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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [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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Kaiser J. Sequencing projects will screen 200,000 newborns for disease. Science 2022; 378:1159. [PMID: 36520905 DOI: 10.1126/science.adg2858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
U.K. and New York City efforts face cost and ethical issues.
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Perez-Sepulveda BM, Heavens D, Pulford CV, Predeus AV, Low R, Webster H, Dykes GF, Schudoma C, Rowe W, Lipscombe J, Watkins C, Kumwenda B, Shearer N, Costigan K, Baker KS, Feasey NA, Hinton JCD, Hall N. An accessible, efficient and global approach for the large-scale sequencing of bacterial genomes. Genome Biol 2021; 22:349. [PMID: 34930397 PMCID: PMC8690886 DOI: 10.1186/s13059-021-02536-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 10/21/2021] [Indexed: 12/14/2022] Open
Abstract
We have developed an efficient and inexpensive pipeline for streamlining large-scale collection and genome sequencing of bacterial isolates. Evaluation of this method involved a worldwide research collaboration focused on the model organism Salmonella enterica, the 10KSG consortium. Following the optimization of a logistics pipeline that involved shipping isolates as thermolysates in ambient conditions, the project assembled a diverse collection of 10,419 isolates from low- and middle-income countries. The genomes were sequenced using the LITE pipeline for library construction, with a total reagent cost of less than USD$10 per genome. Our method can be applied to other large bacterial collections to underpin global collaborations.
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Affiliation(s)
| | | | - Caisey V. Pulford
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Alexander V. Predeus
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Ross Low
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - Hermione Webster
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Gregory F. Dykes
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | | | - Will Rowe
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
- University of Birmingham, Birmingham, UK
| | | | - Chris Watkins
- Earlham Institute, Norwich Research Park, Norwich, UK
| | | | - Neil Shearer
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - Karl Costigan
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Kate S. Baker
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Nicholas A. Feasey
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK
- Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi
| | - Jay C. D. Hinton
- Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Neil Hall
- Earlham Institute, Norwich Research Park, Norwich, UK
- School of Biological Sciences, University of East Anglia, Norwich, UK
- Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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Brown B, Allard M, Bazaco MC, Blankenship J, Minor T. An economic evaluation of the Whole Genome Sequencing source tracking program in the U.S. PLoS One 2021; 16:e0258262. [PMID: 34614029 PMCID: PMC8494326 DOI: 10.1371/journal.pone.0258262] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/22/2021] [Indexed: 11/20/2022] Open
Abstract
The U.S. Food and Drug Administration (FDA) created the GenomeTrakr Whole Genome Sequencing (WGS) Network in 2013, as a tool to improve food safety. This study presents an analysis of Whole Genome source tracking implementation on potential food contamination and related illnesses through theoretical, empirical, and cost benefit analyses. We conduct empirical tests using data from FDA regulated food commodity outbreaks garnering FDA response from 1999 through 2019 and examine the effect of the National Center for Biotechnology Information (NCBI) Pathogen detection program of source tracking WGS isolates collected in the U.S. on outbreak illnesses for three pilot pathogens (E. coli, Listeria, and Salmonella). Empirical results are consistent with the theoretical model and suggest that each additional 1,000 WGS isolates added to the public NCBI database is associated with a reduction of approximately 6 illnesses per WGS pathogen, per year. Empirical results are connected to existing literature for a Monte Carlo analysis to estimate benefits and costs. By 2019, annual health benefits are estimated at nearly $500 million, compared to an approximately $22 million investment by public health agencies. Even under conservative assumptions, the program likely broke even in its second year of implementation and could produce increasing public health benefits as the GenomeTrakr network matures.
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Affiliation(s)
- Brad Brown
- United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, Maryland, United States of America
| | - Marc Allard
- United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, Maryland, United States of America
| | - Michael C. Bazaco
- United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, Maryland, United States of America
| | - Joseph Blankenship
- United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, Maryland, United States of America
| | - Travis Minor
- United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, Maryland, United States of America
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Vogel M, Utpatel C, Corbett C, Kohl TA, Iskakova A, Ahmedov S, Antonenka U, Dreyer V, Ibrahimova A, Kamarli C, Kosimova D, Mohr V, Sahalchyk E, Sydykova M, Umetalieva N, Kadyrov A, Kalmambetova G, Niemann S, Hoffmann H. Implementation of whole genome sequencing for tuberculosis diagnostics in a low-middle income, high MDR-TB burden country. Sci Rep 2021; 11:15333. [PMID: 34321545 PMCID: PMC8319420 DOI: 10.1038/s41598-021-94297-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/06/2021] [Indexed: 11/09/2022] Open
Abstract
Whole genome sequencing (WGS) is revolutionary for diagnostics of TB and its mutations associated with drug-resistances, but its uptake in low- and middle-income countries is hindered by concerns of implementation feasibility. Here, we provide a proof of concept for its successful implementation in such a setting. WGS was implemented in the Kyrgyz Republic. We estimated needs of up to 55 TB-WGS per week and chose the MiSeq platform (Illumina, USA) because of its capacity of up to 60 TB-WGS per week. The project's timeline was completed in 93-weeks. Costs of large equipment and accompanying costs were 222,065 USD and 8462 USD, respectively. The first 174 WGS costed 277 USD per sequence, but this was skewed by training inefficiencies. Based on real prices and presuming optimal utilization of WGS capacities, WGS costs could drop to 167 and 141 USD per WGS using MiSeq Reagent Kits v2 (500-cycles) and v3 (600-cycles), respectively. Five trainings were required to prepare the staff for autonomous WGS which cost 48,250 USD. External assessment confirmed excellent performance of WGS by the Kyrgyz laboratory in an interlaboratory comparison of 30 M. tuberculosis genomes showing complete agreeance of results.
