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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.

Developmental disorders (DDs) are more prevalent in males, thought to be due to X-linked genetic variation. Here, the authors investigate the burden of X-linked coding variants in 11,044 DD patients, showing that this contributes to ~6% of both male and female cases and therefore does not solely explain male bias in DDs.

Estimates of mutation rates differ between species. Here, Lindsay et al. perform side-by-side analyses of germline mutation rates using multi-sibling mouse and human pedigrees and find different mutation rates between species, also stratified by sex and temporal stage of mutation acquisition.