Imperial College London

Dr Nicky Whiffin

Faculty of MedicineNational Heart & Lung Institute

Research Fellow
 
 
 
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Contact

 

n.whiffin

 
 
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Location

 

Hammersmith HospitalHammersmith 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@unpublished{Whiffin:2017:10.1101/180109,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},
publisher = {Cold Spring Harbor Laboratory},
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}
}

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
PB - Cold Spring Harbor Laboratory
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 -