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Adams DR, van Karnebeek CDM, Agulló SB, Faùndes V, Jamuar SS, Lynch SA, Pintos-Morell G, Puri RD, Shai R, Steward CA, Tumiene B, Verloes A. Addressing diagnostic gaps and priorities of the global rare diseases community: Recommendations from the IRDiRC diagnostics scientific committee. Eur J Med Genet 2024; 70:104951. [PMID: 38848991 DOI: 10.1016/j.ejmg.2024.104951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 06/05/2024] [Indexed: 06/09/2024]
Abstract
The International Rare Diseases Research Consortium (IRDiRC) Diagnostic Scientific Committee (DSC) is charged with discussion and contribution to progress on diagnostic aspects of the IRDiRC core mission. Specifically, IRDiRC goals include timely diagnosis, use of globally coordinated diagnostic pipelines, and assessing the impact of rare diseases on affected individuals. As part of this mission, the DSC endeavored to create a list of research priorities to achieve these goals. We present a discussion of those priorities along with aspects of current, global rare disease needs and opportunities that support our prioritization. In support of this discussion, we also provide clinical vignettes illustrating real-world examples of diagnostic challenges.
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Affiliation(s)
- David R Adams
- National Human Genome Research Institute, National Institutes of Health, USA.
| | - Clara D M van Karnebeek
- Departments of Pediatrics and Human Genetics, Emma Center for Personalized Medicine, Amsterdam Gastro-enterology Endocrinology Metabolism, Amsterdam University Medical Centers, the Netherlands
| | - Sergi Beltran Agulló
- Centre Nacional d'Anàlisi Genòmica (CNAG), Spain; Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona (UB), Spain
| | - Víctor Faùndes
- Laboratorio de Genética y Enfermedades Metabólicas, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Chile
| | - Saumya Shekhar Jamuar
- Genetics Service, KK Women's and Children's Hospital and Paediatrics ACP, Duke-NUS Medical School, Singapore; Singhealth Duke-NUS Institute of Precision Medicine, Singapore
| | | | - Guillem Pintos-Morell
- Vall d'Hebron Research Institute (VHIR), Vall d'Hebron Barcelona Hospital, Spain; MPS-Spain Patient Advocacy Organization, Spain
| | - Ratna Dua Puri
- Institute of Medical Genetics and Genomics, Sir Ganga Ram Hospital, India
| | - Ruty Shai
- Pediatric Cancer Molecular Lab, Sheba Medical Center, Israel
| | | | - Biruté Tumiene
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Lithuania
| | - Alain Verloes
- Département de Génétique, CHU Paris - Hôpital Robert Debré, France
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2
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Schmidt A, Danyel M, Grundmann K, Brunet T, Klinkhammer H, Hsieh TC, Engels H, Peters S, Knaus A, Moosa S, Averdunk L, Boschann F, Sczakiel HL, Schwartzmann S, Mensah MA, Pantel JT, Holtgrewe M, Bösch A, Weiß C, Weinhold N, Suter AA, Stoltenburg C, Neugebauer J, Kallinich T, Kaindl AM, Holzhauer S, Bührer C, Bufler P, Kornak U, Ott CE, Schülke M, Nguyen HHP, Hoffjan S, Grasemann C, Rothoeft T, Brinkmann F, Matar N, Sivalingam S, Perne C, Mangold E, Kreiss M, Cremer K, Betz RC, Mücke M, Grigull L, Klockgether T, Spier I, Heimbach A, Bender T, Brand F, Stieber C, Morawiec AM, Karakostas P, Schäfer VS, Bernsen S, Weydt P, Castro-Gomez S, Aziz A, Grobe-Einsler M, Kimmich O, Kobeleva X, Önder D, Lesmann H, Kumar S, Tacik P, Basin MA, Incardona P, Lee-Kirsch MA, Berner R, Schuetz C, Körholz J, Kretschmer T, Di Donato N, Schröck E, Heinen A, Reuner U, Hanßke AM, Kaiser FJ, Manka E, Munteanu M, Kuechler A, Cordula K, Hirtz R, Schlapakow E, Schlein C, Lisfeld J, Kubisch C, Herget T, Hempel M, Weiler-Normann C, Ullrich K, Schramm C, Rudolph C, Rillig F, Groffmann M, Muntau A, Tibelius A, Schwaibold EMC, Schaaf CP, Zawada M, Kaufmann L, Hinderhofer K, Okun PM, Kotzaeridou U, Hoffmann GF, Choukair D, Bettendorf M, Spielmann M, Ripke A, Pauly M, Münchau A, Lohmann K, Hüning I, Hanker B, Bäumer T, Herzog R, Hellenbroich Y, Westphal DS, Strom T, Kovacs R, Riedhammer KM, Mayerhanser K, Graf E, Brugger M, Hoefele J, Oexle K, Mirza-Schreiber N, Berutti R, Schatz U, Krenn M, Makowski C, Weigand H, Schröder S, Rohlfs M, Vill K, Hauck F, Borggraefe I, Müller-Felber W, Kurth I, Elbracht M, Knopp C, Begemann M, Kraft F, Lemke JR, Hentschel J, Platzer K, Strehlow V, Abou Jamra R, Kehrer M, Demidov G, Beck-Wödl S, Graessner H, Sturm M, Zeltner L, Schöls LJ, Magg J, Bevot A, Kehrer C, Kaiser N, Turro E, Horn D, Grüters-Kieslich A, Klein C, Mundlos S, Nöthen M, Riess O, Meitinger T, Krude H, Krawitz PM, Haack T, Ehmke N, Wagner M. Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings. Nat Genet 2024; 56:1644-1653. [PMID: 39039281 PMCID: PMC11319204 DOI: 10.1038/s41588-024-01836-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 06/18/2024] [Indexed: 07/24/2024]
Abstract
Individuals with ultrarare disorders pose a structural challenge for healthcare systems since expert clinical knowledge is required to establish diagnoses. In TRANSLATE NAMSE, a 3-year prospective study, we evaluated a novel diagnostic concept based on multidisciplinary expertise in Germany. Here we present the systematic investigation of the phenotypic and molecular genetic data of 1,577 patients who had undergone exome sequencing and were partially analyzed with next-generation phenotyping approaches. Molecular genetic diagnoses were established in 32% of the patients totaling 370 distinct molecular genetic causes, most with prevalence below 1:50,000. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were identified, mainly in individuals with neurodevelopmental disorders. Sequencing data of the subcohort that consented to computer-assisted analysis of their facial images with GestaltMatcher could be prioritized more efficiently compared with approaches based solely on clinical features and molecular scores. Our study demonstrates the synergy of using next-generation sequencing and phenotyping for diagnosing ultrarare diseases in routine healthcare and discovering novel etiologies by multidisciplinary teams.
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Affiliation(s)
- Axel Schmidt
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Magdalena Danyel
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- BIH Charité Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kathrin Grundmann
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Theresa Brunet
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
- Institut für Medizinische Biometrie, Informatik und Epidemiologie, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Tzung-Chien Hsieh
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Hartmut Engels
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Sophia Peters
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Alexej Knaus
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Shahida Moosa
- Institute for Medical Genetics, Stellenbosch University, Cape Town, South Africa
| | - Luisa Averdunk
- Department of Pediatrics, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Felix Boschann
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- BIH Charité Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Henrike Lisa Sczakiel
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- BIH Charité Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sarina Schwartzmann
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Martin Atta Mensah
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- BIH Charité Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jean Tori Pantel
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
| | - Manuel Holtgrewe
- Core Uni Bioinformatics, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Annemarie Bösch
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia Weiß
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Natalie Weinhold
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Aude-Annick Suter
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Corinna Stoltenburg
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Julia Neugebauer
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tillmann Kallinich
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Angela M Kaindl
- Department of Pediatric Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Center for Chronically Sick Children, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute of Cell and Neurobiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Susanne Holzhauer
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Bührer
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Philip Bufler
- Department of Pediatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Uwe Kornak
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claus-Eric Ott
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Schülke
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Sabine Hoffjan
- Department of Human Genetics, Ruhr University Bochum, Bochum, Germany
| | - Corinna Grasemann
- Department of Pediatrics Bochum and CeSER, Ruhr University Bochum, Bochum, Germany
| | - Tobias Rothoeft
- Department of Pediatrics Bochum and CeSER, Ruhr University Bochum, Bochum, Germany
| | - Folke Brinkmann
- Department of Pediatrics Bochum and CeSER, Ruhr University Bochum, Bochum, Germany
| | - Nora Matar
- Department of Pediatrics Bochum and CeSER, Ruhr University Bochum, Bochum, Germany
| | - Sugirthan Sivalingam
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Claudia Perne
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Elisabeth Mangold
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Martina Kreiss
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Kirsten Cremer
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Regina C Betz
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Martin Mücke
- Center for Rare Diseases, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Lorenz Grigull
- Center for Rare Diseases, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Thomas Klockgether
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Isabel Spier
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - André Heimbach
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Tim Bender
- Center for Rare Diseases, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Fabian Brand
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Christiane Stieber
- Center for Rare Diseases, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Alexandra Marzena Morawiec
- Center for Rare Diseases, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Pantelis Karakostas
- Clinic for Internal Medicine III, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Valentin S Schäfer
- Clinic for Internal Medicine III, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Sarah Bernsen
- Center for Rare Diseases, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Patrick Weydt
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Sergio Castro-Gomez
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Ahmad Aziz
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Marcus Grobe-Einsler
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Okka Kimmich
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Xenia Kobeleva
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Demet Önder
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Hellen Lesmann
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Sheetal Kumar
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Pawel Tacik
- Department of Neurology, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Meghna Ahuja Basin
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Pietro Incardona
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Min Ae Lee-Kirsch
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Reinhard Berner
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Catharina Schuetz
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Julia Körholz
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Tanita Kretschmer
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Nataliya Di Donato
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Evelin Schröck
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - André Heinen
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Ulrike Reuner
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
- Department of Neurology, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Amalia-Mihaela Hanßke
- University Center for Rare Diseases, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Frank J Kaiser
- Institute of Human Genetics, University Hospital Essen, Essen, Germany
| | - Eva Manka
- Department of Pediatrics II, University Hospital Essen, Essen, Germany
| | - Martin Munteanu
- Institute of Human Genetics, University Hospital Essen, Essen, Germany
| | - Alma Kuechler
- Institute of Human Genetics, University Hospital Essen, Essen, Germany
| | - Kiewert Cordula
- Department of Pediatrics II, University Hospital Essen, Essen, Germany
| | - Raphael Hirtz
- Department of Pediatrics II, University Hospital Essen, Essen, Germany
| | - Elena Schlapakow
- Department of Neurology, University Hospital Halle, Halle, Germany
| | - Christian Schlein
- Institute of Human Genetics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Jasmin Lisfeld
- Institute of Human Genetics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Kubisch
- Institute of Human Genetics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Theresia Herget
- Institute of Human Genetics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Maja Hempel
- Institute of Human Genetics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Christina Weiler-Normann
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Kurt Ullrich
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Schramm
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Cornelia Rudolph
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Franziska Rillig
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Maximilian Groffmann
- Martin Zeitz Center for Rare Diseases, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Ania Muntau
- Department of Pediatrics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | | | | | | | - Michal Zawada
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Lilian Kaufmann
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | | | - Pamela M Okun
- Center for Child and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Urania Kotzaeridou
- Center for Child and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Georg F Hoffmann
- Center for Child and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Daniela Choukair
- Center for Child and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Markus Bettendorf
- Center for Child and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Malte Spielmann
- Institute of Human Genetics, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Annekatrin Ripke
- Center for Rare Diseases, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Martje Pauly
- Department of Neurology, University Hospital Schleswig-Holstein, Lübeck, Germany
- Institute for Neurogenetics, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Alexander Münchau
- Center for Rare Diseases, University Hospital Schleswig-Holstein, Lübeck, Germany
- Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany
| | - Katja Lohmann
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Irina Hüning
- Institute of Human Genetics, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Britta Hanker
- Institute of Human Genetics, University of Lübeck, Lübeck, Germany
| | - Tobias Bäumer
- Center for Rare Diseases, University Hospital Schleswig-Holstein, Lübeck, Germany
- Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany
| | - Rebecca Herzog
- Center for Rare Diseases, University Hospital Schleswig-Holstein, Lübeck, Germany
- Department of Neurology, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Yorck Hellenbroich
- Department of Human Genetics, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Dominik S Westphal
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Tim Strom
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Reka Kovacs
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Korbinian M Riedhammer
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
- Department of Nephrology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Katharina Mayerhanser
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Elisabeth Graf
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Melanie Brugger
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Julia Hoefele
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Konrad Oexle
- Institute of Neurogenomics, Helmholtz Zentrum München, München, Germany
| | | | - Riccardo Berutti
- Institute of Neurogenomics, Helmholtz Zentrum München, München, Germany
| | - Ulrich Schatz
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Martin Krenn
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
- Department of Neurology, Medical University of Vienna, Wien, Austria
| | - Christine Makowski
- Department of Paediatrics, Adolescent Medicine and Neonatology, München, Germany
| | - Heike Weigand
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
| | - Sebastian Schröder
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
| | - Meino Rohlfs
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
| | - Katharina Vill
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
| | - Fabian Hauck
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
| | - Ingo Borggraefe
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
| | | | - Ingo Kurth
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
| | - Miriam Elbracht
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
| | - Cordula Knopp
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
| | - Matthias Begemann
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
| | - Florian Kraft
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
| | - Johannes R Lemke
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
- Center for Rare Diseases, University of Leipzig Medical Center, Leipzig, Germany
| | - Julia Hentschel
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Vincent Strehlow
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Rami Abou Jamra
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Martin Kehrer
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - German Demidov
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Stefanie Beck-Wödl
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Holm Graessner
- Center for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Marc Sturm
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Lena Zeltner
- Center for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Ludger J Schöls
- Department of Neurology, University of Tübingen, Tübingen, Germany
| | - Janine Magg
- Center for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Andrea Bevot
- Department of Pediatric Neurology and Developmental Medicine, University of Tübingen, Tübingen, Germany
| | - Christiane Kehrer
- Department of Pediatric Neurology and Developmental Medicine, University of Tübingen, Tübingen, Germany
| | - Nadja Kaiser
- Department of Pediatric Neurology and Developmental Medicine, University of Tübingen, Tübingen, Germany
| | - Ernest Turro
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Denise Horn
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Christoph Klein
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
| | - Stefan Mundlos
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Nöthen
- Institute of Human Genetics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany
| | - Olaf Riess
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Heiko Krude
- Berlin Centre for Rare Diseases, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter M Krawitz
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Medical Faculty and University Hospital Bonn, Bonn, Germany.
