BackgroundChildhood-onset pulmonary arterial hypertension (PAH) is rare and differs from adult-onset disease in clinical presentation, with often unexplained mental retardation and dysmorphic features (MR/DF). Mutations in the major PAH gene, BMPR2, were reported to cause PAH in only 10–16% of childhood-onset patients. We aimed to identify more genes associated with childhood-onset PAH.MethodsWe studied 20 consecutive cases with idiopathic or heritable PAH. In patients with accompanying MR/DF (n=6) array-comparative genomic hybridisation analysis was performed, with the aim of finding common deletion regions containing candidate genes for PAH. Three patients had overlapping deletions of 17q23.2. TBX2 and TBX4 were selected from this area as candidate genes and sequenced in all 20 children. After identifying TBX4 mutations in these children, we subsequently sequenced TBX4 in a cohort of 49 adults with PAH. Because TBX4 mutations are known to cause small patella syndrome (SPS), all patients with newly detected TBX4 mutations were screened for features of SPS. We also screened a third cohort of 23 patients with SPS for PAH.ResultsTBX4 mutations (n=3) or TBX4-containing deletions (n=3) were detected in 6 out of 20 children with PAH (30%). All living patients and two parents with TBX4 mutations appeared to have previously unrecognised SPS. In the adult PAH-cohort, one TBX4 mutation (2%) was detected. Screening in the cohort of (predominantly adult) SPS patients revealed no PAH.ConclusionsThese data indicate that TBX4 mutations are associated with childhood-onset PAH, but that the prevalence of PAH in adult TBX4 mutation carriers is low.

BackgroundClinical and genetic heterogeneity in monogenetic disorders represents a major diagnostic challenge. Although the presence of particular clinical features may aid in identifying a specific cause in some cases, the majority of patients remain undiagnosed.Here, we investigated the utility of whole-exome sequencing as a diagnostic approach for establishing a molecular diagnosis in a highly heterogeneous group of patients with varied intellectual disability and microcephaly.MethodsWhole-exome sequencing was performed in 38 patients, including three sib-pairs, in addition to or in parallel with genetic analyses that were performed during the diagnostic work-up of the study participants.ResultsIn ten out of these 35 families (29 %), we found mutations in genes already known to be related to a disorder in which microcephaly is a main feature. Two unrelated patients had mutations in the ASPM gene. In seven other patients we found mutations in RAB3GAP1, RNASEH2B, KIF11, ERCC8, CASK, DYRK1A and BRCA2. In one of the sib-pairs, mutations were found in the RTTN gene. Mutations were present in seven out of our ten families with an established etiological diagnosis with recessive inheritance.ConclusionsWe demonstrate that whole-exome sequencing is a powerful tool for the diagnostic evaluation of patients with highly heterogeneous neurodevelopmental disorders such as intellectual disability with microcephaly. Our results confirm that autosomal recessive disorders are highly prevalent among patients with microcephaly.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0167-8) contains supplementary material, which is available to authorized users.

We have developed a tool for detecting single exon copy-number variations (CNVs) in targeted next-generation sequencing data: CoNVaDING (Copy Number Variation Detection In Next-generation sequencing Gene panels). CoNVaDING includes a stringent quality control (QC) metric, that excludes or flags low-quality exons. Since this QC shows exactly which exons can be reliably analyzed and which exons are in need of an alternative analysis method, CoNVaDING is not only useful for CNV detection in a research setting, but also in clinical diagnostics. During the validation phase, CoNVaDING detected all known CNVs in high-quality targets in 320 samples analyzed, giving 100% sensitivity and 99.998% specificity for 308,574 exons. CoNVaDING outperforms existing tools by exhibiting a higher sensitivity and specificity and by precisely identifying low-quality samples and regions.