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Affiliation(s)
- Monica Vogel
- Institute of Microbiology and Laboratory Medicine, Department IML Red GmbH, WHO - Supranational Tuberculosis Reference Laboratory Munich-Gauting, Robert Koch-Allee 2, 82131, Gauting, Germany
| | - Christian Utpatel
- Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany
- German Center for Infection Research, Partner Site Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Caroline Corbett
- Institute of Microbiology and Laboratory Medicine, Department IML Red GmbH, WHO - Supranational Tuberculosis Reference Laboratory Munich-Gauting, Robert Koch-Allee 2, 82131, Gauting, Germany
| | - Thomas A Kohl
- Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany
- German Center for Infection Research, Partner Site Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Altyn Iskakova
- Republican Tuberculosis Reference Laboratory, Bishkek, Kyrgyz Republic
| | - Sevim Ahmedov
- USAID, Bureau for Global Health, TB Division, Washington, DC, USA
| | - Uladzimir Antonenka
- Institute of Microbiology and Laboratory Medicine, Department IML Red GmbH, WHO - Supranational Tuberculosis Reference Laboratory Munich-Gauting, Robert Koch-Allee 2, 82131, Gauting, Germany
| | - Viola Dreyer
- Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany
- German Center for Infection Research, Partner Site Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Ainura Ibrahimova
- ABT Associates, Defeat TB Project Management, Bishkek, Kyrgyz Republic
| | | | - Dilorom Kosimova
- ABT Associates, Defeat TB Project Management, Bishkek, Kyrgyz Republic
| | - Vanessa Mohr
- Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany
- German Center for Infection Research, Partner Site Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Evgeni Sahalchyk
- Institute of Microbiology and Laboratory Medicine, Department IML Red GmbH, WHO - Supranational Tuberculosis Reference Laboratory Munich-Gauting, Robert Koch-Allee 2, 82131, Gauting, Germany
| | - Meerim Sydykova
- Republican Tuberculosis Reference Laboratory, Bishkek, Kyrgyz Republic
| | - Nagira Umetalieva
- Institute of Microbiology and Laboratory Medicine, Department IML Red GmbH, WHO - Supranational Tuberculosis Reference Laboratory Munich-Gauting, Robert Koch-Allee 2, 82131, Gauting, Germany
| | - Abdylat Kadyrov
- Republican Tuberculosis Center, National Tuberculosis Project Management, Bishkek, Kyrgyz Republic
| | | | - Stefan Niemann
- Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany
- German Center for Infection Research, Partner Site Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Harald Hoffmann
- Institute of Microbiology and Laboratory Medicine, Department IML Red GmbH, WHO - Supranational Tuberculosis Reference Laboratory Munich-Gauting, Robert Koch-Allee 2, 82131, Gauting, Germany.
- SYNLAB Gauting, SYNLAB Human Genetics, Munich-Gauting, Germany.
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Martin AR, Atkinson EG, Chapman SB, Stevenson A, Stroud RE, Abebe T, Akena D, Alemayehu M, Ashaba FK, Atwoli L, Bowers T, Chibnik LB, Daly MJ, DeSmet T, Dodge S, Fekadu A, Ferriera S, Gelaye B, Gichuru S, Injera WE, James R, Kariuki SM, Kigen G, Koenen KC, Kwobah E, Kyebuzibwa J, Majara L, Musinguzi H, Mwema RM, Neale BM, Newman CP, Newton CRJC, Pickrell JK, Ramesar R, Shiferaw W, Stein DJ, Teferra S, van der Merwe C, Zingela Z. Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations. Am J Hum Genet 2021; 108:656-668. [PMID: 33770507 PMCID: PMC8059370 DOI: 10.1016/j.ajhg.2021.03.012] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 03/05/2021] [Indexed: 12/21/2022] Open
Abstract
Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
| | - Elizabeth G Atkinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Sinéad B Chapman
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Anne Stevenson
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Rocky E Stroud
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Tamrat Abebe
- Department of Microbiology, Immunology, and Parasitology, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Dickens Akena
- Department of Psychiatry, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Melkam Alemayehu
- Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Fred K Ashaba
- Department of Immunology & Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Lukoye Atwoli
- Department of Mental Health, School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Tera Bowers
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Lori B Chibnik
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Institute for Molecular Medicine Finland, Helsinki 00014, Finland
| | - Timothy DeSmet
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Sheila Dodge
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Abebaw Fekadu
- Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia; Centre for Innovative Drug Development & Therapeutic Trials for Africa, Addis Ababa University, Addis Ababa, Ethiopia
| | - Steven Ferriera
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Bizu Gelaye
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Stella Gichuru
- Department of Mental Health, Moi Teaching and Referral Hospital, Eldoret, Kenya
| | - Wilfred E Injera
- Department of Immunology, School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Roxanne James
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Symon M Kariuki
- Neurosciences Unit, Clinical Department, KEMRI-Wellcome Trust Research Programme-Coast, Kilifi, Kenya; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - Gabriel Kigen
- Department of Pharmacology and Toxicology, School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Karestan C Koenen
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Edith Kwobah
- Department of Mental Health, Moi Teaching and Referral Hospital, Eldoret, Kenya
| | - Joseph Kyebuzibwa
- Department of Psychiatry, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Lerato Majara
