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Ellwanger K, Brill JA, de Boer E, Efthymiou S, Elgersma Y, Icmat M, Lecoquierre F, Lobato AG, Morleo M, Ori M, Schaffer AE, Vitobello A, Wells S, Yalcin B, Zhai RG, Sturm M, Zurek B, Graessner H, Bermejo-Sánchez E, Evangelista T, Hoogerbrugge N, Nigro V, Schüle R, Verloes A, Brunner H, Campeau PM, Lasko P, Riess O. Model matchmaking via the Solve-RD Rare Disease Models & Mechanisms Network (RDMM-Europe). Lab Anim (NY) 2024; 53:161-165. [PMID: 38914824 PMCID: PMC11216991 DOI: 10.1038/s41684-024-01395-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Affiliation(s)
- Kornelia Ellwanger
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
| | - Julie A Brill
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Elke de Boer
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Stephanie Efthymiou
- Department of Neuromuscular Disorders, Queen Square Institute of Neurology, University College London, London, UK
| | - Ype Elgersma
- Department of Clinical Genetics, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Marynelle Icmat
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - François Lecoquierre
- Normandie Univ, UNIROUEN, Inserm U1245, CHU Rouen, Department of Genetics, FHU G4 Génomique, Rouen, France
| | - Amanda G Lobato
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA
- Graduate Program in Human Genetics and Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Manuela Morleo
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (Na), Italy
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Napoli, Italy
| | - Michela Ori
- Department of Biology, University of Pisa, Pisa, Italy
| | - Ashleigh E Schaffer
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Antonio Vitobello
- INSERM-Université de Bourgogne UMR1231 GAD «Génétique Des Anomalies du Développement», FHU-TRANSLAD, UFR Des Sciences de Santé, Dijon, France
- Dijon University Hospital- UF innovation en diagnostic génomique, Dijon, France
| | - Sara Wells
- The Mary Lyon Centre at MRC Harwell, Harwell Science Campus, Oxon, UK
| | - Binnaz Yalcin
- Inserm Unit 1231, University of Bourgogne Franche-Comté, Dijon, France
| | - R Grace Zhai
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Birte Zurek
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Holm Graessner
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Eva Bermejo-Sánchez
- Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Teresinha Evangelista
- Sorbonne Université, Inserm, Institut de Myologie, Centre de Recherche en Myologie, Paris, France
| | - Nicoline Hoogerbrugge
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Vincenzo Nigro
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (Na), Italy
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Napoli, Italy
| | - Rebecca Schüle
- Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany
| | - Alain Verloes
- Department of Genetics, Assistance Publique-Hôpitaux de Paris - Université de Paris, Robert DEBRE University Hospital, Paris, France
| | - Han Brunner
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Philippe M Campeau
- Department of Pediatrics, CHU Sainte-Justine and University of Montreal, Montreal, Quebec, Canada
| | - Paul Lasko
- Department of Biology, McGill University, Montreal, Quebec, Canada
| | - Olaf Riess
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
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2
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Scarano C, Veneruso I, De Simone RR, Di Bonito G, Secondino A, D’Argenio V. The Third-Generation Sequencing Challenge: Novel Insights for the Omic Sciences. Biomolecules 2024; 14:568. [PMID: 38785975 PMCID: PMC11117673 DOI: 10.3390/biom14050568] [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] [Received: 04/08/2024] [Revised: 05/05/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The understanding of the human genome has been greatly improved by the advent of next-generation sequencing technologies (NGS). Despite the undeniable advantages responsible for their widespread diffusion, these methods have some constraints, mainly related to short read length and the need for PCR amplification. As a consequence, long-read sequencers, called third-generation sequencing (TGS), have been developed, promising to overcome NGS. Starting from the first prototype, TGS has progressively ameliorated its chemistries by improving both read length and base-calling accuracy, as well as simultaneously reducing the costs/base. Based on these premises, TGS is showing its potential in many fields, including the analysis of difficult-to-sequence genomic regions, structural variations detection, RNA expression profiling, DNA methylation study, and metagenomic analyses. Protocol standardization and the development of easy-to-use pipelines for data analysis will enhance TGS use, also opening the way for their routine applications in diagnostic contexts.
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Affiliation(s)
- Carmela Scarano
- Department of Molecular Medicine and Medical Biotechnologies, Federico II University, Via Sergio Pansini 5, 80131 Napoli, Italy
- CEINGE-Biotecnologie Avanzate Franco Salvatore, Via G. Salvatore 486, 80145 Napoli, Italy
| | - Iolanda Veneruso
- Department of Molecular Medicine and Medical Biotechnologies, Federico II University, Via Sergio Pansini 5, 80131 Napoli, Italy
- CEINGE-Biotecnologie Avanzate Franco Salvatore, Via G. Salvatore 486, 80145 Napoli, Italy
| | - Rosa Redenta De Simone
- Department of Molecular Medicine and Medical Biotechnologies, Federico II University, Via Sergio Pansini 5, 80131 Napoli, Italy
- CEINGE-Biotecnologie Avanzate Franco Salvatore, Via G. Salvatore 486, 80145 Napoli, Italy
| | - Gennaro Di Bonito
- Department of Molecular Medicine and Medical Biotechnologies, Federico II University, Via Sergio Pansini 5, 80131 Napoli, Italy
- CEINGE-Biotecnologie Avanzate Franco Salvatore, Via G. Salvatore 486, 80145 Napoli, Italy
| | - Angela Secondino
- Department of Molecular Medicine and Medical Biotechnologies, Federico II University, Via Sergio Pansini 5, 80131 Napoli, Italy
- CEINGE-Biotecnologie Avanzate Franco Salvatore, Via G. Salvatore 486, 80145 Napoli, Italy
| | - Valeria D’Argenio
- CEINGE-Biotecnologie Avanzate Franco Salvatore, Via G. Salvatore 486, 80145 Napoli, Italy
- Department of Human Sciences and Quality of Life Promotion, San Raffaele Open University, Via di Val Cannuta 247, 00166 Roma, Italy
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3
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Steyaert W, Sagath L, Demidov G, Yépez VA, Esteve-Codina A, Gagneur J, Ellwanger K, Derks R, Weiss M, den Ouden A, van den Heuvel S, Swinkels H, Zomer N, Steehouwer M, O'Gorman L, Astuti G, Neveling K, Schüle R, Xu J, Synofzik M, Beijer D, Hengel H, Schöls L, Claeys KG, Baets J, Van de Vondel L, Ferlini A, Selvatici R, Morsy H, Saeed Abd Elmaksoud M, Straub V, Müller J, Pini V, Perry L, Sarkozy A, Zaharieva I, Muntoni F, Bugiardini E, Polavarapu K, Horvath R, Reid E, Lochmüller H, Spinazzi M, Savarese M, Matalonga L, Laurie S, Brunner HG, Graessner H, Beltran S, Ossowski S, Vissers LELM, Gilissen C, Hoischen A. Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.03.24305331. [PMID: 38746462 PMCID: PMC11092722 DOI: 10.1101/2024.05.03.24305331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilised 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single nucleotide variants (SNVs), insertion-deletions (InDels), and short tandem repeat (STR) expansions in extensively studied RD families without clear molecular diagnoses. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Rare Disease Network (ERN) experts. Of these, 21 families were affected by so-called 'unsolvable' syndromes for which genetic causes remain unknown, and 93 families with at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded thirteen novel genetic diagnoses due to de novo and rare inherited SNVs, InDels, SVs, and STR expansions. In an additional four families, we identified a candidate disease-causing SV affecting several genes including an MCF2 / FGF13 fusion and PSMA3 deletion. However, no common genetic cause was identified in any of the 'unsolvable' syndromes. Taken together, we found (likely) disease-causing genetic variants in 13.0% of previously unsolved families and additional candidate disease-causing SVs in another 4.3% of these families. In conclusion, our results demonstrate the added value of HiFi long-read genome sequencing in undiagnosed rare diseases.
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4
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Dias KR, Shrestha R, Schofield D, Evans CA, O'Heir E, Zhu Y, Zhang F, Standen K, Weisburd B, Stenton SL, Sanchis-Juan A, Brand H, Talkowski ME, Ma A, Ghedia S, Wilson M, Sandaradura SA, Smith J, Kamien B, Turner A, Bakshi M, Adès LC, Mowat D, Regan M, McGillivray G, Savarirayan R, White SM, Tan TY, Stark Z, Brown NJ, Pérez-Jurado LA, Krzesinski E, Hunter MF, Akesson L, Fennell AP, Yeung A, Boughtwood T, Ewans LJ, Kerkhof J, Lucas C, Carey L, French H, Rapadas M, Stevanovski I, Deveson IW, Cliffe C, Elakis G, Kirk EP, Dudding-Byth T, Fletcher J, Walsh R, Corbett MA, Kroes T, Gecz J, Meldrum C, Cliffe S, Wall M, Lunke S, North K, Amor DJ, Field M, Sadikovic B, Buckley MF, O'Donnell-Luria A, Roscioli T. Narrowing the diagnostic gap: Genomes, episignatures, long-read sequencing, and health economic analyses in an exome-negative intellectual disability cohort. Genet Med 2024; 26:101076. [PMID: 38258669 DOI: 10.1016/j.gim.2024.101076] [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: 06/30/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
PURPOSE Genome sequencing (GS)-specific diagnostic rates in prospective tightly ascertained exome sequencing (ES)-negative intellectual disability (ID) cohorts have not been reported extensively. METHODS ES, GS, epigenetic signatures, and long-read sequencing diagnoses were assessed in 74 trios with at least moderate ID. RESULTS The ES diagnostic yield was 42 of 74 (57%). GS diagnoses were made in 9 of 32 (28%) ES-unresolved families. Repeated ES with a contemporary pipeline on the GS-diagnosed families identified 8 of 9 single-nucleotide variations/copy-number variations undetected in older ES, confirming a GS-unique diagnostic rate of 1 in 32 (3%). Episignatures contributed diagnostic information in 9% with GS corroboration in 1 of 32 (3%) and diagnostic clues in 2 of 32 (6%). A genetic etiology for ID was detected in 51 of 74 (69%) families. Twelve candidate disease genes were identified. Contemporary ES followed by GS cost US$4976 (95% CI: $3704; $6969) per diagnosis and first-line GS at a cost of $7062 (95% CI: $6210; $8475) per diagnosis. CONCLUSION Performing GS only in ID trios would be cost equivalent to ES if GS were available at $2435, about a 60% reduction from current prices. This study demonstrates that first-line GS achieves higher diagnostic rate than contemporary ES but at a higher cost.
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Affiliation(s)
- Kerith-Rae Dias
- Neuroscience Research Australia, Sydney, NSW, Australia; Prince of Wales Clinical School, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Rupendra Shrestha
- Centre for Economic Impacts of Genomic Medicine, Macquarie Business School, Macquarie University, Sydney, NSW, Australia
| | - Deborah Schofield
- Centre for Economic Impacts of Genomic Medicine, Macquarie Business School, Macquarie University, Sydney, NSW, Australia
| | - Carey-Anne Evans
- Neuroscience Research Australia, Sydney, NSW, Australia; New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Emily O'Heir
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Ying Zhu
- Neuroscience Research Australia, Sydney, NSW, Australia; New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia; The Genetics of Learning Disability Service, Waratah, NSW, Australia
| | - Futao Zhang
- Neuroscience Research Australia, Sydney, NSW, Australia; New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Krystle Standen
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Ben Weisburd
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Sarah L Stenton
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Alba Sanchis-Juan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Harrison Brand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Michael E Talkowski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Alan Ma
- Department of Clinical Genetics, Children's Hospital at Westmead, Sydney Children's Hospital Network, Sydney, NSW, Australia; Specialty of Genomic Medicine, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Sondy Ghedia
- Department of Clinical Genetics, Royal North Shore Hospital, Sydney, NSW, Australia; Northern Clinical School, Royal North Shore Hospital, Sydney, NSW, Australia
| | - Meredith Wilson
- Department of Clinical Genetics, Children's Hospital at Westmead, Sydney Children's Hospital Network, Sydney, NSW, Australia
| | - Sarah A Sandaradura
- Department of Clinical Genetics, Children's Hospital at Westmead, Sydney Children's Hospital Network, Sydney, NSW, Australia; Disciplines of Child and Adolescent Health and Genetic Medicine, University of Sydney, Sydney, NSW 2050, Australia
| | - Janine Smith
- Department of Clinical Genetics, Children's Hospital at Westmead, Sydney Children's Hospital Network, Sydney, NSW, Australia; Specialty of Genomic Medicine, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Benjamin Kamien
- Genetic Services of Western Australia, Perth, WA, Australia; School of Paediatrics and Child Health, University of Western Australia, Perth, WA, Australia
| | - Anne Turner
- Centre for Clinical Genetics, Sydney Children's Hospital, Sydney, NSW, Australia
| | - Madhura Bakshi
- Department of Clinical Genetics, Liverpool Hospital, Sydney, NSW, Australia
| | - Lesley C Adès
- Department of Clinical Genetics, Children's Hospital at Westmead, Sydney Children's Hospital Network, Sydney, NSW, Australia; Disciplines of Child and Adolescent Health and Genetic Medicine, University of Sydney, Sydney, NSW 2050, Australia
| | - David Mowat
- Centre for Clinical Genetics, Sydney Children's Hospital, Sydney, NSW, Australia; Discipline of Paediatrics & Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Matthew Regan
- Monash Genetics, Monash Health, Melbourne, VIC, Australia
| | - George McGillivray
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia
| | - Ravi Savarirayan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Susan M White
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Tiong Yang Tan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Zornitza Stark
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; Australian Genomics, Melbourne, VIC, Australia
| | - Natasha J Brown
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Luis A Pérez-Jurado
- Genetics Unit, Universitat Pompeu Fabra, Institut Hospital del Mar d'Investigacions Mediques (IMIM), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain; Women's and Children's Hospital, South Australian Health and Medical Research Institute & University of Adelaide, Adelaide, SA, Australia
| | - Emma Krzesinski
- Monash Genetics, Monash Health, Melbourne, VIC, Australia; Department of Paediatrics, Monash University, Melbourne, VIC, Australia
| | - Matthew F Hunter
- Monash Genetics, Monash Health, Melbourne, VIC, Australia; Department of Paediatrics, Monash University, Melbourne, VIC, Australia
| | - Lauren Akesson
- Melbourne Pathology, Melbourne, VIC, Australia; Department of Pathology, The Royal Melbourne Hospital, Melbourne, VIC, Australia; Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia
| | - Andrew Paul Fennell
- Monash Genetics, Monash Health, Melbourne, VIC, Australia; Department of Paediatrics, Monash University, Melbourne, VIC, Australia
| | - Alison Yeung
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Tiffany Boughtwood
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Australian Genomics, Melbourne, VIC, Australia
| | - Lisa J Ewans
- Centre for Clinical Genetics, Sydney Children's Hospital, Sydney, NSW, Australia; Discipline of Paediatrics & Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia; Genomics and Inherited Disease Program, Garvan Institute of Medical Research, University of New South Wales Sydney, Sydney, NSW, Australia
| | - Jennifer Kerkhof
- Verspeeten Clinical Genome Centre London Health Sciences Centre, London, ON, Canada
| | - Christopher Lucas
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Louise Carey
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Hugh French
- Department of Medical Genomics, Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | - Melissa Rapadas
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, University of New South Wales Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Sydney, NSW, Australia
| | - Igor Stevanovski
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, University of New South Wales Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Sydney, NSW, Australia
| | - Ira W Deveson
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, University of New South Wales Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Sydney, NSW, Australia; St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Corrina Cliffe
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - George Elakis
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Edwin P Kirk
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia; Centre for Clinical Genetics, Sydney Children's Hospital, Sydney, NSW, Australia; Discipline of Paediatrics & Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | | | - Janice Fletcher
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Rebecca Walsh
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Mark A Corbett
- Adelaide Medical School and Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
| | - Thessa Kroes
- Adelaide Medical School and Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
| | - Jozef Gecz
- Adelaide Medical School and Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Cliff Meldrum
- State Wide Service, New South Wales Health Pathology, Sydney, NSW, Australia
| | - Simon Cliffe
- State Wide Service, New South Wales Health Pathology, Sydney, NSW, Australia
| | - Meg Wall
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia
| | - Sebastian Lunke
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia
| | - Kathryn North
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; Australian Genomics, Melbourne, VIC, Australia; Global Alliance for Genomics and Health, Toronto, ON, Canada
| | - David J Amor
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Michael Field
- The Genetics of Learning Disability Service, Waratah, NSW, Australia
| | - Bekim Sadikovic
- Verspeeten Clinical Genome Centre London Health Sciences Centre, London, ON, Canada; Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Michael F Buckley
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
| | - Tony Roscioli
- Neuroscience Research Australia, Sydney, NSW, Australia; Prince of Wales Clinical School, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia; New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, NSW, Australia.
