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

Genet Med
Authors
Keywords
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

Year of Publication
2017
Journal
Genet Med
Volume
19
Issue
10
Pages
1151-1158
Date Published
2017 Oct
ISSN
1530-0366
DOI
10.1038/gim.2017.26
PubMed ID
28518168
PubMed Central ID
PMC5563454
Links
Grant list
MC_U120085815 / Medical Research Council / United Kingdom
FS/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