Imperial College London
Alternate

ProfessorStuartCook

Faculty of Medicine, Institute of Clinical Sciences

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 3313 1346stuart.cook

 
 
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Location

 

RF 16Sydney StreetRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Whiffin:2017:10.1101/180109,
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, AE and Najgebauer, H and Francis, C and Wilkinson, S and Monk, T and Brett, L and O'Regan, DP and Prasad, SK and Morris-Rosendahl, DJ and Barton, PJR and Edwards, E and Ware, JS and Cook, SA},
doi = {10.1101/180109},
title = {CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation},
url = {http://dx.doi.org/10.1101/180109},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Purpose</jats:title><jats:p>Internationally-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 (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.cardioclassifier.org">www.cardioclassifier.org</jats:ext-link>), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>CardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support varian 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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We 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 <jats:bold>P</jats:bold>=1.1x10-18), 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.</jats:p></jats:sec><jats:sec><jat
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,AE
AU  - Najgebauer,H
AU  - Francis,C
AU  - Wilkinson,S
AU  - Monk,T
AU  - Brett,L
AU  - O'Regan,DP
AU  - Prasad,SK
AU  - Morris-Rosendahl,DJ
AU  - Barton,PJR
AU  - Edwards,E
AU  - Ware,JS
AU  - Cook,SA
DO  - 10.1101/180109
PY  - 2017///
TI  - CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation
UR  - http://dx.doi.org/10.1101/180109
ER  -
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