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@article{Zhang:2021:10.1038/s41436-020-00972-3,
author = {Zhang, X and Walsh, R and Whiffin, N and Buchan, R and Midwinter, W and Wilk, A and Govind, R and Li, N and Ahmad, M and Mazzarotto, F and Roberts, A and Theotokis, P and Mazaika, E and Allouba, M and de, Marvao A and Pua, CJ and Day, SM and Ashley, E and Colan, SD and Michels, M and Pereira, AC and Jacoby, D and Ho, CY and Olivotto, I and Gunnarsson, GT and Jefferies, J and Semsarian, C and Ingles, J and ORegan, DP and Aguib, Y and Yacoub, MH and Cook, SA and Barton, PJR and Bottolo, L and Ware, JS},
doi = {10.1038/s41436-020-00972-3},
journal = {Genetics in Medicine},
pages = {69--79},
title = {Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions},
url = {http://dx.doi.org/10.1038/s41436-020-00972-3},
volume = {23},
year = {2021}
}

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TY  - JOUR
AB  - Background: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning tools are useful for genome-wide variant prioritisation but remain imprecise. Since the relationship between molecular consequence and likelihood of pathogenicity varies between genes with distinct molecular mechanisms, we hypothesised that a disease-specific classifier may outperform existing genome-wide tools. Methods: We present a novel disease-specific variant classification tool, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias, trained with variants of known clinical effect. To benchmark against state-of-the-art genome-wide pathogenicity classification tools, we assessed classification of hold-out test variants using both overall performance metrics, and metrics of high-confidence (>90%) classifications relevant to variant interpretation. We further evaluated the prioritisation of variants associated with disease and patient clinical outcomes, providing validations that are robust to potential mis-classification in gold-standard reference datasets.Results: CardioBoost has higher discriminating power than published genome-wide variant classification tools in distinguishing between pathogenic and benign variants based on overall classification performance measures with the highest area under the Precision-Recall Curve as 91% for cardiomyopathies and as 96% for inherited arrhythmias. When assessed at high-confidence (>90%) classification thresholds, prediction accuracy is improved by at least 120% over existing tools for both cardiomyopathies and arrhythmias, with significantly improved sensitivity and specificity. Finally, CardioBoost improves prioritisation of variants significantly associated with disease, and stratifies survival of patients with cardiomyopathies, confirming biologically relevant vari
AU  - Zhang,X
AU  - Walsh,R
AU  - Whiffin,N
AU  - Buchan,R
AU  - Midwinter,W
AU  - Wilk,A
AU  - Govind,R
AU  - Li,N
AU  - Ahmad,M
AU  - Mazzarotto,F
AU  - Roberts,A
AU  - Theotokis,P
AU  - Mazaika,E
AU  - Allouba,M
AU  - de,Marvao A
AU  - Pua,CJ
AU  - Day,SM
AU  - Ashley,E
AU  - Colan,SD
AU  - Michels,M
AU  - Pereira,AC
AU  - Jacoby,D
AU  - Ho,CY
AU  - Olivotto,I
AU  - Gunnarsson,GT
AU  - Jefferies,J
AU  - Semsarian,C
AU  - Ingles,J
AU  - ORegan,DP
AU  - Aguib,Y
AU  - Yacoub,MH
AU  - Cook,SA
AU  - Barton,PJR
AU  - Bottolo,L
AU  - Ware,JS
DO  - 10.1038/s41436-020-00972-3
EP  - 79
PY  - 2021///
SN  - 1098-3600
SP  - 69
TI  - Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
T2  - Genetics in Medicine
UR  - http://dx.doi.org/10.1038/s41436-020-00972-3
UR  - http://hdl.handle.net/10044/1/83594
VL  - 23
ER  -

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