Using high-resolution variant frequencies to empower clinical genome interpretation
| Autor: | Eric Vallabh Minikel, Nicola Whiffin, Roddy Walsh, Birgit Funke, Daniel G. MacArthur, Stuart A. Cook, Paul J.R. Barton, Konrad J. Karczewski, Alexander Y Ing, Anne H. O’Donnell-Luria, James S. Ware |
|---|---|
| Přispěvatelé: | Fondation Leducq, Wellcome Trust, Department of Health |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Genetics & Heredity
Genetics 0604 Genetics 0303 health sciences 1103 Clinical Sciences Genomics 030204 cardiovascular system & hematology Biology Genome Penetrance DNA sequencing 03 medical and health sciences symbols.namesake 0302 clinical medicine Mendelian inheritance symbols Allelic heterogeneity Allele frequency Exome 030304 developmental biology |
| DOI: | 10.1101/073114 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|