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Dong X, Lu Y, Guo L, Li C, Ni Q, Wu B, Wang H, Yang L, Wu S, Sun Q, Zheng H, Zhou W, Wang S. PICOTEES: a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants from Chinese children cohorts. J Genet Genomics 2024; 51:243-251. [PMID: 37714454 DOI: 10.1016/j.jgg.2023.09.003] [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: 04/06/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 09/17/2023]
Abstract
The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, but it requires significant trust between users and providers. To alleviate privacy concerns, the most commonly used strategy is to anonymize sensitive data. Unfortunately, studies have shown that anonymization is insufficient to protect against reidentification attacks. Recently, privacy-preserving technologies have been applied to preserve application utility while protecting the privacy of biomedical data. We present the PICOTEES framework, a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants (https://birthdefectlab.cn:3000/). PICOTEES enables privacy-preserving queries of the phenotype spectrum for a single variant by utilizing trusted execution environment technology, which can protect the privacy of the user's query information, backend models, and data, as well as the final results. We demonstrate the utility and performance of PICOTEES by exploring a bioinformatics dataset. The dataset is from a cohort containing 20,909 genetic testing patients with 3,152,508 variants from the Children's Hospital of Fudan University in China, dominated by the Chinese Han population (>99.9%). Our query results yield a large number of unreported diagnostic variants and previously reported pathogenicity.
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Affiliation(s)
- Xinran Dong
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China; Key Laboratory of Birth Defects, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Yulan Lu
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China; Key Laboratory of Birth Defects, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Lanting Guo
- Department of Bioinformatics, Hangzhou Nuowei Information Technology Co., Ltd, Hangzhou, Zhejiang 310000, China
| | - Chuan Li
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Qi Ni
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China; Key Laboratory of Birth Defects, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Bingbing Wu
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China; Key Laboratory of Birth Defects, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Huijun Wang
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China; Key Laboratory of Birth Defects, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Lin Yang
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China; Key Laboratory of Birth Defects, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Songyang Wu
- The Third Research Institute of the Ministry of Public Security, Shanghai 200031, China
| | - Qi Sun
- Department of Bioinformatics, Hangzhou Nuowei Information Technology Co., Ltd, Hangzhou, Zhejiang 310000, China
| | - Hao Zheng
- Department of Bioinformatics, Hangzhou Nuowei Information Technology Co., Ltd, Hangzhou, Zhejiang 310000, China
| | - Wenhao Zhou
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai 201102, China; Xiamen Campus of Children's Hospital of Fudan University, Xiamen, Fujian 361006, China.
| | - Shuang Wang
- Department of Bioinformatics, Hangzhou Nuowei Information Technology Co., Ltd, Hangzhou, Zhejiang 310000, China; Institutes for Systems Genetics, West China Hospital, Chengdu, Sichuan 610041, China; Shanghai Putuo People's Hospital, Tongji University, Shanghai 200060, China.
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2
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Szakszon K, Lourenco CM, Callewaert BL, Geneviève D, Rouxel F, Morin D, Denommé-Pichon AS, Vitobello A, Patterson WG, Louie R, Pinto E Vairo F, Klee E, Kaiwar C, Gavrilova RH, Agre KE, Jacquemont S, Khadijé J, Giltay J, van Gassen K, Merő G, Gerkes E, Van Bon BW, Rinne T, Pfundt R, Brunner HG, Caluseriu O, Grasshoff U, Kehrer M, Haack TB, Khelifa MM, Bergmann AK, Cueto-González AM, Martorell AC, Ramachandrappa S, Sawyer LB, Fasel P, Braun D, Isis A, Superti-Furga A, McNiven V, Chitayat D, Ahmed SA, Brennenstuhl H, Schwaibolf EM, Battisti G, Parmentier B, Stevens SJC. Further delineation of the rare GDACCF (global developmental delay, absent or hypoplastic corpus callosum, dysmorphic facies syndrome): genotype and phenotype of 22 patients with ZNF148 mutations. J Med Genet 2024; 61:132-141. [PMID: 37580113 DOI: 10.1136/jmg-2022-109030] [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: 11/14/2022] [Accepted: 07/27/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Pathogenic variants in the zinc finger protein coding genes are rare causes of intellectual disability and congenital malformations. Mutations in the ZNF148 gene causing GDACCF syndrome (global developmental delay, absent or hypoplastic corpus callosum, dysmorphic facies; MIM #617260) have been reported in five individuals so far. METHODS As a result of an international collaboration using GeneMatcher Phenome Central Repository and personal communications, here we describe the clinical and molecular genetic characteristics of 22 previously unreported individuals. RESULTS The core clinical phenotype is characterised by developmental delay particularly in the domain of speech development, postnatal growth retardation, microcephaly and facial dysmorphism. Corpus callosum abnormalities appear less frequently than suggested by previous observations. The identified mutations concerned nonsense or frameshift variants that were mainly located in the last exon of the ZNF148 gene. Heterozygous deletion including the entire ZNF148 gene was found in only one case. Most mutations occurred de novo, but were inherited from an affected parent in two families. CONCLUSION The GDACCF syndrome is clinically diverse, and a genotype-first approach, that is, exome sequencing is recommended for establishing a genetic diagnosis rather than a phenotype-first approach. However, the syndrome may be suspected based on some recurrent, recognisable features. Corpus callosum anomalies were not as constant as previously suggested, we therefore recommend to replace the term 'GDACCF syndrome' with 'ZNF148-related neurodevelopmental disorder'.
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Affiliation(s)
- Katalin Szakszon
- Faculty of Medicine Institute of Pediatrics, University of Debrecen, Debrecen, Hungary
- Rare Congenital Malformations and Rare intellectual Disability (ERN ITHACA), European Reference Networks, Debrecen, Hungary
| | - Charles Marques Lourenco
- Neurogenetics Unit - Inborn Errors of Metabolism Clinics, National Reference Center for Rare Diseases, Medicine School of Sao Jose do Rio Preto, Sao Jose do Rio Preto, Brazil
| | - Bert Louis Callewaert
- Center for Medical Genetics, University Hospital Ghent, Gent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - David Geneviève
- Montpellier University, Inserm Unit U1183, Reference Center for Rare Disease: Developmental Anomalies. Clinical Genetic Unit, CHU Montpellier, Montpellier, France
- Rare Congenital Malformations and Rare Intellectual Disability (ERN ITHACA), European Reference Networks, Montpellier, France
| | - Flavien Rouxel
- Génétique Clinique, Départment de Génétique Médicale, Maladies Rares et Médecine Personnalisée, CHU Montpellier, Montpellier University, Centre de Référence Anomalies du Développement SOOR, Montpellier, France
| | - Denis Morin
- Rare Kidney Disease Center, Montpellier University Hospital, Montpellier, France
| | - Anne-Sophie Denommé-Pichon
- Functional Unity of Innovative Diagnosis for Rare Diseases, University of Burgundy, Dijon, France
- Inserm UMR1231 team GAD, University of Burgundy, Dijon, France
| | - Antonio Vitobello
- Functional Unity of Innovative Diagnosis for Rare Diseases, University of Burgundy, Dijon, France
- Inserm UMR1231 team GAD, University of Burgundy, Dijon, France
| | | | - Raymond Louie
- Greenwood Genetic Center Inc, Greenwood, South Carolina, USA
| | - Filippo Pinto E Vairo
- Department of Clinical Genomics, Center for Individualized Medicine, Mayo Clinic Research Rochester, Rochester, Minnesota, USA
| | - Eric Klee
- Department of Clinical Genomics, Center for Individualized Medicine, Mayo Clinic Research Rochester, Rochester, Minnesota, USA
| | - Charu Kaiwar
- Department of Clinical Genomics, Center for Individualized Medicine, Mayo Clinic Research Rochester, Rochester, Minnesota, USA
| | - Ralitza H Gavrilova
- Department of Clinical Genomics, Center for Individualized Medicine, Mayo Clinic Research Rochester, Rochester, Minnesota, USA
| | - Katherine E Agre
- Department of Clinical Genomics, Center for Individualized Medicine, Mayo Clinic Research Rochester, Rochester, Minnesota, USA
| | - Sebastien Jacquemont
- Sainte-Justine Research Center, Sainte-Justine Hospital, University of Montreal, Montreal, Quebec, Canada
- Department of Medical Genetics, Sainte-Justine Hospital, University of Montreal, Montreal, Quebec, Canada
| | - Jizi Khadijé
- Department of Medical Genetics, Sainte-Justine Hospital, University of Montreal, Montreal, Quebec, Canada
| | - Jacques Giltay
- Department of Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Koen van Gassen
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gabriella Merő
- Faculty of Medicine Institute of Pediatrics, University of Debrecen, Debrecen, Hungary
| | - Erica Gerkes
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Bregje W Van Bon
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tuula Rinne
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rolph Pfundt
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Han G Brunner
- Klinische Genetica, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Oana Caluseriu
- Medical Genetics Clinic, University of Alberta, Edmonton, Alberta, Canada
| | - Ute Grasshoff
- Institute of Medical Genetics and Applied Genomics, University Clinic, Tübingen University, Tübingen, Germany
| | - Martin Kehrer
- Institute of Medical Genetics and Applied Genomics, University Clinic, Tübingen University, Tübingen, Germany
| | - Tobias B Haack
- Institute of Medical Genetics and Applied Genomics, University Clinic, Tübingen University, Tübingen, Germany
| | | | | | - Anna Maria Cueto-González
- Department of Clinical and Molecular Genetics, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
- Rare Congenital Malformations and Rare intellectual Disability (ERN ITHACA), European Reference Networks, Barcelona, Spain
| | - Ariadna Campos Martorell
- Pediatric Endocrinology Department, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
- Endocrinology Group, Vall d'Hebron Barcelona Hospital Campus, Autonomous University of Barcelona, Vall d'Hebron Research Institute, Barcelona, Spain
| | | | - Lindsey B Sawyer
- Department of Medical Genetics, Children's Hospital of The King's Daughters, Norfolk, Virginia, USA
| | - Pascale Fasel
- Department of Human Genetics, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Dominique Braun
- Department of Human Genetics, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Atallah Isis
- Division of Genetic Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Andrea Superti-Furga
- Division of Genetic Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Vanda McNiven
- University Health Network and Mount Sinai Hospital, Fred A Litwin Family Centre in Genetic Medicine, Toronto, Ontario, Canada
- Division of Clinical and Metabolic Genetics, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - David Chitayat
- Division of Clinical and Metabolic Genetics, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
- The Prenatal Diagnosis and Medical Genetics Program, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Syed Anas Ahmed
- University Health Network and Mount Sinai Hospital, Fred A Litwin Family Centre in Genetic Medicine, Toronto, Ontario, Canada
| | | | - Eva Mc Schwaibolf
- Insittute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Gladys Battisti
- Centre de Génétique Humaine, Institut de Pathologie et de Genetique asbl, Gosselies, Belgium
| | - Benoit Parmentier
- Centre de Génétique Humaine, Institut de Pathologie et de Genetique asbl, Gosselies, Belgium
| | - Servi J C Stevens
- Klinische Genetica, Maastricht University Medical Center, Maastricht, The Netherlands
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3
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Licata L, Via A, Turina P, Babbi G, Benevenuta S, Carta C, Casadio R, Cicconardi A, Facchiano A, Fariselli P, Giordano D, Isidori F, Marabotti A, Martelli PL, Pascarella S, Pinelli M, Pippucci T, Russo R, Savojardo C, Scafuri B, Valeriani L, Capriotti E. Resources and tools for rare disease variant interpretation. Front Mol Biosci 2023; 10:1169109. [PMID: 37234922 PMCID: PMC10206239 DOI: 10.3389/fmolb.2023.1169109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.
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Affiliation(s)
- Luana Licata
- Department of Biology, University of Rome Tor Vergata, Roma, Italy
| | - Allegra Via
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Paola Turina
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | - Claudio Carta
- National Centre for Rare Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Andrea Cicconardi
- Department of Physics, University of Genova, Genova, Italy
- Italiano di Tecnologia—IIT, Genova, Italy
| | - Angelo Facchiano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Deborah Giordano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Federica Isidori
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Anna Marabotti
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | - Pier Luigi Martelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Michele Pinelli
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
| | - Tommaso Pippucci
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Roberta Russo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, Napoli, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Bernardina Scafuri
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | | | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
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4
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Pei XM, Yeung MHY, Wong ANN, Tsang HF, Yu ACS, Yim AKY, Wong SCC. Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases. Cells 2023; 12:493. [PMID: 36766834 PMCID: PMC9913990 DOI: 10.3390/cells12030493] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases.
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Affiliation(s)
- Xiao Meng Pei
- Department of Applied Biology & Chemical Technology, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Alex Ngai Nick Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Hin Fung Tsang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
- Department of Clinical Laboratory and Pathology, Hong Kong Adventist Hospital, Hong Kong, China
| | - Allen Chi Shing Yu
- Codex Genetics Limited, Unit 212, 2/F., Building 16W, No. 16 Science Park West Avenue, The Hong Kong Science Park, Hong Kong 852, China
| | - Aldrin Kay Yuen Yim
- Codex Genetics Limited, Unit 212, 2/F., Building 16W, No. 16 Science Park West Avenue, The Hong Kong Science Park, Hong Kong 852, China
| | - Sze Chuen Cesar Wong
- Department of Applied Biology & Chemical Technology, The Hong Kong Polytechnic University, Hong Kong 999077, China
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5
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Gupta N. Deciphering Intellectual Disability. Indian J Pediatr 2023; 90:160-167. [PMID: 36441387 DOI: 10.1007/s12098-022-04345-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/05/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022]
Abstract
Intellectual disability (ID) is a common cause of referral to the pediatricians, geneticists, and pediatric neurologists. A thorough clinical evaluation and a stepwise investigative approach using a combination of traditional genetic techniques and appropriate latest genomic technologies can help in arriving at a diagnosis. In the current "omics" era, adopting a multiomics approach would further assist in solving the undiagnosed cases with intellectual disability.
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Affiliation(s)
- Neerja Gupta
- Division of Genetics, Department of Pediatrics, All India Institute of Medical Sciences, Ansari Nagar, Old OT Block, New Delhi, 110029, India.
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6
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Muffels IJ, Schene IF, Rehmann H, Massink MP, van der Wal MM, Bauder C, Labeur M, Armando NG, Lequin MH, Houben ML, Giltay JC, Haitjema S, Huisman A, Vansenne F, Bluvstein J, Pappas J, Shailee LV, Zarate YA, Mokry M, van Haaften GW, Nieuwenhuis EE, Refojo D, van Wijk F, Fuchs SA, van Hasselt PM. Bi-allelic variants in NAE1 cause intellectual disability, ischiopubic hypoplasia, stress-mediated lymphopenia and neurodegeneration. Am J Hum Genet 2023; 110:146-160. [PMID: 36608681 PMCID: PMC9892777 DOI: 10.1016/j.ajhg.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Neddylation has been implicated in various cellular pathways and in the pathophysiology of numerous diseases. We identified four individuals with bi-allelic variants in NAE1, which encodes the neddylation E1 enzyme. Pathogenicity was supported by decreased NAE1 abundance and overlapping clinical and cellular phenotypes. To delineate how cellular consequences of NAE1 deficiency would lead to the clinical phenotype, we focused primarily on the rarest phenotypic features, based on the assumption that these would best reflect the pathophysiology at stake. Two of the rarest features, neuronal loss and lymphopenia worsening during infections, suggest that NAE1 is required during cellular stress caused by infections to protect against cell death. In support, we found that stressing the proteasome system with MG132-requiring upregulation of neddylation to restore proteasomal function and proteasomal stress-led to increased cell death in fibroblasts of individuals with NAE1 genetic variants. Additionally, we found decreased lymphocyte counts after CD3/CD28 stimulation and decreased NF-κB translocation in individuals with NAE1 variants. The rarest phenotypic feature-delayed closure of the ischiopubic rami-correlated with significant downregulation of RUN2X and SOX9 expression in transcriptomic data of fibroblasts. Both genes are involved in the pathophysiology of ischiopubic hypoplasia. Thus, we show that NAE1 plays a major role in (skeletal) development and cellular homeostasis during stress. Our approach suggests that a focus on rare phenotypic features is able to provide significant pathophysiological insights in diseases caused by mutations in genes with pleiotropic effects.
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Affiliation(s)
- Irena J.J. Muffels
- Department of Metabolic Diseases, Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, 3584 EA Utrecht, the Netherlands,Center for Translational Immunology (CTI), Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Imre F. Schene
- Department of Metabolic Diseases, Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, 3584 EA Utrecht, the Netherlands
| | - Holger Rehmann
- Department of Energy and Biotechnology, Flensburg University of Applied Sciences, Flensburg, Germany
| | - Maarten P.G. Massink
- Department of Genetics, Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Maria M. van der Wal
- Center for Translational Immunology (CTI), Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Corinna Bauder
- Department of Neuroendocrinology, Max Planck Institute of Psychiatry, Munich, Germany,Institute of Developmental Genetics, Helmholtz Zentrum München, Munich, Germany
| | - Martha Labeur
- Department of Neuroendocrinology, Max Planck Institute of Psychiatry, Munich, Germany
| | - Natalia G. Armando
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina
| | - Maarten H. Lequin
- Division Imaging and Oncology University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Michiel L. Houben
- Department of General Pediatrics, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jaques C. Giltay
- Department of Genetics, Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Saskia Haitjema
- Central Diagnostics Laboratory, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Albert Huisman
- Central Diagnostics Laboratory, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Fleur Vansenne
- Department of Medical Genetics, University Medical Center Groningen, Groningen, the Netherlands
| | - Judith Bluvstein
- Dravet Center and Comprehensive Epilepsy Center, NYU School of Medicine, New York, NY, USA
| | - John Pappas
- NYU Clinical Genetic Services, NYU Grossman School of Medicine, New York, NY, USA
| | - Lala V. Shailee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yuri A. Zarate
- Section of Genetics and Metabolism, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Michal Mokry
- Laboratory of Experimental Cardiology, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, the Netherlands
| | - Gijs W. van Haaften
- Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Edward E.S. Nieuwenhuis
- Department of Biomedical and Life Sciences, University College Roosevelt, Middelburg, the Netherlands
| | - Damian Refojo
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina,Molecular Neurobiology, Max Planck Institute of Psychiatry, Munich, Germany
| | - Femke van Wijk
- Department of Genetics, Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Sabine A. Fuchs
- Department of Metabolic Diseases, Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, 3584 EA Utrecht, the Netherlands
| | - Peter M. van Hasselt
- Department of Metabolic Diseases, Division Pediatrics, Wilhelmina Children’s Hospital University Medical Center Utrecht, Utrecht University, 3584 EA Utrecht, the Netherlands,Corresponding author
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7
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Papakonstantinou E, Efthymiou V, Dragoumani K, Christodoulou M, Vlachakis D. Collaborative Platforms and Matchmaking Algorithms for Research and Education, Establishment, and Optimization of Consortia. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:125-133. [PMID: 37486486 DOI: 10.1007/978-3-031-31982-2_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Matchmaking has a great position in the rational allocation of resources in several fields, ranging from market operation to people's daily lives. Matchmakers have evolved through artificial intelligence technologies and are being introduced in numerous aspects of industry, research, and academia in solving decision issues, research innovation design, and building robust and efficient networks. The goal of this report is to describe the collaborative platforms and matchmaking algorithms for research and education, as well as the establishment and optimization of consortia.
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Affiliation(s)
- Eleni Papakonstantinou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Vasiliki Efthymiou
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - Konstantina Dragoumani
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Maria Christodoulou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Dimitrios Vlachakis
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece.
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece.
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
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8
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Boycott KM, Hartley T, Kernohan KD, Dyment DA, Howley H, Innes AM, Bernier FP, Brudno M. Care4Rare Canada: Outcomes from a decade of network science for rare disease gene discovery. Am J Hum Genet 2022; 109:1947-1959. [PMID: 36332610 PMCID: PMC9674964 DOI: 10.1016/j.ajhg.2022.10.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
The past decade has witnessed a rapid evolution in rare disease (RD) research, fueled by the availability of genome-wide (exome and genome) sequencing. In 2011, as this transformative technology was introduced to the research community, the Care4Rare Canada Consortium was launched: initially as FORGE, followed by Care4Rare, and Care4Rare SOLVE. Over what amounted to three eras of diagnosis and discovery, the Care4Rare Consortium used exome sequencing and, more recently, genome and other 'omic technologies to identify the molecular cause of unsolved RDs. We achieved a diagnostic yield of 34% (623/1,806 of participating families), including the discovery of deleterious variants in 121 genes not previously associated with disease, and we continue to study candidate variants in novel genes for 145 families. The Consortium has made significant contributions to RD research, including development of platforms for data collection and sharing and instigating a Canadian network to catalyze functional characterization research of novel genes. The Consortium was instrumental to implementing genome-wide sequencing as a publicly funded test for RD diagnosis in Canada. Despite the successes of the past decade, the challenge of solving all RDs remains enormous, and the work is far from over. We must leverage clinical and 'omic data for secondary use, develop tools and policies to support safe data sharing, continue to explore the utility of new and emerging technologies, and optimize research protocols to delineate complex disease mechanisms. Successful approaches in each of these realms is required to offer diagnostic clarity to all families with RDs.
