas routine clinical implementation remains constrained by functional validation, assay standardization, prospective evaluation, and regulatory requirements.