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Peer-reviewedCC BY-NC-ND 4.0Genome Medicine

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

  • Qifei Li1,2,3
  • Sarah U. Morton2,3,4,5,6
  • Shiyu Luo1
  • Zornitza Stark7,8
  • Pankaj B. Agrawal1,2,3
8 affiliations
  1. 1Division of Neonatology, Department of Pediatrics, University of Miami Miller School of Medicine and Holtz Children’s Hospital, Jackson Health System, Miami, FL USA
  2. 2Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA
  3. 3The Broad Institute of MIT and Harvard, Cambridge, MA USA
  4. 4Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA USA
  5. 5Department of Pediatrics, Harvard Medical School, Boston, MA USA
  6. 6The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA USA
  7. 7Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, VIC Australia
  8. 8Department of Paediatrics, University of Melbourne, Melbourne, VIC Australia
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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.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13073-026-01740-9.

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Cite

BibTeXRIS
BibTeX
@article{li2026challenges,
  title = {{Challenges and approaches to genetic variants of uncertain significance in clinical practice and research}},
  author = {Qifei Li and Sarah U. Morton and Shiyu Luo and Zornitza Stark and Pankaj B. Agrawal},
  journal = {Genome Medicine},
  year = {2026},
  month = aug,
  doi = {10.1186/s13073-026-01740-9},
  url = {https://doi.org/10.1186/s13073-026-01740-9},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Challenges and approaches to genetic variants of uncertain significance in clinical practice and research
AU  - Qifei Li
AU  - Sarah U. Morton
AU  - Shiyu Luo
AU  - Zornitza Stark
AU  - Pankaj B. Agrawal
PY  - 2026
DA  - 2026/08/10
JO  - Genome Medicine
DO  - 10.1186/s13073-026-01740-9
UR  - https://doi.org/10.1186/s13073-026-01740-9
AB  - 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.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13073-026-01740-9.
SN  - 1756-994X
ER  -