| | - Tobias Haack
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Nadja Ehmke
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- BIH Charité Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Matias Wagner
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, München, Germany
- Dr. von Hauner Children's Hospital, University Hospital Munich, München, Germany
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3
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Record CJ, Reilly MM. Lessons and pitfalls of whole genome sequencing. Pract Neurol 2024; 24:263-274. [PMID: 38548322 DOI: 10.1136/pn-2023-004083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2024] [Indexed: 07/18/2024]
Abstract
Whole-genome sequencing (WGS) has recently become the first-line genetic investigation for many suspected genetic neurological disorders. While its diagnostic capabilities are innumerable, as with any test, it has its limitations. Clinicians should be aware of where WGS is extremely reliable (detecting single-nucleotide variants), where its reliability is much improved (detecting copy number variants and small repeat expansions) and where it may miss/misinterpret a variant (large repeat expansions, balanced structural variants or low heteroplasmy mitochondrial DNA variants). Bioinformatic technology and virtual gene panels are constantly evolving, and it is important to know what genes and what types of variant are being tested; the current National Health Service Genomic Medicine Service WGS offers more than early iterations of the 100 000 Genomes Project analysis. Close communication between clinician and laboratory, ideally through a multidisciplinary team meeting, is encouraged where there is diagnostic uncertainty.
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Affiliation(s)
- Christopher J Record
- Centre for Neuromuscular Diseases, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Mary M Reilly
- Centre for Neuromuscular Diseases, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
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4
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Kakar N, Rehman FU, Kaur R, Bhavani GS, Goyal M, Shah H, Kaur K, Sodhi KS, Kubisch C, Borck G, Panigrahi I, Girisha KM, Kornak U, Spielmann M. Multi-gene panel sequencing in highly consanguineous families and patients with congenital forms of skeletal dysplasias. Clin Genet 2024; 106:47-55. [PMID: 38378010 DOI: 10.1111/cge.14509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
Skeletal dysplasias (SKDs) are a heterogeneous group of more than 750 genetic disorders characterized by abnormal development, growth, and maintenance of bones or cartilage in the human skeleton. SKDs are often caused by variants in early patterning genes and in many cases part of multiple malformation syndromes and occur in combination with non-skeletal phenotypes. The aim of this study was to investigate the underlying genetic cause of congenital SKDs in highly consanguineous Pakistani families, as well as in sporadic and familial SKD cases from India using multigene panel sequencing analysis. Therefore, we performed panel sequencing of 386 bone-related genes in 7 highly consanguineous families from Pakistan and 27 cases from India affected with SKDs. In the highly consanguineous families, we were able to identify the underlying genetic cause in five out of seven families, resulting in a diagnostic yield of 71%. Whereas, in the sporadic and familial SKD cases, we identified 12 causative variants, corresponding to a diagnostic yield of 44%. The genetic heterogeneity in our cohorts was very high and we were able to detect various types of variants, including missense, nonsense, and frameshift variants, across multiple genes known to cause different types of SKDs. In conclusion, panel sequencing proved to be a highly effective way to decipher the genetic basis of SKDs in highly consanguineous families as well as sporadic and or familial cases from South Asia. Furthermore, our findings expand the allelic spectrum of skeletal dysplasias.
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Affiliation(s)
- Naseebullah Kakar
- Institut für Humangenetik, Universitätsklinikum Schleswig-Holstein, University of Lübeck and University of Kiel, Lübeck, Germany
- Department of Biotechnology, BUITEMS, Quetta, Pakistan
- Institute of Human Genetics, Ulm University, Ulm, Germany
| | - Fazal Ur Rehman
- Department of Pathology, Bolan Medical College, Quetta, Pakistan
| | - Ramandeep Kaur
- Department of Pediatrics, APC, PGIMER, Chandigarh, India
| | - Gandham SriLakshmi Bhavani
- Department of Medical Genetics, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Manisha Goyal
- Pediatrics Genetic & Research Laboratory, Department of Pediatrics, Lok Nayak Hospital, New Delhi, India
| | - Hitesh Shah
- Department of Pediatric Orthopedics, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Karandeep Kaur
- Department of Pediatrics, APC, PGIMER, Chandigarh, India
| | | | - Christian Kubisch
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Guntram Borck
- Institute of Human Genetics, Ulm University, Ulm, Germany
| | | | - Katta Mohan Girisha
- Department of Medical Genetics, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Uwe Kornak
- Institute of Human Genetics, University Medical Center Göttingen, Göttingen, Germany
| | - Malte Spielmann
- Institut für Humangenetik, Universitätsklinikum Schleswig-Holstein, University of Lübeck and University of Kiel, Lübeck, Germany
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5
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Bridges Y, de Souza V, Cortes KG, Haendel M, Harris NL, Korn DR, Marinakis NM, Matentzoglu N, McLaughlin JA, Mungall CJ, Osumi-Sutherland D, Robinson PN, Smedley D, Jacobsen JO. Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598672. [PMID: 38915571 PMCID: PMC11195176 DOI: 10.1101/2024.06.13.598672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Background Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools. Results In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets. Conclusions PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.
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Affiliation(s)
- Yasemin Bridges
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Katherina G Cortes
- School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Melissa Haendel
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Daniel R Korn
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Nikolaos M Marinakis
- Laboratory of Medical Genetics, National and Kapodistrian University of Athens, Athens, 11527, Greece
| | | | - James A McLaughlin
- Samples, Phenotypes, and Ontologies (SPOT), European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Peter N Robinson
- Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, 10117, Germany
| | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Julius Ob Jacobsen
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
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6
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Danis D, Bamshad MJ, Bridges Y, Cacheiro P, Carmody LC, Chong JX, Coleman B, Dalgleish R, Freeman PJ, Graefe ASL, Groza T, Jacobsen JOB, Klocperk A, Kusters M, Ladewig MS, Marcello AJ, Mattina T, Mungall CJ, Munoz-Torres MC, Reese JT, Rehburg F, Reis BCS, Schuetz C, Smedley D, Strauss T, Sundaramurthi JC, Thun S, Wissink K, Wagstaff JF, Zocche D, Haendel MA, Robinson PN. A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24308104. [PMID: 38854034 PMCID: PMC11160806 DOI: 10.1101/2024.05.29.24308104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.
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Affiliation(s)
- Daniel Danis
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael J Bamshad
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA
- Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle WA 98195, USA
- Department of Pediatrics, Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98195, USA
| | - Yasemin Bridges
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Pilar Cacheiro
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Leigh C Carmody
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
| | - Jessica X Chong
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA
- Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle WA 98195, USA
| | - Ben Coleman
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
| | - Raymond Dalgleish
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Peter J Freeman
- Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - Adam S L Graefe
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tudor Groza
- Rare Care Centre, Perth Children's Hospital, Nedlands, WA 6009, Australia
- SingHealth Duke-NUS Institute of Precision Medicine, 5 Hospital Drive Level 9, Singapore 169609, Singapore
- Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Julius O B Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Adam Klocperk
- Department of Immunology, 2nd Faculty of Medicine, Charles University and University Hospital in Motol, Prague, Czech Republic
| | - Maaike Kusters
- Department of Paediatric Immunology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- University College London Institute of Child Health, London, United Kingdom
| | - Markus S Ladewig
- Department of Ophthalmology, University Clinic Marburg - Campus Fulda, Fulda, Germany
| | - Anthony J Marcello
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA
| | - Teresa Mattina
- Medica Genetics University of Catania Italy
- Morgagni foundation and Clinic, Catania, Italy
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Monica C Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Ccampus
| | - Justin T Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Filip Rehburg
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Bárbara C S Reis
- Department of Immunology, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil
- High Complexity Laboratory, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil
| | - Catharina Schuetz
- Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Timmy Strauss
- Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | - Sylvia Thun
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kyran Wissink
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Utrecht University, Utrecht, the Netherlands
| | | | - David Zocche
- North West Thames Regional Genetics Service, Northwick Park & St Mark's Hospitals, London, UK
| | | | - Peter N Robinson
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
- ELLIS-European Laboratory for Learning and Intelligent Systems
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7
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Althagafi A, Zhapa-Camacho F, Hoehndorf R. Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning. Bioinformatics 2024; 40:btae301. [PMID: 38696757 PMCID: PMC11132820 DOI: 10.1093/bioinformatics/btae301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/05/2024] [Accepted: 04/30/2024] [Indexed: 05/04/2024] Open
Abstract
MOTIVATION Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability. RESULTS We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information. AVAILABILITY AND IMPLEMENTATION EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.
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Affiliation(s)
- Azza Althagafi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Fernando Zhapa-Camacho
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence, King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
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8
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Lee B, Nasanovsky L, Shen L, Maglinte DT, Pan Y, Gai X, Schmidt RJ, Raca G, Biegel JA, Roytman M, An P, Saunders CJ, Farrow EG, Shams S, Ji J. Significance Associated with Phenotype Score Aids in Variant Prioritization for Exome Sequencing Analysis. J Mol Diagn 2024; 26:337-348. [PMID: 38360210 DOI: 10.1016/j.jmoldx.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 12/04/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduced a novel metric Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing an individual's observed phenotypes against existing gene-phenotype associations. To evaluate the SAP score, a retrospective analysis was performed on 219 exomes. Among them, 82 family-based and 35 singleton exomes had at least one disease-causing variant that explained the patient's clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared with a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77 of 82) of the family-based exomes and in first place for 73% of these cases. For singleton exomes, the SAP score analysis ranked the known pathogenic variants within the top 10 for 80% (28 of 35) of cases. The SAP score, which is independent of detected variants, demonstrates comparable performance with Exomiser, which considers both phenotype and variant-level evidence simultaneously. Among 102 cases with negative results or variants of uncertain significance, SAP score analysis revealed two cases with a potential new diagnosis based on rank. The SAP score, a phenotypic quantitative metric, can be used in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis.
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Affiliation(s)
- Brian Lee
- Bionano Genomics, San Diego, California
| | | | - Lishuang Shen
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California
| | - Dennis T Maglinte
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California
| | - Yachen Pan
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California
| | - Xiaowu Gai
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California; Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Ryan J Schmidt
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California; Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Gordana Raca
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California; Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Jaclyn A Biegel
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California; Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | | | - Paul An
- Bionano Genomics, San Diego, California
| | - Carol J Saunders
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, Missouri; University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Emily G Farrow
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, Missouri; University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | | | - Jianling Ji
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California; Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California.
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9
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Werren EA, Kalsner L, Ewald J, Peracchio M, King C, Vats P, Audano PA, Robinson PN, Adams MD, Kelly MA, Matson AP. A de novo variant in PAK2 detected in an individual with Knobloch type 2 syndrome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590108. [PMID: 38712026 PMCID: PMC11071314 DOI: 10.1101/2024.04.18.590108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
P21-activated kinase 2 (PAK2) is a serine/threonine kinase essential for a variety of cellular processes including signal transduction, cellular survival, proliferation, and migration. A recent report proposed monoallelic PAK2 variants cause Knobloch syndrome type 2 (KNO2)-a developmental disorder primarily characterized by ocular anomalies. Here, we identified a novel de novo heterozygous missense variant in PAK2, NM_002577.4:c.1273G>A, p.(D425N), by whole genome sequencing in an individual with features consistent with KNO2. Notable clinical phenotypes include global developmental delay, congenital retinal detachment, mild cerebral ventriculomegaly, hypotonia, FTT, pyloric stenosis, feeding intolerance, patent ductus arteriosus, and mild facial dysmorphism. The p.(D425N) variant lies within the protein kinase domain and is predicted to be functionally damaging by in silico analysis. Previous clinical genetic testing did not report this variant due to unknown relevance of PAK2 variants at the time of testing, highlighting the importance of reanalysis. Our findings also substantiate the candidacy of PAK2 variants in KNO2 and expand the KNO2 clinical spectrum.
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Affiliation(s)
- Elizabeth A Werren
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, 06032, USA
| | - Louisa Kalsner
- Connecticut Children's Medical Center, Hartford, CT 06106, USA
| | - Jessica Ewald
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, 06032, USA
| | | | - Cameron King
- Connecticut Children's Medical Center, Hartford, CT 06106, USA
| | - Purva Vats
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, 06032, USA
| | - Peter A Audano
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, 06032, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, 06032, USA
| | - Mark D Adams
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, 06032, USA
| | - Melissa A Kelly
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, 06032, USA
| | - Adam P Matson
- Connecticut Children's Medical Center, Hartford, CT 06106, USA
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10
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Durmaz D, Aslanger AD, Yavas Abali Z, Yilmaz Y, Karaman V, Yesil Sayin G, Toksoy G, Unuvar A, Uyguner ZO. A Rare Inherited Bone Marrow Failure Syndrome Disclosed by Reanalysis of the Exome Data of a Patient Evaluated for Cytopenia and Dysmorphic Features. J Pediatr Hematol Oncol 2024; 46:e214-e219. [PMID: 38408162 PMCID: PMC10956657 DOI: 10.1097/mph.0000000000002839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/27/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Multisystemic findings of inherited bone marrow failure syndromes may cause difficulty in diagnosis. Exome sequencing (ES) helps to define the etiology of rare diseases and reanalysis offers a valuable new diagnostic approach. Herein, we present the clinical and molecular characteristics of a girl who was referred for cytopenia and frequent infections. CASE REPORT A 5-year-old girl with cytopenia, dysmorphism, short stature, developmental delay, and myopia was referred for genetic counseling. Reanalysis of the ES data revealed a homozygous splice-site variant in the DNAJC21 (NM_001012339.3:c.983+1G>A), causing Shwachman-Diamond Syndrome (SDS). It was shown by the RNA sequencing that exon 7 was skipped, causing an 88-nucleotide deletion. CONCLUSIONS Precise genetic diagnosis enables genetic counseling and improves patient management by avoiding inappropriate treatment and unnecessary testing. This report would contribute to the clinical and molecular understanding of this rare type of SDS caused by DNAJC21 variants and expand the phenotypic features of this condition.
