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      Genetic landscape and novel disease mechanisms from a large LGMD cohort of 4656 patients

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          Abstract

          Objective

          Limb‐girdle muscular dystrophies ( LGMDs), one of the most heterogeneous neuromuscular disorders ( NMDs), involves predominantly proximal‐muscle weakness with >30 genes associated with different subtypes. The clinical‐genetic overlap among subtypes and with other NMDs complicate disease‐subtype identification lengthening diagnostic process, increases overall costs hindering treatment/clinical‐trial recruitment. Currently seven LGMD clinical trials are active but still no gene‐therapy‐related treatment is available. Till‐date no nation‐wide large‐scale LGMD sequencing program was performed. Our objectives were to understand LGMD genetic basis, different subtypes’ relative prevalence across US and investigate underlying disease mechanisms.

          Methods

          A total of 4656 patients with clinically suspected‐ LGMD across US were recruited to conduct next‐generation sequencing ( NGS)‐based gene‐panel testing during June‐2015 to June‐2017 in CLIACAP‐certified Emory‐Genetics‐Laboratory. Thirty‐five LGMD‐subtypes‐associated or LGMD‐like other NMD‐associated genes were investigated. Main outcomes were diagnostic yield, gene‐variant spectrum, and LGMD subtypes’ prevalence in a large US LGMD‐suspected population.

          Results

          Molecular diagnosis was established in 27% (1259 cases; 95% CI, 26–29%) of the patients with major contributing genes to LGMD phenotypes being: CAPN3 (17%), DYSF (16%), FKRP (9%) and ANO5 (7%). We observed an increased prevalence of genetically confirmed late‐onset Pompe disease, DNAJB6‐ associated LGMD subtype1E and CAPN3 ‐associated autosomal‐dominant LGMDs. Interestingly, we identified a high prevalence of patients with pathogenic variants in more than one LGMD gene suggesting possible synergistic heterozygosity/digenic/multigenic contribution to disease presentation/progression that needs consideration as a part of diagnostic modality.

          Interpretation

          Overall, this study has improved our understanding of the relative prevalence of different LGMD subtypes, their respective genetic etiology, and the changing paradigm of their inheritance modes and novel mechanisms that will allow for improved timely treatment, management, and enrolment of molecularly diagnosed individuals in clinical trials.

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          Most cited references49

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          Digenic inheritance of an SMCHD1 mutation and an FSHD-permissive D4Z4 allele causes facioscapulohumeral muscular dystrophy type 2

          Facioscapulohumeral dystrophy (FSHD) is characterized by chromatin relaxation of the D4Z4 macrosatellite array on chromosome 4 and expression of the D4Z4-encoded DUX4 gene in skeletal muscle. The more common form, autosomal dominant FSHD1, is caused by a contraction of the D4Z4 array, whereas the genetic determinants and inheritance of D4Z4 array contraction-independent FSHD2 are unclear. Here we show that mutations in SMCHD1 (structural maintenance of chromosomes flexible hinge domain containing 1) on chromosome 18 reduce SMCHD1 protein levels and segregate with genome-wide D4Z4 CpG hypomethylation in human kindreds. FSHD2 occurs in individuals who inherited both the SMCHD1 mutation and a normal-sized D4Z4 array on a chromosome 4 haplotype permissive for DUX4 expression. Reducing SMCHD1 levels in skeletal muscle results in contraction-independent DUX4 expression. Our study identifies SMCHD1 as an epigenetic modifier of the D4Z4 metastable epiallele and as a causal genetic determinant of FSHD2 and possibly other human diseases subject to epigenetic regulation.
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            Improving genetic diagnosis in Mendelian disease with transcriptome sequencing

            Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI–like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.
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              Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test

              Purpose Genetic testing is an integral diagnostic component of pediatric medicine. Standard of care is often a time-consuming stepwise approach involving chromosomal microarray analysis and targeted gene sequencing panels, which can be costly and inconclusive. Whole-genome sequencing (WGS) provides a comprehensive testing platform that has the potential to streamline genetic assessments, but there are limited comparative data to guide its clinical use. Methods We prospectively recruited 103 patients from pediatric non-genetic subspecialty clinics, each with a clinical phenotype suggestive of an underlying genetic disorder, and compared the diagnostic yield and coverage of WGS with those of conventional genetic testing. Results WGS identified diagnostic variants in 41% of individuals, representing a significant increase over conventional testing results (24% P = 0.01). Genes clinically sequenced in the cohort (n = 1,226) were well covered by WGS, with a median exonic coverage of 40 × ±8 × (mean ±SD). All the molecular diagnoses made by conventional methods were captured by WGS. The 18 new diagnoses made with WGS included structural and non-exonic sequence variants not detectable with whole-exome sequencing, and confirmed recent disease associations with the genes PIGG, RNU4ATAC, TRIO, and UNC13A. Conclusion WGS as a primary clinical test provided a higher diagnostic yield than conventional genetic testing in a clinically heterogeneous cohort.
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                Author and article information

                Contributors
                mhegde@emory.edu
                Journal
                Ann Clin Transl Neurol
                Ann Clin Transl Neurol
                10.1002/(ISSN)2328-9503
                ACN3
                Annals of Clinical and Translational Neurology
                John Wiley and Sons Inc. (Hoboken )
                2328-9503
                01 December 2018
                December 2018
                : 5
                : 12 ( doiID: 10.1002/acn3.2018.5.issue-12 )
                : 1574-1587
                Affiliations
                [ 1 ] Emory University Department of Human Genetics Atlanta Georgia 30322
                [ 2 ] EGL Genetics‐Eurofins Tucker Atlanta Georgia 30084
                [ 3 ] Augusta University Augusta Georgia 30912
                [ 4 ] Department of Neurology Bombay Hospital Mumbai Maharashtra India
                [ 5 ] Department of Neurology Sir J J Group of Hospitals Grant Medical College Mumbai Maharashtra India
                [ 6 ] Centre for Advanced Molecular Diagnostics in Neuromuscular Disorders (CAMDND) 400022 Mumbai India
                [ 7 ] Neurology The University of Colorado at Denver ‐ Anschutz Medical Campus Aurora Colorado 80045
                [ 8 ] Neurology University of California, Irvine Orange California 92868
                [ 9 ] Department of Neurology Columbia University New York New York 10032
                [ 10 ] Jain Foundation Seattle Wisconsin 98115
                [ 11 ] In‐Depth Genomics Bellevue Washington
                Author notes
                [*] [* ] Correspondence

                Madhuri Hegde, Emory University School of Medicine, Department of Human Genetics, Whitehead Building Suite 301, 615 Michael Street NE, Atlanta, GA 30322. Tel: +1 470 337 2847/+1 404 727 1197; Fax: +1 404 727 3949; E‐mail: mhegde@ 123456emory.edu

                Article
                ACN3649
                10.1002/acn3.649
                6292381
                30564623
                b8b731d5-7854-40e2-905e-e2966754fa69
                © 2018 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 27 August 2018
                : 28 August 2018
                Page count
                Figures: 5, Tables: 2, Pages: 14, Words: 8591
                Funding
                Funded by: Jain Foundation
                Funded by: Muscular Dystrophy Association
                This work was funded by Jain Foundation grant ; Muscular Dystrophy Association grant .
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                acn3649
                December 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.5.4 mode:remove_FC converted:13.12.2018

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