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CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

View ORCID ProfileNicola Whiffin, View ORCID ProfileRoddy Walsh, View ORCID ProfileRisha Govind, Matthew Edwards, Mian Ahmad, Xiaolei Zhang, Upasana Tayal, Rachel Buchan, William Midwinter, Alicja E Wilk, Hanna Najgebauer, Catherine Francis, Sam Wilkinson, Thomas Monk, Laura Brett, View ORCID ProfileDeclan P O'Regan, Sanjay K Prasad, View ORCID ProfileDeborah J Morris-Rosendahl, View ORCID ProfilePaul JR Barton, Elizabeth Edwards, View ORCID ProfileJames S Ware, View ORCID ProfileStuart A Cook
doi: https://doi.org/10.1101/180109
Nicola Whiffin
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
3MRC London Institute of Medical Sciences, Imperial College London
PhD
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  • ORCID record for Nicola Whiffin
  • For correspondence: n.whiffin@imperial.ac.uk
Roddy Walsh
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
MSc
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  • ORCID record for Roddy Walsh
Risha Govind
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
MSc
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Matthew Edwards
4Clinical Genetics and Genomics Laboratory, Royal Brompton and Harefield NHS Foundation Trust, London, UK
MSc
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Mian Ahmad
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
PhD
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Xiaolei Zhang
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
MSc
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Upasana Tayal
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
BMBCh, MRCP
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Rachel Buchan
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
MSc
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William Midwinter
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
BSc
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Alicja E Wilk
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
BSc
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Hanna Najgebauer
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
PhD
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Catherine Francis
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
MA, MRCP
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Sam Wilkinson
4Clinical Genetics and Genomics Laboratory, Royal Brompton and Harefield NHS Foundation Trust, London, UK
BSc
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Thomas Monk
4Clinical Genetics and Genomics Laboratory, Royal Brompton and Harefield NHS Foundation Trust, London, UK
MSc
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Laura Brett
4Clinical Genetics and Genomics Laboratory, Royal Brompton and Harefield NHS Foundation Trust, London, UK
MPhil
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Declan P O'Regan
3MRC London Institute of Medical Sciences, Imperial College London
PhD, FRCR
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  • ORCID record for Declan P O'Regan
Sanjay K Prasad
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
MD, FRCP
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Deborah J Morris-Rosendahl
1National Heart & Lung Institute, Imperial College London
4Clinical Genetics and Genomics Laboratory, Royal Brompton and Harefield NHS Foundation Trust, London, UK
PhD
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Paul JR Barton
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
PhD
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Elizabeth Edwards
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
PhD
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James S Ware
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
3MRC London Institute of Medical Sciences, Imperial College London
PhD,MRCP
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Stuart A Cook
1National Heart & Lung Institute, Imperial College London
2Cardiovascular Research Centre at Royal Brompton and Harefield NHS Foundation Trust, London, UK
5National Heart Centre Singapore, Singapore
6Duke-National University of Singapore, Singapore
PhD MRCP
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ABSTRACT

Purpose 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 (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).

Methods 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.

Results 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 P=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.

Conclusion CardioClassifier 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 01, 2017.
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CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation
Nicola Whiffin, Roddy Walsh, Risha Govind, Matthew Edwards, Mian Ahmad, Xiaolei Zhang, Upasana Tayal, Rachel Buchan, William Midwinter, Alicja E Wilk, Hanna Najgebauer, Catherine Francis, Sam Wilkinson, Thomas Monk, Laura Brett, Declan P O'Regan, Sanjay K Prasad, Deborah J Morris-Rosendahl, Paul JR Barton, Elizabeth Edwards, James S Ware, Stuart A Cook
bioRxiv 180109; doi: https://doi.org/10.1101/180109
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CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation
Nicola Whiffin, Roddy Walsh, Risha Govind, Matthew Edwards, Mian Ahmad, Xiaolei Zhang, Upasana Tayal, Rachel Buchan, William Midwinter, Alicja E Wilk, Hanna Najgebauer, Catherine Francis, Sam Wilkinson, Thomas Monk, Laura Brett, Declan P O'Regan, Sanjay K Prasad, Deborah J Morris-Rosendahl, Paul JR Barton, Elizabeth Edwards, James S Ware, Stuart A Cook
bioRxiv 180109; doi: https://doi.org/10.1101/180109

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