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa; SA MRC Human Genetics Research Unit, Division of Human Genetics, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, South Africa
| | - Henry Musinguzi
- Department of Immunology & Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Rehema M Mwema
- Neurosciences Unit, Clinical Department, KEMRI-Wellcome Trust Research Programme-Coast, Kilifi, Kenya
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Carter P Newman
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Charles R J C Newton
- Neurosciences Unit, Clinical Department, KEMRI-Wellcome Trust Research Programme-Coast, Kilifi, Kenya; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | | | - Raj Ramesar
- SA MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Welelta Shiferaw
- Department of Microbiology, Immunology, and Parasitology, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa; SA MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town and Neuroscience Institute, Cape Town, South Africa
| | - Solomon Teferra
- Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Celia van der Merwe
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Zukiswa Zingela
- Department of Psychiatry and Human Behavioral Sciences, Walter Sisulu University, Mthatha, South Africa
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8
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Ford L, Glass K, Williamson DA, Sintchenko V, Robson JMB, Lancsar E, Stafford R, Kirk MD. Cost of whole genome sequencing for non-typhoidal Salmonella enterica. PLoS One 2021; 16:e0248561. [PMID: 33739986 PMCID: PMC7978342 DOI: 10.1371/journal.pone.0248561] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 03/01/2021] [Indexed: 11/23/2022] Open
Abstract
Background While whole genome sequencing (WGS) may be more expensive than traditional testing and polymerase chain reaction (PCR), simple cost comparisons ignore the potential for WGS to reduce the societal costs of non-typhoidal Salmonella enterica through public health action to prevent illness. Methods We determined how many cases the use of WGS data would need to prevent to be cost-equal to serotyping and MLVA, or culture independent testing based on PCR in Australia. We then examined the costs and cost-savings of current typing methods compared with WGS in outbreak scenarios. Results A median of 275 (90% CrI -55-775) or 1.9% (90% CrI -0.4%-5.4%) of notified serotyped Salmonella cases would need to be prevented for WGS to be cost-equal to current typing methods and 1,550 (90% CrI 820–2,725) or 9.6% of all notified Salmonella cases would need to be prevented to be cost-equal to PCR. WGS is likely to result in cost savings in prolonged outbreaks, where data can support earlier public health action. Conclusions Despite currently having a higher cost per isolate, routine WGS of Salmonella was no more expensive than existing typing methods or PCR where >2% of illness was averted.
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Affiliation(s)
- Laura Ford
- Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia
- * E-mail:
| | - Kathryn Glass
- Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Deborah A. Williamson
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
| | - Vitali Sintchenko
- Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Westmead, New South Wales, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, NSW Health Pathology, Sydney, New South Wales, Australia
| | | | - Emily Lancsar
- Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia
| | | | - Martyn D. Kirk
- Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia
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9
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Schwabl P, Maiguashca Sánchez J, Costales JA, Ocaña-Mayorga S, Segovia M, Carrasco HJ, Hernández C, Ramírez JD, Lewis MD, Grijalva MJ, Llewellyn MS. Culture-free genome-wide locus sequence typing (GLST) provides new perspectives on Trypanosoma cruzi dispersal and infection complexity. PLoS Genet 2020; 16:e1009170. [PMID: 33326438 PMCID: PMC7743988 DOI: 10.1371/journal.pgen.1009170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/02/2020] [Indexed: 12/30/2022] Open
Abstract
Analysis of genetic polymorphism is a powerful tool for epidemiological surveillance and research. Powerful inference from pathogen genetic variation, however, is often restrained by limited access to representative target DNA, especially in the study of obligate parasitic species for which ex vivo culture is resource-intensive or bias-prone. Modern sequence capture methods enable pathogen genetic variation to be analyzed directly from host/vector material but are often too complex and expensive for resource-poor settings where infectious diseases prevail. This study proposes a simple, cost-effective 'genome-wide locus sequence typing' (GLST) tool based on massive parallel amplification of information hotspots throughout the target pathogen genome. The multiplexed polymerase chain reaction amplifies hundreds of different, user-defined genetic targets in a single reaction tube, and subsequent agarose gel-based clean-up and barcoding completes library preparation at under 4 USD per sample. Our study generates a flexible GLST primer panel design workflow for Trypanosoma cruzi, the parasitic agent of Chagas disease. We successfully apply our 203-target GLST panel to direct, culture-free metagenomic extracts from triatomine vectors containing a minimum of 3.69 pg/μl T. cruzi DNA and further elaborate on method performance by sequencing GLST libraries from T. cruzi reference clones representing discrete typing units (DTUs) TcI, TcIII, TcIV, TcV and TcVI. The 780 SNP sites we identify in the sample set repeatably distinguish parasites infecting sympatric vectors and detect correlations between genetic and geographic distances at regional (< 150 km) as well as continental scales. The markers also clearly separate TcI, TcIII, TcIV and TcV + TcVI and appear to distinguish multiclonal infections within TcI. We discuss the advantages, limitations and prospects of our method across a spectrum of epidemiological research.