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Affiliation(s)
- Durmus Durmaz
- Department of Medical Genetics, Istanbul Faculty of Medicine
| | | | - Zehra Yavas Abali
- Department of Medical Genetics, Istanbul Faculty of Medicine
- Institute of Health Sciences
| | - Yasin Yilmaz
- Division of Pediatric Hematology and Oncology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Volkan Karaman
- Department of Medical Genetics, Istanbul Faculty of Medicine
| | | | - Guven Toksoy
- Department of Medical Genetics, Istanbul Faculty of Medicine
| | - Aysegul Unuvar
- Division of Pediatric Hematology and Oncology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
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Gold J, Kripke CM, Drivas TG. Universal Exome Sequencing in Critically Ill Adults: A Diagnostic Yield of 25% and Race-Based Disparities in Access to Genetic Testing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.11.24304088. [PMID: 38559092 PMCID: PMC10980115 DOI: 10.1101/2024.03.11.24304088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Numerous studies have underscored the diagnostic and therapeutic potential of exome or genome sequencing in critically ill pediatric populations. However, an equivalent investigation in critically ill adults remains conspicuously absent. We retrospectively analyzed whole exome sequencing (WES) data available through the PennMedicine Biobank (PMBB) from all 365 young adult patients, aged 18-40 years, with intensive care unit (ICU) admissions at the University of Pennsylvania Health System who met inclusion criteria for our study. For each participant, two Medical Genetics and Internal Medicine-trained clinicians reviewed WES reports and patient charts for variant classification, result interpretation, and identification of genetic diagnoses related to their critical illness. Of the 365 individuals in our study, 90 (24.7%) were found to have clearly diagnostic results on WES; an additional 40 (11.0%) had a suspicious variant of uncertain significance (VUS) identified; and an additional 16 (4.4%) had a medically actionable incidental finding. The diagnostic rate of exome sequencing did not decrease with increasing patient age. Affected genes were primarily involved in cardiac function (18.8%), vascular health (16.7%), cancer (16.7%), and pulmonary disease (11.5%). Only half of all diagnostic findings were known and documented in the patient chart at the time of ICU admission. Significant disparities emerged in subgroup analysis by EHR-reported race, with genetic diagnoses known/documented for 63.5% of White patients at the time of ICU admission but only for 28.6% of Black or Hispanic patients. There was a trend towards patients with undocumented genetic diagnoses having a 66% increased mortality rate, making these race-based disparities in genetic diagnosis even more concerning. Altogether, universal exome sequencing in ICU-admitted adult patients was found to yield a new definitive diagnosis in 11.2% of patients. Of these diagnoses, 76.6% conferred specific care-altering medical management recommendations. Our study suggests that the diagnostic utility of exome sequencing in critically ill young adults is similar to that observed in neonatal and pediatric populations and is age-independent. The high diagnostic rate and striking race-based disparities we find in genetic diagnoses argue for broad and universal approaches to genetic testing for critically ill adults. The widespread implementation of comprehensive genetic sequencing in the adult population promises to enhance medical care for all individuals and holds the potential to rectify disparities in genetic testing referrals, ultimately promoting more equitable healthcare delivery.
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Affiliation(s)
- Jessica Gold
- Division of Clinical Genetics, Department of Pediatrics, Northwell Health, Great Neck, NY 11021, USA
| | - Colleen M. Kripke
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
| | | | | | - Theodore G. Drivas
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
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12
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Gravel B, Renaux A, Papadimitriou S, Smits G, Nowé A, Lenaerts T. Prioritization of oligogenic variant combinations in whole exomes. Bioinformatics 2024; 40:btae184. [PMID: 38603604 PMCID: PMC11037482 DOI: 10.1093/bioinformatics/btae184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/29/2024] [Accepted: 04/10/2024] [Indexed: 04/13/2024] Open
Abstract
MOTIVATION Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion. RESULTS We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores. AVAILABILITY AND IMPLEMENTATION Hop is available at https://github.com/oligogenic/HOP.
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Affiliation(s)
- Barbara Gravel
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Alexandre Renaux
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Sofia Papadimitriou
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Brussels Interuniversity Genomics High Throughput core (BRIGHTcore), UZ Brussel, Vrije Universiteit Brussel (VUB) - Université Libre de Bruxelles (ULB), 1090 Brussels, Belgium
| | - Guillaume Smits
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Center of Human Genetics, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Ann Nowé
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
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13
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Kader MA, Devarajan B, Vijayan S, Ramakrishnan R, Sundaresan P, Uduman MS, Krishnadas SR, Kuppamuthu D. Myocilin Mutation N480K Leads to Early Onset Juvenile and Adult-onset Primary Open Angle Glaucoma in a Six Generation Family. J Glaucoma 2024; 33:218-224. [PMID: 37670504 DOI: 10.1097/ijg.0000000000002286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/29/2023] [Indexed: 09/07/2023]
Abstract
PRCIS A pathogenic autosomal dominant MYOC mutation N480K detected in 6 generations of an Indian family is primarily responsible for juvenile open angle glaucoma (JOAG) and adult-onset primary open angle glaucoma (POAG), emphasizing the importance of screening this mutation at a younger age. PURPOSE To screen myocilin mutations in a large South Indian family with early-onset JOAG and adult-onset POAG. METHODS In a large South Indian family with 20 members, 8 members diagnosed as JOAG, 7 members as POAG, 4 members as JOAG suspect, and 1 member as POAG suspect were screened for myocilin ( MYOC) mutations using Sanger sequencing. Whole exome sequencing was performed on clinically suspected JOAG/POAG individuals. RESULTS Myocilin gene mutation N480K (c.1440C>G) was detected in 20 family members, including proband, of whom 8 were JOAG and 7 were POAG patients, 3 were JOAG suspects, and 2 were unaffected. Among the unaffected carriers, 1 was less than 5 years old, and another was 25 years old. The earliest to develop the disease was a 10-year-old child. The penetrance of the mutation was 95% over 10 years of age. This family had JOAG/POAG suspects with no N480K MYOC mutation, and they were further screened for other mutations using whole-exome sequencing. Polymorphisms CYP1B1 L432V and MYOC R76K were detected in 3 JOAG/POAG suspects, and among these 3, one had another CYP1B1 polymorphic variant R368H. The presence of the CYP1B1 polymorphism along with an MYOC polymorphic variant among the JOAG/POAG suspects needs additional studies to explore their combined role in the onset of glaucoma. CONCLUSIONS This study reveals that MYOC mutation is primarily responsible for JOAG and adult-onset POAG in a family, emphasizing the importance of screening for this mutation at a younger age for early treatment.
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Affiliation(s)
| | | | - Saravanan Vijayan
- Department of Genetics, Aravind Medical Research Foundation, Madurai
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14
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Yang J, Liu C, Deng W, Wu D, Weng C, Zhou Y, Wang K. Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT. PATTERNS (NEW YORK, N.Y.) 2024; 5:100887. [PMID: 38264716 PMCID: PMC10801236 DOI: 10.1016/j.patter.2023.100887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 01/25/2024]
Abstract
To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models-PhenoBCBERT and PhenoGPT-for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models to automate the detection of phenotype terms, including those not in the current HPO. We compare these models with PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also show strong performance in case studies on biomedical literature. We evaluate the strengths and weaknesses of BERT- and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.
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Affiliation(s)
- Jingye Yang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Wendy Deng
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Da Wu
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Yunyun Zhou
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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15
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Canavati C, Sherill-Rofe D, Kamal L, Bloch I, Zahdeh F, Sharon E, Terespolsky B, Allan IA, Rabie G, Kawas M, Kassem H, Avraham KB, Renbaum P, Levy-Lahad E, Kanaan M, Tabach Y. Using multi-scale genomics to associate poorly annotated genes with rare diseases. Genome Med 2024; 16:4. [PMID: 38178268 PMCID: PMC10765705 DOI: 10.1186/s13073-023-01276-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 12/15/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes. METHODS EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool. RESULTS In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes. CONCLUSIONS We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .
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Affiliation(s)
- Christina Canavati
- Department of Developmental Biology and Cancer Research, Institute of Medical Research - Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel
- Molecular Genetics Lab, Istishari Arab Hospital, Ramallah, Palestine
| | - Dana Sherill-Rofe
- Department of Developmental Biology and Cancer Research, Institute of Medical Research - Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel
| | - Lara Kamal
- Molecular Genetics Lab, Istishari Arab Hospital, Ramallah, Palestine
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Idit Bloch
- Department of Developmental Biology and Cancer Research, Institute of Medical Research - Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel
| | - Fouad Zahdeh
- Medical Genetics Institute, Shaare Zedek Medical Center, Jerusalem, 91031, Israel
| | - Elad Sharon
- Department of Developmental Biology and Cancer Research, Institute of Medical Research - Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel
| | - Batel Terespolsky
- Department of Developmental Biology and Cancer Research, Institute of Medical Research - Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel
- Medical Genetics Institute, Shaare Zedek Medical Center, Jerusalem, 91031, Israel
| | - Islam Abu Allan
- Molecular Genetics Lab, Istishari Arab Hospital, Ramallah, Palestine
| | - Grace Rabie
- Hereditary Research Laboratory and Department of Life Sciences, Bethlehem University, Bethlehem, 72372, Palestine
| | - Mariana Kawas
- Hereditary Research Laboratory and Department of Life Sciences, Bethlehem University, Bethlehem, 72372, Palestine
| | - Hanin Kassem
- Molecular Genetics Lab, Istishari Arab Hospital, Ramallah, Palestine
| | - Karen B Avraham
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Paul Renbaum
- Medical Genetics Institute, Shaare Zedek Medical Center, Jerusalem, 91031, Israel
| | - Ephrat Levy-Lahad
- Medical Genetics Institute, Shaare Zedek Medical Center, Jerusalem, 91031, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel
| | - Moien Kanaan
- Molecular Genetics Lab, Istishari Arab Hospital, Ramallah, Palestine
- Hereditary Research Laboratory and Department of Life Sciences, Bethlehem University, Bethlehem, 72372, Palestine
| | - Yuval Tabach
- Department of Developmental Biology and Cancer Research, Institute of Medical Research - Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel.
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16
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Shen L, Falk MJ, Gai X. MSeqDR Quick-Mitome (QM): Combining Phenotype-Guided Variant Interpretation and Machine Learning Classifiers to Aid Primary Mitochondrial Disease Genetic Diagnosis. Curr Protoc 2024; 4:e955. [PMID: 38284225 PMCID: PMC11046528 DOI: 10.1002/cpz1.955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
The international Mitochondrial Disease Sequence Data Resource Consortium (MSeqDR) Quick-Mitome (QM) is a web-based platform enabling automated variant interpretation of whole-exome sequencing (WES) datasets for the genetic diagnosis of primary mitochondrial diseases (PMD). Designed specifically to address the unique dual genome nature of PMD etiologies, QM includes features for both nuclear and mitochondrial DNA (mtDNA) genome analysis. QM requires VCF variant lists, HPO ID clinical phenotypes, and pedigree files for multiple-sample VCF inputs. QM maps phenotypes to HPO terms before analysis. QM analysis requires 2 to 20 min for 100,000 variants on an 8-vCPU AWS server using Exomiser's "PASS_ONLY" mode for nuclear variants. QM ranks variants based on allele frequency, phenotype-gene association, functional impact, and inheritance mode. Variants are further annotated with multiple data sources such as OMIM, ClinVar, dbNSFP, gnoMAD, MITOMAP, and MSeqDR. In addition to standard Exomiser results, QM generates an Analysis Report and QM Integrated Report with add-on mtDNA-specific analyses, including haplogroup prediction with Phy-Mer, heteroplasmy calculation, and mvTool annotations. We developed the Mitochondrial Disease Variant (MDV) classifier using XGBoost to predict variant pathogenicity for PMD. The MDV classifier was trained on >120 features and performance benchmarking showed that it correctly classified >98% of nuclear gene variants as being pathogenic or benign, and predicted PMD-causing variants with 94% precision. The MSeqDR QM server is an open-access resource for phenotype-driven dual-genome analyses for PMD diagnosis by the global mitochondrial disease community. It is publicly available for non-commercial, non-clinical research use at https://mseqdr.org/quickmitome.php. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Standardizing clinical phenotypes into human phenotype ontology (HPO) terms as the phenotype input for Quick-Mitome (QM) Basic Protocol 2: Prepare the pedigree input for multiple-sample VCF Basic Protocol 3: Quick-Mitome (QM) analysis Basic Protocol 4: Reviewing and understanding the QM Integrated Report and Analysis Report.
