Using high-resolution variant frequencies to empower clinical genome interpretation
File(s)
Published version
Accepted version
Author(s)
Type
Journal Article
Abstract
Purpose: Whole exome and genome sequencing have transformed the discovery of genetic variants that cause human Mendelian disease, but discriminating pathogenic from benign variants remains a daunting challenge. Rarity is recognised as a necessary, although not sufficient, criterion for pathogenicity, but frequency cutoffs used in Mendelian analysis are often arbitrary and overly lenient. Recent very large reference datasets, such as the Exome Aggregation Consortium (ExAC), provide an unprecedented opportunity to obtain robust frequency estimates even for very rare variants.
Methods: We present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets.
Results: Using the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, without removing true pathogenic variants (false positive rate<0.001).
Conclusion: We outline a statistically robust framework for assessing whether a variant is 'too common' to be causative for a Mendelian disorder of interest. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.
Methods: We present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets.
Results: Using the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, without removing true pathogenic variants (false positive rate<0.001).
Conclusion: We outline a statistically robust framework for assessing whether a variant is 'too common' to be causative for a Mendelian disorder of interest. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.
Date Issued
2017-05-18
Date Acceptance
2017-02-02
Citation
Genetics in Medicine, 2017, 19, pp.1151-1158
ISSN
1530-0366
Publisher
Nature Publishing Group
Start Page
1151
End Page
1158
Journal / Book Title
Genetics in Medicine
Volume
19
Copyright Statement
This work is licensed under a Creative Commons
Attribution 4.0 International License. The
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included in the article’s Creative Commons license, unless
indicated otherwise in the credit line; if the material is not
included under the Creative Commons license, users will
need to obtain permission from the license holder to
reproduce the material. To view a copy of this license, visit
http://creativecommons.org/licenses/by/4.0/
© The Author(s) 2017
Attribution 4.0 International License. The
images or other third party material in this article are
included in the article’s Creative Commons license, unless
indicated otherwise in the credit line; if the material is not
included under the Creative Commons license, users will
need to obtain permission from the license holder to
reproduce the material. To view a copy of this license, visit
http://creativecommons.org/licenses/by/4.0/
© The Author(s) 2017
License URL
Sponsor
Fondation Leducq
Wellcome Trust
Department of Health
Wellcome Trust
Grant Number
11 CVD-01
HICF-R6-373
HICF-R6-373
107469/Z/15/Z
Subjects
0604 Genetics
1103 Clinical Sciences
Genetics & Heredity
Publication Status
Published online
Date Publish Online
2017-05-18