Evaluating the use of paralogous protein domains to increase data availability for missense variant classification.

ABSTRACT:

Background

Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains ("meta-domains").

Methods

We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint sco

SUBMITTER: Gunning AC 

PROVIDER: S-EPMC10714540 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature