“Matching” consent to purpose: The example of the Matchmaker ExchangeDyke, Stephanie O. M.; Knoppers, Bartha M.; Hamosh, Ada; Firth, Helen V.; Hurles, Matthew; Brudno, Michael; Boycott, Kym M.; Philippakis, Anthony A.; Rehm, Heidi L.
doi: 10.1002/humu.23278pmid: 28699299
The Matchmaker Exchange (MME) connects rare disease clinicians and researchers to facilitate the sharing of data from undiagnosed patients for the purpose of novel gene discovery. Such sharing raises the odds that two or more similar patients with candidate genes in common may be found, thereby allowing their condition to be more readily studied and understood. Consent considerations for data sharing in MME included both the ethical and legal differences between clinical and research settings and the level of privacy risk involved in sharing varying amounts of rare disease patient data to enable patient matches. In this commentary, we discuss these consent considerations and the resulting MME Consent Policy as they may be relevant to other international data sharing initiatives.
IFN‐γR1 defects: Mutation update and description of the IFNGR1 variation databasede Vosse, Esther; Dissel, Jaap T.
doi: 10.1002/humu.23302pmid: 28744922
IFN‐γ signaling is essential for the innate immune defense against mycobacterial infections. IFN‐γ signals through the IFN‐γ receptor, which consists of a tetramer of two IFN‐γR1 chains in complex with two IFN‐γR2 chains, where IFN‐γR1 is the ligand‐binding chain of the interferon‐γ receptor and IFN‐γR2 is the signal‐transducing chain of the IFN‐γ receptor. Germline mutations in the gene IFNGR1 encoding the IFN‐γR1 cause a primary immunodeficiency that mainly leads to mycobacterial infections. Here, we review the molecular basis of this immunodeficiency in the 130 individuals described to date, and report mutations in five new individuals, bringing the total number to 135 individuals from 98 kindreds. Forty unique IFNGR1 mutations have been reported and they exert either an autosomal dominant or an autosomal recessive effect. Mutations resulting in premature stopcodons represent the majority of IFNGR1 mutations (60%; 24 out of 40), followed by amino acid substitutions (28%, 11 out of 40). All known mutations, as well as 287 other variations, have been deposited in the online IFNGR1 variation database (www.LOVD.nl/IFNGR1). In this article, we review the function of IFN‐γR1 and molecular genetics of human IFNGR1.
MiSynPat: An integrated knowledge base linking clinical, genetic, and structural data for disease‐causing mutations in human mitochondrial aminoacyl‐tRNA synthetasesMoulinier, Luc; Ripp, Raymond; Castillo, Gaston; Poch, Olivier; Sissler, Marie
doi: 10.1002/humu.23277pmid: 28608363
Numerous mutations in each of the mitochondrial aminoacyl‐tRNA synthetases (aaRSs) have been implicated in human diseases. The mutations are autosomal and recessive and lead mainly to neurological disorders, although with pleiotropic effects. The processes and interactions that drive the etiology of the disorders associated with mitochondrial aaRSs (mt‐aaRSs) are far from understood. The complexity of the clinical, genetic, and structural data requires concerted, interdisciplinary efforts to understand the molecular biology of these disorders. Toward this goal, we designed MiSynPat, a comprehensive knowledge base together with an ergonomic Web server designed to organize and access all pertinent information (sequences, multiple sequence alignments, structures, disease descriptions, mutation characteristics, original literature) on the disease‐linked human mt‐aaRSs. With MiSynPat, a user can also evaluate the impact of a possible mutation on sequence‐conservation‐structure in order to foster the links between basic and clinical researchers and to facilitate future diagnosis. The proposed integrated view, coupled with research on disease‐related mt‐aaRSs, will help to reveal new functions for these enzymes and to open new vistas in the molecular biology of the cell. The purpose of MiSynPat, freely available at http://misynpat.org, is to constitute a reference and a converging resource for scientists and clinicians.
Semi‐automated cancer genome analysis using high‐performance computingCrispatzu, Giuliano; Kulkarni, Pranav; Toliat, Mohammad R.; Nürnberg, Peter; Herling, Marco; Herling, Carmen D.; Frommolt, Peter
doi: 10.1002/humu.23275pmid: 28598576
Next‐generation sequencing (NGS) has turned from a new and experimental technology into a standard procedure for cancer genome studies and clinical investigation. While a multitude of software packages for cancer genome data analysis have been made available, these need to be combined into efficient analytical workflows that cover multiple aspects relevant to a clinical environment and that deliver handy results within a reasonable time frame. Here, we introduce QuickNGS Cancer as a new suite of bioinformatics pipelines that is focused on cancer genomics and significantly reduces the analytical hurdles that still limit a broader applicability of NGS technology, particularly to clinically driven research. QuickNGS Cancer allows a highly efficient analysis of a broad variety of NGS data types, specifically considering cancer‐specific issues, such as biases introduced by tumor impurity and aneuploidy or the assessment of genomic variations regarding their biomedical relevance. It delivers highly reproducible analysis results ready for interpretation within only a few days after sequencing, as shown by a reanalysis of 140 tumor/normal pairs from The Cancer Genome Atlas (TCGA) in which QuickNGS Cancer detected a significant number of mutations in key cancer genes missed by a well‐established mutation calling pipeline. Finally, QuickNGS Cancer obtained several unexpected mutations in leukemias that could be confirmed by Sanger sequencing.
Investigating DNA‐, RNA‐, and protein‐based features as a means to discriminate pathogenic synonymous variantsLivingstone, Mark; Folkman, Lukas; Yang, Yuedong; Zhang, Ping; Mort, Matthew; Cooper, David N.; Liu, Yunlong; Stantic, Bela; Zhou, Yaoqi
doi: 10.1002/humu.23283pmid: 28649752
Synonymous single‐nucleotide variants (SNVs), although they do not alter the encoded protein sequences, have been implicated in many genetic diseases. Experimental studies indicate that synonymous SNVs can lead to changes in the secondary and tertiary structures of DNA and RNA, thereby affecting translational efficiency, cotranslational protein folding as well as the binding of DNA‐/RNA‐binding proteins. However, the importance of these various features in disease phenotypes is not clearly understood. Here, we have built a support vector machine (SVM) model (termed DDIG‐SN) as a means to discriminate disease‐causing synonymous variants. The model was trained and evaluated on nearly 900 disease‐causing variants. The method achieves robust performance with the area under the receiver operating characteristic curve of 0.84 and 0.85 for protein‐stratified 10‐fold cross‐validation and independent testing, respectively. We were able to show that the disease‐causing effects in the immediate proximity to exon–intron junctions (1–3 bp) are driven by the loss of splicing motif strength, whereas the gain of splicing motif strength is the primary cause in regions further away from the splice site (4–69 bp). The method is available as a part of the DDIG server at http://sparks-lab.org/ddig.