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Affiliation(s)
- Lishuang Shen
- Center for Personalized Medicine, Department of Pathology & Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, California, USA
| | - Marni J. Falk
- Mitochondrial Medicine Frontier Program, Division of Human Genetics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Xiaowu Gai
- Center for Personalized Medicine, Department of Pathology & Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, California, USA
- Keck School of Medicine, University of Southern California, California, USA
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17
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Borrell A, Ordoñez E, Pauta M, Otaño J, Paz-y-Miño F, de Almeida M, León M, Cirigliano V. Prenatal Exome Sequencing Analysis in Fetuses with Various Ultrasound Findings. J Clin Med 2023; 13:181. [PMID: 38202188 PMCID: PMC10780147 DOI: 10.3390/jcm13010181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
OBJECTIVES To evaluate the use of Exome Sequencing (ES) for the detection of genome-wide Copy Number Variants (CNVs) and the frequency of SNVs-InDels in selected genes related to developmental disorders in a cohort of consecutive pregnancies undergoing invasive diagnostic procedures for minor or simple ultrasound findings with no indication of ES. METHODS Women undergoing invasive diagnostic testing (chorionic villus sampling or amniocentesis) for QF-PCR and chromosomal microarray analysis (CMA) due to prenatal ultrasound findings without an indication for ES were selected over a five-month period (May-September 2021). ES was performed to compare the efficiency of genome-wide CNV detection against CMA analysis and to detect monogenic disorders. Virtual gene panels were selected to target genes related to ultrasound findings and bioinformatic analysis was performed, prioritizing variants based on the corresponding HPO terms. The broad Fetal Gene panel for developmental disorders developed by the PAGE group was also included in the analysis. RESULTS A total of 59 out of 61 women consented to participate in this study. There were 36 isolated major fetal anomalies, 11 aneuploidy markers, 6 minor fetal anomalies, 4 multiple anomalies, and 2 other ultrasound signs. Following QF-PCR analysis, two uncultured samples were excluded from this study, and six (10%) common chromosome aneuploidies were detected. In the remaining 51 cases, no pathogenic CNVs were detected at CMA, nor were any pathogenic variants observed in gene panels only targeting the ultrasound indications. Two (3.9%) monogenic diseases, apparently unrelated to the fetal phenotype, were detected: blepharo-cheilo-odontic syndrome (spina bifida) and Duchenne muscular dystrophy (pyelocaliceal dilation). CONCLUSIONS In our series of pregnancies with ultrasound findings, common aneuploidies were the only chromosomal abnormalities present, which were detected in 10% of cases. ES CNV analysis was concordant with CMA results in all cases. No additional findings were provided by only targeting selected genes based on ultrasound findings. Broadening the analysis to a larger number of genes involved in fetal developmental disorders revealed monogenic diseases in 3.9% of cases, which, although apparently not directly related to the indications, were clinically relevant.
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Affiliation(s)
- Antoni Borrell
- Barcelona Centre for Maternal-Fetal and Neonatal Medicine (BCNatal), Hospital Clínic Barcelona, Universitat de Barcelona, 08007 Barcelona, Spain; (J.O.); (F.P.-y.-M.)
| | - Elena Ordoñez
- Veritas Genetics, c/Zamora 46-48, 08005 Barcelona, Spain; (E.O.); (M.d.A.); (M.L.); (V.C.)
| | - Montse Pauta
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Juan Otaño
- Barcelona Centre for Maternal-Fetal and Neonatal Medicine (BCNatal), Hospital Clínic Barcelona, Universitat de Barcelona, 08007 Barcelona, Spain; (J.O.); (F.P.-y.-M.)
| | - Fernanda Paz-y-Miño
- Barcelona Centre for Maternal-Fetal and Neonatal Medicine (BCNatal), Hospital Clínic Barcelona, Universitat de Barcelona, 08007 Barcelona, Spain; (J.O.); (F.P.-y.-M.)
| | - Mafalda de Almeida
- Veritas Genetics, c/Zamora 46-48, 08005 Barcelona, Spain; (E.O.); (M.d.A.); (M.L.); (V.C.)
| | - Miriam León
- Veritas Genetics, c/Zamora 46-48, 08005 Barcelona, Spain; (E.O.); (M.d.A.); (M.L.); (V.C.)
| | - Vincenzo Cirigliano
- Veritas Genetics, c/Zamora 46-48, 08005 Barcelona, Spain; (E.O.); (M.d.A.); (M.L.); (V.C.)
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18
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Pagnamenta AT, Camps C, Giacopuzzi E, Taylor JM, Hashim M, Calpena E, Kaisaki PJ, Hashimoto A, Yu J, Sanders E, Schwessinger R, Hughes JR, Lunter G, Dreau H, Ferla M, Lange L, Kesim Y, Ragoussis V, Vavoulis DV, Allroggen H, Ansorge O, Babbs C, Banka S, Baños-Piñero B, Beeson D, Ben-Ami T, Bennett DL, Bento C, Blair E, Brasch-Andersen C, Bull KR, Cario H, Cilliers D, Conti V, Davies EG, Dhalla F, Dacal BD, Dong Y, Dunford JE, Guerrini R, Harris AL, Hartley J, Hollander G, Javaid K, Kane M, Kelly D, Kelly D, Knight SJL, Kreins AY, Kvikstad EM, Langman CB, Lester T, Lines KE, Lord SR, Lu X, Mansour S, Manzur A, Maroofian R, Marsden B, Mason J, McGowan SJ, Mei D, Mlcochova H, Murakami Y, Németh AH, Okoli S, Ormondroyd E, Ousager LB, Palace J, Patel SY, Pentony MM, Pugh C, Rad A, Ramesh A, Riva SG, Roberts I, Roy N, Salminen O, Schilling KD, Scott C, Sen A, Smith C, Stevenson M, Thakker RV, Twigg SRF, Uhlig HH, van Wijk R, Vona B, Wall S, Wang J, Watkins H, Zak J, Schuh AH, Kini U, Wilkie AOM, Popitsch N, Taylor JC. Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases. Genome Med 2023; 15:94. [PMID: 37946251 PMCID: PMC10636885 DOI: 10.1186/s13073-023-01240-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 09/27/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.
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Affiliation(s)
- Alistair T Pagnamenta
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Carme Camps
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Edoardo Giacopuzzi
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Human Technopole, Viale Rita Levi Montalcini 1, 20157, Milan, Italy
| | - John M Taylor
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Mona Hashim
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Eduardo Calpena
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Pamela J Kaisaki
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Akiko Hashimoto
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Jing Yu
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Edward Sanders
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Ron Schwessinger
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Jim R Hughes
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Gerton Lunter
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- University Medical Center Groningen, Groningen University, PO Box 72, 9700 AB, Groningen, The Netherlands
| | - Helene Dreau
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Matteo Ferla
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Lukas Lange
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Yesim Kesim
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Vassilis Ragoussis
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Dimitrios V Vavoulis
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Holger Allroggen
- Neurosciences Department, UHCW NHS Trust, Clifford Bridge Road, Coventry, CV2 2DX, UK
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Christian Babbs
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Siddharth Banka
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Oxford Road, Manchester, M13 9WL, UK
| | - Benito Baños-Piñero
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - David Beeson
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Tal Ben-Ami
- Pediatric Hematology-Oncology Unit, Kaplan Medical Center, Rehovot, Israel
| | - David L Bennett
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Celeste Bento
- Hematology Department, Hospitais da Universidade de Coimbra, Coimbra, Portugal
| | - Edward Blair
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Charlotte Brasch-Andersen
- Department of Clinical Genetics, Odense University Hospital and Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Katherine R Bull
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Holger Cario
- Department of Pediatrics and Adolescent Medicine, University Medical Center, Eythstrasse 24, 89075, Ulm, Germany
| | - Deirdre Cilliers
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Valerio Conti
- Neuroscience Department, Meyer Children's Hospital IRCCS, Viale Pieraccini 24, 50139, Florence, Italy
| | - E Graham Davies
- Department of Immunology, Great Ormond Street Hospital for Children NHS Trust and UCL Great Ormond Street Institute of Child Health, Zayed Centre for Research, 2Nd Floor, 20C Guilford Street, London, WC1N 1DZ, UK
| | - Fatima Dhalla
- Department of Paediatrics, Institute of Developmental and Regenerative Medicine, IMS-Tetsuya Nakamura Building, Old Road Campus, Roosevelt Drive, Oxford, OX3 7TY, UK
| | - Beatriz Diez Dacal
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Yin Dong
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - James E Dunford
- Oxford NIHR Musculoskeletal BRC and Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Nuffield Orthopaedic Centre, Old Road, Oxford, OX3 7HE, UK
| | - Renzo Guerrini
- Neuroscience Department, Meyer Children's Hospital IRCCS, Viale Pieraccini 24, 50139, Florence, Italy
| | - Adrian L Harris
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK
| | - Jane Hartley
- Liver Unit, Birmingham Women's & Children's Hospital and University of Birmingham, Steelhouse Lane, Birmingham, B4 6NH, UK
| | - Georg Hollander
- Department of Paediatrics, University of Oxford, Level 2, Children's Hospital, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Kassim Javaid
- Oxford NIHR Musculoskeletal BRC and Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Nuffield Orthopaedic Centre, Old Road, Oxford, OX3 7HE, UK
| | - Maureen Kane
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Pharmacy Hall North, Room 731, 20 N. Pine Street, Baltimore, MD, 21201, USA
| | - Deirdre Kelly
- Liver Unit, Birmingham Women's & Children's Hospital and University of Birmingham, Steelhouse Lane, Birmingham, B4 6NH, UK
| | - Dominic Kelly
- Children's Hospital, OUH NHS Foundation Trust, NIHR Oxford BRC, Headley Way, Oxford, OX3 9DU, UK
| | - Samantha J L Knight
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Alexandra Y Kreins
- Department of Immunology, Great Ormond Street Hospital for Children NHS Trust and UCL Great Ormond Street Institute of Child Health, Zayed Centre for Research, 2Nd Floor, 20C Guilford Street, London, WC1N 1DZ, UK
| | - Erika M Kvikstad
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Craig B Langman
- Feinberg School of Medicine, Northwestern University, 211 E Chicago Avenue, Chicago, IL, MS37, USA
| | - Tracy Lester
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Kate E Lines
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- University of Oxford, Academic Endocrine Unit, OCDEM, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Simon R Lord
- Early Phase Clinical Trials Unit, Department of Oncology, University of Oxford, Cancer and Haematology Centre, Level 2 Administration Area, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Xin Lu
- Nuffield Department of Clinical Medicine, Ludwig Institute for Cancer Research, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK
| | - Sahar Mansour
- St George's University Hospitals NHS Foundation Trust, Blackshore Road, Tooting, London, SW17 0QT, UK
| | - Adnan Manzur
- MRC Centre for Neuromuscular Diseases, National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK
| | - Reza Maroofian
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK
| | - Brian Marsden
- Nuffield Department of Medicine, Kennedy Institute, University of Oxford, Oxford, OX3 7BN, UK
| | - Joanne Mason
- Yourgene Health Headquarters, Skelton House, Lloyd Street North, Manchester Science Park, Manchester, M15 6SH, UK
| | - Simon J McGowan
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Davide Mei
- Neuroscience Department, Meyer Children's Hospital IRCCS, Viale Pieraccini 24, 50139, Florence, Italy
| | - Hana Mlcochova
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Yoshiko Murakami
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Steven Okoli
- Imperial College NHS Trust, Department of Haematology, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK
| | - Elizabeth Ormondroyd
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- University of Oxford, Level 6 West Wing, Oxford, OX3 9DU, JR, UK
| | - Lilian Bomme Ousager
- Department of Clinical Genetics, Odense University Hospital and Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jacqueline Palace
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Smita Y Patel
- Clinical Immunology, John Radcliffe Hospital, Level 4A, Oxford, OX3 9DU, UK
| | - Melissa M Pentony
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Chris Pugh
- Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Aboulfazl Rad
- Department of Otolaryngology-Head & Neck Surgery, Tübingen Hearing Research Centre, Eberhard Karls University, Elfriede-Aulhorn-Str. 5, 72076, Tübingen, Germany
| | - Archana Ramesh
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Simone G Riva
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Irene Roberts
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- Department of Paediatrics, University of Oxford, Level 2, Children's Hospital, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Noémi Roy
- Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Level 4, Haematology, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Outi Salminen
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Kyleen D Schilling
- Ann & Robert H. Lurie Children's Hospital of Chicago, 225 E Chicago Avenue, Chicago, IL, 60611, USA
| | - Caroline Scott
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Arjune Sen
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Conrad Smith
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Mark Stevenson
- University of Oxford, Academic Endocrine Unit, OCDEM, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Rajesh V Thakker
- University of Oxford, Academic Endocrine Unit, OCDEM, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Stephen R F Twigg
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Holm H Uhlig
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Paediatrics, University of Oxford, Level 2, Children's Hospital, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Richard van Wijk
- UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Barbara Vona
- Department of Otolaryngology-Head & Neck Surgery, Tübingen Hearing Research Centre, Eberhard Karls University, Elfriede-Aulhorn-Str. 5, 72076, Tübingen, Germany
- Institute of Human Genetics, University Medical Center Göttingen, Heinrich-Düker-Weg 12, 37073, Göttingen, Germany
- Institute for Auditory Neuroscience and InnerEarLab, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany
| | - Steven Wall
- Oxford Craniofacial Unit, John Radcliffe Hospital, Level LG1, West Wing, Oxford, OX3 9DU, UK
| | - Jing Wang
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Hugh Watkins
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- University of Oxford, Level 6 West Wing, Oxford, OX3 9DU, JR, UK
| | - Jaroslav Zak
- Nuffield Department of Clinical Medicine, Ludwig Institute for Cancer Research, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Anna H Schuh
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Usha Kini
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Andrew O M Wilkie
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Niko Popitsch
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Biochemistry and Cell Biology, Max Perutz Labs, University of Vienna, Vienna BioCenter(VBC), Dr.-Bohr-Gasse 9, 1030, Vienna, Austria
| | - Jenny C Taylor
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK.