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Affiliation(s)
- Kym M. Boycott
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada,Corresponding author
| | - Taila Hartley
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - Kristin D. Kernohan
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - David A. Dyment
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - Heather Howley
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - A. Micheil Innes
- Department of Medical Genetics and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Francois P. Bernier
- Department of Medical Genetics and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
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9
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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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10
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Harnish JM, Li L, Rogic S, Poirier-Morency G, Kim SY, Boycott KM, Wangler MF, Bellen HJ, Hieter P, Pavlidis P, Liu Z, Yamamoto S. ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research. Hum Mutat 2022; 43:743-759. [PMID: 35224820 PMCID: PMC9133126 DOI: 10.1002/humu.24364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/12/2022] [Accepted: 02/23/2022] [Indexed: 11/08/2022]
Abstract
Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.
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Affiliation(s)
- J. Michael Harnish
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital (TCH), Houston, TX, 77030, USA
| | - Lucian Li
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital (TCH), Houston, TX, 77030, USA
- Department of Pediatrics, Division of Neurology and Developmental Neuroscience, BCM, Houston, TX, 77030, USA
| | - Sanja Rogic
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - Guillaume Poirier-Morency
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - Seon-Young Kim
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital (TCH), Houston, TX, 77030, USA
- Department of Pediatrics, Division of Neurology and Developmental Neuroscience, BCM, Houston, TX, 77030, USA
| | | | - Kym M. Boycott
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H8L1, Canada
| | - Michael F. Wangler
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital (TCH), Houston, TX, 77030, USA
- Development, Disease Models & Therapeutics Graduate Program, BCM, Houston, TX, 77030, USA
| | - Hugo J. Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital (TCH), Houston, TX, 77030, USA
- Development, Disease Models & Therapeutics Graduate Program, BCM, Houston, TX, 77030, USA
- Department of Neuroscience, BCM, Houston, TX, 77030, USA
| | - Philip Hieter
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital (TCH), Houston, TX, 77030, USA
- Department of Pediatrics, Division of Neurology and Developmental Neuroscience, BCM, Houston, TX, 77030, USA
- Quantitative and Computational Biosciences Graduate Program, BCM, Houston, TX, 77030, USA
| | - Shinya Yamamoto
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital (TCH), Houston, TX, 77030, USA
- Development, Disease Models & Therapeutics Graduate Program, BCM, Houston, TX, 77030, USA
- Department of Neuroscience, BCM, Houston, TX, 77030, USA
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11
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Driver HG, Hartley T, Price EM, Turinsky AL, Buske OJ, Osmond M, Ramani AK, Kirby E, Kernohan KD, Couse M, Elrick H, Lu K, Mashouri P, Mohan A, So D, Klamann C, Le HGBH, Herscovich A, Marshall CR, Statia A, Canada Consortium C, Knoppers BM, Brudno M, Boycott KM. Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery. Hum Mutat 2022; 43:800-811. [PMID: 35181971 PMCID: PMC9311832 DOI: 10.1002/humu.24354] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/08/2022] [Accepted: 02/16/2022] [Indexed: 11/06/2022]
Abstract
Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R‐SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web‐accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.
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Affiliation(s)
- Hannah G. Driver
- Children's Hospital of Eastern Ontario Research InstituteUniversity of OttawaOttawaCanada
| | - Taila Hartley
- Children's Hospital of Eastern Ontario Research InstituteUniversity of OttawaOttawaCanada
| | - E. Magda Price
- Children's Hospital of Eastern Ontario Research InstituteUniversity of OttawaOttawaCanada
| | - Andrei L. Turinsky
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | | | - Matthew Osmond
- Children's Hospital of Eastern Ontario Research InstituteUniversity of OttawaOttawaCanada
| | - Arun K. Ramani
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Emily Kirby
- Centre of Genomics and PolicyMcGill UniversityMontrealCanada
| | - Kristin D. Kernohan
- Children's Hospital of Eastern Ontario Research InstituteUniversity of OttawaOttawaCanada
- Newborn Screening OntarioChildren's Hospital of Eastern OntarioOttawaCanada
- Genomics4RD Steering CommitteeChildren's Hospital of Eastern Ontario Research InstituteOttawaCanada
| | - Madeline Couse
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Hillary Elrick
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Kevin Lu
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Pouria Mashouri
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Aarthi Mohan
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Delvin So
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Conor Klamann
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Hannah G. B. H. Le
- Centre for Computational MedicineThe Hospital for Sick ChildrenTorontoCanada
| | - Andrea Herscovich
- Genomics4RD Steering CommitteeChildren's Hospital of Eastern Ontario Research InstituteOttawaCanada
| | - Christian R. Marshall
- Genomics4RD Steering CommitteeChildren's Hospital of Eastern Ontario Research InstituteOttawaCanada
- Genome DiagnosticsThe Hospital for Sick ChildrenTorontoCanada
| | - Andrew Statia
- Genomics4RD Steering CommitteeChildren's Hospital of Eastern Ontario Research InstituteOttawaCanada
| | | | | | - Michael Brudno
- PhenoTips, The Hospital for Sick ChildrenTorontoCanada
- Genomics4RD Steering CommitteeChildren's Hospital of Eastern Ontario Research InstituteOttawaCanada
- Techna InstituteUniversity Health NetworkTorontoCanada
- Department of Computer ScienceUniversity of TorontoTorontoCanada
| | - Kym M. Boycott
- Children's Hospital of Eastern Ontario Research InstituteUniversity of OttawaOttawaCanada
- Genomics4RD Steering CommitteeChildren's Hospital of Eastern Ontario Research InstituteOttawaCanada
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12
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Boycott KM, Azzariti DR, Hamosh A, Rehm HL. Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking. Hum Mutat 2022; 43:659-667. [PMID: 35537081 PMCID: PMC9133175 DOI: 10.1002/humu.24373] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 03/22/2022] [Indexed: 11/09/2022]
Abstract
The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in the years to come.
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Affiliation(s)
- Kym M. Boycott
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Danielle R. Azzariti
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Heidi L. Rehm
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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13
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Compound Heterozygous Mutations Presented with Quadriparesis and Menopause. A Case Report. Twin Res Hum Genet 2022; 25:74-76. [DOI: 10.1017/thg.2022.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract
Mitochondrion regulates cellular metabolism with the aid of its respiratory complexes; any defect within these complexes can result in mitochondrial malfunction and various conditions. One such mutation can occur in SLC25A10, resulting in mitochondrial DNA depletion syndrome. It should be noted that the pattern of inheritance of this syndrome is autosomal recessive. However, we present a case with compound heterozygous mutations within this gene resulting in disease. An 18-year-old female was referred to our clinic due to menopause with a medical history of hearing loss, spasticity, hypotonia and quadriparesis. The child’s birth and development were uneventful until the initiation of movement reduction and hypotonia when she was 12 months old. Afterward, the hypotonia progressed to quadriparesis and spasticity throughout the years. Our patient became completely quadriplegic up to the age of 3 and became completely deaf at 10. Her puberty onset was at the age of 9, and no significant event took place until she was 17 years old when suddenly her periods, which were regular until that time, became irregular and ceased after a year; hence, a thorough evaluation began, but similar to her previous evaluations all tests were insignificant. Nonetheless, we suspected an underlying metabolic or genetic defect; thus, we ordered a whole-exome sequencing (WES) workup and found simultaneous heterozygous mutations within SLC25A10, HFE and TTN genes that could explain her condition. When all other tests fail, and we suspect an underlying genetic or metabolic cause, WES can be of great value.
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14
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Thistlethwaite LR, Li X, Burrage LC, Riehle K, Hacia JG, Braverman N, Wangler MF, Miller MJ, Elsea SH, Milosavljevic A. Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data. Sci Rep 2022; 12:6556. [PMID: 35449147 PMCID: PMC9023513 DOI: 10.1038/s41598-022-10415-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 03/14/2022] [Indexed: 02/06/2023] Open
Abstract
Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.
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Affiliation(s)
- Lillian R Thistlethwaite
- Quantitative and Computational Biosciences Program, Baylor College of Medicine, One Baylor Plaza, 400D, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xiqi Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Lindsay C Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Joseph G Hacia
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Nancy Braverman
- Department of Pediatrics and Human Genetics, McGill University, Montreal, QC, Canada
| | - Michael F Wangler
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
- Jan and Dan Duncan Texas Children's Hospital Neurological Research Institute, Houston, TX, USA
| | - Marcus J Miller
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sarah H Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Aleksandar Milosavljevic
- Quantitative and Computational Biosciences Program, Baylor College of Medicine, One Baylor Plaza, 400D, Houston, TX, 77030, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
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15
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CoMent: relationships between biomedical concepts inferred from the scientific literature. J Mol Biol 2022; 434:167568. [DOI: 10.1016/j.jmb.2022.167568] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 01/22/2023]
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16
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Lee JH. Invertebrate Model Organisms as a Platform to Investigate Rare Human Neurological Diseases. Exp Neurobiol 2022; 31:1-16. [PMID: 35256540 PMCID: PMC8907251 DOI: 10.5607/en22003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/07/2022] [Accepted: 02/07/2022] [Indexed: 01/16/2023] Open
Abstract
Patients suffering from rare human diseases often go through a painful journey for finding a definite molecular diagnosis prerequisite of appropriate cures. With a novel variant isolated from a single patient, determination of its pathogenicity to end such "diagnostic odyssey" requires multi-step processes involving experts in diverse areas of interest, including clinicians, bioinformaticians and research scientists. Recent efforts in building large-scale genomic databases and in silico prediction platforms have facilitated identification of potentially pathogenic variants causative of rare human diseases of a Mendelian basis. However, the functional significance of individual variants remains elusive in many cases, thus requiring incorporation of versatile and rapid model organism (MO)-based platforms for functional analyses. In this review, the current scope of rare disease research is briefly discussed. In addition, an overview of invertebrate MOs for their key features relevant to rare neurological diseases is provided, with the characteristics of two representative invertebrate MOs, Drosophila melanogaster and Caenorhabditis elegans, as well as the challenges against them. Finally, recently developed research networks integrating these MOs in collaborative research are portraited with an array of bioinformatical analyses embedded. A comprehensive survey of MO-based research activities provided in this review will help us to design a wellstructured analysis of candidate genes or potentially pathogenic variants for their roles in rare neurological diseases in future.
