Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity

Abstract

BACKGROUND: With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation. METHODS: We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual's disease. We present models for genes causing inherited cardiac conditions, though..