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Genet Med. 2017 Oct;19(10):1151-1158. doi: 10.1038/gim.2017.26. Epub 2017 May 18.

Using high-resolution variant frequencies to empower clinical genome interpretation.

Author information

1
Cardiovascular Genetics and Genomics, National Heart and Lung Institute, Imperial College London, London, UK.
2
NIHR Royal Brompton Cardiovascular Biomedical Research Unit, Royal Brompton &Harefield Hospitals &Imperial College London, London, UK.
3
Analytic &Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
4
Program in Medical and Population Genetics, Broad Institute of MIT &Harvard, Cambridge, Massachusetts, USA.
5
Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA.
6
Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
7
National Heart Centre Singapore, Singapore, Singapore.
8
Duke-National University of Singapore, Singapore, Singapore.
9
Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
10
MRC London Institute of Medical Sciences, Imperial College London, London, UK.

Abstract

PurposeWhole-exome and whole-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 recognized 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.MethodsWe 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.ResultsUsing 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).ConclusionWe 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.

PMID:
28518168
PMCID:
PMC5563454
DOI:
10.1038/gim.2017.26
[Indexed for MEDLINE]
Free PMC Article

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