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Affiliation(s)
- Ji-Hye Lee
- Department of Oral Pathology & Life Science in Dentistry, School of Dentistry, Pusan National University, Yangsan 50612, Korea.,Dental Life Science Institute, Pusan National University, Yangsan 50612, Korea.,Periodontal Disease Signaling Network Research Center, Pusan National University, Yangsan 50612, Korea
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17
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Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med 2022; 14:23. [PMID: 35220969 PMCID: PMC8883622 DOI: 10.1186/s13073-022-01026-w] [Citation(s) in RCA: 105] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/10/2022] [Indexed: 02/07/2023] Open
Abstract
Rare diseases affect 30 million people in the USA and more than 300-400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25-35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.
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Affiliation(s)
- Shruti Marwaha
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.
| | - Joshua W Knowles
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
- Department of Medicine, Diabetes Research Center, Cardiovascular Institute and Prevention Research Center, Stanford, CA, USA
| | - Euan A Ashley
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.
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18
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Osmond M, Hartley T, Johnstone B, Andjic S, Girdea M, Gillespie M, Buske O, Dumitriu S, Koltunova V, Ramani A, Boycott KM, Brudno M. PhenomeCentral: 7 years of rare disease matchmaking. Hum Mutat 2022; 43:674-681. [PMID: 35165961 DOI: 10.1002/humu.24348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/04/2022] [Accepted: 02/08/2022] [Indexed: 11/08/2022]
Abstract
A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions in order to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral's features have been expanded and its dataset has consistently grown. There are currently 1,615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Matthew Osmond
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada
| | - Taila Hartley
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada
| | - Brittney Johnstone
- Cancer Genetics and High Risk Program, Sunnybrook Health Sciences Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Sasha Andjic
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Marta Girdea
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Meredith Gillespie
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada
| | | | - Sergiu Dumitriu
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Veronika Koltunova
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Arun Ramani
- Hospital for Sick Children, Toronto, ON, Canada
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada.,Department of Genetics, Children's Hospital of Eastern Ontario, ON, Canada
| | - Michael Brudno
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, ON, Canada.,Hospital for Sick Children, Toronto, ON, Canada
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19
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Foreman J, Brent S, Perrett D, Bevan AP, Hunt SE, Cunningham F, Hurles ME, Firth HV. DECIPHER: Supporting the interpretation and sharing of rare disease phenotype-linked variant data to advance diagnosis and research. Hum Mutat 2022; 43:682-697. [PMID: 35143074 PMCID: PMC9303633 DOI: 10.1002/humu.24340] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/17/2022] [Accepted: 02/07/2022] [Indexed: 11/12/2022]
Abstract
DECIPHER (https://www.deciphergenomics.org) is a free web platform for sharing anonymised phenotype-linked variant data from rare disease patients. Its dynamic interpretation interfaces contextualise genomic and phenotypic data to enable more informed variant interpretation, incorporating international standards for variant classification. DECIPHER supports almost all types of germline and mosaic variation in the nuclear and mitochondrial genome: sequence variants, short tandem repeats, copy-number variants and large structural variants. Patient phenotypes are deposited using Human Phenotype Ontology (HPO) terms, supplemented by quantitative data, which is aggregated to derive gene-specific phenotypic summaries. It hosts data from >250 projects from ~40 countries, openly sharing >40,000 patient records containing >51,000 variants and >172,000 phenotype terms. The rich phenotype-linked variant data in DECIPHER drives rare disease research and diagnosis by enabling patient matching within DECIPHER and with other resources, and has been cited in >2,600 publications. In this paper, we describe the types of data deposited to DECIPHER, the variant interpretation tools, and patient matching interfaces which make DECIPHER an invaluable rare disease resource. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Julia Foreman
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom
| | - Simon Brent
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom
| | - Daniel Perrett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom
| | - Andrew P Bevan
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom
| | - Sarah E Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Matthew E Hurles
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom
| | - Helen V Firth
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom.,East Anglian Medical Genetics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, United Kingdom
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20
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Fujiwara T, Shin JM, Yamaguchi A. Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm. Hum Mutat 2022; 43:734-742. [PMID: 35143083 PMCID: PMC9305291 DOI: 10.1002/humu.24341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/17/2022] [Accepted: 02/07/2022] [Indexed: 11/11/2022]
Abstract
Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Toyofumi Fujiwara
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, 277-0871, Japan
| | - Jae-Moon Shin
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, 277-0871, Japan
| | - Atsuko Yamaguchi
- Graduate School of Integrative Science and Engineering, Tokyo City University, Setagaya-ku, Tokyo, 158-8557, Japan
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21
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Maia N, Nabais Sá MJ, Melo-Pires M, de Brouwer APM, Jorge P. Intellectual disability genomics: current state, pitfalls and future challenges. BMC Genomics 2021; 22:909. [PMID: 34930158 PMCID: PMC8686650 DOI: 10.1186/s12864-021-08227-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/02/2021] [Indexed: 12/18/2022] Open
Abstract
Intellectual disability (ID) can be caused by non-genetic and genetic factors, the latter being responsible for more than 1700 ID-related disorders. The broad ID phenotypic and genetic heterogeneity, as well as the difficulty in the establishment of the inheritance pattern, often result in a delay in the diagnosis. It has become apparent that massive parallel sequencing can overcome these difficulties. In this review we address: (i) ID genetic aetiology, (ii) clinical/medical settings testing, (iii) massive parallel sequencing, (iv) variant filtering and prioritization, (v) variant classification guidelines and functional studies, and (vi) ID diagnostic yield. Furthermore, the need for a constant update of the methodologies and functional tests, is essential. Thus, international collaborations, to gather expertise, data and resources through multidisciplinary contributions, are fundamental to keep track of the fast progress in ID gene discovery.
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Affiliation(s)
- Nuno Maia
- Centro de Genética Médica Jacinto de Magalhães (CGM), Centro Hospitalar Universitário do Porto (CHUPorto), Porto, Portugal. .,Unit for Multidisciplinary Research in Biomedicine (UMIB), Institute of Biomedical Sciences Abel Salazar (ICBAS), and ITR - Laboratory for Integrative and Translational Research in Population Health, University of Porto, Porto, Portugal.
| | - Maria João Nabais Sá
- Unit for Multidisciplinary Research in Biomedicine (UMIB), Institute of Biomedical Sciences Abel Salazar (ICBAS), and ITR - Laboratory for Integrative and Translational Research in Population Health, University of Porto, Porto, Portugal
| | - Manuel Melo-Pires
- Serviço de Neuropatologia, Centro Hospitalar e Universitário do Porto (CHUPorto), Porto, Portugal
| | - Arjan P M de Brouwer
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Paula Jorge
- Centro de Genética Médica Jacinto de Magalhães (CGM), Centro Hospitalar Universitário do Porto (CHUPorto), Porto, Portugal.,Unit for Multidisciplinary Research in Biomedicine (UMIB), Institute of Biomedical Sciences Abel Salazar (ICBAS), and ITR - Laboratory for Integrative and Translational Research in Population Health, University of Porto, Porto, Portugal
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22
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Outcome of over 1500 matches through the Matchmaker Exchange for rare disease gene discovery: The 2-year experience of Care4Rare Canada. Genet Med 2021; 24:100-108. [PMID: 34906465 DOI: 10.1016/j.gim.2021.08.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 06/15/2021] [Accepted: 08/23/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Matchmaking has emerged as a useful strategy for building evidence toward causality of novel disease genes in patients with undiagnosed rare diseases. The Matchmaker Exchange (MME) is a collaborative initiative that facilitates international data sharing for matchmaking purposes; however, data on user experience is limited. METHODS Patients enrolled as part of the Finding of Rare Disease Genes in Canada (FORGE) and Care4Rare Canada research programs had their exome sequencing data reanalyzed by a multidisciplinary research team over a 2-year period. Compelling variants in genes not previously associated with a human phenotype were submitted through the MME node PhenomeCentral, and outcomes were collected. RESULTS In this study, 194 novel candidate genes were submitted to the MME, resulting in 1514 matches, and 15% of the genes submitted resulted in collaborations. Most submissions resulted in at least 1 match, and most matches were with GeneMatcher (82%), where additional email exchange was required to evaluate the match because of the lack of phenotypic or inheritance information. CONCLUSION Matchmaking through the MME is an effective way to investigate novel candidate genes; however, it is a labor-intensive process. Engagement from the community to contribute phenotypic, genotypic, and inheritance data will ensure that matchmaking continues to be a useful approach in the future.
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23
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Brokamp E, Koziura ME, Phillips JA, Tang LA, Cogan JD, Rives LC, Robertson AK, Duncan L, Bican A, Peterson JF, Newman JH, Hamid R, Bastarache L. One is the loneliest number: genotypic matchmaking using the electronic health record. Genet Med 2021; 23:1830-1832. [PMID: 34230636 PMCID: PMC11140587 DOI: 10.1038/s41436-021-01179-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 04/05/2021] [Indexed: 01/02/2023] Open
Affiliation(s)
- Elly Brokamp
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Mary E Koziura
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John A Phillips
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Leigh Anne Tang
- Center for Precision Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joy D Cogan
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lynette C Rives
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy K Robertson
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laura Duncan
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anna Bican
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Josh F Peterson
- Center for Precision Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John H Newman
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rizwan Hamid
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lisa Bastarache
- Center for Precision Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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24
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Sleiman S, Marshall AE, Dong X, Mhanni A, Alidou-D'Anjou I, Frosk P, Marin SE, Stark Z, Del Bigio MR, McBride A, Sadedin S, Gallacher L, Christodoulou J, Boycott KM, Dragon F, Kernohan KD. Compound heterozygous variants in SHQ1 are associated with a spectrum of neurological features, including early-onset dystonia. Hum Mol Genet 2021; 31:614-624. [PMID: 34542157 DOI: 10.1093/hmg/ddab247] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/06/2021] [Accepted: 08/23/2021] [Indexed: 01/29/2023] Open
Abstract
SHQ1 is essential for biogenesis of H/ACA ribonucleoproteins, a class of molecules important for processing ribosomal RNAs, modifying spliceosomal small nuclear RNAs, and stabilizing telomerase. Components of the H/ACA ribonucleoprotein complex have been linked to neurological developmental defects. Here we report two sibling pairs from unrelated families with compound heterozygous variants in SHQ1. Exome sequencing was used to detect disease causing variants which were submitted to 'matching' platforms linked to MatchMaker Exchange. Phenotype comparisons supported these matches. The affected individuals present with early-onset dystonia, with individuals from one family displaying additional neurological phenotypes, including neurodegeneration. As a result of CSF studies suggesting possible abnormal dopamine metabolism, a trial of levodopa replacement therapy was started but no clear response was noted. We show that fibroblasts from affected individuals have dramatic loss of SHQ1 protein. Variants from both families were expressed in S. cerevisiae, resulting in a strong reduction in H/ACA snoRNA production and remarkable defects in rRNA processing and ribosome formation. Our study identifies SHQ1 as associated with neurological disease, including early-onset dystonia, and begins to delineate the molecular etiology of this novel condition.
