Computational and experimental methods for classifying variants of unknown clinical significance.
Malte SpielmannMartin KircherPublished in: Cold Spring Harbor molecular case studies (2022)
The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.
Keyphrases
- copy number
- healthcare
- genome wide
- machine learning
- end stage renal disease
- public health
- newly diagnosed
- endothelial cells
- dna methylation
- biofilm formation
- mental health
- chronic kidney disease
- single cell
- single molecule
- ejection fraction
- quality improvement
- big data
- current status
- prognostic factors
- high throughput
- staphylococcus aureus
- risk assessment
- high resolution
- induced pluripotent stem cells
- circulating tumor cells
- peritoneal dialysis
- climate change
- pseudomonas aeruginosa
- social media
- deep learning
- gestational age
- bioinformatics analysis
- health promotion