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Affiliation(s)
- Philipp Schwabl
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Jalil Maiguashca Sánchez
- Centro de Investigación para la Salud en América Latina, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
| | - Jaime A. Costales
- Centro de Investigación para la Salud en América Latina, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
| | - Sofía Ocaña-Mayorga
- Centro de Investigación para la Salud en América Latina, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
| | - Maikell Segovia
- Laboratorio de Biología Molecular de Protozoarios, Instituto de Medicina Tropical, Universidad Central de Venezuela, Caracas, Venezuela
| | - Hernán J. Carrasco
- Laboratorio de Biología Molecular de Protozoarios, Instituto de Medicina Tropical, Universidad Central de Venezuela, Caracas, Venezuela
| | - Carolina Hernández
- Grupo de Investigaciones Microbiológicas-UR (GIMUR), Departamento de Biología, Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Juan David Ramírez
- Grupo de Investigaciones Microbiológicas-UR (GIMUR), Departamento de Biología, Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Michael D. Lewis
- London School of Hygiene & Tropical Medicine, Keppel Street, London, United Kingdom
| | - Mario J. Grijalva
- Centro de Investigación para la Salud en América Latina, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
- Infectious and Tropical Disease Institute, Biomedical Sciences Department, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, United States of America
| | - Martin S. Llewellyn
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
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10
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Samsom KG, Bosch LJW, Schipper LJ, Roepman P, de Bruijn E, Hoes LR, Riethorst I, Schoenmaker L, van der Kolk LE, Retèl VP, Frederix GWJ, Buffart TE, van der Hoeven JJM, Voest EE, Cuppen E, Monkhorst K, Meijer GA. Study protocol: Whole genome sequencing Implementation in standard Diagnostics for Every cancer patient (WIDE). BMC Med Genomics 2020; 13:169. [PMID: 33167975 PMCID: PMC7654005 DOI: 10.1186/s12920-020-00814-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 10/25/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND 'Precision oncology' can ensure the best suitable treatment at the right time by tailoring treatment towards individual patient and comprehensive tumour characteristics. In current molecular pathology, diagnostic tests which are part of the standard of care (SOC) only cover a limited part of the spectrum of genomic changes, and often are performed in an iterative way. This occurs at the expense of valuable patient time, available tissue sample, and interferes with 'first time right' treatment decisions. Whole Genome Sequencing (WGS) captures a near complete view of genomic characteristics of a tumour in a single test. Moreover, WGS facilitates faster implementation of new treatment relevant biomarkers. At present, WGS mainly has been applied in study settings, but its performance in a routine diagnostic setting remains to be evaluated. The WIDE study aims to investigate the feasibility and validity of WGS-based diagnostics in clinical practice. METHODS 1200 consecutive patients in a single comprehensive cancer centre with (suspicion of) a metastasized solid tumour will be enrolled with the intention to analyse tumour tissue with WGS, in parallel to SOC diagnostics. Primary endpoints are (1) feasibility of implementation of WGS-based diagnostics into routine clinical care and (2) clinical validation of WGS by comparing identification of treatment-relevant variants between WGS and SOC molecular diagnostics. Secondary endpoints entail (1) added clinical value in terms of additional treatment options and (2) cost-effectiveness of WGS compared to SOC diagnostics through a Health Technology Assessment (HTA) analysis. Furthermore, the (3) perceived impact of WGS-based diagnostics on clinical decision making will be evaluated through questionnaires. The number of patients included in (experimental) therapies initiated based on SOC or WGS diagnostics will be reported with at least 3 months follow-up. The clinical efficacy is beyond the scope of WIDE. Key performance indicators will be evaluated after every 200 patients enrolled, and procedures optimized accordingly, to continuously improve the diagnostic performance of WGS in a routine clinical setting. DISCUSSION WIDE will yield the optimal conditions under which WGS can be implemented in a routine molecular diagnostics setting and establish the position of WGS compared to SOC diagnostics in routine clinical care.
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Affiliation(s)
- Kris G Samsom
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Linda J W Bosch
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Luuk J Schipper
- Department of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Paul Roepman
- Hartwig Medical Foundation, Amsterdam, The Netherlands
| | | | - Louisa R Hoes
- Department of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | | | | | | | | | - Tineke E Buffart
- Department of Gastrointestinal Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Emile E Voest
- Department of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Edwin Cuppen
- Hartwig Medical Foundation, Amsterdam, The Netherlands
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Kim Monkhorst
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Gerrit A Meijer
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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11
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Quick C, Anugu P, Musani S, Weiss ST, Burchard EG, White MJ, Keys KL, Cucca F, Sidore C, Boehnke M, Fuchsberger C. Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations. Genet Epidemiol 2020; 44:537-549. [PMID: 32519380 PMCID: PMC7449570 DOI: 10.1002/gepi.22326] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 04/02/2020] [Accepted: 05/22/2020] [Indexed: 01/03/2023]
Abstract
A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.
