Robust identification of deletions in exome and genome sequence data based on clustering of Mendelian errors. Issue 6 (22nd March 2018) - Manheimer, Kathryn B.; Patel, Nihir; Richter, Felix; Gorham, Joshua; Tai, Angela C.; Homsy, Jason; Boskovski, Marko T.; Parfenov, Michael; Goldmuntz, Elizabeth; Chung, Wendy K.; Brueckner, Martina; Tristani‐Firouzi, Martin; et al.
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. Abstract : Here, we introduce the Mendelian Error Method (MEM), a novel tool to identify deletion CNVs based on the clustering of Mendelian errors (MEs) in whole exome and genome sequencing data from of trios. We show MEM deletion calls achieve validation rates of 88–97%, and can identify additional de novo deletions that are missed by read‐depth based methods such as XHMM. In addition, MEM is able to robustly identify cases of uniparental disomy, sample switches, and DNA contamination.