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Affiliation(s)
- Sophie Sleiman
- Centre d'excellence en recherche sur les maladies orphelines - Fondation Courtois (CERMO-FC), Département des sciences biologiques, Université du Québec à Montréal, Montréal, Québec, H3C 3P8, Canada
| | - Aren E Marshall
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, K1H 8L1, Canada
| | - Xiaomin Dong
- Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Aziz Mhanni
- Departments of Pediatrics and Child Health.,Biochemistry and Medical Genetics
| | - Ismaël Alidou-D'Anjou
- Centre d'excellence en recherche sur les maladies orphelines - Fondation Courtois (CERMO-FC), Département des sciences biologiques, Université du Québec à Montréal, Montréal, Québec, H3C 3P8, Canada
| | - Patrick Frosk
- Departments of Pediatrics and Child Health.,Biochemistry and Medical Genetics
| | | | - Zornitza Stark
- Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Marc R Del Bigio
- Pathology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Arran McBride
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, K1H 8L1, Canada
| | - Simon Sadedin
- Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Lyndon Gallacher
- Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, VIC 3052, Australia
| | | | - John Christodoulou
- Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, K1H 8L1, Canada
| | - François Dragon
- Centre d'excellence en recherche sur les maladies orphelines - Fondation Courtois (CERMO-FC), Département des sciences biologiques, Université du Québec à Montréal, Montréal, Québec, H3C 3P8, Canada
| | - Kristin D Kernohan
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, K1H 8L1, Canada.,Newborn Screening Ontario, Ottawa, Canada, K1H 8L1
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25
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Umlai UKI, Bangarusamy DK, Estivill X, Jithesh PV. Genome sequencing data analysis for rare disease gene discovery. Brief Bioinform 2021; 23:6366880. [PMID: 34498682 DOI: 10.1093/bib/bbab363] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/24/2021] [Accepted: 08/17/2021] [Indexed: 12/14/2022] Open
Abstract
Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.
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Affiliation(s)
- Umm-Kulthum Ismail Umlai
- Division of Genomics & Translational Biomedicine, College of Health & Life Sciences, Hamad Bin Khalifa University, B-147, Penrose House, PO Box 34110, Education City, Doha, Qatar
| | - Dhinoth Kumar Bangarusamy
- Division of Genomics & Translational Biomedicine, College of Health & Life Sciences, Hamad Bin Khalifa University, B-147, Penrose House, PO Box 34110, Education City, Doha, Qatar
| | - Xavier Estivill
- Quantitative Genomics Laboratories (qGenomics), Barcelona, Catalonia, Spain
| | - Puthen Veettil Jithesh
- Division of Genomics & Translational Biomedicine, College of Health & Life Sciences, Hamad Bin Khalifa University, B-147, Penrose House, PO Box 34110, Education City, Doha, Qatar
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26
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de Macena Sobreira NL, Hamosh A. Next-generation sequencing and the evolution of data sharing. Am J Med Genet A 2021; 185:2633-2635. [PMID: 33960641 DOI: 10.1002/ajmg.a.62239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/26/2021] [Accepted: 04/10/2021] [Indexed: 12/17/2022]
Abstract
Disease gene identification often relies on identifying multiple affected individuals with similar phenotypes and candidate variants in the same gene. Phenotypic and genomic data sharing tools have facilitated connections that led to novel disease gene discoveries and better characterization and recognition of rare diseases. Additionally, data sharing has evolved. From gene-based matches to variant-level information with increasing use of phenotypic information. We expect that these initiatives will continue to expand in the future affording clinicians, researchers, and most importantly, patients and their families faster and more comprehensive answers.
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Affiliation(s)
- Nara Lygia de Macena Sobreira
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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27
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Yubero D, Natera-de Benito D, Pijuan J, Armstrong J, Martorell L, Fernàndez G, Maynou J, Jou C, Roldan M, Ortez C, Nascimento A, Hoenicka J, Palau F. The Increasing Impact of Translational Research in the Molecular Diagnostics of Neuromuscular Diseases. Int J Mol Sci 2021; 22:4274. [PMID: 33924139 PMCID: PMC8074304 DOI: 10.3390/ijms22084274] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/13/2021] [Accepted: 04/16/2021] [Indexed: 12/12/2022] Open
Abstract
The diagnosis of neuromuscular diseases (NMDs) has been progressively evolving from the grouping of clinical symptoms and signs towards the molecular definition. Optimal clinical, biochemical, electrophysiological, electrophysiological, and histopathological characterization is very helpful to achieve molecular diagnosis, which is essential for establishing prognosis, treatment and genetic counselling. Currently, the genetic approach includes both the gene-targeted analysis in specific clinically recognizable diseases, as well as genomic analysis based on next-generation sequencing, analyzing either the clinical exome/genome or the whole exome or genome. However, as of today, there are still many patients in whom the causative genetic variant cannot be definitely established and variants of uncertain significance are often found. In this review, we address these drawbacks by incorporating two additional biological omics approaches into the molecular diagnostic process of NMDs. First, functional genomics by introducing experimental cell and molecular biology to analyze and validate the variant for its biological effect in an in-house translational diagnostic program, and second, incorporating a multi-omics approach including RNA-seq, metabolomics, and proteomics in the molecular diagnosis of neuromuscular disease. Both translational diagnostics programs and omics are being implemented as part of the diagnostic process in academic centers and referral hospitals and, therefore, an increase in the proportion of neuromuscular patients with a molecular diagnosis is expected. This improvement in the process and diagnostic performance of patients will allow solving aspects of their health problems in a precise way and will allow them and their families to take a step forward in their lives.
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Affiliation(s)
- Dèlia Yubero
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.Y.); (J.A.); (L.M.); (G.F.); (J.M.); (M.R.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain;
| | - Daniel Natera-de Benito
- Neuromuscular Unit, Department of Pediatric Neurology, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.N.-d.B.); (C.O.)
| | - Jordi Pijuan
- Laboratory of Neurogenetics and Molecular Medicine—IPER, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain;
| | - Judith Armstrong
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.Y.); (J.A.); (L.M.); (G.F.); (J.M.); (M.R.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain;
| | - Loreto Martorell
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.Y.); (J.A.); (L.M.); (G.F.); (J.M.); (M.R.)
- Laboratory of Neurogenetics and Molecular Medicine—IPER, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain;
| | - Guerau Fernàndez
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.Y.); (J.A.); (L.M.); (G.F.); (J.M.); (M.R.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain;
| | - Joan Maynou
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.Y.); (J.A.); (L.M.); (G.F.); (J.M.); (M.R.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain;
| | - Cristina Jou
- Department of Pathology, Hospital Sant Joan de Déu, Pediatric Biobank for Research, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain;
| | - Mònica Roldan
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.Y.); (J.A.); (L.M.); (G.F.); (J.M.); (M.R.)
- Confocal Microscopy and Cellular Imaging Unit, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain
| | - Carlos Ortez
- Neuromuscular Unit, Department of Pediatric Neurology, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.N.-d.B.); (C.O.)
- Division of Pediatrics, Clinic Institute of Medicine & Dermatology, Hospital Clínic, University of Barcelona School of Medicine and Health Sciences, 08950 Barcelona, Spain
| | - Andrés Nascimento
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain;
- Neuromuscular Unit, Department of Pediatric Neurology, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.N.-d.B.); (C.O.)
| | - Janet Hoenicka
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain;
- Laboratory of Neurogenetics and Molecular Medicine—IPER, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain;
| | - Francesc Palau
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (D.Y.); (J.A.); (L.M.); (G.F.); (J.M.); (M.R.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain;
- Laboratory of Neurogenetics and Molecular Medicine—IPER, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain;
- Department of Pathology, Hospital Sant Joan de Déu, Pediatric Biobank for Research, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain;
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Aly KA, Moutaoufik MT, Phanse S, Zhang Q, Babu M. From fuzziness to precision medicine: on the rapidly evolving proteomics with implications in mitochondrial connectivity to rare human disease. iScience 2021; 24:102030. [PMID: 33521598 PMCID: PMC7820543 DOI: 10.1016/j.isci.2020.102030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Mitochondrial (mt) dysfunction is linked to rare diseases (RDs) such as respiratory chain complex (RCC) deficiency, MELAS, and ARSACS. Yet, how altered mt protein networks contribute to these ailments remains understudied. In this perspective article, we identified 21 mt proteins from public repositories that associate with RCC deficiency, MELAS, or ARSACS, engaging in a relatively small number of protein-protein interactions (PPIs), underscoring the need for advanced proteomic and interactomic platforms to uncover the complete scope of mt connectivity to RDs. Accordingly, we discuss innovative untargeted label-free proteomics in identifying RD-specific mt or other macromolecular assemblies and mapping of protein networks in complex tissue, organoid, and stem cell-differentiated neurons. Furthermore, tag- and label-based proteomics, genealogical proteomics, and combinatorial affinity purification-mass spectrometry, along with advancements in detecting and integrating transient PPIs with single-cell proteomics and transcriptomics, collectively offer seminal follow-ups to enrich for RD-relevant networks, with implications in RD precision medicine.
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Affiliation(s)
- Khaled A. Aly
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | | | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Qingzhou Zhang
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, SK, Canada
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Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, Danis D, Balagura G, Baynam G, Brower AM, Callahan TJ, Chute CG, Est JL, Galer PD, Ganesan S, Griese M, Haimel M, Pazmandi J, Hanauer M, Harris NL, Hartnett M, Hastreiter M, Hauck F, He Y, Jeske T, Kearney H, Kindle G, Klein C, Knoflach K, Krause R, Lagorce D, McMurry JA, Miller JA, Munoz-Torres M, Peters RL, Rapp CK, Rath AM, Rind SA, Rosenberg A, Segal MM, Seidel MG, Smedley D, Talmy T, Thomas Y, Wiafe SA, Xian J, Yüksel Z, Helbig I, Mungall CJ, Haendel MA, Robinson PN. The Human Phenotype Ontology in 2021. Nucleic Acids Res 2021; 49:D1207-D1217. [PMID: 33264411 PMCID: PMC7778952 DOI: 10.1093/nar/gkaa1043] [Citation(s) in RCA: 547] [Impact Index Per Article: 182.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/11/2020] [Accepted: 11/16/2020] [Indexed: 12/21/2022] Open
Abstract
The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.