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Affiliation(s)
- Corbin Quick
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Pramod Anugu
- University of Mississippi Medical CenterJacksonMississippi
| | - Solomon Musani
- University of Mississippi Medical CenterJacksonMississippi
| | - Scott T. Weiss
- Harvard Medical SchoolBostonMassachusetts
- Channing Department of Network MedicineBrigham and Women's HospitalBostonCalifornia
- Partners HealthCare Personalized MedicineBostonMassachusetts
| | - Esteban G. Burchard
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCalifornia
| | - Marquitta J. White
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Kevin L. Keys
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
- Dipartimento di Scienze BiomedicheUniversità di SassariSassariItaly
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
- Department of Genetics and Pharmacology, Institute of Genetic EpidemiologyMedical University of InnsbruckInnsbruckAustria
- Institute for Biomedicine, Eurac ResearchAffiliated Institute of the University of LübeckBolzanoItaly
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12
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Lee XJ, Elliott TM, Harris PNA, Douglas J, Henderson B, Watson C, Paterson DL, Schofield DS, Graves N, Gordon LG. Clinical and Economic Outcomes of Genome Sequencing Availability on Containing a Hospital Outbreak of Resistant Escherichia coli in Australia. Value Health 2020; 23:994-1002. [PMID: 32828227 DOI: 10.1016/j.jval.2020.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 02/12/2020] [Accepted: 03/15/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVES To evaluate the outbreak size and hospital cost effects of bacterial whole-genome sequencing availability in managing a large-scale hospital outbreak. METHODS We built a hybrid discrete event/agent-based simulation model to replicate a serious bacterial outbreak of resistant Escherichia coli in a large metropolitan public hospital during 2017. We tested the 3 strategies of using whole-genome sequencing early, late (actual outbreak), or not using it and assessed their associated outbreak size and hospital cost. The model included ward dynamics, pathogen transmission, and associated hospital costs during a 5-month outbreak. Model parameters were determined using data from the Queensland Hospital Admitted Patient Data Collection (N = 4809 patient admissions) and local clinical knowledge. Sensitivity analyses were performed to address model and parameter uncertainty. RESULTS An estimated 197 patients were colonized during the outbreak, with 75 patients detected. The total outbreak cost was A$460 137 (US$317 117), with 6.1% spent on sequencing. Without sequencing, the outbreak was estimated to result in 352 colonized patients, costing A$766 921 (US$528 547). With earlier detection from use of routine sequencing, the estimated outbreak size was 3 patients and cost A$65 374 (US$45 054). CONCLUSIONS Using whole-genome sequencing in hospital outbreak management was associated with smaller outbreaks and cost savings, with sequencing costs as a small fraction of total hospital costs, supporting the further investigation of the use of routine whole-genome sequencing in hospitals.
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Affiliation(s)
- Xing J Lee
- Queensland University of Technology (QUT), Australian Centre for Health Services Innovation, Institute of Health and Biomedical Innovations, Kelvin Grove, Queensland, Australia
| | - Thomas M Elliott
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Patrick N A Harris
- Queensland Health, Pathology Queensland, Herston, Queensland, Australia; University of Queensland Centre for Clinical Research, Herston, Queensland, Australia
| | - Joel Douglas
- Queensland Health, Pathology Queensland, Herston, Queensland, Australia
| | - Belinda Henderson
- Infection Management Services, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
| | - Catherine Watson
- Infection Management Services, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
| | - David L Paterson
- University of Queensland Centre for Clinical Research, Herston, Queensland, Australia
| | | | - Nicholas Graves
- Queensland University of Technology (QUT), Australian Centre for Health Services Innovation, Institute of Health and Biomedical Innovations, Kelvin Grove, Queensland, Australia
| | - Louisa G Gordon
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia; Queensland Health, Pathology Queensland, Herston, Queensland, Australia; Queensland University of Technology (QUT), School of Nursing, Kelvin Grove, Queensland, Australia; School of Public Health, The University of Queensland, Herston, Queensland, Australia.
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13
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Marine RL, Magaña LC, Castro CJ, Zhao K, Montmayeur AM, Schmidt A, Diez-Valcarce M, Ng TFF, Vinjé J, Burns CC, Nix WA, Rota PA, Oberste MS. Comparison of Illumina MiSeq and the Ion Torrent PGM and S5 platforms for whole-genome sequencing of picornaviruses and caliciviruses. J Virol Methods 2020; 280:113865. [PMID: 32302601 PMCID: PMC9119587 DOI: 10.1016/j.jviromet.2020.113865] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 02/04/2020] [Accepted: 04/06/2020] [Indexed: 02/06/2023]
Abstract
Next-generation sequencing is a powerful tool for virological surveillance. While Illumina® and Ion Torrent® sequencing platforms are used extensively for generating viral RNA genome sequences, there is limited data comparing different platforms. The Illumina MiSeq, Ion Torrent PGM and Ion Torrent S5 platforms were evaluated using a panel of sixteen specimens containing picornaviruses and human caliciviruses (noroviruses and sapoviruses). The specimens were processed, using combinations of three library preparation and five sequencing kits, to assess the quality and completeness of assembled viral genomes, and an estimation of cost per sample to generate the data was calculated. The choice of library preparation kit and sequencing platform was found to impact the breadth of genome coverage and accuracy of consensus viral genomes. The Ion Torrent S5 510 chip runs produced more reads at a lower cost per sample than the highest output Ion Torrent PGM 318 chip run, and generated the highest proportion of reads for enterovirus D68 samples. However, indels at homopolymer regions impacted the accuracy of consensus genome sequences. For lower throughput sequencing runs (i.e., Ion Torrent 510 and Illumina MiSeq Nano V2), the cost per sample was lower on the MiSeq platform, whereas with higher throughput runs (Ion Torrent 530 and Illumina MiSeq V2) there is less of a difference in the cost per sample between the two sequencing platforms ($5.47-$10.25 more per sample for an Ion Torrent 530 chip run when multiplexing 24 samples). These findings suggest that the Ion Torrent S5 and Illumina MiSeq platforms are both viable options for genomic sequencing of RNA viruses, each with specific advantages and tradeoffs.
