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Genet Med DOI:10.1038/gim.2017.26

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

Publication TypeJournal Article
Year of Publication2017
AuthorsWhiffin, N, Minikel, E, Walsh, R, O'Donnell-Luria, AH, Karczewski, K, Ing, AY, Barton, PJR, Funke, B, Cook, SA, Macarthur, D, Ware, JS
JournalGenet Med
Date Published2017 Oct

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.


Alternate JournalGenet. Med.
PubMed ID28518168
PubMed Central IDPMC5563454
Grant ListFS/15/81/31817 / / British Heart Foundation / United Kingdom
F31 AI122592 / AI / NIAID NIH HHS / United States
100124 / / Wellcome Trust / United Kingdom
U54 DK105566 / DK / NIDDK NIH HHS / United States
/ / Wellcome Trust / United Kingdom
R01 GM104371 / GM / NIGMS NIH HHS / United States
T32 GM007748 / GM / NIGMS NIH HHS / United States