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      Exome-first approach identified novel INDELs and gene deletions in Mowat-Wilson Syndrome patients

      case-report

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          Abstract

          Mowat-Wilson syndrome (MWS) is characterized by severe intellectual disability, absent or impaired speech and microcephaly, with a gradual post-natal onset. The syndrome is often confused with other Angelman-like syndromes (ALS) during infancy, but in older children and adults, the characteristic facial gestalt of Mowat–Wilson syndrome allows it to be distinguished easily from ALS. We report two cases in which an exome-first approach of patients with MWS identified two novel deletions in the ZEB2 gene ranging from a 4 base deletion (case 1) to at least a 573 Kb deletion (case 2).

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          The ExAC browser: displaying reference data information from over 60 000 exomes

          Konrad J. Karczewski, Ben Weisburd, Brett Thomas … (2016)
          Worldwide, hundreds of thousands of humans have had their genomes or exomes sequenced, and access to the resulting data sets can provide valuable information for variant interpretation and understanding gene function. Here, we present a lightweight, flexible browser framework to display large population datasets of genetic variation. We demonstrate its use for exome sequence data from 60 706 individuals in the Exome Aggregation Consortium (ExAC). The ExAC browser provides gene- and transcript-centric displays of variation, a critical view for clinical applications. Additionally, we provide a variant display, which includes population frequency and functional annotation data as well as short read support for the called variant. This browser is open-source, freely available at http://exac.broadinstitute.org, and has already been used extensively by clinical laboratories worldwide.
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            Detection of clinically relevant copy-number variants by exome sequencing in a large cohort of genetic disorders

            Rolph Pfundt, Marisol del Rosario, Lisenka Vissers … (2016)
            Purpose: Copy-number variation is a common source of genomic variation and an important genetic cause of disease. Microarray-based analysis of copy-number variants (CNVs) has become a first-tier diagnostic test for patients with neurodevelopmental disorders, with a diagnostic yield of 10–20%. However, for most other genetic disorders, the role of CNVs is less clear and most diagnostic genetic studies are generally limited to the study of single-nucleotide variants (SNVs) and other small variants. With the introduction of exome and genome sequencing, it is now possible to detect both SNVs and CNVs using an exome- or genome-wide approach with a single test. Methods: We performed exome-based read-depth CNV screening on data from 2,603 patients affected by a range of genetic disorders for which exome sequencing was performed in a diagnostic setting. Results: In total, 123 clinically relevant CNVs ranging in size from 727 bp to 15.3 Mb were detected, which resulted in 51 conclusive diagnoses and an overall increase in diagnostic yield of ~2% (ranging from 0 to –5.8% per disorder). Conclusions: This study shows that CNVs play an important role in a broad range of genetic disorders and that detection via exome-based CNV profiling results in an increase in the diagnostic yield without additional testing, bringing us closer to single-test genomics. Genet Med advance online publication 27 October 2016
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              Using XHMM Software to Detect Copy Number Variation in Whole-Exome Sequencing Data.

              Menachem Fromer, Shaun Purcell (2014)
              Copy number variation (CNV) has emerged as an important genetic component in human diseases, which are increasingly being studied for large numbers of samples by sequencing the coding regions of the genome, i.e., exome sequencing. Nonetheless, detecting this variation from such targeted sequencing data is a difficult task, involving sorting out signal from noise, for which we have recently developed a set of statistical and computational tools called XHMM. In this unit, we give detailed instructions on how to run XHMM and how to use the resulting CNV calls in biological analyses.
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                Author and article information

                Contributors
                Martin Vazquez: +54 9 341 5863588 , martin.vazquez@heritas.com.ar
                Journal
                Journal ID (nlm-ta): Hum Genome Var
                Journal ID (iso-abbrev): Hum Genome Var
                Title: Human Genome Variation
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2054-345X
                Publication date (Electronic): 1 August 2018
                Publication date PMC-release: 1 August 2018
                Publication date Collection: 2018
                Volume: 5
                Electronic Location Identifier: 21
                Affiliations
                [1 ]Heritas - CIBIC S.A, Zeballos 249, Rosario, Argentina
                [2 ]Heritas - INDEAR, Ocampo 210bis, Rosario, Argentina
                [3 ]Casa Angelman, Esmeralda 280, Tigre, Buenos Aires Argentina
                Article
                Publisher ID: 21
                DOI: 10.1038/s41439-018-0021-y
                PMC ID: 6070557
                SO-VID: 13ee2568-fa9a-46cd-8a09-40964b8dcbeb
                Copyright © © The Author(s) 2018
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 8 May 2018
                Date revision received : 29 May 2018
                Date accepted : 17 June 2018
                Categories
                Subject: Data Report
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

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