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      The complex genetic landscape of familial MDS and AML reveals pathogenic germline variants

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      1 , , 2 , , 3 , 2 , 4 , 2 , 2 , 2 , 5 , 2 , 1 , 2 , 1 , 1 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 17 , 24 , 20 , 25 , 26 , 22 , 27 , 28 , 29 , 30 , 31 , 6 , 32 , 32 , 33 , 33 , 33 , 34 , 35 , 35 , 36 , 35 , 37 , 38 , 3 , 1 , , 2 , 39 ,
      Nature Communications
      Nature Publishing Group UK
      Cancer genomics, Acute myeloid leukaemia

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          Abstract

          The inclusion of familial myeloid malignancies as a separate disease entity in the revised WHO classification has renewed efforts to improve the recognition and management of this group of at risk individuals. Here we report a cohort of 86 acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) families with 49 harboring germline variants in 16 previously defined loci (57%). Whole exome sequencing in a further 37 uncharacterized families (43%) allowed us to rationalize 65 new candidate loci, including genes mutated in rare hematological syndromes ( ADA, GP6, IL17RA, PRF1 and SEC23B), reported in prior MDS/AML or inherited bone marrow failure series ( DNAH9, NAPRT1 and  SH2B3) or variants at novel loci ( DHX34) that appear specific to inherited forms of myeloid malignancies. Altogether, our series of MDS/AML families offer novel insights into the etiology of myeloid malignancies and provide a framework to prioritize variants for inclusion into routine diagnostics and patient management.

          Abstract

          Familial myeloid malignancies have recently been classified as separate disease entities. Here, using whole-exome sequencing of affected pedigrees - the authors highlight genetic variants associated with these conditions.

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          Most cited references78

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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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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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                Author and article information