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK.
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Yang J, Liu C, Deng W, Wu D, Weng C, Zhou Y, Wang K. Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT. ARXIV 2023:arXiv:2308.06294v2. [PMID: 37986722 PMCID: PMC10659449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models - PhenoBCBERT and PhenoGPT - for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes, due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models (LLMs) to automate the detection of phenotype terms, including those not in the current HPO. We compared these models to PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also showed strong performance in case studies on biomedical literature. We evaluated the strengths and weaknesses of BERT-based and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.
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Affiliation(s)
- Jingye Yang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Wendy Deng
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Da Wu
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Yunyun Zhou
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Biostatistics and Bioinformatics facility, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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20
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Zhou Z, Huang X, Tang X, Chen W, Chen Q, Zhang C, Li Y, Zhao D, Zheng Z, Hu S, Wang J, Kullo IJ, Ding K. Heterozygous nonsense variants in laminin subunit 3α resulting in Ebstein's anomaly. HGG ADVANCES 2023; 4:100227. [PMID: 37635785 PMCID: PMC10450520 DOI: 10.1016/j.xhgg.2023.100227] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/26/2023] [Indexed: 08/29/2023] Open
Abstract
Ebstein's anomaly is a rare congenital heart disease characterized by tricuspid valve downward displacement and is associated with additional cardiac phenotypes such as left ventricle non-compaction. The genetic basis of Ebstein's anomaly has yet to be fully elucidated, although several genes (e.g., NKX2-5, MYH7, TPM1, and FLNA) may contribute to Ebstein's anomaly. Here, in two Ebstein's anomaly families (a three-generation family and a trio), we identified independent heterozygous nonsense variants in laminin subunit 3 α (LAMA3), cosegregated with phenotypes in families with reduced penetrance. Furthermore, knocking out Lama3 in mice revealed that haploinsufficiency of Lama3 led to Ebstein's malformation of the tricuspid valve and an abnormal basement membrane structure. In conclusion, we identified a novel gene-disease association of LAMA3 implicated in Ebstein's anomaly, and the findings extended our understanding of the role of the extracellular matrix in Ebstein's anomaly etiology.
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Affiliation(s)
- Zhou Zhou
- Department of Laboratory Medicine, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, P.R. China
| | - Xumei Huang
- Department of Cardiovascular Diseases, Wenzhou Central Hospital, Wenzhou, Zhejiang 325000, P.R. China
| | - Xia Tang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, P.R. China
| | - Wen Chen
- Department of Laboratory Medicine, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, P.R. China
| | - Qianlong Chen
- Department of Laboratory Medicine, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, P.R. China
| | - Chaohui Zhang
- Henan Key Laboratory of Medical Tissue Regeneration, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Yuxin Li
- Henan Key Laboratory of Medical Tissue Regeneration, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Dachun Zhao
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, P.R. China
| | - Zhe Zheng
- Department of Laboratory Medicine, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, P.R. China
| | - Shengshou Hu
- Department of Laboratory Medicine, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, P.R. China
| | - Jikui Wang
- Henan Key Laboratory of Medical Tissue Regeneration, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China
| | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Keyue Ding
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
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21
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Huang D, Jiang J, Zhao T, Wu S, Li P, Lyu Y, Feng J, Wei M, Zhu Z, Gu J, Ren Y, Yu G, Lu H. diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype. Bioinformatics 2023; 39:btad517. [PMID: 37647638 PMCID: PMC10500091 DOI: 10.1093/bioinformatics/btad517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/24/2023] [Accepted: 08/29/2023] [Indexed: 09/01/2023] Open
Abstract
SUMMARY The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods. AVAILABILITY AND IMPLEMENTATION diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.
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Affiliation(s)
- Daoyi Huang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jianping Jiang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Tingting Zhao
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai, China
| | - Shengnan Wu
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Pin Li
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yongfen Lyu
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jincai Feng
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mingyue Wei
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhixing Zhu
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai, China
| | - Jianlei Gu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yongyong Ren
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai, China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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22
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Dingemans AJM, Hinne M, Truijen KMG, Goltstein L, van Reeuwijk J, de Leeuw N, Schuurs-Hoeijmakers J, Pfundt R, Diets IJ, den Hoed J, de Boer E, Coenen-van der Spek J, Jansen S, van Bon BW, Jonis N, Ockeloen CW, Vulto-van Silfhout AT, Kleefstra T, Koolen DA, Campeau PM, Palmer EE, Van Esch H, Lyon GJ, Alkuraya FS, Rauch A, Marom R, Baralle D, van der Sluijs PJ, Santen GWE, Kooy RF, van Gerven MAJ, Vissers LELM, de Vries BBA. PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework. Nat Genet 2023; 55:1598-1607. [PMID: 37550531 PMCID: PMC11414844 DOI: 10.1038/s41588-023-01469-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 07/05/2023] [Indexed: 08/09/2023]
Abstract
Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.
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Affiliation(s)
- Alexander J M Dingemans
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Max Hinne
- Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kim M G Truijen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Lia Goltstein
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen van Reeuwijk
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nicole de Leeuw
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Janneke Schuurs-Hoeijmakers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Rolph Pfundt
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Illja J Diets
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Joery den Hoed
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Elke de Boer
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jet Coenen-van der Spek
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Sandra Jansen
- Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bregje W van Bon
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Noraly Jonis
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Charlotte W Ockeloen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Anneke T Vulto-van Silfhout
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Tjitske Kleefstra
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - David A Koolen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Philippe M Campeau
- Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada
| | - Elizabeth E Palmer
- Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia
- Sydney Children's Hospitals Network, Sydney, New South Wales, Australia
| | - Hilde Van Esch
- Center for Human Genetics, University Hospitals Leuven, University of Leuven, Leuven, Belgium
| | - Gholson J Lyon
- Department of Human Genetics and George A. Jervis Clinic, Institute for Basic Research in Developmental Disabilities (IBR), Staten Island, NY, USA
- Biology PhD Program, The Graduate Center, The City University of New York, New York City, NY, USA
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Anita Rauch
- Institute of Medical Genetics, University of Zürich, Zürich, Switzerland
| | - Ronit Marom
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Diana Baralle
- Faculty of Medicine, University of Southampton, Southampton, UK
| | | | - Gijs W E Santen
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - R Frank Kooy
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
| | - Marcel A J van Gerven
- Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Lisenka E L M Vissers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - Bert B A de Vries
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
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23
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Aradhya S, Facio FM, Metz H, Manders T, Colavin A, Kobayashi Y, Nykamp K, Johnson B, Nussbaum RL. Applications of artificial intelligence in clinical laboratory genomics. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2023; 193:e32057. [PMID: 37507620 DOI: 10.1002/ajmg.c.32057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.
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Affiliation(s)
- Swaroop Aradhya
- Invitae Corporation, San Francisco, California, USA
- Adjunct Clinical Faculty, Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | | | - Hillery Metz
- Invitae Corporation, San Francisco, California, USA
| | - Toby Manders
- Invitae Corporation, San Francisco, California, USA
| | | | | | - Keith Nykamp
- Invitae Corporation, San Francisco, California, USA
| | | | - Robert L Nussbaum
- Invitae Corporation, San Francisco, California, USA
- Volunteer Faculty, School of Medicine, University of California San Francisco, San Francisco, California, USA
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24
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Nunes S, Sousa R, Pesquita C. Multi-domain knowledge graph embeddings for gene-disease association prediction. J Biomed Semantics 2023; 14:11. [PMID: 37580835 PMCID: PMC10426189 DOI: 10.1186/s13326-023-00291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/29/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of genes and diseases based on the scientific knowledge described in ontologies, which can then be explored by machine learning algorithms. However, state-of-the-art knowledge graph embeddings are produced over a single ontology or multiple but disconnected ones, ignoring the impact that considering multiple interconnected domains can have on complex tasks such as gene-disease association prediction. RESULTS We propose a novel approach to predict gene-disease associations using rich semantic representations based on knowledge graph embeddings over multiple ontologies linked by logical definitions and compound ontology mappings. The experiments showed that considering richer knowledge graphs significantly improves gene-disease prediction and that different knowledge graph embeddings methods benefit more from distinct types of semantic richness. CONCLUSIONS This work demonstrated the potential for knowledge graph embeddings across multiple and interconnected biomedical ontologies to support gene-disease prediction. It also paved the way for considering other ontologies or tackling other tasks where multiple perspectives over the data can be beneficial. All software and data are freely available.
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Affiliation(s)
- Susana Nunes
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Rita T. Sousa
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Catia Pesquita
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
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25
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Reiley J, Botas P, Miller CE, Zhao J, Malone Jenkins S, Best H, Grubb PH, Mao R, Isla J, Brunelli L. Open-Source Artificial Intelligence System Supports Diagnosis of Mendelian Diseases in Acutely Ill Infants. CHILDREN (BASEL, SWITZERLAND) 2023; 10:991. [PMID: 37371223 PMCID: PMC10296792 DOI: 10.3390/children10060991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/18/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023]
Abstract
Mendelian disorders are prevalent in neonatal and pediatric intensive care units and are a leading cause of morbidity and mortality in these settings. Current diagnostic pipelines that integrate phenotypic and genotypic data are expert-dependent and time-intensive. Artificial intelligence (AI) tools may help address these challenges. Dx29 is an open-source AI tool designed for use by clinicians. It analyzes the patient's phenotype and genotype to generate a ranked differential diagnosis. We used Dx29 to retrospectively analyze 25 acutely ill infants who had been diagnosed with a Mendelian disorder, using a targeted panel of ~5000 genes. For each case, a trio (proband and both parents) file containing gene variant information was analyzed, alongside patient phenotype, which was provided to Dx29 by three approaches: (1) AI extraction from medical records, (2) AI extraction with manual review/editing, and (3) manual entry. We then identified the rank of the correct diagnosis in Dx29's differential diagnosis. With these three approaches, Dx29 ranked the correct diagnosis in the top 10 in 92-96% of cases. These results suggest that non-expert use of Dx29's automated phenotyping and subsequent data analysis may compare favorably to standard workflows utilized by bioinformatics experts to analyze genomic data and diagnose Mendelian diseases.
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Affiliation(s)
- Joseph Reiley
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA
| | - Pablo Botas
- Foundation Twenty-Nine, 28223 Madrid, Spain
- Nostos Genomics, 10625 Berlin, Germany
| | - Christine E. Miller
- ARUP Laboratories, University of Utah Health Sciences Center, Salt Lake City, UT 84108, USA
- Valley Children’s Healthcare, Madera, CA 93636, USA
| | - Jian Zhao
- ARUP Laboratories, University of Utah Health Sciences Center, Salt Lake City, UT 84108, USA
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Sabrina Malone Jenkins
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA
| | - Hunter Best
- ARUP Laboratories, University of Utah Health Sciences Center, Salt Lake City, UT 84108, USA
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Peter H. Grubb
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA
| | - Rong Mao
- ARUP Laboratories, University of Utah Health Sciences Center, Salt Lake City, UT 84108, USA
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | | | - Luca Brunelli
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA
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26
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Lesmann H, Klinkhammer H, M. Krawitz PDMDPP. The future role of facial image analysis in ACMG classification guidelines. MED GENET-BERLIN 2023; 35:115-121. [PMID: 38840866 PMCID: PMC10842539 DOI: 10.1515/medgen-2023-2014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
The use of next-generation sequencing (NGS) has dramatically improved the diagnosis of rare diseases. However, the analysis of genomic data has become complex with the increasing detection of variants by exome and genome sequencing. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed a 5-tier classification scheme in 2015 for variant interpretation, that has since been widely adopted. Despite efforts to minimise discrepancies in the application of these criteria, inconsistencies still occur. Further specifications for individual genes were developed by Variant Curation Expert Panels (VCEPs) of the Clinical Genome Resource (ClinGen) consortium, that also take into consideration gene or disease specific features. For instance, in disorders with a highly characerstic facial gestalt a "phenotypic match" (PP4) has higher pathogenic evidence than e.g. in a non-syndromic form of intellectual disability. With computational approaches for quantifying the similarity of dysmorphic features results of such analysis can now be used in a refined Bayesian framework for the ACMG/AMP criteria.
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Affiliation(s)
- Hellen Lesmann
- University of Bonn, Medical Faculty & University Hospital BonnInstitute of Human GeneticsVenusberg-Campus 153127BonnGermany
| | - Hannah Klinkhammer
- University of BonnInstitute for Genomic Statistics and BioinformaticsBonnGermany
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27
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Licata L, Via A, Turina P, Babbi G, Benevenuta S, Carta C, Casadio R, Cicconardi A, Facchiano A, Fariselli P, Giordano D, Isidori F, Marabotti A, Martelli PL, Pascarella S, Pinelli M, Pippucci T, Russo R, Savojardo C, Scafuri B, Valeriani L, Capriotti E. Resources and tools for rare disease variant interpretation. Front Mol Biosci 2023; 10:1169109. [PMID: 37234922 PMCID: PMC10206239 DOI: 10.3389/fmolb.2023.1169109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.
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Affiliation(s)
- Luana Licata
- Department of Biology, University of Rome Tor Vergata, Roma, Italy
| | - Allegra Via
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Paola Turina
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | - Claudio Carta
- National Centre for Rare Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Andrea Cicconardi
- Department of Physics, University of Genova, Genova, Italy
- Italiano di Tecnologia—IIT, Genova, Italy
| | - Angelo Facchiano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Deborah Giordano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Federica Isidori
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Anna Marabotti
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | - Pier Luigi Martelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Michele Pinelli
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
| | - Tommaso Pippucci
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Roberta Russo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, Napoli, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Bernardina Scafuri
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | | | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
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28
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Ladewig MS, Jacobsen JOB, Wagner AH, Danis D, El Kassaby B, Gargano M, Groza T, Baudis M, Steinhaus R, Seelow D, Bechrakis NE, Mungall CJ, Schofield PN, Elemento O, Smith L, McMurry JA, Munoz‐Torres M, Haendel MA, Robinson PN. GA4GH Phenopackets: A Practical Introduction. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2200016. [PMID: 36910590 PMCID: PMC10000265 DOI: 10.1002/ggn2.202200016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/30/2022] [Indexed: 11/08/2022]
Abstract
The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.
