Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions.

ABSTRACT:

Purpose

Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene-disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance.

Methods

We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost's ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and strati

SUBMITTER: Zhang X 

PROVIDER: S-EPMC7790749 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature