Use of machine learning to classify high-risk variants of uncertain significance in lamin A/C cardiac disease.

ABSTRACT:

Background

Variation in lamin A/C results in a spectrum of clinical disease, including arrhythmias and cardiomyopathy. Benign variation is rare, and classification of LMNA missense variants via in silico prediction tools results in a high rate of variants of uncertain significance (VUSs).

Objective

The goal of this study was to use a machine learning (ML) approach for in silico prediction of LMNA pathogenic variation.

Methods

Genetic sequencing was performed on family members with conduction system disease, and patient cell lines were examined for LMNA expression. In silico predictions of conservation and pathogenicity of published LMNA variants were visualized with uniform manifold approximation and projection. K-means clustering was used to identify variant groups w

SUBMITTER: Bennett JS 

PROVIDER: S-EPMC10082443 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature