When analysis of a patient’s genome identifies a variant, but it is unclear whether that variant is actually connected to a health condition, the finding is called a variant of uncertain significance (abbreviated VUS).
In many cases, these variants are so rare in the population that little information is available about them. Typically, more information is required to determine if the variant is disease related. Such information may include more extensive population data, functional studies, and tracing the variant in other family members who have or do not have the same health condition.
AI and Machine Learning are poised to play a significant role in advancing Genetic Testing practices.
These study findings offer hope that further acceleration of VUS reduction is possible through advances in these rapidly evolving technologies.
Conclusions and Relevance In this cohort study of individuals undergoing genetic testing, the empirically estimated accuracy of pathogenic, likely pathogenic, benign, and likely benign classifications exceeded the certainty thresholds set by current VC guidelines, suggesting the need to reevaluate definitions of these classifications. The relative contribution of various strategies to resolve VUS, including emerging machine learning–based computational methods, RNA analysis, and cascade family testing, provides useful insights that can be applied toward further improving VC methods, reducing the rate of VUS, and generating more definitive results for patients.
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2825808
Lynch Syndrome Ireland 