In recent years, the use of genomic sequencing in a clinical setting has increased exponentially. In parallel, so too have the number of variants within patient genomes with an unclear classification. For example, variants can be classified by the American College of Medical Genetics (ACMG) as being of uncertain significance (VUS) or by the ClinVar database as having a conflicting interpretation of pathogenicity (CIP). To diagnose and treat patients with diseases and other conditions with a genetic component, it is necessary to have a clear interpretation of identified genetic variants.
ClinVar is a publicly available database of human genomic variants and their known association with disease. This database is a resource, not only for medical professionals, but also for patients. Variants of uncertain significance represent the largest class within this database (Favalli et al. 2021). This reflects the magnitude of the problem within the greater population. I aim to further the understanding of uncertain and conflicting variant interpretation using the ClinVar database. To do this I will summarise features of uncertain and conflicting variants and identify both the shared and unique features of each category. I hypothesize that there may be unexplored features between uncertain and conflicting variants that will aid in their classification into benign or pathogenic variant classes. To test this hypothesis, I will use the information compiled from ClinVar to determine if there are further identifiable subclasses of variants within the uncertain and conflicting categories that share similar features. This knowledge could be used to improve and inform the guidelines for variant interpretation going forward, which would greatly impact the treatment of patients. Due to the ever-increasing number of these variants, any methods that improve on their classification regarding pathogenicity will be of enormous benefit to patients and their families undergoing genetic testing in the future.
