VarClaMM: A reference meta-model to understand DNA variant classification

Abstract

Determining the significance of a DNA variant in patients’ health status – a complex process known as variant classification – is highly critical for precision medicine applications. However, there is still debate on how to combine and weigh diverse available evidence to achieve proper and consistent conclusions. Indeed, currently, there are more than 200 different variant classification guidelines available to the scientific community, aiming to establish a framework for standardizing the classification process. Yet, these guidelines are qualitative and vague by nature, hindering their practical application and potential automation. Consequently, more precise definitions are needed.

In this work, we discuss our efforts to create VarClaMM, a UML meta-model that aims to provide a clear specification of the key concepts involved in variant classification, serving as a common framework for the process. Through this accurate characterization of the domain, we were able to find contradictions or inconsistencies that might have an effect on the classification results. VarClaMM’s conceptualization efforts will lay the ground for the operationalization of variant classification, enabling any potential automation to be based on precise definitions.