Autosomal recessive polycystic kidney disease (ARPKD), usually considered to be a genetically homogeneous disease caused by mutations in PKHD1, has been associated with ciliary dysfunction. Here, we describe mutations in the DAZ interacting protein 1-like (DZIP1L) gene in patients with ARPKD, findings we have further validated by loss-of-function studies in mice and zebrafish. DZIP1L localizes to centrioles and at the distal end of basal bodies, and interacts with septin2, a protein implicated in maintenance of the periciliary diffusion barrier at the ciliary transition zone. Consistent with a defect in the diffusion barrier, we found that the ciliary membrane translocation of the PKD proteins, polycystin-1 and −2, is compromised in DZIP1L mutant cells. Together, these data provide the first conclusive evidence that ARPKD is not a homogeneous disorder, and establishes DZIP1L as a second gene involved in its pathogenesis.

Pediatric stroke is a rare but highly penetrant disease with a strong genetic background. Although there are an increasing number of genome-wide association studies (GWASs) for stroke in adults, such studies for stroke of pediatric onset are lacking. Here we report the results of the first GWAS on pediatric stroke using a large cohort of 270 family-based trios. GWAS was performed using the Illumina 370 CNV single nucleotide polymorphisms array and analyzed using the transmission disequilibrium test as implemented in PLINK. An enrichment analysis was performed to identify additional true association signals among lower P value signals and searched for cumulatively associated genes within protein interaction data using dmGWAS. We observed clustering of association signals in 4 genes belonging to one family of metalloproteinases at high (ADAMTS12, P ‫؍‬ 2.9 ؋ 10 ؊6 ; ADAMTS2, P ‫؍‬ 8.0 ؋ 10 ؊6 ) and moderate (ADAMTS13, P ‫؍‬ 9.3 ؋ 10 ؊4 ; ADAMTS17, P ‫؍‬ 8.5 ؋ 10 ؊4 ) significance levels. Over-representation and gene-network IntroductionPediatric stroke (PS) is a heterogeneous disorder associated with significant morbidity and mortality. It is recognized as an important childhood disease, with an incidence of 2.6-6.4 per 100 000 children per year. 1 Although PS is relatively rare, it is devastating to those affected as half of the survivors develop cognitive or motor disabilities. 2 Risk factors for stroke are different in children and adults, and classic risk factors, such as smoking, arteriosclerosis, or diabetes, are unlikely to contribute to pediatric stroke. The most prominent risk factors for stroke in children include underlying medical conditions (ie, cardiac disorders, metabolic diseases, cerebrovascular pathologies, and infectious diseases) 1 as well as many prothrombotic abnormalities. 3,4 Numerous association studies have established several genetic polymorphisms contributing to pediatric stroke risk. These candidate gene approaches identified several susceptibility genes for PS (ie, prothrombin, and GPX3). 3,4 However, genetic predisposition, environmental effects, and other risk factors can often not be disentangled because of the complex nature of PS etiology. Furthermore, a large proportion of missing heritability remains to be accounted for. 5 Genome-wide association studies (GWASs) offer a powerful approach to gene function discovery and are the current method of choice to dissect the genetic basis of complex diseases. Up-to-date, studies in families with a known first onset of pediatric stroke are lacking mainly because of limited sample size.Therefore, we performed a family-based GWAS for pediatric stroke in 270 families from Muenster, Germany composed of affected children and their parents. We identified several single nucleotide polymorphisms (SNPs), which are associated with PS at significance levels P Ͻ 10 Ϫ5 . In addition, we determined potential combined effects of SNPs contributing to stroke risk that may be missed through conventional single marker or haplotype association. We assessed ...

Renal cysts are clinically and genetically heterogeneous conditions. Autosomal dominant polycystic kidney disease (ADPKD) is the most frequent life-threatening genetic disease and mainly caused by mutations in PKD1. The presence of six PKD1 pseudogenes and tremendous allelic heterogeneity make molecular genetic testing challenging requiring laborious locus-specific amplification. Increasing evidence suggests a major role for PKD1 in early and severe cases of ADPKD and some patients with a recessive form. Furthermore it is becoming obvious that clinical manifestations can be mimicked by mutations in a number of other genes with the necessity for broader genetic testing. We established and validated a sequence capture based NGS testing approach for all genes known for cystic and polycystic kidney disease including PKD1. Thereby, we demonstrate that the applied standard mapping algorithm specifically aligns reads to the PKD1 locus and overcomes the complication of unspecific capture of pseudogenes. Employing careful and experienced assessment of NGS data, the method is shown to be very specific and equally sensitive as established methods. An additional advantage over conventional Sanger sequencing is the detection of copy number variations (CNVs). Sophisticated bioinformatic read simulation increased the high analytical depth of the validation study and further demonstrated the strength of the approach. We further raise some awareness of limitations and pitfalls of common NGS workflows when applied in complex regions like PKD1 demonstrating that quality of NGS needs more than high coverage of the target region. By this, we propose a time- and cost-efficient diagnostic strategy for comprehensive molecular genetic testing of polycystic kidney disease which is highly automatable and will be of particular value when therapeutic options for PKD emerge and genetic testing is needed for larger numbers of patients.

Recent genome-wide association studies (GWAS) have confirmed known risk mutations for venous thromboembolism (VTE) and identified a number of novel susceptibility loci in adults. Here we present a GWAS in 212 nuclear families with pediatric VTE followed by targeted next-generation sequencing (NGS) to identify causative mutations contributing to the association. Three single nucleotide polymorphisms (SNPs) exceeded the threshold for genome-wide significance as determined by permutation testing using 100 000 bootstrap permutations ( < 10). These SNPs reside in a region on chromosome 6q13 comprising the genes small ARF GAP1 (), an ARF6 guanosine triphosphatase-activating protein that functions in clathrin-dependent endocytosis, and β-1,3-glucoronyltransferase 2 (), a member of the human natural killer 1 carbohydrate pathway. Rs1304029 and rs2748331 are associated with pediatric VTE with unpermuted/permuted values of = 1.42 × 10/2.0 × 10 and = 6.11 × 10/1.8 × 10, respectively. Rs2748331 was replicated ( = .00719) in an independent study sample coming from our GWAS on pediatric thromboembolic stroke (combined = 7.88 × 10). Subsequent targeted NGS in 24 discordant sibling pairs identified 17 nonsynonymous coding variants, of which 1 located in and 3 in, a member of the RIM family of active zone proteins, are predicted as damaging by Protein Variation Effect Analyzer and/or sorting intolerant from tolerant scores. Three SNPs curtly missed statistical significance in the transmission-disequilibrium test in the full cohort (rs112439957: = .08326,; rs767118962: = .08326,; and rs41265501: = .05778,). In conjunction, our data provide compelling evidence for ,, and as novel susceptibility loci for pediatric VTE and warrant future functional studies to unravel the underlying molecular mechanisms leading to VTE.

We present a comprehensive toolkit for post-processing, visualization and advanced analysis of GWAS results. In the spirit of comparable tools for gene-expression analysis, we attempt to unify and simplify several procedures that are essential for the interpretation of GWAS results. This includes the generation of advanced Manhattan and regional association plots including rare variant display as well as novel interaction network analysis tools for the investigation of systems-biology aspects. Our package supports virtually all model organisms and represents the first cohesive implementation of such tools for the popular language R. Previous software of that range is dispersed over a wide range of platforms and mostly not adaptable for custom work pipelines. We demonstrate the utility of this package by providing an example workflow on a publicly available dataset.