Increasing the diagnostic yield of exome sequencing by copy number variant analysis

As whole exome sequencing (WES) becomes more widely used in the clinical realm, a wealth of unanalyzed information will be routinely generated. Using WES read depth data to predict copy number variation (CNV) could extend the diagnostic utility of this previously underutilized data by providing clinically important information such as previously unsuspected deletions or duplications. We evaluated ExomeDepth, a free R package, in addition to an aneuploidy prediction method, to detect CNVs in WES data. First, in a blinded pilot study, five out of five genomic alterations were correctly identified from clinical samples with previously defined chromosomal gains or losses, including submicroscopic deletions, duplications, and chromosomal trisomy. We then examined CNV calls among 53 patients participating in the NCGENES research study and undergoing WES, who had existing clinical chromosomal microarray (CMA) data that could be used for validation. For unique CNVs that overlap well with WES coverage regions, sensitivity was 89% for deletions and 65% for duplications. While specificity of the algorithm calls remains a concern, this is less of an issue at high threshold filtering levels. When applied to all 672 patients from the exome sequencing study, ExomeDepth identified eleven diagnostically relevant CNVs ranging in size from a two exon deletion to whole chromosome duplications, as well as numerous other CNVs with varying clinical significance. This opportunistic analysis of WES data yields an additional 1.6% of patients in this study with pathogenic or likely pathogenic CNVs that are clinically relevant to their phenotype as well as clinically relevant secondary findings. Finally, we demonstrate the potential value of copy number analysis in cases where a single heterozygous likely or known pathogenic single nucleotide alteration is identified in a gene associated with an autosomal recessive condition.