Schizophrenia is a severe psychiatric disease with complex etiology, affecting approximately 1% of the general population. Most genetics studies so far have focused on disease association with common genetic variation, such as single-nucleotide polymorphisms (SNPs), but it has recently become apparent that large-scale genomic copy-number variants (CNVs) are involved in disease development as well. To assess the role of rare CNVs in schizophrenia, we screened 54 patients with deficit schizophrenia using Affymetrix's GeneChip 250K SNP arrays. We identified 90 CNVs in total, 77 of which have been reported previously in unaffected control cohorts. Among the genes disrupted by the remaining rare CNVs are MYT1L, CTNND2, NRXN1, and ASTN2, genes that play an important role in neuronal functioning but--except for NRXN1--have not been associated with schizophrenia before. We studied the occurrence of CNVs at these four loci in an additional cohort of 752 patients and 706 normal controls from The Netherlands. We identified eight additional CNVs, of which the four that affect coding sequences were found only in the patient cohort. Our study supports a role for rare CNVs in schizophrenia susceptibility and identifies at least three candidate genes for this complex disorder.

A homozygous mutation of the CNTNAP2 gene has been associated with a syndrome of focal epilepsy, mental retardation, language regression and other neuropsychiatric problems in children of the Old Order Amish community. Here we report genomic rearrangements resulting in haploinsufficiency of the CNTNAP2 gene in association with epilepsy and schizophrenia. Genomic deletions of varying sizes affecting the CNTNAP2 gene were identified in three nonrelated Caucasian patients. In contrast, we did not observe any dosage variation for this gene in 512 healthy controls. Moreover, this genomic region has not been identified as showing large-scale copy number variation. Our data thus confirm an association of CNTNAP2 to epilepsy outside the Old Order Amish population and suggest that dosage alteration of this gene may lead to a complex phenotype of schizophrenia, epilepsy and cognitive impairment.

Although the benefits of next-generation sequencing (NGS) for the diagnosis of heterogeneous diseases such as intellectual disability (ID) are undisputed, there is little consensus on the relative merits of targeted enrichment, whole-exome sequencing (WES) or whole-genome sequencing (WGS). To answer this question, WES and WGS data from the same nine samples were compared, and WES was shown not to miss any variants identified by WGS in a gene panel including ∼500 genes linked to ID (500GP). Additionally, deeply sequenced WES data were shown to adequately cover ∼99% of the 500GP; thus, little additional benefit was to be expected from a targeted enrichment approach. To reduce costs, minimal sequencing criteria were determined by investigating the relation between sequenced reads and outcome parameters such as coverage and variant yield. Our analysis indicated that 60 million reads yielded a mean coverage of ∼60×: ∼97% of the 500GP sequences were sufficiently covered to exclude variants, whereas variant yield was ∼99.5% and false-positive and false-negative rates were controlled. Our findings indicate that WES is currently the optimal approach to ID diagnostics. This result depends on the capture kit and sequencing strategy used. The developed framework however is amenable to other sequencing approaches.

Clinical application of whole-exome and whole-genome sequencing (WES and WGS) has led to an increasing interest in how it could drive healthcare decisions. As with any healthcare innovation, implementation of next-generation sequencing in the clinic raises questions on affordability and costing impact for society as a whole. We retrospectively analyzed medical records of 370 patients with ID who had undergone WES at various stages of their diagnostic trajectory. We collected all medical interventions performed on these patients at the University Medical Center Utrecht (UMCU), Utrecht, the Netherlands. We categorized the patients according to their WES-based preliminary diagnosis ("yes", "no", and "uncertain"), and assessed the per-patient healthcare activities and corresponding costs before (pre) and after (post) genetic diagnosis. The WES-specific diagnostic yield among the 370 patients was 35% (128 patients). Pre-WES costs were €7.225 on average. Highest average costs were observed for laboratory-based tests, including genetics, followed by consults. Compared to pre-WES costs, the post-WES costs were on average 80% lower per patient, irrespective of the WES-based diagnostic outcome. Application of WES results in a considerable reduction of healthcare costs, not just in current settings, but even more so when applied earlier in the diagnostic trajectory (genetics-first). In such context, WES may replace less cost-effective traditional technologies without compromising the diagnostic yield. Moreover, WES appears to harbor an intrinsic "end-of-trajectory" effect; regardless of the diagnosis, downstream medical interventions decrease substantially in both number and costs.