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Affiliation(s)
- Markus S. Ladewig
- Department of OphthalmologyKlinikum Saarbrücken66119SaarbrückenGermany
| | - Julius O. B. Jacobsen
- William Harvey Research InstituteCharterhouse SquareBarts and the London School of Medicine and Dentistry QueenQueen Mary University of LondonLondonEC1M 6BQUK
| | - Alex H. Wagner
- Departments of Pediatrics and Biomedical InformaticsThe Ohio State University College of MedicineColumbusOH43210USA
- The Steve and Cindy Rasmussen Institute for Genomic MedicineNationwide Children's HospitalColumbusOH43215USA
| | - Daniel Danis
- The Jackson Laboratory for Genomic Medicine10 Discovery DriveFarmingtonCT06032USA
| | - Baha El Kassaby
- The Jackson Laboratory for Genomic Medicine10 Discovery DriveFarmingtonCT06032USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine10 Discovery DriveFarmingtonCT06032USA
| | - Tudor Groza
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)CambridgeCB10 1SDUK
| | - Michael Baudis
- Department of Molecular Life Sciences and Swiss Institute of BioinformaticsUniversity of ZurichZurichSwitzerland
| | - Robin Steinhaus
- Exploratory Diagnostic SciencesBerlin Institute of Health at Charité – Universitätsmedizin Berlin10178BerlinGermany
- Institute of Medical Genetics and Human GeneticsCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität Berlin and Humboldt‐Universität zu Berlin13353BerlinGermany
| | - Dominik Seelow
- Exploratory Diagnostic SciencesBerlin Institute of Health at Charité – Universitätsmedizin Berlin10178BerlinGermany
- Institute of Medical Genetics and Human GeneticsCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität Berlin and Humboldt‐Universität zu Berlin13353BerlinGermany
| | | | - Christopher J. Mungall
- Lawrence Berkeley National LaboratoryEnvironmental Genomics and Systems BiologyBerkeleyCA94720USA
| | - Paul N. Schofield
- Department of Physiology Development and NeuroscienceUniversity of CambridgeDowning StreetCambridgeCB2 3EGUK
- The Jackson LaboratoryBar HarborME04609USA
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision MedicineWeill Cornell MedicineNew YorkNY10021USA
| | - Lindsay Smith
- Ontario Institute for Cancer ResearchAdaptive OncologyTorontoCAM5G0A3USA
- Global Alliance for Genomics and HealthTorontoCAM5G0A3USA
| | - Julie A. McMurry
- Center for Health AIUniversity of Colorado Anschutz Medical CampusAuroraCO80045USA
| | - Monica Munoz‐Torres
- Center for Health AIUniversity of Colorado Anschutz Medical CampusAuroraCO80045USA
| | - Melissa A. Haendel
- Center for Health AIUniversity of Colorado Anschutz Medical CampusAuroraCO80045USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine10 Discovery DriveFarmingtonCT06032USA
- Institute for Systems GenomicsUniversity of ConnecticutFarmingtonCT06032USA
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29
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Pei XM, Yeung MHY, Wong ANN, Tsang HF, Yu ACS, Yim AKY, Wong SCC. Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases. Cells 2023; 12:493. [PMID: 36766834 PMCID: PMC9913990 DOI: 10.3390/cells12030493] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases.
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Affiliation(s)
- Xiao Meng Pei
- Department of Applied Biology & Chemical Technology, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Alex Ngai Nick Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Hin Fung Tsang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
- Department of Clinical Laboratory and Pathology, Hong Kong Adventist Hospital, Hong Kong, China
| | - Allen Chi Shing Yu
- Codex Genetics Limited, Unit 212, 2/F., Building 16W, No. 16 Science Park West Avenue, The Hong Kong Science Park, Hong Kong 852, China
| | - Aldrin Kay Yuen Yim
- Codex Genetics Limited, Unit 212, 2/F., Building 16W, No. 16 Science Park West Avenue, The Hong Kong Science Park, Hong Kong 852, China
| | - Sze Chuen Cesar Wong
- Department of Applied Biology & Chemical Technology, The Hong Kong Polytechnic University, Hong Kong 999077, China
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30
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Draetta EL, Lazarević D, Provero P, Cittaro D. The frequency of somatic mutations in cancer predicts the phenotypic relevance of germline mutations. Front Genet 2023; 13:1045301. [PMID: 36699457 PMCID: PMC9868957 DOI: 10.3389/fgene.2022.1045301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
Genomic sequence mutations can be pathogenic in both germline and somatic cells. Several authors have observed that often the same genes are involved in cancer when mutated in somatic cells and in genetic diseases when mutated in the germline. Recent advances in high-throughput sequencing techniques have provided us with large databases of both types of mutations, allowing us to investigate this issue in a systematic way. Hence, we applied a machine learning based framework to this problem, comparing multiple models. The models achieved significant predictive power as shown by both cross-validation and their application to recently discovered gene/phenotype associations not used for training. We found that genes characterized by high frequency of somatic mutations in the most common cancers and ancient evolutionary age are most likely to be involved in abnormal phenotypes and diseases. These results suggest that the combination of tolerance for mutations at the cell viability level (measured by the frequency of somatic mutations in cancer) and functional relevance (demonstrated by evolutionary conservation) are the main predictors of disease genes. Our results thus confirm the deep relationship between pathogenic mutations in somatic and germline cells, provide new insight into the common origin of cancer and genetic diseases, and can be used to improve the identification of new disease genes.
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Affiliation(s)
- Edoardo Luigi Draetta
- University of Milan, Milan, Italy,Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Dejan Lazarević
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Provero
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy,Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Davide Cittaro
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy,*Correspondence: Davide Cittaro ,
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Raza R, Chhabra G, Bilal M, Ndiaye MA, Liaqat K, Nawaz S, Sgro JY, Rayment I, Ahmad W, Ahmad N. A Homozygous Missense Variant in K25 Underlying Overlapping Phenotype with Woolly Hair and Dental Anomalies. J Invest Dermatol 2023; 143:173-176.e3. [PMID: 35926655 DOI: 10.1016/j.jid.2022.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Rubab Raza
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Biochemistry, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Gagan Chhabra
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Muhammad Bilal
- Department of Biochemistry, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Mary A Ndiaye
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Khurram Liaqat
- Department of Biotechnology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Shoaib Nawaz
- Department of Biotechnology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Jean-Yves Sgro
- Department of Biochemistry, College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ivan Rayment
- Department of Biochemistry, College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Wasim Ahmad
- Department of Biochemistry, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Nihal Ahmad
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA; William S. Middleton VA Medical Center, Madison, Wisconsin, USA.
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32
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Reese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, Laraway B, Antonescu C, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Caufield JH, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine 2023; 87:104413. [PMID: 36563487 PMCID: PMC9769411 DOI: 10.1016/j.ebiom.2022.104413] [Citation(s) in RCA: 59] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/23/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.
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Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Elena Casiraghi
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Johanna J Loomba
- The Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Bryan Laraway
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Kenneth J Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Nariman Ammar
- Health Science Center, University of Tennessee, Memphis, TN, USA
| | - Blessy Antony
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - J Harry Caufield
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Julie A McMurry
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA; Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Richard Moffitt
- Department of Biomedical Informatics and Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | | | | | | | - Kristin Kostka
- Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Christopher G Chute
- Schools of Medicine, Public Health and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA.
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Cisterna A, González-Vidal A, Ruiz D, Ortiz J, Gómez-Pascual A, Chen Z, Nalls M, Faghri F, Hardy J, Díez I, Maietta P, Álvarez S, Ryten M, Botía JA. PhenoExam: gene set analyses through integration of different phenotype databases. BMC Bioinformatics 2022; 23:567. [PMID: 36587217 PMCID: PMC9805686 DOI: 10.1186/s12859-022-05122-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 12/22/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. RESULTS PhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. CONCLUSIONS We developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G' and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam . Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/ .
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Affiliation(s)
- Alejandro Cisterna
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Aurora González-Vidal
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Daniel Ruiz
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Jordi Ortiz
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Alicia Gómez-Pascual
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Zhongbo Chen
- Department of Neurodegenerative Disease, UCL, Institute of Neurology, London, UK
| | - Mike Nalls
- Data Tecnica International LLC, Glen Echo, MD, USA
- Laboratory of Neurogenetics, NIA/NIH, Bethesda, MD, USA
- Center for Alzheimer's and Related Dememtias, NIH, Bethesda, MD, USA
| | - Faraz Faghri
- Data Tecnica International LLC, Glen Echo, MD, USA
- Laboratory of Neurogenetics, NIA/NIH, Bethesda, MD, USA
- Center for Alzheimer's and Related Dememtias, NIH, Bethesda, MD, USA
| | - John Hardy
- Department of Neurodegenerative Disease, UCL, Institute of Neurology, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Irene Díez
- NIMGenetics Genómica y Medicina S.L, Madrid, Spain
| | | | - Sara Álvarez
- NIMGenetics Genómica y Medicina S.L, Madrid, Spain
| | - Mina Ryten
- Department of Neurodegenerative Disease, UCL, Institute of Neurology, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, WC1E 6BT, UK
| | - Juan A Botía
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain.
- Department of Neurodegenerative Disease, UCL, Institute of Neurology, London, UK.
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Lieberwirth JK, Büttner B, Klöckner C, Platzer K, Popp B, Abou Jamra R. AutoCaSc: Prioritizing candidate genes for neurodevelopmental disorders. Hum Mutat 2022; 43:1795-1807. [PMID: 35998261 DOI: 10.1002/humu.24451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/24/2022] [Accepted: 08/18/2022] [Indexed: 01/24/2023]
Abstract
Routine exome sequencing (ES) in individuals with neurodevelopmental disorders (NDD) remains inconclusive in >50% of the cases. Research analysis of unsolved cases can identify novel candidate genes but is time-consuming, subjective, and hard to compare between labs. The field, therefore, requires automated and standardized assessment methods to prioritize candidates for matchmaking. We developed AutoCaSc (https://autocasc.uni-leipzig.de) based on our candidate scoring scheme. We validated our approach using synthetic trios and real in-house trio ES data. AutoCaSc consistently (94.5% of all cases) scored the relevant variants in valid novel NDD genes in the top three ranks. In 93 real trio exomes, AutoCaSc identified most (97.5%) previously manually scored variants while evaluating additional high-scoring variants missed in manual evaluation. It identified candidate variants in previously undescribed NDD candidate genes (CNTN2, DLGAP1, SMURF1, NRXN3, and PRICKLE1). AutoCaSc enables anybody to quickly screen a variant for its plausibility in NDD. After contributing >40 descriptions of NDD-associated genes, we provide usage recommendations based on our extensive experience. Our implementation is capable of pipeline integration and therefore allows the screening of large cohorts for candidate genes. AutoCaSc empowers even small labs to a standardized matchmaking collaboration and to contribute to the ongoing identification of novel NDD entities.
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Affiliation(s)
| | - Benjamin Büttner
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Chiara Klöckner
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Bernt Popp
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany.,Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Center of Functional Genomics, Berlin, Germany
| | - Rami Abou Jamra
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
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Phenotype-aware prioritisation of rare Mendelian disease variants. Trends Genet 2022; 38:1271-1283. [PMID: 35934592 PMCID: PMC9950798 DOI: 10.1016/j.tig.2022.07.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/06/2022] [Accepted: 07/05/2022] [Indexed: 01/24/2023]
Abstract
A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.
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Tábuas-Pereira M, Guerreiro R, Kun-Rodrigues C, Almeida MR, Brás J, Santana I. Whole-exome sequencing reveals PSEN1 and ATP7B combined variants as a possible cause of early-onset Lewy body dementia: a case study of genotype-phenotype correlation. Neurogenetics 2022; 23:279-283. [PMID: 36114914 PMCID: PMC9669161 DOI: 10.1007/s10048-022-00699-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 09/02/2022] [Indexed: 10/14/2022]
Abstract
Dementia with Lewy bodies is a neurodegenerative disease, sharing features with Parkinson's and Alzheimer's diseases. We report a case of a patient dementia with Lewy bodies carrying combined PSEN1 and ATP7B mutations. A man developed dementia with Lewy bodies starting at the age of 60 years. CSF biomarkers were of Alzheimer's disease and DaTSCAN was abnormal. Whole-exome sequencing revealed a heterozygous p.Ile408Thr PSEN1 variant and a homozygous p.Arg616Trp ATP7B variant. This case reinstates the need of considering ATP7B mutations when evaluating a patient with parkinsonism and supports p.Ile408Thr as a pathogenic PSEN1 variant.
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Affiliation(s)
- Miguel Tábuas-Pereira
- Neurology Department, Centro Hospitalar E Universitário de Coimbra, Praceta Prof. Mota Pinto, 3000-045, Coimbra, Portugal.
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.