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Affiliation(s)
- Rachel L Marine
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
| | - Laura C Magaña
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
| | - Christina J Castro
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
| | - Kun Zhao
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | - Marta Diez-Valcarce
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
| | - Terry Fei Fan Ng
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jan Vinjé
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Cara C Burns
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - W Allan Nix
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Paul A Rota
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - M Steven Oberste
- Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
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14
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Kim HS, Jeon S, Kim C, Kim YK, Cho YS, Kim J, Blazyte A, Manica A, Lee S, Bhak J. Chromosome-scale assembly comparison of the Korean Reference Genome KOREF from PromethION and PacBio with Hi-C mapping information. Gigascience 2019; 8:giz125. [PMID: 31794015 PMCID: PMC6889754 DOI: 10.1093/gigascience/giz125] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/02/2019] [Accepted: 09/28/2019] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Long DNA reads produced by single-molecule and pore-based sequencers are more suitable for assembly and structural variation discovery than short-read DNA fragments. For de novo assembly, Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are the favorite options. However, PacBio's SMRT sequencing is expensive for a full human genome assembly and costs more than $40,000 US for 30× coverage as of 2019. ONT PromethION sequencing, on the other hand, is 1/12 the price of PacBio for the same coverage. This study aimed to compare the cost-effectiveness of ONT PromethION and PacBio's SMRT sequencing in relation to the quality. FINDINGS We performed whole-genome de novo assemblies and comparison to construct an improved version of KOREF, the Korean reference genome, using sequencing data produced by PromethION and PacBio. With PromethION, an assembly using sequenced reads with 64× coverage (193 Gb, 3 flowcell sequencing) resulted in 3,725 contigs with N50s of 16.7 Mb and a total genome length of 2.8 Gb. It was comparable to a KOREF assembly constructed using PacBio at 62× coverage (188 Gb, 2,695 contigs, and N50s of 17.9 Mb). When we applied Hi-C-derived long-range mapping data, an even higher quality assembly for the 64× coverage was achieved, resulting in 3,179 scaffolds with an N50 of 56.4 Mb. CONCLUSION The pore-based PromethION approach provided a high-quality chromosome-scale human genome assembly at a low cost with long maximum contig and scaffold lengths and was more cost-effective than PacBio at comparable quality measurements.
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Affiliation(s)
- Hui-Su Kim
- KOGIC, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
| | - Sungwon Jeon
- KOGIC, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
- Department of Biomedical Engineering, School of Life Sciences, UNIST-gil 50, Eonyang-eup, Ulju-gun, UNIST, Ulsan 44919, Republic of Korea
| | - Changjae Kim
- KOGIC, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
| | - Yeon Kyung Kim
- KOGIC, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
| | - Yun Sung Cho
- Clinomics Inc., UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
| | - Jungeun Kim
- Personal Genomics Institute, Genome Research Foundation, Osong saengmyong1ro, Cheongju 28160, Republic of Korea
| | - Asta Blazyte
- KOGIC, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
| | - Andrea Manica
- Department of Zoology, Cambridge University, Downing street, Cambridge CB2 3EJ, UK
| | - Semin Lee
- KOGIC, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
- Department of Biomedical Engineering, School of Life Sciences, UNIST-gil 50, Eonyang-eup, Ulju-gun, UNIST, Ulsan 44919, Republic of Korea
| | - Jong Bhak
- KOGIC, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
- Department of Biomedical Engineering, School of Life Sciences, UNIST-gil 50, Eonyang-eup, Ulju-gun, UNIST, Ulsan 44919, Republic of Korea
- Clinomics Inc., UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
- Personal Genomics Institute, Genome Research Foundation, Osong saengmyong1ro, Cheongju 28160, Republic of Korea
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Affiliation(s)
- Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, University of Sydney, Sydney NSW 2042, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney
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Balloux F, Brønstad Brynildsrud O, van Dorp L, Shaw LP, Chen H, Harris KA, Wang H, Eldholm V. From Theory to Practice: Translating Whole-Genome Sequencing (WGS) into the Clinic. Trends Microbiol 2018; 26:1035-1048. [PMID: 30193960 PMCID: PMC6249990 DOI: 10.1016/j.tim.2018.08.004] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 07/20/2018] [Accepted: 08/10/2018] [Indexed: 12/12/2022]
Abstract
Hospitals worldwide are facing an increasing incidence of hard-to-treat infections. Limiting infections and providing patients with optimal drug regimens require timely strain identification as well as virulence and drug-resistance profiling. Additionally, prophylactic interventions based on the identification of environmental sources of recurrent infections (e.g., contaminated sinks) and reconstruction of transmission chains (i.e., who infected whom) could help to reduce the incidence of nosocomial infections. WGS could hold the key to solving these issues. However, uptake in the clinic has been slow. Some major scientific and logistical challenges need to be solved before WGS fulfils its potential in clinical microbial diagnostics. In this review we identify major bottlenecks that need to be resolved for WGS to routinely inform clinical intervention and discuss possible solutions.
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Affiliation(s)
- Francois Balloux
- UCL Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK; These authors made equal contributions.
