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

Abstract

PurposeStructural variants such as multi-exon 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 to genome-wide low-resolution and exon-resolution chromosomal microarrays (CMA), and to characterise the properties of de novo CNVs in a large clinical cohort. MethodsWe performed CNV detection using ES of 13,462 parent-offspring trios in the Deciphering Developmental Disorders (DDD) study, and compared them to CNVs detected from exon-resolution array comparative genomic hybridization (aCGH) in 5,197 probands from the DDD study. ResultsIntegrating calls from multiple ES-based CNV algorithms using random forest machine learning generated a higher quality dataset than using individual algorithms. Both ES- and aCGH-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 pre-screened with low resolution CMA, 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. ConclusionES-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, exomebased CNV detection could replace low-resolution CMA for detecting pathogenic CNVs.