- Centro Académico Clínico de Coimbra, University of Coimbra, Coimbra, Portugal.
| | - Rita Guerreiro
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
| | - Célia Kun-Rodrigues
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Maria Rosário Almeida
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - José Brás
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
| | - Isabel Santana
- Neurology Department, Centro Hospitalar E Universitário de Coimbra, Praceta Prof. Mota Pinto, 3000-045, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
- Centro Académico Clínico de Coimbra, University of Coimbra, Coimbra, Portugal
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Mendoza-Alvarez A, Tosco-Herrera E, Muñoz-Barrera A, Rubio-Rodríguez LA, Alonso-Gonzalez A, Corrales A, Iñigo-Campos A, Almeida-Quintana L, Martin-Fernandez E, Martinez-Beltran D, Perez-Rodriguez E, Callero A, Garcia-Robaina JC, González-Montelongo R, Marcelino-Rodriguez I, Lorenzo-Salazar JM, Flores C. A catalog of the genetic causes of hereditary angioedema in the Canary Islands (Spain). Front Immunol 2022; 13:997148. [PMID: 36203598 PMCID: PMC9531158 DOI: 10.3389/fimmu.2022.997148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Hereditary angioedema (HAE) is a rare disease where known causes involve C1 inhibitor dysfunction or dysregulation of the kinin cascade. The updated HAE management guidelines recommend performing genetic tests to reach a precise diagnosis. Unfortunately, genetic tests are still uncommon in the diagnosis routine. Here, we characterized for the first time the genetic causes of HAE in affected families from the Canary Islands (Spain). Whole-exome sequencing data was obtained from 41 affected patients and unaffected relatives from 29 unrelated families identified in the archipelago. The Hereditary Angioedema Database Annotation (HADA) tool was used for pathogenicity classification and causal variant prioritization among the genes known to cause HAE. Manual reclassification of prioritized variants was used in those families lacking known causal variants. We detected a total of eight different variants causing HAE in this patient series, affecting essentially SERPING1 and F12 genes, one of them being a novel SERPING1 variant (c.686-12A>G) with a predicted splicing effect which was reclassified as likely pathogenic in one family. Altogether, the diagnostic yield by assessing previously reported causal genes and considering variant reclassifications according to the American College of Medical Genetics guidelines reached 66.7% (95% Confidence Interval [CI]: 30.1-91.0) in families with more than one affected member and 10.0% (95% CI: 1.8-33.1) among cases without family information for the disease. Despite the genetic causes of many patients remain to be identified, our results reinforce the need of genetic tests as first-tier diagnostic tool in this disease, as recommended by the international WAO/EAACI guidelines for the management of HAE.
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Affiliation(s)
| | - Eva Tosco-Herrera
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Adrian Muñoz-Barrera
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Luis A. Rubio-Rodríguez
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Aitana Alonso-Gonzalez
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Universidad de Santiago de Compostela, Santiago de Compostela, Spain
| | - Almudena Corrales
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Antonio Iñigo-Campos
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Lourdes Almeida-Quintana
- Allergy Service, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain
| | - Elena Martin-Fernandez
- Allergy Service, Hospital Universitario Dr. Molina Orosa, Las Palmas de Gran Canaria, Spain
| | - Dara Martinez-Beltran
- Allergy Service, Hospital Universitario Insular-Materno Infantil, Las Palmas de Gran Canaria, Spain
| | - Eva Perez-Rodriguez
- Allergy Service, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Ariel Callero
- Allergy Service, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Jose C. Garcia-Robaina
- Allergy Service, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | | | - Itahisa Marcelino-Rodriguez
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Public Health and Preventive Medicine Area, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Jose M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Carlos Flores
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
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Chen J, Zhang P, Peng M, Liu B, Wang X, Du S, Lu Y, Mu X, Lu Y, Wang S, Wu Y. An additional whole-exome sequencing study in 102 panel-undiagnosed patients: A retrospective study in a Chinese craniosynostosis cohort. Front Genet 2022; 13:967688. [PMID: 36118902 PMCID: PMC9481236 DOI: 10.3389/fgene.2022.967688] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Craniosynostosis (CRS) is a disease with prematurely fused cranial sutures. In the last decade, the whole-exome sequencing (WES) was widely used in Caucasian populations. The WES largely contributed in genetic diagnosis and exploration on new genetic mechanisms of CRS. In this study, we enrolled 264 CRS patients in China. After a 17-gene-panel sequencing designed in the previous study, 139 patients were identified with pathogenic/likely pathogenic (P/LP) variants according to the ACMG guideline as positive genetic diagnosis. WES was then performed on 102 patients with negative genetic diagnosis by panel. Ten P/LP variants were additionally identified in ten patients, increasing the genetic diagnostic yield by 3.8% (10/264). The novel variants in ANKH, H1-4, EIF5A, SOX6, and ARID1B expanded the mutation spectra of CRS. Then we designed a compatible research pipeline (RP) for further exploration. The RP could detect all seven P/LP SNVs and InDels identified above, in addition to 15 candidate variants found in 13 patients with worthy of further study. In sum, the 17-gene panel and WES identified positive genetic diagnosis for 56.4% patients (149/264) in 16 genes. At last, in our estimation, the genetic testing strategy of “Panel-first” saves 24.3% of the cost compared with “WES only”, suggesting the “Panel-first” is an economical strategy.
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Affiliation(s)
- Jieyi Chen
- Department of Plastic Surgery, Huashan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering at School of Life Sciences, Fudan University, Shanghai, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ping Zhang
- Center for Molecular Medicine, Pediatrics Research Institute, Children’s Hospital of Fudan University, Shanghai, China
| | - Meifang Peng
- The Core Laboratory in Medical Center of Clinical Research, Department of Molecular Diagnostics & Endocrinology, Shanghai Ninth People’s Hospital, State Key Laboratory of Medical Genomics, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bo Liu
- Center for Molecular Medicine, Pediatrics Research Institute, Children’s Hospital of Fudan University, Shanghai, China
| | - Xiao Wang
- Center for Molecular Medicine, Pediatrics Research Institute, Children’s Hospital of Fudan University, Shanghai, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yao Lu
- School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Xiongzheng Mu
- Department of Plastic Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Yulan Lu
- Center for Molecular Medicine, Pediatrics Research Institute, Children’s Hospital of Fudan University, Shanghai, China
- *Correspondence: Yingzhi Wu, ; Sijia Wang, ; Yulan Lu,
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- *Correspondence: Yingzhi Wu, ; Sijia Wang, ; Yulan Lu,
| | - Yingzhi Wu
- Department of Plastic Surgery, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Yingzhi Wu, ; Sijia Wang, ; Yulan Lu,
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Havrilla JM, Singaravelu A, Driscoll DM, Minkovsky L, Helbig I, Medne L, Wang K, Krantz I, Desai BR. PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care. BMC Med Inform Decis Mak 2022; 22:198. [PMID: 35902925 PMCID: PMC9335954 DOI: 10.1186/s12911-022-01927-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 07/06/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Clinical phenotype information greatly facilitates genetic diagnostic interpretations pipelines in disease. While post-hoc extraction using natural language processing on unstructured clinical notes continues to improve, there is a need to improve point-of-care collection of patient phenotypes. Therefore, we developed "PheNominal", a point-of-care web application, embedded within Epic electronic health record (EHR) workflows, to permit capture of standardized phenotype data. METHODS Using bi-directional web services available within commercial EHRs, we developed a lightweight web application that allows users to rapidly browse and identify relevant terms from the Human Phenotype Ontology (HPO). Selected terms are saved discretely within the patient's EHR, permitting reuse both in clinical notes as well as in downstream diagnostic and research pipelines. RESULTS In the 16 months since implementation, PheNominal was used to capture discrete phenotype data for over 1500 individuals and 11,000 HPO terms during clinic and inpatient encounters for a genetic diagnostic consultation service within a quaternary-care pediatric academic medical center. An average of 7 HPO terms were captured per patient. Compared to a manual workflow, the average time to enter terms for a patient was reduced from 15 to 5 min per patient, and there were fewer annotation errors. CONCLUSIONS Modern EHRs support integration of external applications using application programming interfaces. We describe a practical application of these interfaces to facilitate deep phenotype capture in a discrete, structured format within a busy clinical workflow. Future versions will include a vendor-agnostic implementation using FHIR. We describe pilot efforts to integrate structured phenotyping through controlled dictionaries into diagnostic and research pipelines, reducing manual effort for phenotype documentation and reducing errors in data entry.
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Affiliation(s)
- James M. Havrilla
- grid.239552.a0000 0001 0680 8770Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Anbumalar Singaravelu
- grid.239552.a0000 0001 0680 8770Emerging Technology and Transformation Team, Information Services, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Dennis M. Driscoll
- grid.239552.a0000 0001 0680 8770Emerging Technology and Transformation Team, Information Services, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Leonard Minkovsky
- grid.239552.a0000 0001 0680 8770Emerging Technology and Transformation Team, Information Services, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Ingo Helbig
- grid.239552.a0000 0001 0680 8770Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA ,grid.239552.a0000 0001 0680 8770The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, USA ,grid.239552.a0000 0001 0680 8770Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA ,grid.25879.310000 0004 1936 8972Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104 USA
| | - Livija Medne
- grid.239552.a0000 0001 0680 8770Roberts Individualized Medical Genetics Center, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Kai Wang
- grid.239552.a0000 0001 0680 8770Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA ,grid.239552.a0000 0001 0680 8770Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA ,grid.25879.310000 0004 1936 8972Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104 USA
| | - Ian Krantz
- grid.239552.a0000 0001 0680 8770Roberts Individualized Medical Genetics Center, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Bimal R. Desai
- grid.25879.310000 0004 1936 8972Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104 USA
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Reese JT, Blau H, Bergquist T, Loomba JJ, Callahan T, Laraway B, Antonescu C, Casiraghi E, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.05.24.22275398. [PMID: 35665012 PMCID: PMC9164456 DOI: 10.1101/2022.05.24.22275398] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
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Mehinovic E, Gray T, Campbell M, Ekholm J, Wenger A, Rowell W, Grudo A, Grimwood J, Korlach J, Gurnett C, Constantino JN, Turner TN. Germline mosaicism of a missense variant in KCNC2 in a multiplex family with autism and epilepsy characterized by long-read sequencing. Am J Med Genet A 2022; 188:2071-2081. [PMID: 35366058 PMCID: PMC9197999 DOI: 10.1002/ajmg.a.62743] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/04/2022] [Accepted: 02/18/2022] [Indexed: 02/06/2023]
Abstract
Currently, protein-coding de novo variants and large copy number variants have been identified as important for ~30% of individuals with autism. One approach to identify relevant variation in individuals who lack these types of events is by utilizing newer genomic technologies. In this study, highly accurate PacBio HiFi long-read sequencing was applied to a family with autism, epileptic encephalopathy, cognitive impairment, and mild dysmorphic features (two affected female siblings, unaffected parents, and one unaffected male sibling) with no known clinical variant. From our long-read sequencing data, a de novo missense variant in the KCNC2 gene (encodes Kv3.2) was identified in both affected children. This variant was phased to the paternal chromosome of origin and is likely a germline mosaic. In silico assessment revealed the variant was not in controls, highly conserved, and predicted damaging. This specific missense variant (Val473Ala) has been shown in both an ortholog and paralog of Kv3.2 to accelerate current decay, shift the voltage dependence of activation, and prevent the channel from entering a long-lasting open state. Seven additional missense variants have been identified in other individuals with neurodevelopmental disorders (p = 1.03 × 10-5 ). KCNC2 is most highly expressed in the brain; in particular, in the thalamus and is enriched in GABAergic neurons. Long-read sequencing was useful in discovering the relevant variant in this family with autism that had remained a mystery for several years and will potentially have great benefits in the clinic once it is widely available.
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Affiliation(s)
- Elvisa Mehinovic
- Department of GeneticsWashington University School of MedicineSt. LouisMissouriUSA
| | - Teddi Gray
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Meghan Campbell
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | | | | | | | - Ari Grudo
- Pacific BiosciencesMenlo ParkCaliforniaUSA
| | - Jane Grimwood
- HudsonAlpha Institute for BiotechnologyHuntsvilleAlabamaUSA
| | | | - Christina Gurnett
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - John N. Constantino
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Tychele N. Turner
- Department of GeneticsWashington University School of MedicineSt. LouisMissouriUSA
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New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches. Int J Mol Sci 2022; 23:ijms23126792. [PMID: 35743235 PMCID: PMC9224427 DOI: 10.3390/ijms23126792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 11/21/2022] Open
Abstract
Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.