| | - Ola Brønstad Brynildsrud
- Infectious Diseases and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 8, Oslo 0456, Norway; These authors made equal contributions
| | - Lucy van Dorp
- UCL Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK; These authors made equal contributions
| | - Liam P Shaw
- UCL Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK
| | - Hongbin Chen
- UCL Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK; Department of Clinical Laboratory, Peking University People's Hospital, Beijing, 100044, China
| | - Kathryn A Harris
- Great Ormond Street Hospital NHS Foundation Trust, Department of Microbiology, Virology & Infection Prevention & Control, London WC1N 3JH, UK
| | - Hui Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, 100044, China
| | - Vegard Eldholm
- Infectious Diseases and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 8, Oslo 0456, Norway
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Christensen KD, Phillips KA, Green RC, Dukhovny D. Cost Analyses of Genomic Sequencing: Lessons Learned from the MedSeq Project. Value Health 2018; 21:1054-1061. [PMID: 30224109 PMCID: PMC6444358 DOI: 10.1016/j.jval.2018.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 06/11/2018] [Indexed: 05/17/2023]
Abstract
OBJECTIVE To summarize lessons learned while analyzing the costs of integrating whole genome sequencing into the care of cardiology and primary care patients in the MedSeq Project by conducting the first randomized controlled trial of whole genome sequencing in general and specialty medicine. METHODS Case study that describes key methodological and data challenges that were encountered or are likely to emerge in future work, describes the pros and cons of approaches considered by the study team, and summarizes the solutions that were implemented. RESULTS Major methodological challenges included defining whole genome sequencing, structuring an appropriate comparator, measuring downstream costs, and examining clinical outcomes. Discussions about solutions addressed conceptual and practical issues that arose because of definitions and analyses around the cost of genomic sequencing in trial-based studies. CONCLUSIONS The MedSeq Project provides an instructive example of how to conduct a cost analysis of whole genome sequencing that feasibly incorporates best practices while being sensitive to the varied applications and diversity of results it may produce. Findings provide guidance for researchers to consider when conducting or analyzing economic analyses of whole genome sequencing and other next-generation sequencing tests, particularly regarding costs.
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Affiliation(s)
- Kurt D Christensen
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Kathryn A Phillips
- Department of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA; Philip R. Lee Institute for Health Policy and Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Robert C Green
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Partners HealthCare Personalized Medicine, Boston, MA, USA
| | - Dmitry Dukhovny
- Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA
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Pereira S, Clayton EW. Commercial Interests, the Technological Imperative, and Advocates: Three Forces Driving Genomic Sequencing in Newborns. Hastings Cent Rep 2018; 48 Suppl 2:S43-S44. [PMID: 30133724 DOI: 10.1002/hast.885] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
While the NSIGHT program was driven by a desire to define and gather data about both the benefits and harms of introducing genomic sequencing into the care of newborns, it remains to be seen how much influence these data will have in shaping the use of this technology in newborns. Ultimately, three additional forces-commercial interests, the technological imperative, and advocates-may play a significant role in shaping the use of sequencing in newborns. Policy-makers and clinicians should be aware of the effects of these additional forces when considering the appropriate use of this technology in clinical practice and public health screening programs.
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Yuen T, Carter MT, Szatmari P, Ungar WJ. Cost-effectiveness of Genome and Exome Sequencing in Children Diagnosed with Autism Spectrum Disorder. Appl Health Econ Health Policy 2018; 16:481-493. [PMID: 29651777 DOI: 10.1007/s40258-018-0390-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
BACKGROUND Genome (GS) and exome sequencing (ES) could potentially identify pathogenic variants with greater sensitivity than chromosomal microarray (CMA) in autism spectrum disorder (ASD) but are costlier and result interpretation can be uncertain. Study objective was to compare the costs and outcomes of four genetic testing strategies in children with ASD. METHODS A microsimulation model estimated the outcomes and costs (in societal and public payer perspectives in Ontario, Canada) of four genetic testing strategies: CMA for all, CMA for all followed by ES for those with negative CMA and syndromic features (CMA+ES), ES or GS for all. RESULTS Compared to CMA, the incremental cost-effectiveness ratio (ICER) per additional child identified with rare pathogenic variants within 18 months of ASD diagnosis was $CAN5997.8 for CMA+ES, $CAN13,504.2 for ES and $CAN10,784.5 for GS in the societal perspective. ICERs were sensitive to changes in ES or GS diagnostic yields, wait times for test results or pre-test genetic counselling, but were robust to changes in the ES or GS costs. CONCLUSION Strategic integration of ES into ASD care could be a cost-effective strategy. Long wait times for genetic services and uncertain utility, both clinical and personal, of sequencing results could limit broader clinical implementation.
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Affiliation(s)
- Tracy Yuen
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Child Health Evaluative Sciences, The Hospital for Sick Children Peter Gilgan Centre for Research and Learning, 11/F, 686 Bay St, Toronto, M5G 0A4, Canada
| | - Melissa T Carter
- Regional Genetics Program, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Peter Szatmari
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Child Health Evaluative Sciences, The Hospital for Sick Children Peter Gilgan Centre for Research and Learning, 11/F, 686 Bay St, Toronto, M5G 0A4, Canada
- Centre for Addiction and Mental Health, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Wendy J Ungar
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
- Child Health Evaluative Sciences, The Hospital for Sick Children Peter Gilgan Centre for Research and Learning, 11/F, 686 Bay St, Toronto, M5G 0A4, Canada.
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23
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Alleweldt F, Kara S, Osinski A, Van Baal P, Kellerborg K, Aarestrup FM, Koopmans M. Developing a framework to assess the costeffectiveness of COMPARE - a global platform for the exchange of sequence-based pathogen data. REV SCI TECH OIE 2018; 36:311-322. [PMID: 28926006 DOI: 10.20506/rst.36.1.2631] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Analysing the genomic data of pathogens with the help of next-generation sequencing (NGS) is an increasingly important part of disease outbreak investigations and helps guide responses. While this technology has already been successfully employed to elucidate and control disease outbreaks, wider implementation of NGS also depends on its cost-effectiveness. COMPARE - short for 'Collaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks' - is a major project, funded by the European Union, to develop a global platform for sharing and analysing NGS data and thereby improve the rapid identification, containment and mitigation of emerging infectious diseases and foodborne outbreaks. This article introduces the project and presents the results of a review of the literature, composed of previous relevant cost-benefit and cost-effectiveness analyses. The authors also outline the implications for a methodological framework to assess the costeffectiveness of COMPARE and similar systems.
