Journal article

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

Dace Ruklisa, James S Ware, Roddy Walsh, David J Balding, Stuart A Cook

GENOME MEDICINE | BMC | Published : 2015


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..

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University of Melbourne Researchers


Awarded by Academy of Medical Sciences (AMS)

Awarded by British Heart Foundation

Awarded by Medical Research Council

Funding Acknowledgements

This work was supported by the Academy of Medical Sciences, the Wellcome Trust, the British Heart Foundation, Arthritis Research UK, the Fondation Leducq, the NIHR Cardiovascular Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College London and the NIHR University College London Hospitals Biomedical Research Centre. We thank Jon and Christine Seidman for access to training data for HCM genes.