                Contributors
                a.rio-machin@qmul.ac.uk
                t.vulliamy@qmul.ac.uk
                j.fitzgibbon@qmul.ac.uk
                i.dokal@qmul.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 February 2020
                25 February 2020
                2020
                : 11
                : 1044
                Affiliations
                [1 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, Centre for Haemato-Oncology, Barts Cancer Institute, , Queen Mary University of London, ; London, UK
                [2 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, Centre for Genomics and Child Health, Blizard Institute, , Queen Mary University of London, ; London, UK
                [3 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, , University of Edinburgh, ; Edinburgh, UK
                [4 ]ISNI 0000 0004 0622 5016, GRID grid.120073.7, Department of Haematology, , Addenbrooke’s Hospital, ; Cambridge, UK
                [5 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, Centre for Molecular Oncology, Barts Cancer Institute, , Queen Mary University of London, ; London, UK
                [6 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, The Leukemia/BMT Program of British Columbia, Division of Hematology, Department of Medicine, Faculty of Medicine, , University of British Columbia, ; Vancouver, BC Canada
                [7 ]ISNI 0000 0004 0399 8363, GRID grid.415720.5, Department of Haematology, , Christie Hospital, ; Manchester, UK
                [8 ]ISNI 0000 0001 0942 9821, GRID grid.11804.3c, MTA-SE Lendulet Molecular Oncohematology Research Group, 1st Department of Pathology and Experimental Cancer Research, , Semmelweis University, ; Budapest, Hungary
                [9 ]GRID grid.443984.6, Department of Haematology, , St James’s University Hospital, ; Leeds, UK
                [10 ]Service d’hématologie Séniors, Hôpital St Louis/Université Paris, Paris, France
                [11 ]ISNI 0000 0004 0516 3853, GRID grid.417322.1, National Centre for Medical Genetics, , Our Lady’s Children’s Hospital, ; Crumlin, Dublin, Ireland
                [12 ]Children’s Health Queensland Hospital and Health Service, Queensland Children’s Hospital, South Brisbane, QLD Australia
                [13 ]ISNI 0000 0001 1516 2393, GRID grid.5947.f, Department of Hematology, , St Olavs Hospital and Institute of Cancer Research and Molecular Medicine (IKM) Norwegian University of Science and Technology (NTNU), ; Trondheim, Norway
                [14 ]ISNI 0000 0004 0581 2008, GRID grid.451052.7, Department of Haematology, Whipps Cross Hospital, , Barts NHS Trust, ; London, UK
                [15 ]ISNI 0000 0000 9910 8169, GRID grid.416098.2, Department of Haematology, , The Royal Bournemouth Hospital NHS Foundation Trust, ; Bournemouth, UK
                [16 ]ISNI 0000 0004 0399 7272, GRID grid.415246.0, Department of Haematology, , Birmingham Children’s Hospital, ; Birmingham, UK
                [17 ]ISNI 0000 0001 0705 4923, GRID grid.413629.b, Centre for Haematology, Imperial College London, , Hammersmith Hospital, ; London, UK
                [18 ]GRID grid.420468.c, Clinic Genetics Unit, , Great Ormond Street Hospital, ; London, UK
                [19 ]ISNI 0000 0004 0391 9020, GRID grid.46699.34, Department of Haematological Medicine, Haematology Institute, , King’s College Hospital, ; London, UK
                [20 ]ISNI 0000 0004 0380 7336, GRID grid.410421.2, Bristol Haematology Unit, , University Hospitals Bristol NHS Foundation Trust, ; Bristol, UK
                [21 ]UMC Utrecht Cancer Center, Universitair Medisch Centrum Utrecht, Huispostnummer, Utrecht, Netherlands
                [22 ]GRID grid.7080.f, Laboratori d´Hematologia, Hospital de la Santa Creu i Sant Pau, , Universitat Autònoma de Barcelona, ; Barcelona, Spain
                [23 ]ISNI 0000 0004 0469 2139, GRID grid.414959.4, Division of Hematology and Hematological Malignancies, , Foothills Medical Centre, ; Calgary, AB Canada
                [24 ]ISNI 0000000121901201, GRID grid.83440.3b, Department of Haematology, UCL Cancer Institute, , University College London, ; London, UK
                [25 ]ISNI 0000 0004 0471 8845, GRID grid.410463.4, Laboratory of Hematology, Biology and Pathology Center, , Centre Hospitalier Regional Universitaire de Lille, ; Lille, France
                [26 ]ISNI 0000 0001 2242 6780, GRID grid.503422.2, Jean-Pierre Aubert Research Center, INSERM, , Universitaire de Lille, ; Lille, France
                [27 ]GRID grid.66859.34, Broad Institute of Harvard and MIT, ; Cambridge, MA USA
                [28 ]ISNI 0000 0001 0440 1889, GRID grid.240404.6, Centre for Clinical Haematology, , Nottingham University Hospitals NHS Trust, ; Nottingham, UK
                [29 ]ISNI 0000 0000 8546 682X, GRID grid.264200.2, Clinical Genetics, , St George’s Hospital Medical School, ; London, UK
                [30 ]ISNI 0000 0001 0695 6255, GRID grid.414531.6, Servicio de Hematologia y Oncologia, , Hospital de Pediatría “Prof. Dr. Juan P. Garrahan”, ; Ciudad Autonoma de Buenos Aires, Argentina
                [31 ]ISNI 0000 0004 0417 0461, GRID grid.424926.f, Haemato-oncology Department, , Royal Marsden Hospital, ; Sutton, UK
                [32 ]ISNI 0000 0004 0626 3338, GRID grid.410569.f, Department of Hematology, , University Hospitals Leuven, ; Leuven, Belgium
                [33 ]ISNI 0000 0000 9529 9877, GRID grid.10423.34, Institut für Humangenetik, , Medizinische Hochschule Hannover, ; Hannover, Germany
                [34 ]ISNI 0000 0001 0684 7788, GRID grid.414137.4, British Columbia Children’s Hospital, ; Vancouver, BC Canada
                [35 ]GRID grid.498025.2, West Midlands Regional Genetics Laboratory, , Birmingham Women’s NHS Foundation Trust, ; Birmingham, UK
                [36 ]Department of Laboratory Medicine & Pathology, Qatar Rehabilitation Institute, Hamad Bin Khalifa Medical City (HBKM), Doha, Qatar
                [37 ]ISNI 0000 0004 0581 2008, GRID grid.451052.7, Department of Haematology, St Bartholomew’s Hospital, , Barts NHS Trust, ; London, UK
                [38 ]ISNI 0000000121901201, GRID grid.83440.3b, Genetics Institute, , University College London, ; London, UK
                [39 ]ISNI 0000 0001 0372 5777, GRID grid.139534.9, Barts Health NHS Trust, ; London, UK
                Author information
                http://orcid.org/0000-0001-6733-9752
                http://orcid.org/0000-0002-8490-8512
                http://orcid.org/0000-0003-2509-9599
                http://orcid.org/0000-0003-1292-7965
                http://orcid.org/0000-0002-0729-692X
                http://orcid.org/0000-0002-2670-5696
                http://orcid.org/0000-0003-3399-346X
                http://orcid.org/0000-0001-5219-5637
                http://orcid.org/0000-0001-5841-778X
                http://orcid.org/0000-0001-5644-424X
                http://orcid.org/0000-0003-4719-1935
                http://orcid.org/0000-0001-8797-0816
                http://orcid.org/0000-0002-5597-9215
                http://orcid.org/0000-0001-8025-6169
                Article
                14829
                10.1038/s41467-020-14829-5
                7042299
                32098966
                bab073c4-529c-4dbb-a83a-cb894be93965
                © The Author(s) 2020

                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
                : 4 February 2019
                : 27 January 2020
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                © The Author(s) 2020

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                cancer genomics,acute myeloid leukaemia
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                cancer genomics, acute myeloid leukaemia

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