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Dhombres F, Morgan P, Chaudhari BP, Filges I, Sparks TN, Lapunzina P, Roscioli T, Agarwal U, Aggarwal S, Beneteau C, Cacheiro P, Carmody LC, Collardeau‐Frachon S, Dempsey EA, Dufke A, Duyzend MH, el Ghosh M, Giordano JL, Glad R, Grinfelde I, Iliescu DG, Ladewig MS, Munoz‐Torres MC, Pollazzon M, Radio FC, Rodo C, Silva RG, Smedley D, Sundaramurthi JC, Toro S, Valenzuela I, Vasilevsky NA, Wapner RJ, Zemet R, Haendel MA, Robinson PN. Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2022; 190:231-242. [PMID: 35872606 PMCID: PMC9588534 DOI: 10.1002/ajmg.c.31989] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/01/2022] [Indexed: 01/07/2023]
Abstract
Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype-driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision-making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal-perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype-driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne University, GRC26, INSERM, Limics, Armand Trousseau Hospital, Fetal Medicine Department, APHPParisFrance
| | - Patricia Morgan
- American College of Medical Genetics and Genomics, Newborn Screening Translational Research NetworkBethesdaMarylandUSA
| | - Bimal P. Chaudhari
- Institute for Genomic MedicineNationwide Children's HospitalColumbusOhioUSA
| | - Isabel Filges
- University Hospital Basel and University of Basel, Medical GeneticsBaselSwitzerland
| | - Teresa N. Sparks
- Department of Obstetrics, Gynecology, & Reproductive SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Pablo Lapunzina
- CIBERER and Hospital Universitario La Paz, INGEMM‐Institute of Medical and Molecular GeneticsMadridSpain
| | - Tony Roscioli
- Neuroscience Research Australia (NeuRA), University of New South WalesSydneyNew South WalesAustralia
| | - Umber Agarwal
- Department of Maternal and Fetal MedicineLiverpool Women's NHS Foundation TrustLiverpoolUK
| | - Shagun Aggarwal
- Department of Medical GeneticsNizam's Institute of Medical SciencesHyderabadTelanganaIndia
| | - Claire Beneteau
- Service de Génétique Médicale, UF 9321 de Fœtopathologie et Génétique, CHU de NantesNantesFrance
| | - Pilar Cacheiro
- William Harvey Research InstituteQueen Mary University of LondonLondonUK
| | - Leigh C. Carmody
- Department of Genomic MedicineThe Jackson LaboratoryFarmingtonConnecticutUSA
| | | | - Esther A. Dempsey
- St George's University of London, Molecular and Clinical Sciences Research InstituteLondonUK
| | - Andreas Dufke
- University of Tübingen, Institute of Medical Genetics and Applied GenomicsTübingenGermany
| | | | | | - Jessica L. Giordano
- Department of Obstetrics and GynecologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Ragnhild Glad
- Department of Obstetrics and GynecologyUniversity Hospital of North NorwayTromsøNorway
| | - Ieva Grinfelde
- Department of Medical Genetics and Prenatal diagnosisChildren's University HospitalRigaLatvia
| | - Dominic G. Iliescu
- Department of Obstetrics and GynecologyUniversity of Medicine and Pharmacy CraiovaCraiovaDoljRomania
| | - Markus S. Ladewig
- Department of OphthalmologyKlinikum SaarbrückenSaarbrückenSaarlandGermany
| | - Monica C. Munoz‐Torres
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Marzia Pollazzon
- Azienda USL‐IRCCS di Reggio EmiliaMedical Genetics UnitReggio EmiliaItaly
| | | | - Carlota Rodo
- Vall d'Hebron Hospital Campus, Maternal & Fetal MedicineBarcelonaSpain
| | - Raquel Gouveia Silva
- Hospital Santa Maria, Serviço de Genética, Departamento de PediatriaHospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte, Centro Académico de Medicina de LisboaLisboaPortugal
| | - Damian Smedley
- William Harvey Research InstituteQueen Mary University of LondonLondonUK
| | | | - Sabrina Toro
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Irene Valenzuela
- Hospital Vall d'Hebron, Clinical and Molecular Genetics AreaBarcelonaSpain
| | - Nicole A. Vasilevsky
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Ronald J. Wapner
- Department of Obstetrics and GynecologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Roni Zemet
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Melissa A Haendel
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Peter N. Robinson
- Department of Genomic MedicineThe Jackson LaboratoryFarmingtonConnecticutUSA
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Pais LS, Snow H, Weisburd B, Zhang S, Baxter SM, DiTroia S, O’Heir E, England E, Chao KR, Lemire G, Osei-Owusu I, VanNoy GE, Wilson M, Nguyen K, Arachchi H, Phu W, Solomonson M, Mano S, O’Leary M, Lovgren A, Babb L, Austin-Tse CA, Rehm HL, MacArthur DG, O’Donnell-Luria A. seqr: A web-based analysis and collaboration tool for rare disease genomics. Hum Mutat 2022; 43:698-707. [PMID: 35266241 PMCID: PMC9903206 DOI: 10.1002/humu.24366] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 02/23/2022] [Accepted: 03/04/2022] [Indexed: 02/04/2023]
Abstract
Exome and genome sequencing have become the tools of choice for rare disease diagnosis, leading to large amounts of data available for analyses. To identify causal variants in these datasets, powerful filtering and decision support tools that can be efficiently used by clinicians and researchers are required. To address this need, we developed seqr - an open-source, web-based tool for family-based monogenic disease analysis that allows researchers to work collaboratively to search and annotate genomic callsets. To date, seqr is being used in several research pipelines and one clinical diagnostic lab. In our own experience through the Broad Institute Center for Mendelian Genomics, seqr has enabled analyses of over 10,000 families, supporting the diagnosis of more than 3,800 individuals with rare disease and discovery of over 300 novel disease genes. Here, we describe a framework for genomic analysis in rare disease that leverages seqr's capabilities for variant filtration, annotation, and causal variant identification, as well as support for research collaboration and data sharing. The seqr platform is available as open source software, allowing low-cost participation in rare disease research, and a community effort to support diagnosis and gene discovery in rare disease.
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Affiliation(s)
- Lynn S. Pais
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hana Snow
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Ben Weisburd
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Shifa Zhang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Samantha M. Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Stephanie DiTroia
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Emily O’Heir
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Katherine R. Chao
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Gabrielle Lemire
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ikeoluwa Osei-Owusu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Grace E. VanNoy
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Michael Wilson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kevin Nguyen
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Harindra Arachchi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - William Phu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Matthew Solomonson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Stacy Mano
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Melanie O’Leary
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Alysia Lovgren
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Lawrence Babb
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Christina A. Austin-Tse
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Heidi L. Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel G. MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, Australia,Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Anne O’Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Bhat GR, Sethi I, Rah B, Kumar R, Afroze D. Innovative in Silico Approaches for Characterization of Genes and Proteins. Front Genet 2022; 13:865182. [PMID: 35664302 PMCID: PMC9159363 DOI: 10.3389/fgene.2022.865182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.
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Affiliation(s)
- Gh. Rasool Bhat
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
| | - Itty Sethi
- Institute of Human Genetics, University of Jammu, Jammu, India
| | - Bilal Rah
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
| | - Rakesh Kumar
- School of Biotechnology, Shri Mata Vaishno Devi University, Katra, India
| | - Dil Afroze
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
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Steinhaus R, Proft S, Seelow E, Schalau T, Robinson PN, Seelow D. Deep phenotyping: symptom annotation made simple with SAMS. Nucleic Acids Res 2022; 50:W677-W681. [PMID: 35524573 PMCID: PMC9252818 DOI: 10.1093/nar/gkac329] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/16/2022] [Accepted: 04/24/2022] [Indexed: 11/14/2022] Open
Abstract
Precision medicine needs precise phenotypes. The Human Phenotype Ontology (HPO) uses clinical signs instead of diagnoses and has become the standard annotation for patients’ phenotypes when describing single gene disorders. Use of the HPO beyond human genetics is however still limited. With SAMS (Symptom Annotation Made Simple), we want to bring sign-based phenotyping to routine clinical care, to hospital patients as well as to outpatients. Our web-based application provides access to three widely used annotation systems: HPO, OMIM, Orphanet. Whilst data can be stored in our database, phenotypes can also be imported and exported as Global Alliance for Genomics and Health (GA4GH) Phenopackets without using the database. The web interface can easily be integrated into local databases, e.g. clinical information systems. SAMS offers users to share their data with others, empowering patients to record their own signs and symptoms (or those of their children) and thus provide their doctors with additional information. We think that our approach will lead to better characterised patients which is not only helpful for finding disease mutations but also to better understand the pathophysiology of diseases and to recruit patients for studies and clinical trials. SAMS is freely available at https://www.genecascade.org/SAMS/.
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Affiliation(s)
- Robin Steinhaus
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.,Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany
| | - Sebastian Proft
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.,Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany
| | - Evelyn Seelow
- Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Tobias Schalau
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.,FB Mathematik und Informatik, Freie Universität Berlin, Berlin 14195, Germany
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.,Institute for Systems Genomics, University of Connecticut, Farmington, CT 06030, USA
| | - Dominik Seelow
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.,Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany
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Wang Q, Tang X, Yang K, Huo X, Zhang H, Ding K, Liao S. Deep phenotyping and whole-exome sequencing improved the diagnostic yield for nuclear pedigrees with neurodevelopmental disorders. Mol Genet Genomic Med 2022; 10:e1918. [PMID: 35266334 PMCID: PMC9034680 DOI: 10.1002/mgg3.1918] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 12/21/2022] Open
Abstract
Background Neurodevelopmental disorders, a group of early‐onset neurological disorders with significant clinical and genetic heterogeneity, remain a diagnostic challenge for clinical genetic evaluation. Therefore, we assessed the diagnostic yield by combining standard phenotypes and whole‐exome sequencing in families with these disorders that were “not yet diagnosed” by the traditional testing methods. Methods Using a standardized vocabulary of phenotypic abnormalities from human phenotype ontology (HPO), we performed deep phenotyping for 45 “not yet diagnosed” pedigrees to characterize multiple clinical features extracted from Chinese electronic medical records (EMRs). By matching HPO terms with known human diseases and phenotypes from model organisms, together with whole‐exome sequencing data, we prioritized candidate mutations/genes. We made probable genetic diagnoses for the families. Results We obtained a diagnostic yield of 29% (13 out of 45) with probably genetic diagnosis, of which compound heterozygosity and de novo mutations accounted for 77% (10/13) of the diagnosis. Of note, these pedigrees are accompanied by a more significant number of non‐neurological features. Conclusions Deep phenotyping and whole‐exome sequencing improve the etiological evaluation for neurodevelopmental disorders in the clinical setting.
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Affiliation(s)
- Qingqing Wang
- Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.,NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, China
| | - Xia Tang
- Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.,NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, China
| | - Ke Yang
- Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.,NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, China
| | - Xiaodong Huo
- Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.,NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, China
| | - Hui Zhang
- Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.,NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, China
| | - Keyue Ding
- Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.,NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, China
| | - Shixiu Liao
- Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.,NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, China
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48
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Steinhaus R, Boschann F, Vogel M, Fischer-Zirnsak B, Seelow D. AutozygosityMapper: Identification of disease-mutations in consanguineous families. Nucleic Acids Res 2022; 50:W83-W89. [PMID: 35489060 PMCID: PMC9252840 DOI: 10.1093/nar/gkac280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/06/2022] [Accepted: 04/12/2022] [Indexed: 11/14/2022] Open
Abstract
With the shift from SNP arrays to high-throughput sequencing, most researchers studying diseases in consanguineous families do not rely on linkage analysis any longer, but simply search for deleterious variants which are homozygous in all patients. AutozygosityMapper allows the fast and convenient identification of disease mutations in patients from consanguineous pedigrees by focussing on homozygous segments shared by all patients. Users can upload multi-sample VCF files, including WGS data, without any pre-processing. Genome-wide runs of homozygosity and the underlying genotypes are presented in graphical interfaces. AutozygosityMapper extends the functions of its predecessor, HomozygosityMapper, to the search for autozygous regions, in which all patients share the same homozygous genotype. We provide export of VCF files containing only the variants found in homozygous regions, this usually reduces the number of variants by two orders of magnitude. These regions can also directly be analysed with our disease mutation identification tool MutationDistiller. The application comes with simple and intuitive graphical interfaces for data upload, analysis, and results. We kept the structure of HomozygosityMapper so that previous users will find it easy to switch. With AutozygosityMapper, we provide a fast web-based way to identify disease mutations in consanguineous families. AutozygosityMapper is freely available at https://www.genecascade.org/AutozygosityMapper/.
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Affiliation(s)
- Robin Steinhaus
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.,Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany
| | - Felix Boschann
- Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany
| | - Melanie Vogel
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.,FB Mathematik und Informatik, Freie Universität Berlin, Berlin 14195, Germany
| | - Björn Fischer-Zirnsak
- Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany
| | - Dominik Seelow
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.,Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany
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49
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Danis D, Jacobsen JOB, Balachandran P, Zhu Q, Yilmaz F, Reese J, Haimel M, Lyon GJ, Helbig I, Mungall CJ, Beck CR, Lee C, Smedley D, Robinson PN. SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing. Genome Med 2022; 14:44. [PMID: 35484572 PMCID: PMC9047340 DOI: 10.1186/s13073-022-01046-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 04/12/2022] [Indexed: 01/18/2023] Open
Abstract
Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnn a .
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Affiliation(s)
- Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Julius O B Jacobsen
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | | | - Qihui Zhu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Feyza Yilmaz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Matthias Haimel
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- St. Anna Children's Cancer Research Institute, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Present address: Global Computational Biology and Digital Sciences, Boehringer Ingelheim Regional Center Vienna GmbH & Co KG, 1120, Vienna, Austria
| | - Gholson J Lyon
- Department of Human Genetics, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, New York, USA
- Biology PhD Program, The Graduate Center, The City University of New York, New York, USA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Christine R Beck
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, 06032, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, 06269, USA
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Damian Smedley
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, 06032, USA.
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50
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Implementation of Exome Sequencing in Prenatal Diagnosis and Impact on Genetic Counseling: The Polish Experience. Genes (Basel) 2022; 13:genes13050724. [PMID: 35627109 PMCID: PMC9140952 DOI: 10.3390/genes13050724] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 11/23/2022] Open
Abstract
Background: Despite advances in routine prenatal cytogenetic testing, most anomalous fetuses remain without a genetic diagnosis. Exome sequencing (ES) is a molecular technique that identifies sequence variants across protein-coding regions and is now increasingly used in clinical practice. Fetal phenotypes differ from postnatal and, therefore, prenatal ES interpretation requires a large amount of data deriving from prenatal testing. The aim of our study was to present initial results of the implementation of ES to prenatal diagnosis in Polish patients and to discuss its possible clinical impact on genetic counseling. Methods: In this study we performed a retrospective review of all fetal samples referred to our laboratory for ES from cooperating centers between January 2017 and June 2021. Results: During the study period 122 fetuses were subjected to ES at our institution. There were 52 abnormal ES results: 31 in the group of fetuses with a single organ system anomaly and 21 in the group of fetuses with multisystem anomalies. The difference between groups was not statistically significant. There were 57 different pathogenic or likely pathogenic variants reported in 33 different genes. The most common were missense variants. In 17 cases the molecular diagnosis had an actual clinical impact on subsequent pregnancies or other family members. Conclusions: Exome sequencing increases the detection rate in fetuses with structural anomalies and improves genetic counseling for both the affected couple and their relatives.
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