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<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Although rare-missense variants in Mendelian disease-genes have been noted to cluster in specific regions of proteins, it is not clear how to consider this information when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene-association and variant-interpretation that utilise this powerful signal.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We present a case-control rare-variant association test, <jats:italic>ClusterBurden</jats:italic>, that combines information on both variant-burden and variant-clustering. We then introduce a data-driven modelling framework to estimate mutational hotspots in genes with missense variant-clustering and integrate further <jats:italic>in-silico</jats:italic> predictors into the models.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We show that <jats:italic>ClusterBurden</jats:italic> can increase statistical power to scan for putative disease-genes, driven by missense variants, in simulated data and a 34-gene panel dataset of 5,338 cases of hypertrophic cardiomyopathy. We demonstrate that data-driven models can allow quantitative application of the ACMG criteria PM1 and PP3, to resolve a wide range of pathogenicity potential amongst variants of uncertain significance. A web application (<jats:italic>Pathogenicity_by_Position</jats:italic>) is accessible for missense variant risk prediction of six sarcomeric genes and an R package is available for association testing using <jats:italic>ClusterBurden</jats:italic>.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The inclusion of missense residue position enhances the power of disease-gene association and improves rare-variant pathogenicity interpretation.</jats:p></jats:sec>

Original publication

DOI

10.1101/826164

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

01/11/2019