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5
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Schobers G, Derks R, den Ouden A, Swinkels H, van Reeuwijk J, Bosgoed E, Lugtenberg D, Sun SM, Corominas Galbany J, Weiss M, Blok MJ, Olde Keizer RACM, Hofste T, Hellebrekers D, de Leeuw N, Stegmann A, Kamsteeg EJ, Paulussen ADC, Ligtenberg MJL, Bradley XZ, Peden J, Gutierrez A, Pullen A, Payne T, Gilissen C, van den Wijngaard A, Brunner HG, Nelen M, Yntema HG, Vissers LELM. Genome sequencing as a generic diagnostic strategy for rare disease. Genome Med 2024; 16:32. [PMID: 38355605 PMCID: PMC10868087 DOI: 10.1186/s13073-024-01301-y] [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: 09/21/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND To diagnose the full spectrum of hereditary and congenital diseases, genetic laboratories use many different workflows, ranging from karyotyping to exome sequencing. A single generic high-throughput workflow would greatly increase efficiency. We assessed whether genome sequencing (GS) can replace these existing workflows aimed at germline genetic diagnosis for rare disease. METHODS We performed short-read GS (NovaSeq™6000; 150 bp paired-end reads, 37 × mean coverage) on 1000 cases with 1271 known clinically relevant variants, identified across different workflows, representative of our tertiary diagnostic centers. Variants were categorized into small variants (single nucleotide variants and indels < 50 bp), large variants (copy number variants and short tandem repeats) and other variants (structural variants and aneuploidies). Variant calling format files were queried per variant, from which workflow-specific true positive rates (TPRs) for detection were determined. A TPR of ≥ 98% was considered the threshold for transition to GS. A GS-first scenario was generated for our laboratory, using diagnostic efficacy and predicted false negative as primary outcome measures. As input, we modeled the diagnostic path for all 24,570 individuals referred in 2022, combining the clinical referral, the transition of the underlying workflow(s) to GS, and the variant type(s) to be detected. RESULTS Overall, 95% (1206/1271) of variants were detected. Detection rates differed per variant category: small variants in 96% (826/860), large variants in 93% (341/366), and other variants in 87% (39/45). TPRs varied between workflows (79-100%), with 7/10 being replaceable by GS. Models for our laboratory indicate that a GS-first strategy would be feasible for 84.9% of clinical referrals (750/883), translating to 71% of all individuals (17,444/24,570) receiving GS as their primary test. An estimated false negative rate of 0.3% could be expected. CONCLUSIONS GS can capture clinically relevant germline variants in a 'GS-first strategy' for the majority of clinical indications in a genetics diagnostic lab.
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Affiliation(s)
- Gaby Schobers
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
| | - Ronny Derks
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | - Amber den Ouden
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | - Hilde Swinkels
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | - Jeroen van Reeuwijk
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
| | - Ermanno Bosgoed
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | | | - Su Ming Sun
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | - Jordi Corominas Galbany
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
| | - Marjan Weiss
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | - Marinus J Blok
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | - Richelle A C M Olde Keizer
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
| | - Tom Hofste
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | - Debby Hellebrekers
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | - Nicole de Leeuw
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | - Alexander Stegmann
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | | | - Aimee D C Paulussen
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | - Marjolijn J L Ligtenberg
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
| | | | | | | | | | | | - Christian Gilissen
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
| | | | - Han G Brunner
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | - Marcel Nelen
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
| | - Helger G Yntema
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands
| | - Lisenka E L M Vissers
- Department of Human Genetics, Radboudumc, Nijmegen, Netherlands.
- Research Institute for Medical Innovation, Radboudumc, Nijmegen, Netherlands.
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6
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Barbitoff YA, Ushakov MO, Lazareva TE, Nasykhova YA, Glotov AS, Predeus AV. Bioinformatics of germline variant discovery for rare disease diagnostics: current approaches and remaining challenges. Brief Bioinform 2024; 25:bbad508. [PMID: 38271481 PMCID: PMC10810331 DOI: 10.1093/bib/bbad508] [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: 08/09/2023] [Revised: 11/18/2023] [Accepted: 12/12/2023] [Indexed: 01/27/2024] Open
Abstract
Next-generation sequencing (NGS) has revolutionized the field of rare disease diagnostics. Whole exome and whole genome sequencing are now routinely used for diagnostic purposes; however, the overall diagnosis rate remains lower than expected. In this work, we review current approaches used for calling and interpretation of germline genetic variants in the human genome, and discuss the most important challenges that persist in the bioinformatic analysis of NGS data in medical genetics. We describe and attempt to quantitatively assess the remaining problems, such as the quality of the reference genome sequence, reproducible coverage biases, or variant calling accuracy in complex regions of the genome. We also discuss the prospects of switching to the complete human genome assembly or the human pan-genome and important caveats associated with such a switch. We touch on arguably the hardest problem of NGS data analysis for medical genomics, namely, the annotation of genetic variants and their subsequent interpretation. We highlight the most challenging aspects of annotation and prioritization of both coding and non-coding variants. Finally, we demonstrate the persistent prevalence of pathogenic variants in the coding genome, and outline research directions that may enhance the efficiency of NGS-based disease diagnostics.
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Affiliation(s)
- Yury A Barbitoff
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
- Bioinformatics Institute, Kentemirovskaya st. 2A, 197342, St. Petersburg, Russia
| | - Mikhail O Ushakov
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Tatyana E Lazareva
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Yulia A Nasykhova
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Andrey S Glotov
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Alexander V Predeus
- Bioinformatics Institute, Kentemirovskaya st. 2A, 197342, St. Petersburg, Russia
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7
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Al Eissa MM, Alotibi RS, Alhaddad B, Aloraini T, Samman MS, AlAsiri A, Abouelhoda M, AlQahtani AS. Reclassifying variations of unknown significance in diseases affecting Saudi Arabia's population reveal new associations. Front Genet 2023; 14:1250317. [PMID: 38028588 PMCID: PMC10646566 DOI: 10.3389/fgene.2023.1250317] [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: 06/29/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction: Physicians face diagnostic dilemmas upon reports indicating disease variants of unknown significance (VUS). The most puzzling cases are patients with rare diseases, where finding another matched genotype and phenotype to associate their results is challenging. This study aims to prove the value of updating patient files with new classifications, potentially leading to better assessment and prevention. Methodology: We recruited retrospective phenotypic and genotypic data from King Saud Medical City, Riyadh, Kingdom of Saudi Arabia. Between September 2020 and December 2021, 1,080 patients' genetic profiles were tested in a College of American Pathologists accredited laboratory. We excluded all confirmed pathogenic variants, likely pathogenic variants and copy number variations. Finally, we further reclassified 194 VUS using different local and global databases, employing in silico prediction to justify the phenotype-genotype association. Results: Of the 194 VUS, 90 remained VUS, and the other 104 were reclassified as follows: 16 pathogenic, 49 likely pathogenic, nine benign, and 30 likely benign. Moreover, most of these variants had never been observed in other local or international databases. Conclusion: Reclassifying the VUS adds value to understanding the causality of the phenotype if it has been reported in another family or population. The healthcare system should establish guidelines for re-evaluating VUS, and upgrading VUS should reflect on individual/family risks and management strategies.