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Affiliation(s)
| | - Michael Gargano
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Nicolas Matentzoglu
- Monarch Initiative
- Semanticly Ltd, London, UK
- European Bioinformatics Institute (EMBL-EBI)
| | - Leigh C Carmody
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Clinical Neurosciences, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Nicole A Vasilevsky
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
| | | | - Ganna Balagura
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Child Health, University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS ‘G. Gaslini’ Institute, Genoa, Italy
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, King Edward memorial Hospital, Perth, Australia
- Telethon Kids Institute and the Division of Paediatrics, Faculty of Helath and Medical Sciences, University of Western Australia, Perth, Australia
| | - Amy M Brower
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Colorado, USA
| | | | - Johanna L Est
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthias Griese
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Matthias Haimel
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Julia Pazmandi
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Marc Hanauer
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Nomi L Harris
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Michael J Hartnett
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Maximilian Hastreiter
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fabian Hauck
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- German Centre for Infection Research (DZIF), Munich, Germany
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Tim Jeske
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Hugh Kearney
- FutureNeuro, SFI Research Centre for Chronic and Rare Neurological Diseases, Ireland
| | - Gerhard Kindle
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI). Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
- Centre for Biobanking FREEZE, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Christoph Klein
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Katrin Knoflach
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - David Lagorce
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Julie A McMurry
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Jillian A Miller
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Monica C Munoz-Torres
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Rebecca L Peters
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Christina K Rapp
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Ana M Rath
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Shahmir A Rind
- WA Register of Developmental Anomalies
- Curtin University, Western Australia, Australia
| | - Avi Z Rosenberg
- Division of Kidney-Urologic Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | | | - Markus G Seidel
- Research Unit for Pediatric Hematology and Immunology, Division of Pediatric Hemato-Oncology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Damian Smedley
- The William Harvey Research Institute, Charterhouse Square Barts and the London School of Medicine and Dentistry Queen Mary University of London, London EC1M 6BQ, UK
| | - Tomer Talmy
- Genomic Research Department, Emedgene Technologies, Tel Aviv, Israel
- Faculty of Medicine, Hebrew University Hadassah Medical School, Jerusalem, Israel
| | - Yarlalu Thomas
- West Australian Register of Developmental Anomalies, East Perth, WA, Australia
| | | | - Julie Xian
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA, USA
| | - Zafer Yüksel
- Human Genetics, Bioscientia GmbH, Ingelheim, Germany
| | - Ingo Helbig
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Christopher J Mungall
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Melissa A Haendel
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Peter N Robinson
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
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Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders. Comput Struct Biotechnol J 2020; 18:2535-2546. [PMID: 33033576 PMCID: PMC7522539 DOI: 10.1016/j.csbj.2020.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/06/2020] [Accepted: 09/07/2020] [Indexed: 12/30/2022] Open
Abstract
Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.
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Schaaf J, Sedlmayr M, Schaefer J, Storf H. Diagnosis of Rare Diseases: a scoping review of clinical decision support systems. Orphanet J Rare Dis 2020; 15:263. [PMID: 32972444 PMCID: PMC7513302 DOI: 10.1186/s13023-020-01536-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 09/07/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. METHODS We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items "Objective and background of the publication/project", "System or project name", "Functionality", "Type of clinical data", "Rare Diseases covered", "Development status", "System availability", "Data entry and integration", "Last software update" and "Clinical usage". RESULTS The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: "Analysis or comparison of genetic and phenotypic data," "machine learning" and "information retrieval". Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. CONCLUSIONS Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.
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Affiliation(s)
- Jannik Schaaf
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany.
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine Technische Universität Dresden, Dresden, Germany
| | - Johanna Schaefer
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany
| | - Holger Storf
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany
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Peterlin B, Gualandi F, Maver A, Servidei S, van der Maarel SM, Lamy F, Mejat A, Evangelista T, Ferlini A. Genetic testing offer for inherited neuromuscular diseases within the EURO-NMD reference network: A European survey study. PLoS One 2020; 15:e0239329. [PMID: 32946487 PMCID: PMC7500674 DOI: 10.1371/journal.pone.0239329] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 09/03/2020] [Indexed: 11/19/2022] Open
Abstract
The genetic diagnostics of inherited neuromuscular diseases (NMDs) is challenging due to their clinical and genetic heterogeneity. We launched an online survey within the EURO-NMD European Reference Network (ERN) to collect information about the availability/distribution of genetic testing across 61 ERN health care providers (HCPs). A 17 items questionnaire was designed to address methods used, the number of genetic tests available, the clinical pathway to access genetic testing, the use of next-generation sequencing (NGS) and participation to quality assessment schemes (QAs). A remarkable number of HCPs (49%) offers ≥ 500 genetic tests per year, 43,6% offers 100-500 genetic tests per year, and 7,2% ≤ 100 per year. NGS is used by 94% of centres, Sanger sequencing by 84%, MLPA by 66% and Southern blotting by 36%. The majority of centres (60%) offer NGS for all patients that fulfil criteria for NMD of genetic origin. Pipelines for NGS vary amongst centres, even within the same national system. Referral of patients to genetic laboratories by specialists was frequently reported (58%), and 65% of centres participates in genetic testing QAs. We specifically evaluated how many centres cover SMA, DMD, Pompe, LGMDs, and TTR genes/diseases genetic diagnosis, since these rare diseases benefit from personalised therapies. We used the Orphanet EUGT numbers, provided by 82% of HCPs. SMA, DMD, LGMD, TTR and GAA genes are covered by EUGTs although with different numbers and modalities. The number of genetic tests for NMDs offered across HCPs National Health systems is quite high, including routine techniques and NGS. The number and type of tests offered and the clinical practices differ among centres. We provided evidence that survey tools might be useful to learn about the state-of-the-art of ERN health-related activities and to foster harmonisation and standardisation of the complex care for the rare disease patients in the EU.
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Affiliation(s)
- Borut Peterlin
- Clinical Institute of Medical Genetics, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | | | - Ales Maver
- Clinical Institute of Medical Genetics, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Serenella Servidei
- Neurophysiopathology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | | | | | - Teresinha Evangelista
- Neuromuscular Morphology Unit, Myology Institute, GHU Pitié-Salpêtrière, Sorbonne Université, Paris, France
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Salvatore M, Polizzi A, De Stefano MC, Floridia G, Baldovino S, Roccatello D, Sciascia S, Menegatti E, Remuzzi G, Daina E, Iatropoulos P, Bembi B, Da Riol RM, Ferlini A, Neri M, Novelli G, Sangiuolo F, Brancati F, Taruscio D. Improving diagnosis for rare diseases: the experience of the Italian undiagnosed Rare diseases network. Ital J Pediatr 2020; 46:130. [PMID: 32928283 PMCID: PMC7488856 DOI: 10.1186/s13052-020-00883-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 08/17/2020] [Indexed: 12/12/2022] Open
Abstract
Background For a number of persons with rare diseases (RDs) a definite diagnosis remains undiscovered with relevant physical, psychological and social consequences. Undiagnosed RDs (URDs) require other than specialised clinical centres, outstanding molecular investigations, common protocols and dedicated actions at national and international levels; thus, many “Undiagnosed RDs programs” have been gradually developed on the grounds of a well-structured multidisciplinary approach. Methods The Italian Undiagnosed Rare Diseases Network (IURDN) was established in 2016 to improve the level of diagnosis of persons with URD living in Italy. Six Italian Centres of Expertise represented the network. The National Centre for Rare Diseases at the Istituto Superiore di Sanità coordinates the whole project. The software PhenoTips was used to collect the information of the clinical cases. Results One hundred and ten cases were analysed between March 2016 and June 2019. The age of onset of the diseases ranged from prenatal age to 51 years. Conditions were predominantly sporadic; almost all patients had multiple organs involvements. A total of 13/71 family cases were characterized by WES; in some families more than one individual was affected, so leading to 20/71 individuals investigated. Disease causing variants were identified in two cases and were associated to previously undescribed phenotypes. In 5 cases, new candidate genes were identified, although confirmatory tests are pending. In three families, investigations were not completed due to the scarce compliance of members and molecular investigations were temporary suspended. Finally, three cases (one familial) remain still unsolved. Twelve undiagnosed clinical cases were then selected to be shared at International level through PhenomeCentral in accordance to the UDNI statement. Conclusions Our results showed a molecular diagnostic yield of 53,8%; this value is comparable to the diagnostic rates reported in other international studies. Cases collected were also pooled with those collected by UDNI International Network. This represents a unique example of global initiative aimed at sharing and validating knowledge and experience in this field. IURDN is a multidisciplinary and useful initiative linking National and International efforts aimed at making timely and appropriate diagnoses in RD patients who still do not have a confirmed diagnosis even after a long time.
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Affiliation(s)
- Marco Salvatore
- National Centre for Rare Diseases, Undiagnosed Rare Diseases Interdepartmental Unit, Istituto Superiore di Sanità, Rome, Italy.
