Targeted next‐generation‐sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy‐number variations (CNVs) in addition to single‐nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user‐friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state‐of‐the‐art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user‐selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user‐friendliness rendering it highly suitable for routine clinical diagnostics.

Array comparative genomic hybridisation (aCGH) profiling is currently the gold standard for genetic diagnosis of copy number. Next generation sequencing technologies provide an alternative and adaptable method of detecting copy number by comparing the number of sequence reads in non-overlapping windows between patient and control samples. Detection of copy number using the BlueGnome 8×60k oligonucleotide aCGH platform was compared with low resolution next generation sequencing using the Illumina GAIIx on 39 patients with developmental delay and/or learning difficulties who were referred to the Leeds Clinical Cytogenetics Laboratory. Sensitivity and workflow of the two platforms were compared. Customised copy number algorithms assessed sequence counts and detected changes in copy number. Imbalances detected on both platforms were compared. Of the thirty-nine patients analysed, all eleven imbalances detected by array CGH and confirmed by FISH or Q-PCR were also detected by CNV-seq. In addition, CNV-seq reported one purported pathogenic copy number variant that was not detected by array CGH. Non-pathogenic, unconfirmed copy number calls were detected by both platforms; however few were concordant between the two. CNV-seq offers an alternative to array CGH for copy number analysis with resolution and future costs comparable to conventional array CGH platforms and with less stringent sample requirements.

Whole genome sequencing (WGS) has the potential to report on all types of genetic abnormality, thus converging diagnostic testing on a single methodology. Although WGS at sufficient depth for robust detection of point mutations is still some way from being affordable for diagnostic purposes, low-coverage WGS is already an excellent method for detecting copy number variants ("CNVseq"). We report on a family in which individuals presented with a presumed autosomal recessive syndrome of severe intellectual disability and epilepsy. Array comparative genomic hybridization (CGH) analysis had revealed a homozygous deletion apparently lying within intron 3 of CNTNAP2. Since this was too small for confirmation by FISH, CNVseq was used, refining the extent of this mutation to approximately 76.8 kb, encompassing CNTNAP2 exon 3 (an out-of-frame deletion). To characterize the precise breakpoints and provide a rapid molecular diagnostic test, we resequenced the CNVseq library at medium coverage and performed split read mapping. This yielded information for a multiplex polymerase chain reaction (PCR) assay, used for cascade screening and/or prenatal diagnosis in this family. This example demonstrates a rapid, low-cost approach to converting molecular cytogenetic findings into robust PCR-based tests.

Objectives Array comparative genomic hybridization (CGH)