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Affiliation(s)
- Mariam M. Al Eissa
- Public Health Authority, Public Health Lab, Molecular Genetics Laboratory, Riyadh, Saudi Arabia
- Medical School, AlFaisal University, Riyadh, Saudi Arabia
| | - Raniah S. Alotibi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
| | - Bader Alhaddad
- Laboratory Medicine Department, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
- Molecular Genetics Department, King Saud Medical City, Riyadh, Saudi Arabia
| | - Taghrid Aloraini
- Division of Translational Pathology, Department of Laboratory Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia
- Department of Genetics, King Abdullah Specialized Children Hospital, King Abdulaziz Medical City, MNGHA, Riyadh, Saudi Arabia
| | - Manar S. Samman
- Department of Pathology and Clinical Laboratory Medicine Administration, King Fahad Medical City (KFMC), Riyadh, Saudi Arabia
| | - Abdulrahman AlAsiri
- Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Mohamed Abouelhoda
- Chairman Computational Science Department at King Faisal Specialised Hospital and Research Center, KFSHRC, Riyadh, Saudi Arabia
| | - Amerh S. AlQahtani
- Medical Genetics Department, King Saud Medical City, Riyadh, Saudi Arabia
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8
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Lin J, Jia P, Wang S, Kosters W, Ye K. Comparison and benchmark of structural variants detected from long read and long-read assembly. Brief Bioinform 2023:7169138. [PMID: 37200087 DOI: 10.1093/bib/bbad188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/20/2023] Open
Abstract
Structural variant (SV) detection is essential for genomic studies, and long-read sequencing technologies have advanced our capacity to detect SVs directly from read or de novo assembly, also known as read-based and assembly-based strategy. However, to date, no independent studies have compared and benchmarked the two strategies. Here, on the basis of SVs detected by 20 read-based and eight assembly-based detection pipelines from six datasets of HG002 genome, we investigated the factors that influence the two strategies and assessed their performance with well-curated SVs. We found that up to 80% of the SVs could be detected by both strategies among different long-read datasets, whereas variant type, size, and breakpoint detected by read-based strategy were greatly affected by aligners. For the high-confident insertions and deletions at non-tandem repeat regions, a remarkable subset of them (82% in assembly-based calls and 93% in read-based calls), accounting for around 4000 SVs, could be captured by both reads and assemblies. However, discordance between two strategies was largely caused by complex SVs and inversions, which resulted from inconsistent alignment of reads and assemblies at these loci. Finally, benchmarking with SVs at medically relevant genes, the recall of read-based strategy reached 77% on 5X coverage data, whereas assembly-based strategy required 20X coverage data to achieve similar performance. Therefore, integrating SVs from read and assembly is suggested for general-purpose detection because of inconsistently detected complex SVs and inversions, whereas assembly-based strategy is optional for applications with limited resources.
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Affiliation(s)
- Jiadong Lin
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- Genome Institute, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061 China
- Leiden Institute of Advanced Computer Science, Faculty of Science, Leiden University, Leiden 2311 EZ, The Netherlands
| | - Peng Jia
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Songbo Wang
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Walter Kosters
- Leiden Institute of Advanced Computer Science, Faculty of Science, Leiden University, Leiden 2311 EZ, The Netherlands
| | - Kai Ye
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- Genome Institute, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061 China
- The School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
- Faculty of Science, Leiden University, Leiden 2311 , The Netherlands
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9
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Kucuk E, van der Sanden BPGH, O'Gorman L, Kwint M, Derks R, Wenger AM, Lambert C, Chakraborty S, Baybayan P, Rowell WJ, Brunner HG, Vissers LELM, Hoischen A, Gilissen C. Comprehensive de novo mutation discovery with HiFi long-read sequencing. Genome Med 2023; 15:34. [PMID: 37158973 PMCID: PMC10169305 DOI: 10.1186/s13073-023-01183-6] [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] [Received: 08/26/2022] [Accepted: 04/19/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND Long-read sequencing (LRS) techniques have been very successful in identifying structural variants (SVs). However, the high error rate of LRS made the detection of small variants (substitutions and short indels < 20 bp) more challenging. The introduction of PacBio HiFi sequencing makes LRS also suited for detecting small variation. Here we evaluate the ability of HiFi reads to detect de novo mutations (DNMs) of all types, which are technically challenging variant types and a major cause of sporadic, severe, early-onset disease. METHODS We sequenced the genomes of eight parent-child trios using high coverage PacBio HiFi LRS (~ 30-fold coverage) and Illumina short-read sequencing (SRS) (~ 50-fold coverage). De novo substitutions, small indels, short tandem repeats (STRs) and SVs were called in both datasets and compared to each other to assess the accuracy of HiFi LRS. In addition, we determined the parent-of-origin of the small DNMs using phasing. RESULTS We identified a total of 672 and 859 de novo substitutions/indels, 28 and 126 de novo STRs, and 24 and 1 de novo SVs in LRS and SRS respectively. For the small variants, there was a 92 and 85% concordance between the platforms. For the STRs and SVs, the concordance was 3.6 and 0.8%, and 4 and 100% respectively. We successfully validated 27/54 LRS-unique small variants, of which 11 (41%) were confirmed as true de novo events. For the SRS-unique small variants, we validated 42/133 DNMs and 8 (19%) were confirmed as true de novo event. Validation of 18 LRS-unique de novo STR calls confirmed none of the repeat expansions as true DNM. Confirmation of the 23 LRS-unique SVs was possible for 19 candidate SVs of which 10 (52.6%) were true de novo events. Furthermore, we were able to assign 96% of DNMs to their parental allele with LRS data, as opposed to just 20% with SRS data. CONCLUSIONS HiFi LRS can now produce the most comprehensive variant dataset obtainable by a single technology in a single laboratory, allowing accurate calling of substitutions, indels, STRs and SVs. The accuracy even allows sensitive calling of DNMs on all variant levels, and also allows for phasing, which helps to distinguish true positive from false positive DNMs.
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Affiliation(s)
- Erdi Kucuk
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
- Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Bart P G H van der Sanden
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Luke O'Gorman
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Michael Kwint
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Ronny Derks
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | | | | | | | | | | | - Han G Brunner
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Lisenka E L M Vissers
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Alexander Hoischen
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
- Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Internal Medicine, Radboud University Medical Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands.
| | - Christian Gilissen
- Department of Human Genetics, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
- Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
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10
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Yaldiz B, Kucuk E, Hampstead J, Hofste T, Pfundt R, Corominas Galbany J, Rinne T, Yntema HG, Hoischen A, Nelen M, Gilissen C. Twist exome capture allows for lower average sequence coverage in clinical exome sequencing. Hum Genomics 2023; 17:39. [PMID: 37138343 PMCID: PMC10155375 DOI: 10.1186/s40246-023-00485-5] [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: 01/17/2023] [Accepted: 04/20/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. RESULTS We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. CONCLUSION We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques.
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Affiliation(s)
- Burcu Yaldiz
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Erdi Kucuk
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Juliet Hampstead
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Tom Hofste
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Rolph Pfundt
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Jordi Corominas Galbany
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Tuula Rinne
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Helger G Yntema
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Alexander Hoischen
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Marcel Nelen
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Christian Gilissen
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
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11
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Lecoquierre F, Quenez O, Fourneaux S, Coutant S, Vezain M, Rolain M, Drouot N, Boland A, Olaso R, Meyer V, Deleuze JF, Dabbagh D, Gilles I, Gayet C, Saugier-Veber P, Goldenberg A, Guerrot AM, Nicolas G. High diagnostic potential of short and long read genome sequencing with transcriptome analysis in exome-negative developmental disorders. Hum Genet 2023; 142:773-783. [PMID: 37076692 DOI: 10.1007/s00439-023-02553-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/05/2023] [Indexed: 04/21/2023]
Abstract
Exome sequencing (ES) has become the method of choice for diagnosing rare diseases, while the availability of short-read genome sequencing (SR-GS) in a medical setting is increasing. In addition, new sequencing technologies, such as long-read genome sequencing (LR-GS) and transcriptome sequencing, are being increasingly used. However, the contribution of these techniques compared to widely used ES is not well established, particularly in regards to the analysis of non-coding regions. In a pilot study of five probands affected by an undiagnosed neurodevelopmental disorder, we performed trio-based short-read GS and long-read GS as well as case-only peripheral blood transcriptome sequencing. We identified three new genetic diagnoses, none of which affected the coding regions. More specifically, LR-GS identified a balanced inversion in NSD1, highlighting a rare mechanism of Sotos syndrome. SR-GS identified a homozygous deep intronic variant of KLHL7 resulting in a neoexon inclusion, and a de novo mosaic intronic 22-bp deletion in KMT2D, leading to the diagnosis of Perching and Kabuki syndromes, respectively. All three variants had a significant effect on the transcriptome, which showed decreased gene expression, mono-allelic expression and splicing defects, respectively, further validating the effect of these variants. Overall, in undiagnosed patients, the combination of short and long read GS allowed the detection of cryptic variations not or barely detectable by ES, making it a highly sensitive method at the cost of more complex bioinformatics approaches. Transcriptome sequencing is a valuable complement for the functional validation of variations, particularly in the non-coding genome.
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Affiliation(s)
- François Lecoquierre
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France.
| | - Olivier Quenez
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Steeve Fourneaux
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Sophie Coutant
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Myriam Vezain
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Marion Rolain
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Nathalie Drouot
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057, Evry, France
| | - Robert Olaso
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057, Evry, France
| | - Vincent Meyer
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057, Evry, France
| | - Jean-François Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057, Evry, France
| | - Dana Dabbagh
- Department of Pediatrics, Elbeuf Hospital, Elbeuf, France
| | | | - Claire Gayet
- Department of Pediatrics, CHU Rouen, F-76000, Rouen, France
| | - Pascale Saugier-Veber
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Alice Goldenberg
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Anne-Marie Guerrot
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Gaël Nicolas
- Univ Rouen Normandie, Inserm U12045 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France.
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12
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Biallelic CC2D2A variants, SNV and LINE-1 insertion simultaneously identified in siblings using long-read whole-genome sequencing and haplotype phasing. J Hum Genet 2023; 68:431-435. [PMID: 36765129 DOI: 10.1038/s10038-023-01130-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023]
Abstract
Joubert syndrome (JBTS) is characterized by a magnetic resonance imaging appearance called 'molar tooth sign', neonatal breathing dysregulation and hypotonia, and developmental delay. Whole-exome analysis based on short-read sequencing has often contributed to the identification of causative single-nucleotide variants in patients clinically diagnosed with JBTS. However, ~10% of them are still undiagnosed even though a single possible pathogenic variant has been identified. We report a successful identification of biallelic variants using long-read whole-genome sequencing and haplotype phasing analysis in a family with two Japanese siblings having morphological brain abnormalities. The affected siblings had a novel nonsynonymous variant (CC2D2A:NM_001080522.2:c.4454A>G:p.(Tyr1485Cys)) and an exonic insertion of Long INterspercsed Element-1 (LINE-1). The allelicity of these variants was clearly proven without the data of parents. Finally, our survey of in-house genome sequencing data indicates that there are rare carriers of CC2D2A related diseases, who harbour the exonic LINE-1 insertion in the CC2D2A gene.
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13
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The Genetics of Intellectual Disability. Brain Sci 2023; 13:brainsci13020231. [PMID: 36831774 PMCID: PMC9953898 DOI: 10.3390/brainsci13020231] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/23/2022] [Accepted: 01/16/2023] [Indexed: 02/03/2023] Open
Abstract
Intellectual disability (ID) has a prevalence of ~2-3% in the general population, having a large societal impact. The underlying cause of ID is largely of genetic origin; however, identifying this genetic cause has in the past often led to long diagnostic Odysseys. Over the past decades, improvements in genetic diagnostic technologies and strategies have led to these causes being more and more detectable: from cytogenetic analysis in 1959, we moved in the first decade of the 21st century from genomic microarrays with a diagnostic yield of ~20% to next-generation sequencing platforms with a yield of up to 60%. In this review, we discuss these various developments, as well as their associated challenges and implications for the field of ID, which highlight the revolutionizing shift in clinical practice from a phenotype-first into genotype-first approach.
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14
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Sanford Kobayashi E, Batalov S, Wenger AM, Lambert C, Dhillon H, Hall RJ, Baybayan P, Ding Y, Rego S, Wigby K, Friedman J, Hobbs C, Bainbridge MN. Approaches to long-read sequencing in a clinical setting to improve diagnostic rate. Sci Rep 2022; 12:16945. [PMID: 36210382 PMCID: PMC9548499 DOI: 10.1038/s41598-022-20113-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/08/2022] [Indexed: 12/29/2022] Open
Abstract
Over the past decade, advances in genetic testing, particularly the advent of next-generation sequencing, have led to a paradigm shift in the diagnosis of molecular diseases and disorders. Despite our present collective ability to interrogate more than 90% of the human genome, portions of the genome have eluded us, resulting in stagnation of diagnostic yield with existing methodologies. Here we show how application of a new technology, long-read sequencing, has the potential to improve molecular diagnostic rates. Whole genome sequencing by long reads was able to cover 98% of next-generation sequencing dead zones, which are areas of the genome that are not interpretable by conventional industry-standard short-read sequencing. Through the ability of long-read sequencing to unambiguously call variants in these regions, we discovered an immunodeficiency due to a variant in IKBKG in a subject who had previously received a negative genome sequencing result. Additionally, we demonstrate the ability of long-read sequencing to detect small variants on par with short-read sequencing, its superior performance in identifying structural variants, and thirdly, its capacity to determine genomic methylation defects in native DNA. Though the latter technical abilities have been demonstrated, we demonstrate the clinical application of this technology to successfully identify multiple types of variants using a single test.
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Affiliation(s)
- Erica Sanford Kobayashi
- Rady Institute for Genomic Medicine, San Diego, CA USA ,grid.50956.3f0000 0001 2152 9905Department of Pediatrics, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Serge Batalov
- Rady Institute for Genomic Medicine, San Diego, CA USA
| | - Aaron M. Wenger
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Christine Lambert
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Harsharan Dhillon
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Richard J. Hall
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Primo Baybayan
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Yan Ding
- Rady Institute for Genomic Medicine, San Diego, CA USA
| | - Seema Rego
- Rady Institute for Genomic Medicine, San Diego, CA USA
| | - Kristen Wigby
- Rady Institute for Genomic Medicine, San Diego, CA USA ,grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California San Diego and Rady Children’s Hospital, San Diego, CA USA
| | - Jennifer Friedman
- Rady Institute for Genomic Medicine, San Diego, CA USA ,grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California San Diego and Rady Children’s Hospital, San Diego, CA USA ,grid.266100.30000 0001 2107 4242Department of Neuroscience, University of California San Diego and Rady Children’s Hospital, San Diego, CA USA
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15
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Developing CIRdb as a catalog of natural genetic variation in the Canary Islanders. Sci Rep 2022; 12:16132. [PMID: 36168029 PMCID: PMC9514705 DOI: 10.1038/s41598-022-20442-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
The current inhabitants of the Canary Islands have a unique genetic makeup in the European diversity landscape due to the existence of African footprints from recent admixture events, especially of North African components (> 20%). The underrepresentation of non-Europeans in genetic studies and the sizable North African ancestry, which is nearly absent from all existing catalogs of worldwide genetic diversity, justify the need to develop CIRdb, a population-specific reference catalog of natural genetic variation in the Canary Islanders. Based on array genotyping of the selected unrelated donors and comparisons against available datasets from European, sub-Saharan, and North African populations, we illustrate the intermediate genetic differentiation of Canary Islanders between Europeans and North Africans and the existence of within-population differences that are likely driven by genetic isolation. Here we describe the overall design and the methods that are being implemented to further develop CIRdb. This resource will help to strengthen the implementation of Precision Medicine in this population by contributing to increase the diversity in genetic studies. Among others, this will translate into improved ability to fine map disease genes and simplify the identification of causal variants and estimate the prevalence of unattended Mendelian diseases.
