Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification.

ABSTRACT: As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorpo

SUBMITTER: Padigepati SR 

PROVIDER: S-EPMC11303574 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature