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      High-throughput STELA provides a rapid test for the diagnosis of telomere biology disorders

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          Abstract

          Telomere biology disorders are complex clinical conditions that arise due to mutations in genes required for telomere maintenance. Telomere length has been utilised as part of the diagnostic work-up of patients with these diseases; here, we have tested the utility of high-throughput STELA (HT-STELA) for this purpose. HT-STELA was applied to a cohort of unaffected individuals ( n = 171) and a retrospective cohort of mutation carriers ( n = 172). HT-STELA displayed a low measurement error with inter- and intra-assay coefficient of variance of 2.3% and 1.8%, respectively. Whilst telomere length in unaffected individuals declined as a function of age, telomere length in mutation carriers appeared to increase due to a preponderance of shorter telomeres detected in younger individuals (< 20 years of age). These individuals were more severely affected, and age-adjusted telomere length differentials could be used to stratify the cohort for overall survival (Hazard Ratio = 5.6 (1.5–20.5); p < 0.0001). Telomere lengths of asymptomatic mutation carriers were shorter than controls ( p < 0.0001), but longer than symptomatic mutation carriers ( p < 0.0001) and telomere length heterogeneity was dependent on the diagnosis and mutational status. Our data show that the ability of HT-STELA to detect short telomere lengths, that are not readily detected with other methods, means it can provide powerful diagnostic discrimination and prognostic information. The rapid format, with a low measurement error, demonstrates that HT-STELA is a new high-quality laboratory test for the clinical diagnosis of an underlying telomeropathy.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00439-021-02257-4.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

              The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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                Author and article information

                Contributors
                t.vulliamy@qmul.ac.uk , bairddm@cardiff.ac.uk
                t.vulliamy@qmul.ac.uk , bairddm@cardiff.ac.uk
                Journal
                Hum Genet
                Hum Genet
                Human Genetics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-6717
                1432-1203
                11 March 2021
                11 March 2021
                2021
                : 140
                : 6
                : 945-955
                Affiliations
                [1 ]GRID grid.5600.3, ISNI 0000 0001 0807 5670, Division of Cancer and Genetics, School of Medicine, , Cardiff University, ; Heath Park, Cardiff, CF14 4XN UK
                [2 ]GRID grid.4868.2, ISNI 0000 0001 2171 1133, Centre for Genomics and Child Health, , Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, ; London, E1 2AT UK
                [3 ]GRID grid.241103.5, ISNI 0000 0001 0169 7725, Immunodeficiency Centre for Wales, , University Hospital of Wales, ; Heath Park, Cardiff, CF14 4XW UK
                [4 ]GRID grid.5600.3, ISNI 0000 0001 0807 5670, Division of Infection, Inflammation and Immunity, School of Medicine, , Cardiff University, ; Heath Park, Cardiff, CF14 4XN UK
                Author information
                http://orcid.org/0000-0001-8408-5467
                Article
                2257
                10.1007/s00439-021-02257-4
                8099822
                33709208
                dd4d4d90-1394-4634-bfe8-ea51be3f4eb5
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 October 2020
                : 13 January 2021
                Funding
                Funded by: cancer research UK
                Award ID: A18246/A29202
                Award Recipient :
                Funded by: Health and Care Research Wales
                Award ID: WCRC
                Award Recipient :
                Funded by: Medical Research Council UK
                Award ID: MR/P018440/1
                Award Recipient :
                Funded by: Blood Cancer UK
                Award ID: 14032
                Award Recipient :
                Categories
                Original Investigation
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2021

                Genetics
                Genetics

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