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16
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Otsuki A, Okamura Y, Ishida N, Tadaka S, Takayama J, Kumada K, Kawashima J, Taguchi K, Minegishi N, Kuriyama S, Tamiya G, Kinoshita K, Katsuoka F, Yamamoto M. Construction of a trio-based structural variation panel utilizing activated T lymphocytes and long-read sequencing technology. Commun Biol 2022; 5:991. [PMID: 36127505 PMCID: PMC9489684 DOI: 10.1038/s42003-022-03953-1] [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: 10/03/2021] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
Abstract
Long-read sequencing technology enable better characterization of structural variants (SVs). To adapt the technology to population-scale analyses, one critical issue is to obtain sufficient amount of high-molecular-weight genomic DNA. Here, we propose utilizing activated T lymphocytes, which can be established efficiently in a biobank to stably supply high-grade genomic DNA sufficiently. We conducted nanopore sequencing of 333 individuals constituting 111 trios with high-coverage long-read sequencing data (depth 22.2x, N50 of 25.8 kb) and identified 74,201 SVs. Our trio-based analysis revealed that more than 95% of the SVs were concordant with Mendelian inheritance. We also identified SVs associated with clinical phenotypes, all of which appear to be stably transmitted from parents to offspring. Our data provide a catalog of SVs in the general Japanese population, and the applied approach using the activated T-lymphocyte resource will contribute to biobank-based human genetic studies focusing on SVs at the population scale. Long-read sequencing on activated T-cells from a sample of 333 Japanese individuals (representing 111 parent-offspring trios) provides a useful reference dataset of structural variation in the Japanese population.
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Affiliation(s)
- Akihito Otsuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Yasunobu Okamura
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Noriko Ishida
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Jun Takayama
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15 F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.,Department of AI and Innovative Medicine, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Kazuki Kumada
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Junko Kawashima
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Keiko Taguchi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Naoko Minegishi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15 F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.,Department of AI and Innovative Medicine, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Graduate School of Information Sciences, Tohoku University, 6-3-09 Aramaki Aza-Aoba, Aoba-ku, Sendai, Miyagi, 980-8579, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan. .,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. .,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
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17
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Wortmann SB, Oud MM, Alders M, Coene KLM, van der Crabben SN, Feichtinger RG, Garanto A, Hoischen A, Langeveld M, Lefeber D, Mayr JA, Ockeloen CW, Prokisch H, Rodenburg R, Waterham HR, Wevers RA, van de Warrenburg BPC, Willemsen MAAP, Wolf NI, Vissers LELM, van Karnebeek CDM. How to proceed after "negative" exome: A review on genetic diagnostics, limitations, challenges, and emerging new multiomics techniques. J Inherit Metab Dis 2022; 45:663-681. [PMID: 35506430 PMCID: PMC9539960 DOI: 10.1002/jimd.12507] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 11/28/2022]
Abstract
Exome sequencing (ES) in the clinical setting of inborn metabolic diseases (IMDs) has created tremendous improvement in achieving an accurate and timely molecular diagnosis for a greater number of patients, but it still leaves the majority of patients without a diagnosis. In parallel, (personalized) treatment strategies are increasingly available, but this requires the availability of a molecular diagnosis. IMDs comprise an expanding field with the ongoing identification of novel disease genes and the recognition of multiple inheritance patterns, mosaicism, variable penetrance, and expressivity for known disease genes. The analysis of trio ES is preferred over singleton ES as information on the allelic origin (paternal, maternal, "de novo") reduces the number of variants that require interpretation. All ES data and interpretation strategies should be exploited including CNV and mitochondrial DNA analysis. The constant advancements in available techniques and knowledge necessitate the close exchange of clinicians and molecular geneticists about genotypes and phenotypes, as well as knowledge of the challenges and pitfalls of ES to initiate proper further diagnostic steps. Functional analyses (transcriptomics, proteomics, and metabolomics) can be applied to characterize and validate the impact of identified variants, or to guide the genomic search for a diagnosis in unsolved cases. Future diagnostic techniques (genome sequencing [GS], optical genome mapping, long-read sequencing, and epigenetic profiling) will further enhance the diagnostic yield. We provide an overview of the challenges and limitations inherent to ES followed by an outline of solutions and a clinical checklist, focused on establishing a diagnosis to eventually achieve (personalized) treatment.
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Affiliation(s)
- Saskia B. Wortmann
- Radboud Center for Mitochondrial and Metabolic Medicine, Department of PediatricsAmalia Children's Hospital, Radboud University Medical CenterNijmegenThe Netherlands
- University Children's Hospital, Paracelsus Medical UniversitySalzburgAustria
| | - Machteld M. Oud
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Department of Human GeneticsDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Mariëlle Alders
- Department of Human GeneticsAmsterdam UMC, University of Amsterdam, Amsterdam Reproduction and Development Research InstituteAmsterdamThe Netherlands
| | - Karlien L. M. Coene
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Translational Metabolic Laboratory, Department of Laboratory MedicineRadboud University Medical CenterNijmegenThe Netherlands
| | - Saskia N. van der Crabben
- Department of Human GeneticsAmsterdam University Medical Centers, University of AmsterdamAmsterdamThe Netherlands
| | - René G. Feichtinger
- University Children's Hospital, Paracelsus Medical UniversitySalzburgAustria
| | - Alejandro Garanto
- Radboud Center for Mitochondrial and Metabolic Medicine, Department of PediatricsAmalia Children's Hospital, Radboud University Medical CenterNijmegenThe Netherlands
- Department of PediatricsAmalia Children's Hospital, Radboud Institute for Molecular LifesciencesNijmegenThe Netherlands
- Department of Human GeneticsRadboud Institute for Molecular LifesciencesNijmegenThe Netherlands
| | - Alex Hoischen
- Department of Human Genetics, Department of Internal Medicine and Radboud Center for Infectious DiseasesRadboud Institute of Medical Life Sciences, Radboud University Medical CenterNijmegenthe Netherlands
| | - Mirjam Langeveld
- Department of Endocrinology and MetabolismAmsterdam University Medical Centers, location AMC, University of AmsterdamAmsterdamThe Netherlands
| | - Dirk Lefeber
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Translational Metabolic Laboratory, Department of Laboratory MedicineRadboud University Medical CenterNijmegenThe Netherlands
- Department of Neurology, Donders Institute for BrainCognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Johannes A. Mayr
- University Children's Hospital, Paracelsus Medical UniversitySalzburgAustria
| | - Charlotte W. Ockeloen
- Department of Human GeneticsRadboud Institute for Molecular LifesciencesNijmegenThe Netherlands
| | - Holger Prokisch
- School of MedicineInstitute of Human Genetics, Technical University Munich and Institute of NeurogenomicsNeuherbergGermany
| | - Richard Rodenburg
- Radboud Center for Mitochondrial and Metabolic MedicineTranslational Metabolic Laboratory, Department of Pediatrics, Radboud University Medical CenterNijmegenThe Netherlands
| | - Hans R. Waterham
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Laboratory Genetic Metabolic Diseases, Department of Clinical ChemistryAmsterdam University Medical Centers, location AMC, University of AmsterdamAmsterdamThe Netherlands
| | - Ron A. Wevers
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Translational Metabolic Laboratory, Department of Laboratory MedicineRadboud University Medical CenterNijmegenThe Netherlands
| | - Bart P. C. van de Warrenburg
- Department of Neurology, Donders Institute for BrainCognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Michel A. A. P. Willemsen
- Departments of Pediatric Neurology and PediatricsAmalia Children's Hospital, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical CenterNijmegenThe Netherlands
| | - Nicole I. Wolf
- Amsterdam Leukodystrophy Center, Department of Child NeurologyEmma Children's Hospital, Amsterdam University Medical Centers, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Lisenka E. L. M. Vissers
- Department of Human GeneticsDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Clara D. M. van Karnebeek
- Radboud Center for Mitochondrial and Metabolic Medicine, Department of PediatricsAmalia Children's Hospital, Radboud University Medical CenterNijmegenThe Netherlands