| | - Agata Polizzi
- Department of Educational Science, University of Catania, Catania, Italy
| | | | | | - Simone Baldovino
- Department of Clinical and Biological Sciences, University of Turin and S. Giovanni Bosco Hospital, Centre of Research of Immunopathology and Rare Diseases - Regional Coordinating Centre of the National Network for Rare Diseases, Turin, Italy
| | - Dario Roccatello
- Department of Clinical and Biological Sciences, University of Turin and S. Giovanni Bosco Hospital, Centre of Research of Immunopathology and Rare Diseases - Regional Coordinating Centre of the National Network for Rare Diseases, Turin, Italy
| | - Savino Sciascia
- Department of Clinical and Biological Sciences, University of Turin and S. Giovanni Bosco Hospital, Centre of Research of Immunopathology and Rare Diseases - Regional Coordinating Centre of the National Network for Rare Diseases, Turin, Italy
| | - Elisa Menegatti
- Department of Clinical and Biological Sciences, University of Turin and S. Giovanni Bosco Hospital, Centre of Research of Immunopathology and Rare Diseases - Regional Coordinating Centre of the National Network for Rare Diseases, Turin, Italy
| | - Giuseppe Remuzzi
- IRCCS Mario Negri Pharmacological Research Institute, Regional Coordinating Centre of the National Network for Rare Diseases, Clinical Research Centre for Rare Diseases "Aldo e Cele Daccò", Ranica, Bergamo, Italy
| | - Erica Daina
- IRCCS Mario Negri Pharmacological Research Institute, Regional Coordinating Centre of the National Network for Rare Diseases, Clinical Research Centre for Rare Diseases "Aldo e Cele Daccò", Ranica, Bergamo, Italy
| | - Paraskevas Iatropoulos
- IRCCS Mario Negri Pharmacological Research Institute, Regional Coordinating Centre of the National Network for Rare Diseases, Clinical Research Centre for Rare Diseases "Aldo e Cele Daccò", Ranica, Bergamo, Italy
| | - Bruno Bembi
- S.O.C. Regional Coordinating Centre of the National Network for Rare Diseases, S. Maria della Misericordia Hospital, Udine, Italy
| | - Rosalia Maria Da Riol
- S.O.C. Regional Coordinating Centre of the National Network for Rare Diseases, S. Maria della Misericordia Hospital, Udine, Italy
| | - Alessandra Ferlini
- Department of Experimental and Diagnostic Medicine, University of Ferrara, Ferrara, Italy
| | - Marcella Neri
- Department of Experimental and Diagnostic Medicine, University of Ferrara, Ferrara, Italy
| | - Giuseppe Novelli
- Department of Biomedicine and Prevention, University of Tor Vergata and University Hospital Tor Vergata, Unit of Medical Genetics Rome & IRCCS Neuromed, Pozzilli, Italy
| | - Federica Sangiuolo
- Department of Biomedicine and Prevention, University of Tor Vergata and University Hospital Tor Vergata, Unit of Medical Genetics, Rome, Italy
| | - Francesco Brancati
- Department of Life, Health and Environmental Sciences, Unit of Medical Genetics University of L'Aquila, L'Aquila, Italy
| | - Domenica Taruscio
- National Centre for Rare Diseases, Undiagnosed Rare Diseases Interdepartmental Unit, Istituto Superiore di Sanità, Rome, Italy
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Hartley T, Lemire G, Kernohan KD, Howley HE, Adams DR, Boycott KM. New Diagnostic Approaches for Undiagnosed Rare Genetic Diseases. Annu Rev Genomics Hum Genet 2020; 21:351-372. [DOI: 10.1146/annurev-genom-083118-015345] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.
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Affiliation(s)
- Taila Hartley
- CHEO Research Institute, University of Ottawa, Ottawa, Ontario K1H 8L1, Canada;, , , ,
| | - Gabrielle Lemire
- CHEO Research Institute, University of Ottawa, Ottawa, Ontario K1H 8L1, Canada;, , , ,
- Department of Genetics, CHEO, Ottawa, Ontario K1H 8L1, Canada
| | - Kristin D. Kernohan
- CHEO Research Institute, University of Ottawa, Ottawa, Ontario K1H 8L1, Canada;, , , ,
- Newborn Screening Ontario, CHEO, Ottawa, Ontario K1H 9M8, Canada
| | - Heather E. Howley
- CHEO Research Institute, University of Ottawa, Ottawa, Ontario K1H 8L1, Canada;, , , ,
| | - David R. Adams
- Office of the Clinical Director, National Human Genome Research Institute and Undiagnosed Diseases Program, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Kym M. Boycott
- CHEO Research Institute, University of Ottawa, Ottawa, Ontario K1H 8L1, Canada;, , , ,
- Department of Genetics, CHEO, Ottawa, Ontario K1H 8L1, Canada
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35
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Jespersgaard C, Syed A, Chmura P, Løngreen P. Supercomputing and Secure Cloud Infrastructures in Biology and Medicine. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-012920-013357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The increasing amounts of healthcare data stored in health registries, in combination with genomic and other types of data, have the potential to enable better decision making and pave the path for personalized medicine. However, reaping the full benefits of big, sensitive data for the benefit of patients requires greater access to data across organizations and institutions in various regions. This overview first introduces cloud computing and takes stock of the challenges to enhancing data availability in the healthcare system. Four models for ensuring higher data accessibility are then discussed. Finally, several cases are discussed that explore how enhanced access to data would benefit the end user.
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Affiliation(s)
| | - Ali Syed
- Danish National Genome Center, DK-2300 Copenhagen S, Denmark
| | - Piotr Chmura
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Peter Løngreen
- Danish National Genome Center, DK-2300 Copenhagen S, Denmark
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36
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Bruel A, Vitobello A, Tran Mau‐Them F, Nambot S, Sorlin A, Denommé‐Pichon A, Delanne J, Moutton S, Callier P, Duffourd Y, Philippe C, Faivre L, Thauvin‐Robinet C. Next‐generation
sequencing approaches and challenges in the diagnosis of developmental anomalies and intellectual disability. Clin Genet 2020; 98:433-444. [DOI: 10.1111/cge.13764] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 04/20/2020] [Accepted: 04/22/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Ange‐Line Bruel
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
- Centre de Référence Maladies Rares Déficiences Intellectuelles de causes rares, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Antonio Vitobello
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Frédéric Tran Mau‐Them
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Sophie Nambot
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Arthur Sorlin
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
- Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
- Centre de Référence Maladies Rares Maladies dermatologiques en mosaïque Service de dermatologie, CHU Dijon Bourgogne Dijon France
| | - Anne‐Sophie Denommé‐Pichon
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
- Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Julian Delanne
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Sébastien Moutton
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Patrick Callier
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Yannis Duffourd
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Christophe Philippe
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Laurence Faivre
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
| | - Christel Thauvin‐Robinet
- Inserm UMR1231 GAD Université Bourgogne‐Franche Comté Dijon France
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
- Centre de Référence Maladies Rares Déficiences Intellectuelles de causes rares, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
- Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs, Centre de Génétique, FHU‐TRANSLAD, CHU Dijon Bourgogne Dijon France
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37
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Chang WH, Mashouri P, Lozano AX, Johnstone B, Husić M, Olry A, Maiella S, Balci TB, Sawyer SL, Robinson PN, Rath A, Brudno M. Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes. Genet Med 2020; 22:1391-1400. [PMID: 32366968 DOI: 10.1038/s41436-020-0812-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/09/2020] [Accepted: 04/10/2020] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments. METHODS We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data. RESULTS Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs. CONCLUSION Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.
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Affiliation(s)
- Willie H Chang
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.,Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Pouria Mashouri
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada
| | - Alexander X Lozano
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.,Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Materials Science & Engineering, Stanford University, Stanford, CA, USA
| | - Brittney Johnstone
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.,Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Mia Husić
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada
| | - Annie Olry
- Orphanet, Institut national de la santé et de la recherche médicale, Paris, France
| | - Sylvie Maiella
- Orphanet, Institut national de la santé et de la recherche médicale, Paris, France
| | - Tugce B Balci
- Medical Genetics Program of Southwestern Ontario, London Health Sciences Centre, London, ON, Canada
| | - Sarah L Sawyer
- Department of Genetics, Children's Hospital of Eastern Ontario and Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.,Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Ana Rath
- Orphanet, Institut national de la santé et de la recherche médicale, Paris, France
| | - Michael Brudno
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada. .,Department of Computer Science, University of Toronto, Toronto, ON, Canada. .,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada. .,University Health Network, Toronto, ON, Canada.
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38
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Azzariti DR, Hamosh A. Genomic Data Sharing for Novel Mendelian Disease Gene Discovery: The Matchmaker Exchange. Annu Rev Genomics Hum Genet 2020; 21:305-326. [PMID: 32339034 DOI: 10.1146/annurev-genom-083118-014915] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the last decade, exome and/or genome sequencing has become a common test in the diagnosis of individuals with features of a rare Mendelian disorder. Despite its success, this test leaves the majority of tested individuals undiagnosed. This review describes the Matchmaker Exchange (MME), a federated network established to facilitate the solving of undiagnosed rare-disease cases through data sharing. MME supports genomic matchmaking, the act of connecting two or more parties looking for cases with similar phenotypes and variants in the same candidate genes. An application programming interface currently connects six matchmaker nodes-the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER), GeneMatcher, PhenomeCentral, seqr, MyGene2, and the Initiative on Rare and Undiagnosed Diseases (IRUD) Exchange-resulting in a collective data set spanning more than 150,000 cases from more than 11,000 contributors in 88 countries. Here, we describe the successes and challenges of MME, its individual matchmaking nodes, plans for growing the network, and considerations for future directions.
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Affiliation(s)
- Danielle R Azzariti
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA;
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland 21287, USA;
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39
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Macnamara EF, D’Souza P, Tifft CJ. The undiagnosed diseases program: Approach to diagnosis. TRANSLATIONAL SCIENCE OF RARE DISEASES 2020; 4:179-188. [PMID: 32477883 PMCID: PMC7250153 DOI: 10.3233/trd-190045] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Undiagnosed and rare conditions are collectively common and affect millions of people worldwide. The NIH Undiagnosed Diseases Program (UDP) strives to achieve both a comprehensive diagnosis and a better understanding of the mechanisms of disease for many of these individuals. Through the careful review of records, a well-orchestrated inpatient evaluation, genomic sequencing and testing, and with the use of emerging strategies such as matchmaking programs, the UDP succeeds nearly 30 percent of the time for these highly selective cases. Although the UDP process is built on a unique set of resources, case examples demonstrate steps genetic professionals can take, in both clinical and research settings, to arrive at a diagnosis for their most challenging cases.
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Affiliation(s)
- Ellen F. Macnamara
- National Institutes of Health, Undiagnosed Diseases Program, Common Fund, Office of the Director, Bethesda, MD, USA
| | - Precilla D’Souza
- National Institutes of Health, Undiagnosed Diseases Program, Common Fund, Office of the Director, Bethesda, MD, USA
| | - Undiagnosed Diseases Network
- National Institutes of Health, Undiagnosed Diseases Program, Common Fund, Office of the Director, Bethesda, MD, USA
- Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cynthia J. Tifft
- National Institutes of Health, Undiagnosed Diseases Program, Common Fund, Office of the Director, Bethesda, MD, USA
- Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
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40
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Taruscio D, Baynam G, Cederroth H, Groft SC, Klee EW, Kosaki K, Lasko P, Melegh B, Riess O, Salvatore M, Gahl WA. The Undiagnosed Diseases Network International: Five years and more! Mol Genet Metab 2020; 129:243-254. [PMID: 32033911 DOI: 10.1016/j.ymgme.2020.01.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/08/2020] [Accepted: 01/08/2020] [Indexed: 11/23/2022]
Abstract
Undiagnosed rare diseases (URDs) account for a significant portion of the overall rare disease burden, depending upon the country. Hence, URDs represent an unmet medical need. A specific challenge posed by the ensemble of the URD patient cohort is the heterogeneity of its composition; the group, indeed, includes very rare, still unidentified conditions as well as clinical variants of recognized rare diseases. Exact disease recognition requires new approaches that cut across national and institutional boundaries, may need the implementation of methods new to diagnostics, and embrace clinical care and research. To address these issues, the Undiagnosed Diseases Network International (UDNI) was established in 2014, with the major aims of providing diagnoses to patients, implementing additional diagnostic tools, and fostering research on novel diseases, their mechanisms, and their pathways. The UDNI involves centres with internationally recognized expertise, and its scientific resources and know-how aim to fill the knowledge gaps that impede diagnosis, in particularly for ultra-rare diseases. Consequently, the UDNI fosters the translation of research into medical practice, aided by active patient involvement. The goals of the UDNI are to work collaboratively and at an international scale to: 1) provide diagnoses for individuals who have conditions that have eluded diagnosis by clinical experts; 2) gain insights into the etiology and pathogenesis of novel diseases; 3) contribute to standards of diagnosing unsolved patients; and 4) share the results of UDNI research in a timely manner and as broadly as possible.
