Article

A Bayesian method using sparse data to estimate penetrance of disease-associated genetic variants

2019-03-07

Abstract excerpt

<h4>Purpose</h4> A major challenge in genomic medicine is how to best predict risk of disease from rare variants discovered in Mendelian disease genes but with limited phenotypic data. We have recently used Bayesian methods to show that in vitro functional measurements and computational pathogenicity classification of variants in the cardiac gene SCN5A correlate with rare arrhythmia penetrance. We hypothesized...

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Literature Corpus work: 1bad9092-b79d-53fc-ab10-97833a4ed09a
DOI: 10.1101/571158

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