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Department of Human GeneticsAmsterdam UMC, University of Amsterdam, Amsterdam Reproduction and Development Research InstituteAmsterdamThe Netherlands
- Department of Pediatrics, Emma Center for Personalized MedicineAmsterdam University Medical Centers, Amsterdam, Amsterdam Genetics Endocrinology Metabolism Research Institute, University of AmsterdamAmsterdamThe Netherlands
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18
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Schobers G, Schieving JH, Yntema HG, Pennings M, Pfundt R, Derks R, Hofste T, de Wijs I, Wieskamp N, van den Heuvel S, Galbany JC, Gilissen C, Nelen M, Brunner HG, Kleefstra T, Kamsteeg EJ, Willemsen MAAP, Vissers LELM. Reanalysis of exome negative patients with rare disease: a pragmatic workflow for diagnostic applications. Genome Med 2022; 14:66. [PMID: 35710456 PMCID: PMC9204949 DOI: 10.1186/s13073-022-01069-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 06/07/2022] [Indexed: 11/10/2022] Open
Abstract
Background Approximately two third of patients with a rare genetic disease remain undiagnosed after exome sequencing (ES). As part of our post-test counseling procedures, patients without a conclusive diagnosis are advised to recontact their referring clinician to discuss new diagnostic opportunities in due time. We performed a systematic study of genetically undiagnosed patients 5 years after their initial negative ES report to determine the efficiency of diverse reanalysis strategies. Methods We revisited a cohort of 150 pediatric neurology patients originally enrolled at Radboud University Medical Center, of whom 103 initially remained genetically undiagnosed. We monitored uptake of physician-initiated routine clinical and/or genetic re-evaluation (ad hoc re-evaluation) and performed systematic reanalysis, including ES-based resequencing, of all genetically undiagnosed patients (systematic re-evaluation). Results Ad hoc re-evaluation was initiated for 45 of 103 patients and yielded 18 diagnoses (including 1 non-genetic). Subsequent systematic re-evaluation identified another 14 diagnoses, increasing the diagnostic yield in our cohort from 31% (47/150) to 53% (79/150). New genetic diagnoses were established by reclassification of previously identified variants (10%, 3/31), reanalysis with enhanced bioinformatic pipelines (19%, 6/31), improved coverage after resequencing (29%, 9/31), and new disease-gene associations (42%, 13/31). Crucially, our systematic study also showed that 11 of the 14 further conclusive genetic diagnoses were made in patients without a genetic diagnosis that did not recontact their referring clinician. Conclusions We find that upon re-evaluation of undiagnosed patients, both reanalysis of existing ES data as well as resequencing strategies are needed to identify additional genetic diagnoses. Importantly, not all patients are routinely re-evaluated in clinical care, prolonging their diagnostic trajectory, unless systematic reanalysis is facilitated. We have translated our observations into considerations for systematic and ad hoc reanalysis in routine genetic care. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01069-z.
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Affiliation(s)
- Gaby Schobers
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Jolanda H Schieving
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.,Department of Pediatric Neurology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Helger G Yntema
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maartje Pennings
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rolph Pfundt
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ronny Derks
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tom Hofste
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ilse de Wijs
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nienke Wieskamp
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Simone van den Heuvel
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jordi Corominas Galbany
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.,Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | - Christian Gilissen
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.,Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | - Marcel Nelen
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Han G Brunner
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.,Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Tjitske Kleefstra
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Erik-Jan Kamsteeg
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michèl A A P Willemsen
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.,Department of Pediatric Neurology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lisenka E L M Vissers
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands. .,Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
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19
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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] [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
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Julius O. B. Jacobsen
- grid.4868.20000 0001 2171 1133William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ UK
| | - Parithi Balachandran
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Qihui Zhu
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Feyza Yilmaz
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Justin Reese
- grid.184769.50000 0001 2231 4551Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
| | - Matthias Haimel
- grid.511293.d0000 0004 6104 8403Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria ,grid.416346.2St. Anna Children’s Cancer Research Institute, Vienna, Austria ,grid.418729.10000 0004 0392 6802CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria ,grid.486422.e0000000405446183Present address: Global Computational Biology and Digital Sciences, Boehringer Ingelheim Regional Center Vienna GmbH & Co KG, 1120 Vienna, Austria
| | - Gholson J. Lyon
- grid.420001.70000 0000 9813 9625Department of Human Genetics, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, New York, USA ,grid.212340.60000000122985718Biology PhD Program, The Graduate Center, The City University of New York, New York, USA
| | - Ingo Helbig
- grid.239552.a0000 0001 0680 8770Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA USA ,grid.239552.a0000 0001 0680 8770The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA USA ,grid.239552.a0000 0001 0680 8770Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
| | - Christopher J. Mungall
- grid.184769.50000 0001 2231 4551Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
| | - Christine R. Beck
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA ,grid.208078.50000000419370394Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT 06032 USA ,grid.63054.340000 0001 0860 4915Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269 USA
| | - Charles Lee
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Damian Smedley
- grid.4868.20000 0001 2171 1133William 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
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA ,grid.208078.50000000419370394Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT 06032 USA
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20
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Noyes MD, Harvey WT, Porubsky D, Sulovari A, Li R, Rose NR, Audano PA, Munson KM, Lewis AP, Hoekzema K, Mantere T, Graves-Lindsay TA, Sanders AD, Goodwin S, Kramer M, Mokrab Y, Zody MC, Hoischen A, Korbel JO, McCombie WR, Eichler EE. Familial long-read sequencing increases yield of de novo mutations. Am J Hum Genet 2022; 109:631-646. [PMID: 35290762 DOI: 10.1016/j.ajhg.2022.02.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/16/2022] [Indexed: 12/11/2022] Open
Abstract
Studies of de novo mutation (DNM) have typically excluded some of the most repetitive and complex regions of the genome because these regions cannot be unambiguously mapped with short-read sequencing data. To better understand the genome-wide pattern of DNM, we generated long-read sequence data from an autism parent-child quad with an affected female where no pathogenic variant had been discovered in short-read Illumina sequence data. We deeply sequenced all four individuals by using three sequencing platforms (Illumina, Oxford Nanopore, and Pacific Biosciences) and three complementary technologies (Strand-seq, optical mapping, and 10X Genomics). Using long-read sequencing, we initially discovered and validated 171 DNMs across two children-a 20% increase in the number of de novo single-nucleotide variants (SNVs) and indels when compared to short-read callsets. The number of DNMs further increased by 5% when considering a more complete human reference (T2T-CHM13) because of the recovery of events in regions absent from GRCh38 (e.g., three DNMs in heterochromatic satellites). In total, we validated 195 de novo germline mutations and 23 potential post-zygotic mosaic mutations across both children; the overall true substitution rate based on this integrated callset is at least 1.41 × 10-8 substitutions per nucleotide per generation. We also identified six de novo insertions and deletions in tandem repeats, two of which represent structural variants. We demonstrate that long-read sequencing and assembly, especially when combined with a more complete reference genome, increases the number of DNMs by >25% compared to previous studies, providing a more complete catalog of DNM compared to short-read data alone.
