P336: Testing concordance of pathogenicity predictions from orthogonal machine learning algorithms and signal to noise analysis

Since the adoption of the ACMG/AMP guidelines, many methods and algorithms, using diverse and distinct types of data, have been developed to predict the pathogenicity and assess the functional effects of variants. While concordance between different models has traditionally been desired to build confidence in predictions, the lack of agreement between individually validated models that rely on different types of data can tell us important information about specific genes and genetic variants. Here, we compare the predictions of several machine learning models developed by a clinical laboratory to a recently created computational tool (DiscoVari) that is based on signal to noise (S:N) analysis in nine cardiomyopathy- and channelopathy-related genes.