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Hayeems RZ, Bhawra J, Tsiplova K, Meyn MS, Monfared N, Bowdin S, Stavropoulos DJ, Marshall CR, Basran R, Shuman C, Ito S, Cohn I, Hum C, Girdea M, Brudno M, Cohn RD, Scherer SW, Ungar WJ. Care and cost consequences of pediatric whole genome sequencing compared to chromosome microarray. Eur J Hum Genet 2017; 25:1303-1312. [PMID: 29158552 PMCID: PMC5865210 DOI: 10.1038/s41431-017-0020-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 08/10/2017] [Accepted: 09/09/2017] [Indexed: 01/14/2023] Open
Abstract
The clinical use of whole-genome sequencing (WGS) is expected to alter pediatric medical management. The study aimed to describe the type and cost of healthcare activities following pediatric WGS compared to chromosome microarray (CMA). Healthcare activities prompted by WGS and CMA were ascertained for 101 children with developmental delay over 1 year. Activities following receipt of non-diagnostic CMA were compared to WGS diagnostic and non-diagnostic results. Activities were costed in 2016 Canadian dollars (CDN). Ongoing care accounted for 88.6% of post-test activities. The mean number of lab tests was greater following CMA than WGS (0.55 vs. 0.09; p = 0.007). The mean number of specialist visits was greater following WGS than CMA (0.41 vs. 0; p = 0.016). WGS results (diagnostic vs. non-diagnostic) modified the effect of test type on mean number of activities (p < 0.001). The cost of activities prompted by diagnostic WGS exceeded $557CDN for 10% of cases. In complex pediatric care, CMA prompted additional diagnostic investigations while WGS prompted tailored care guided by genotypic variants. Costs for prompted activities were low for the majority and constitute a small proportion of total test costs. Optimal use of WGS depends on robust evaluation of downstream care and cost consequences.
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Affiliation(s)
- Robin Z Hayeems
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada.
| | - Jasmin Bhawra
- School of Public Health and Health Systems, University of Waterloo, Waterloo, Canada
| | - Kate Tsiplova
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - M Stephen Meyn
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Department of Pediatrics, University of Toronto, Toronto, Canada
| | - Nasim Monfared
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Canada
- Department of Genetic Counselling, The Hospital for Sick Children, Toronto, Canada
| | - Sarah Bowdin
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Canada
- Department of Pediatrics, University of Toronto, Toronto, Canada
| | - D James Stavropoulos
- Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Christian R Marshall
- Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
| | - Raveen Basran
- Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Cheryl Shuman
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Department of Genetic Counselling, The Hospital for Sick Children, Toronto, Canada
| | - Shinya Ito
- Division of Clinical Pharmacology and Toxicology, The Hospital for Sick Children, Toronto, Canada
| | - Iris Cohn
- Division of Clinical Pharmacology and Toxicology, The Hospital for Sick Children, Toronto, Canada
| | - Courtney Hum
- Prenatal Diagnosis and Medical Genetics Program, Sinai Health System, Toronto, Canada
| | - Marta Girdea
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Michael Brudno
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Program in Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Canada
| | - Ronald D Cohn
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Department of Pediatrics, University of Toronto, Toronto, Canada
- Program in Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Canada
- Division of Pediatric Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Stephen W Scherer
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
- Program in Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Canada
- McLaughlin Centre, University of Toronto, Toronto, Canada
| | - Wendy J Ungar
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada
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Plöthner M, Frank M, von der Schulenburg JMG. Cost analysis of whole genome sequencing in German clinical practice. Eur J Health Econ 2017; 18:623-633. [PMID: 27380512 DOI: 10.1007/s10198-016-0815-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 06/23/2016] [Indexed: 05/23/2023]
Abstract
OBJECTIVES Whole genome sequencing (WGS) is an emerging tool in clinical diagnostics. However, little has been said about its procedure costs, owing to a dearth of related cost studies. This study helps fill this research gap by analyzing the execution costs of WGS within the setting of German clinical practice. METHODOLOGY First, to estimate costs, a sequencing process related to clinical practice was undertaken. Once relevant resources were identified, a quantification and monetary evaluation was conducted using data and information from expert interviews with clinical geneticists, and personnel at private enterprises and hospitals. This study focuses on identifying the costs associated with the standard sequencing process, and the procedure costs for a single WGS were analyzed on the basis of two sequencing platforms-namely, HiSeq 2500 and HiSeq Xten, both by Illumina, Inc. In addition, sensitivity analyses were performed to assess the influence of various uses of sequencing platforms and various coverage values on a fixed-cost degression. RESULTS In the base case scenario-which features 80 % utilization and 30-times coverage-the cost of a single WGS analysis with the HiSeq 2500 was estimated at €3858.06. The cost of sequencing materials was estimated at €2848.08; related personnel costs of €396.94 and acquisition/maintenance costs (€607.39) were also found. In comparison, the cost of sequencing that uses the latest technology (i.e., HiSeq Xten) was approximately 63 % cheaper, at €1411.20. CONCLUSIONS The estimated costs of WGS currently exceed the prediction of a 'US$1000 per genome', by more than a factor of 3.8. In particular, the material costs in themselves exceed this predicted cost.
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Affiliation(s)
- Marika Plöthner
- Center for Health Economics Research Hannover (CHERH), Leibniz University Hannover, Otto-Brenner-Straße 1, 30159, Hannover, Germany.
| | - Martin Frank
- Center for Health Economics Research Hannover (CHERH), Leibniz University Hannover, Otto-Brenner-Straße 1, 30159, Hannover, Germany
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