Detection and characterization of copy-number variants from exome sequencing in the DDD study

Purpose: Structural variants such as multiexon deletions and duplications are an important cause of disease but are often overlooked in standard exome/genome sequencing analysis. We aimed to evaluate the detection of copy-number variants (CNVs) from exome sequencing (ES) in comparison with genome-wide low-resolution and exon-resolution chromosomal microarrays (CMAs) and to characterize the properties of de novo CNVs in a large clinical cohort. Methods: We performed CNV detection using ES of 9859 parent-offspring trios in the Deciphering Developmental Disorders (DDD) study and compared them with CNVs detected from exon-resolution array comparative genomic hybridization in 5197 probands from the DDD study. Results: Integrating calls from multiple ES-based CNV algorithms using random forest machine learning generated a higher quality data set than using individual algorithms. Both ES- and array comparative genomic hybridization–based approaches had the same sensitivity of 89% and detected the same number of unique pathogenic CNVs not called by the other approach. Of DDD probands prescreened with low-resolution CMAs, 2.6% had a pathogenic CNV detected by higher-resolution assays. De novo CNVs were strongly enriched in known DD-associated genes and exhibited no bias in parental age or sex. Conclusion: ES-based CNV calling has higher sensitivity than low-resolution CMAs currently in clinical use and comparable sensitivity to exon-resolution CMA. With sufficient investment in bioinformatic analysis, exome-based CNV detection could replace low-resolution CMA for detecting pathogenic CNVs.