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Affiliation(s)
- Michelle D Noyes
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Ruiyang Li
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Nicholas R Rose
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Tuomo Mantere
- Department of Human Genetics, Radboud University Medical Center, 6500 Nijmegen, the Netherlands; Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit and Biocenter Oulu, University of Oulu, 90220 Oulu, Finland
| | | | - Ashley D Sanders
- European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
| | - Sara Goodwin
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Melissa Kramer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Younes Mokrab
- Department of Human Genetics, Sidra Medicine, PO Box 26999, Doha, Qatar; Weill Cornell Medicine, PO Box 24144, Doha, Qatar; College of Health and Life Sciences, Hamad Bin Khalifa University, PO Box 34110, Doha, Qatar
| | | | - Alexander Hoischen
- Department of Human Genetics, Radboud University Medical Center, 6500 Nijmegen, the Netherlands; Radboud Institute of Medical Life Sciences and Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6500 Nijmegen, the Netherlands
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
| | - W Richard McCombie
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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21
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Rowlands CF, Taylor A, Rice G, Whiffin N, Hall HN, Newman WG, Black GCM, O'Keefe RT, Hubbard S, Douglas AGL, Baralle D, Briggs TA, Ellingford JM. MRSD: A quantitative approach for assessing suitability of RNA-seq in the investigation of mis-splicing in Mendelian disease. Am J Hum Genet 2022; 109:210-222. [PMID: 35065709 PMCID: PMC8874219 DOI: 10.1016/j.ajhg.2021.12.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 12/12/2021] [Indexed: 12/16/2022] Open
Abstract
Variable levels of gene expression between tissues complicates the use of RNA sequencing of patient biosamples to delineate the impact of genomic variants. Here, we describe a gene- and tissue-specific metric to inform the feasibility of RNA sequencing. This overcomes limitations of using expression values alone as a metric to predict RNA-sequencing utility. We have derived a metric, minimum required sequencing depth (MRSD), that estimates the depth of sequencing required from RNA sequencing to achieve user-specified sequencing coverage of a gene, transcript, or group of genes. We applied MRSD across four human biosamples: whole blood, lymphoblastoid cell lines (LCLs), skeletal muscle, and cultured fibroblasts. MRSD has high precision (90.1%-98.2%) and overcomes transcript region-specific sequencing biases. Applying MRSD scoring to established disease gene panels shows that fibroblasts, of these four biosamples, are the optimum source of RNA for 63.1% of gene panels. Using this approach, up to 67.8% of the variants of uncertain significance in ClinVar that are predicted to impact splicing could be assayed by RNA sequencing in at least one of the biosamples. We demonstrate the utility and benefits of MRSD as a metric to inform functional assessment of splicing aberrations, in particular in the context of Mendelian genetic disorders to improve diagnostic yield.
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Affiliation(s)
- Charlie F Rowlands
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Algy Taylor
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Gillian Rice
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Nicola Whiffin
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Hildegard Nikki Hall
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - William G Newman
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Graeme C M Black
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Raymond T O'Keefe
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Simon Hubbard
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Andrew G L Douglas
- Wessex Clinical Genetics Service, Princess Anne Hospital, University Hospital Southampton NHS Foundation Trust, Coxford Rd, Southampton SO16 5YA, UK; Faculty of Medicine, University of Southampton, Duthie Building, Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK
| | - Diana Baralle
- Wessex Clinical Genetics Service, Princess Anne Hospital, University Hospital Southampton NHS Foundation Trust, Coxford Rd, Southampton SO16 5YA, UK; Faculty of Medicine, University of Southampton, Duthie Building, Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK
| | - Tracy A Briggs
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Jamie M Ellingford
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK.
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22
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Boschann F, Moreno DA, Mensah MA, Sczakiel HL, Skipalova K, Holtgrewe M, Mundlos S, Fischer-Zirnsak B. Xq27.1 palindrome mediated interchromosomal insertion likely causes familial congenital bilateral laryngeal abductor paralysis (Plott syndrome). J Hum Genet 2022; 67:405-410. [PMID: 35095096 PMCID: PMC9233990 DOI: 10.1038/s10038-022-01018-z] [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: 11/03/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 01/27/2023]
Abstract
Bilateral laryngeal abductor paralysis is a rare entity and the second most common cause of stridor in newborns. So far, no conclusive genetic or chromosomal aberration has been reported for X-linked isolated bilateral vocal cord paralysis, also referred to as Plott syndrome. Via whole genome sequencing (WGS), we identified a complex interchromosomal insertion in a large family with seven affected males. The 404 kb inserted fragment originates from chromosome 10q21.3, contains no genes and is inserted inversionally into the intergenic chromosomal region Xq27.1, 82 kb centromeric to the nearest gene SOX3. The patterns found at the breakpoint junctions resemble typical characteristics that arise in replication-based mechanisms with long-distance template switching. Non protein-coding insertions into the same genomic region have been described to result in different phenotypes, indicating that the phenotypic outcome likely depends on the introduction of regulatory elements. In conclusion, our data adds Plott syndrome as another entity, likely caused by the insertion of non-coding DNA into the intergenic chromosomal region Xq27.1. In this regard, we demonstrate the importance of WGS as a powerful diagnostic test in unsolved genetic diseases, as this genomic rearrangement has not been detected by current first-line diagnostic tests, i.e., exome sequencing and chromosomal microarray analysis.
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Long-read technologies identify a hidden inverted duplication in a family with choroideremia. HGG ADVANCES 2021; 2:100046. [PMID: 35047838 PMCID: PMC8756506 DOI: 10.1016/j.xhgg.2021.100046] [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: 04/22/2021] [Accepted: 07/01/2021] [Indexed: 12/03/2022] Open
Abstract
The lack of molecular diagnoses in rare genetic diseases can be explained by limitations of current standard genomic technologies. Upcoming long-read techniques have complementary strengths to overcome these limitations, with a particular strength in identifying structural variants. By using optical genome mapping and long-read sequencing, we aimed to identify the pathogenic variant in a large family with X-linked choroideremia. In this family, aberrant splicing of exon 12 of the choroideremia gene CHM was detected in 2003, but the underlying genomic defect remained elusive. Optical genome mapping and long-read sequencing approaches now revealed an intragenic 1,752 bp inverted duplication including exon 12 and surrounding regions, located downstream of the wild-type copy of exon 12. Both breakpoint junctions were confirmed with Sanger sequencing and segregate with the X-linked inheritance in the family. The breakpoint junctions displayed sequence microhomology suggestive for an erroneous replication mechanism as the origin of the structural variant. The inverted duplication is predicted to result in a hairpin formation of the pre-mRNA with the wild-type exon 12, leading to exon skipping in the mature mRNA. The identified inverted duplication is deemed the hidden pathogenic cause of disease in this family. Our study shows that optical genome mapping and long-read sequencing have significant potential for the identification of (hidden) structural variants in rare genetic diseases.
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McNeill A. What's new in EJHG in April. Eur J Hum Genet 2021; 29:539-540. [PMID: 33837296 PMCID: PMC8035134 DOI: 10.1038/s41431-021-00841-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Alisdair McNeill
- Department of Neuroscience, The University of Sheffield, Sheffield, UK.
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25
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Bolz HJ. Diagnostic Analyses of Retinal Dystrophy Genes: Current Status and Perspective. Klin Monbl Augenheilkd 2021; 238:261-266. [PMID: 33784789 DOI: 10.1055/a-1386-5361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Over the past decade, novel high-throughput DNA sequencing technologies have revolutionised both research and diagnostic testing for monogenic disorders. This applies particularly to genetically very heterogeneous disorders like retinal dystrophies (RDs). Next-generation sequencing (NGS) today is considered as reliable as Sanger sequencing, which had been the gold standard for decades. Today, comprehensive NGS-based diagnostic testing reveals the causative mutations in the majority of RD patients, with important implications for genetic counselling for recurrence risks and personalised medical management (from interdisciplinary surveillance to prophylactic measures and, albeit yet rare, [gene] therapy). While DNA sequencing is - in most cases - no longer the diagnostic bottleneck, one needs to be aware of interpretation pitfalls and dead ends. The advent of new (NGS) technologies will solve some of these issues. However, specialised medical geneticists who are familiar with the peculiarities of certain RD genes and closely interact with ophthalmologists will remain key to successful RD research and diagnostic testing for the benefit of the patients. This review sheds light on the current state of the field, its challenges and potential solutions.
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Affiliation(s)
- Hanno Jörn Bolz
- Humangenetik, Senckenberg Zentrum für Humangenetik, Frankfurt, Germany.,Humangenetik, University Hospital of Cologne, Cologne, Germany
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