RT Journal Article
SR Electronic
T1 Using high-resolution variant frequencies to empower clinical genome interpretation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 073114
DO 10.1101/073114
A1 Nicola Whiffin
A1 Eric Minikel
A1 Roddy Walsh
A1 Anne O’Donnell-Luria
A1 Konrad Karczewski
A1 Alexander Y Ing
A1 Paul J R Barton
A1 Birgit Funke
A1 Stuart A Cook
A1 Daniel G MacArthur
A1 James S Ware
YR 2016
UL http://biorxiv.org/content/early/2016/09/02/073114.abstract
AB 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. Here 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. 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, and identifies 43 variants previously reported as pathogenic that can now be reclassified. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.