Imperial College London

DrDeclanO'Regan

Faculty of MedicineInstitute of Clinical Sciences

Reader in 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}
}

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 -