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e-Article

Robust identification of deletions in exome and genome sequence data based on clustering of Mendelian errors
Document Type
article
Source
Human Mutation. 39(6)
Subject
Biological Sciences
Bioinformatics and Computational Biology
Genetics
Pediatric
Biotechnology
Human Genome
Chromosome Mapping
DNA Copy Number Variations
DNA Mutational Analysis
Exome
Female
Genome
Human
Heart Defects
Congenital
Humans
Male
Sequence Deletion
Exome Sequencing
Whole Genome Sequencing
copy number variant identification
UPD
whole exome sequencing
whole genome sequencing
Clinical Sciences
Genetics & Heredity
Clinical sciences
Language
Abstract
Multiple tools have been developed to identify copy number variants (CNVs) from whole exome (WES) and whole genome sequencing (WGS) data. Current tools such as XHMM for WES and CNVnator for WGS identify CNVs based on changes in read depth. For WGS, other methods to identify CNVs include utilizing discordant read pairs and split reads and genome-wide local assembly with tools such as Lumpy and SvABA, respectively. Here, we introduce a new method to identify deletion CNVs from WES and WGS trio data based on the clustering of Mendelian errors (MEs). Using our Mendelian Error Method (MEM), we identified 127 deletions (inherited and de novo) in 2,601 WES trios from the Pediatric Cardiac Genomics Consortium, with a validation rate of 88% by digital droplet PCR. MEM identified additional de novo deletions compared with XHMM, and a significant enrichment of 15q11.2 deletions compared with controls. In addition, MEM identified eight cases of uniparental disomy, sample switches, and DNA contamination. We applied MEM to WGS data from the Genome In A Bottle Ashkenazi trio and identified deletions with 97% specificity. MEM provides a robust, computationally inexpensive method for identifying deletions, and an orthogonal approach for verifying deletions called by other tools.