Challenges and approaches to genetic variants of uncertain significance in clinical practice and research

Abstract

Despite advances in sequencing technologies and variant interpretation frameworks, many variants identified in genetic testing, particularly missense variants, remain classified as variants of uncertain significance (VUS), posing ongoing challenges in diagnosis and clinical management. This review explores the ongoing challenges of VUS interpretation in clinical genomics and its impact on patients, clinicians, and healthcare systems. We summarize established strategies that support VUS resolution, including large population reference databases, data-sharing initiatives, computational prediction tools, consensus-based guidelines, deep phenotyping, and functional validation assays. Building on these foundations, we highlight emerging approaches that leverage multi-omics analyses, high-throughput experimental platforms (e.g., saturation genome editing and cell-based morphological assays), and artificial intelligence-driven tools to improve scalability and interpretive accuracy. Together, these complementary approaches aim to reduce uncertainty, increase diagnostic yield, and enhance the clinical utility of genomic testing in both research and precision medicine.