Imperial College London
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ProfessorDeclanO'Regan

Faculty of Medicine, Institute of Clinical Sciences

Professor of Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Whiffin:2018:10.1038/gim.2017.258,
author = {Whiffin, N and walsh, R and Govind, R and Edwards, M and Ahmad, M and Zhang, X and Tayal, U and Buchan, R and Midwinter, W and Wilk, A and Najgebauer, H and Francis, C and Wilkinson, S and Monk, T and Brett, L and O'Regan, D and Prasad, S and Morris-Rosendahl, D and Barton, P and Edwards, E and Ware, J and Cook, S},
doi = {10.1038/gim.2017.258},
journal = {Genetics in Medicine},
pages = {1246--1254},
title = {CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation},
url = {http://dx.doi.org/10.1038/gim.2017.258},
volume = {20},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher’s P = 1.1  ×  10−18), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.
AU  - Whiffin,N
AU  - walsh,R
AU  - Govind,R
AU  - Edwards,M
AU  - Ahmad,M
AU  - Zhang,X
AU  - Tayal,U
AU  - Buchan,R
AU  - Midwinter,W
AU  - Wilk,A
AU  - Najgebauer,H
AU  - Francis,C
AU  - Wilkinson,S
AU  - Monk,T
AU  - Brett,L
AU  - O'Regan,D
AU  - Prasad,S
AU  - Morris-Rosendahl,D
AU  - Barton,P
AU  - Edwards,E
AU  - Ware,J
AU  - Cook,S
DO  - 10.1038/gim.2017.258
EP  - 1254
PY  - 2018///
SN  - 1098-3600
SP  - 1246
TI  - CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation
T2  - Genetics in Medicine
UR  - http://dx.doi.org/10.1038/gim.2017.258
UR  - http://hdl.handle.net/10044/1/54482
VL  - 20
ER  -
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