Mutations in the CACNA1A gene, encoding the pore-forming CaV2.1 (P/Q-type) channel α1A subunit, result in heterogeneous human neurological disorders, including familial and sporadic hemiplegic migraine along with episodic and progressive forms of ataxia. Hemiplegic Migraine (HM) mutations induce gain-of-channel function, mainly by shifting channel activation to lower voltages, whereas ataxia mutations mostly produce loss-of-channel function. However, some HM-linked gain-of-function mutations are also associated to congenital ataxia and/or cerebellar atrophy, including the deletion of a highly conserved phenylalanine located at the S6 pore region of α1A domain III (ΔF1502). Functional studies of ΔF1502 CaV2.1 channels, expressed in Xenopus oocytes, using the non-physiological Ba2+ as the charge carrier have only revealed discrete alterations in channel function of unclear pathophysiological relevance. Here, we report a second case of congenital ataxia linked to the ΔF1502 α1A mutation, detected by whole-exome sequencing, and analyze its functional consequences on CaV2.1 human channels heterologously expressed in mammalian tsA-201 HEK cells, using the physiological permeant ion Ca2+. ΔF1502 strongly decreases the voltage threshold for channel activation (by ~ 21 mV), allowing significantly higher Ca2+ current densities in a range of depolarized voltages with physiological relevance in neurons, even though maximal Ca2+ current density through ΔF1502 CaV2.1 channels is 60% lower than through wild-type channels. ΔF1502 accelerates activation kinetics and slows deactivation kinetics of CaV2.1 within a wide range of voltage depolarization. ΔF1502 also slowed CaV2.1 inactivation kinetic and shifted the inactivation curve to hyperpolarized potentials (by ~ 28 mV). ΔF1502 effects on CaV2.1 activation and deactivation properties seem to be of high physiological relevance. Thus, ΔF1502 strongly promotes Ca2+ influx in response to either single or trains of action potential-like waveforms of different durations. Our observations support a causative role of gain-of-function CaV2.1 mutations in congenital ataxia, a neurodevelopmental disorder at the severe-most end of CACNA1A-associated phenotypic spectrum.

Each year diagnostic laboratories in the Netherlands profile thousands of individuals for heritable disease using next‐generation sequencing (NGS). This requires pathogenicity classification of millions of DNA variants on the standard 5‐tier scale. To reduce time spent on data interpretation and increase data quality and reliability, the nine Dutch labs decided to publicly share their classifications. Variant classifications of nearly 100,000 unique variants were catalogued and compared in a centralized MOLGENIS database. Variants classified by more than one center were labeled as “consensus” when classifications agreed, and shared internationally with LOVD and ClinVar. When classifications opposed (LB/B vs. LP/P), they were labeled “conflicting”, while other nonconsensus observations were labeled “no consensus”. We assessed our classifications using the InterVar software to compare to ACMG 2015 guidelines, showing 99.7% overall consistency with only 0.3% discrepancies. Differences in classifications between Dutch labs or between Dutch labs and ACMG were mainly present in genes with low penetrance or for late onset disorders and highlight limitations of the current 5‐tier classification system. The data sharing boosted the quality of DNA diagnostics in Dutch labs, an initiative we hope will be followed internationally. Recently, a positive match with a case from outside our consortium resulted in a more definite disease diagnosis.

Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole‐exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20–30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single‐nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent–child trios. eDiVA combines next‐generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning‐based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state‐of‐the‐art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.

Although large-scale efforts for molecular profiling of cancer samples provide multiple data types for many samples, most approaches for finding candidate cancer genes rely on somatic mutations and DNA copy number only. We present a new method, OncoScape, which exploits five complementary data types across 11 cancer types to identify new candidate cancer genes. We find many rarely mutated genes that are strongly affected by other aberrations. We retrieve the majority of known cancer genes but also new candidates such as STK31 and MSRA with very high confidence. Several genes show a dual oncogene- and tumor suppressor-like behavior depending on the tumor type. Most notably, the well-known tumor suppressor RB1 shows strong oncogene-like signal in colon cancer. We applied OncoScape to cell lines representing ten cancer types, providing the most comprehensive comparison of aberrations in cell lines and tumor samples to date. This revealed that glioblastoma, breast and colon cancer show strong similarity between cell lines and tumors, while head and neck squamous cell carcinoma and bladder cancer, exhibit very little similarity between cell lines and tumors. To facilitate exploration of the cancer aberration landscape, we created a web portal enabling interactive analysis of OncoScape results (http://ccb.nki.nl/software/oncoscape).