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Affiliation(s)
- D Taruscio
- National Centre for Rare Diseases, Undiagnosed Rare Diseases Interdepartmental Unit, Istituto Superiore di Sanità, Rome, Italy.
| | - G Baynam
- Western Australian Register of Developmental Anomalies and Genetic Services of WA, WA Health Department, Perth, Australia; Faculty of Health and Medical Sciences, Division of Paediatrics and Telethon Kids Institute, Perth, Australia
| | | | - S C Groft
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - E W Klee
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - K Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - P Lasko
- Department of Biology, McGill University, Montréal, Québec, Canada; Department of Human Genetics, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - B Melegh
- Department of Medical Genetics, University of Pécs, School of Medicine, Clinical Center, Pecs, Hungary
| | - O Riess
- Institute of Medical Genetics and Applied Genomics, Rare Disease Center, University of Tübingen, Tübingen, Germany
| | - M Salvatore
- National Centre for Rare Diseases, Undiagnosed Rare Diseases Interdepartmental Unit, Istituto Superiore di Sanità, Rome, Italy
| | - W A Gahl
- NIH Undiagnosed Diseases Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA; Office of the Clinical Director, National Human Genome Institute, National Institutes of Health, Bethesda, MD, USA
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41
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Bienstock RJ. Data Sharing Advances Rare and Neglected Disease Clinical Research and Treatments. ACS Pharmacol Transl Sci 2019; 2:491-496. [PMID: 32259080 DOI: 10.1021/acsptsci.9b00034] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Indexed: 11/29/2022]
Abstract
Because of the decreased cost and increased ease of whole genome analysis, the diagnosis of rare, orphan diseases has entered a new era. This new technological advance, combined with the worldwide web connections, now permits sharing, searching, and linking genotype, phenotype, and other information to facilitate diagnosis. Databases currently accessible and searchable by researchers, clinicians, and patients will be presented and discussed.
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Affiliation(s)
- Rachelle J Bienstock
- RJB Computational Modeling LLC, Chapel Hill, North Carolina 27514, United States
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42
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Lablans M, Schmidt EE, Ückert F. An Architecture for Translational Cancer Research As Exemplified by the German Cancer Consortium. JCO Clin Cancer Inform 2019; 2:1-8. [PMID: 30652543 DOI: 10.1200/cci.17.00062] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Networking of medical institutions by means of a capable data infrastructure has the potential to open up vast amounts of routine data to translational cancer research. However, the secondary use of information collected independently in several institutions is a challenging task of data integration. In this review, we discuss the requirements and common challenges involved in the establishment of such a platform. We present methods and tools from the field of medical informatics as solutions to semantic and technical heterogeneity, questions of data protection and record linkage, as well as issues of trust and data ownership. We also describe the architecture of an existing cancer research network as an exemplary application of these methods.
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Affiliation(s)
- Martin Lablans
- All authors: German Cancer Research Center, Heidelberg, Germany
| | | | - Frank Ückert
- All authors: German Cancer Research Center, Heidelberg, Germany
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43
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Köhler S, Øien NC, Buske OJ, Groza T, Jacobsen JOB, McNamara C, Vasilevsky N, Carmody LC, Gourdine JP, Gargano M, McMurry J, Danis D, Mungall CJ, Smedley D, Haendel M, Robinson PN. Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics. CURRENT PROTOCOLS IN HUMAN GENETICS 2019; 103:e92. [PMID: 31479590 PMCID: PMC6814016 DOI: 10.1002/cphg.92] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing human disease phenotypes for computational analysis to support differential diagnostics. The HPO is frequently used to create a set of terms that accurately describe the observed clinical abnormalities of an individual being evaluated for suspected rare genetic disease. This profile is compared with computational disease profiles in the HPO database with the aim of identifying genetic diseases with comparable phenotypic profiles. The computational analysis can be coupled with the analysis of whole-exome or whole-genome sequencing data through applications such as Exomiser. This article explains how to choose an optimal set of HPO terms for these cases and enter them with software, such as PhenoTips and PatientArchive, and demonstrates how to use Phenomizer and Exomiser to generate a computational differential diagnosis. © 2019 by John Wiley & Sons, Inc.
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Affiliation(s)
- Sebastian Köhler
- Charité Centrum für Therapieforschung, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
- Einstein Center Digital Future, Berlin 10117, Germany
- Monarch Initiative, monarchinitiative.org
| | | | | | | | - Julius OB Jacobsen
- Monarch Initiative, monarchinitiative.org
- Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | | | - Nicole Vasilevsky
- Monarch Initiative, monarchinitiative.org
- Oregon Health & Science University, Portland, OR 97217, USA
| | - Leigh C Carmody
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - JP Gourdine
- Monarch Initiative, monarchinitiative.org
- Oregon Health & Science University, Portland, OR 97217, USA
| | - Michael Gargano
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Julie McMurry
- Monarch Initiative, monarchinitiative.org
- Oregon State University, Corvallis, OR, USA
| | - Daniel Danis
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Christopher J Mungall
- Monarch Initiative, monarchinitiative.org
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Damian Smedley
- Monarch Initiative, monarchinitiative.org
- Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Melissa Haendel
- Monarch Initiative, monarchinitiative.org
- Oregon Health & Science University, Portland, OR 97217, USA
- Oregon State University, Corvallis, OR, USA
| | - Peter N Robinson
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
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44
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Nellåker C, Alkuraya FS, Baynam G, Bernier RA, Bernier FP, Boulanger V, Brudno M, Brunner HG, Clayton-Smith J, Cogné B, Dawkins HJ, deVries BB, Douzgou S, Dudding-Byth T, Eichler EE, Ferlaino M, Fieggen K, Firth HV, FitzPatrick DR, Gration D, Groza T, Haendel M, Hallowell N, Hamosh A, Hehir-Kwa J, Hitz MP, Hughes M, Kini U, Kleefstra T, Kooy RF, Krawitz P, Küry S, Lees M, Lyon GJ, Lyonnet S, Marcadier JL, Meyn S, Moslerová V, Politei JM, Poulton CC, Raymond FL, Reijnders MR, Robinson PN, Romano C, Rose CM, Sainsbury DC, Schofield L, Sutton VR, Turnovec M, Van Dijck A, Van Esch H, Wilkie AO. Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative. Front Genet 2019; 10:611. [PMID: 31417602 PMCID: PMC6681681 DOI: 10.3389/fgene.2019.00611] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 06/12/2019] [Indexed: 01/25/2023] Open
Abstract
The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.
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Affiliation(s)
- Christoffer Nellåker
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Institute for Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Fowzan S. Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, and Genetic Services of Western Australia, King Edward Memorial, Subiaco, WA, Australia
- Telethon Kids Institute and School of Paediatrics and Child Health, University of Western Australia, Perth, WA, Australia
- Spatial Sciences, Science and Engineering, Curtin University, Perth, WA, Australia
| | - Raphael A. Bernier
- Department of Psychiatry & Behavioral Science, University of Washington School of Medicine, Seattle, WA, United States
| | | | - Vanessa Boulanger
- National Organization for Rare Disorders, Danbury, CT, United States
| | - Michael Brudno
- Department of Computer Science, University of Toronto and the Hospital for Sick Children, Toronto, Canada
| | - Han G. Brunner
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jill Clayton-Smith
- Manchester Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust, MAHSC, Saint Mary’s Hospital, Manchester, United Kingdom
| | - Benjamin Cogné
- CHU Nantes, Service de Génétique Médicale, Nantes, France
| | - Hugh J.S. Dawkins
- Office of Population Health Genomics, Public and Aboriginal Health Division, Department of Health Government of Western Australia, Perth, WA, Australia
- Sir Walter Murdoch School of Policy and International Affairs, Murdoch University
- Centre for Population Health Research, Curtin University of Technology, Perth, WA, Australia
| | - Bert B.A. deVries
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - Sofia Douzgou
- Manchester Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust, MAHSC, Saint Mary’s Hospital, Manchester, United Kingdom
| | | | - Evan E. Eichler
- Department of Genome Science, University of Washington School of Medicine, Seattle, WA, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, United States
| | - Michael Ferlaino
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
- Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Karen Fieggen
- Division of Human Genetics, Level 3, Wernher and Beit North, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, South Africa
| | - Helen V. Firth
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
| | - David R. FitzPatrick
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
| | - Dylan Gration
- Genetic Services of Western Australia, King Edward Memorial Hospital, Subiaco, WA, Australia
| | - Tudor Groza
- The Garvan Institute, Sydney, NSW, Australia
| | - Melissa Haendel
- Oregon Health & Science University, Portland, OR, United States
| | - Nina Hallowell
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, United Kingdom
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ada Hamosh
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Jayne Hehir-Kwa
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
| | - Marc-Phillip Hitz
- Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital of Schleswig-Holstein–Campus Kiel, Kiel, Germany
| | - Mark Hughes
- Department of Clinical Neurosciences, Western General Hospital, Edinburgh, United Kingdom
| | - Usha Kini
- Oxford Centre for Genomic Medicine, Oxford, United Kingdom
| | - Tjitske Kleefstra
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - R Frank Kooy
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
| | - Peter Krawitz
- Institut für Genomische Statistik und Bioinformatik, Universitätsklinikum Bonn, Rheinische-Friedrich-Wilhelms-Universität, Bonn, Germany
| | - Sébastien Küry
- CHU Nantes, Service de Génétique Médicale, Nantes, France
| | - Melissa Lees
- Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Gholson J. Lyon
- George A. Jervis Clinic and Institute for Basic Research in Developmental Disabilities (IBR), Staten Island, NY, United States
| | | | | | - Stephen Meyn
- Department of Computer Science, University of Toronto and the Hospital for Sick Children, Toronto, Canada
| | - Veronika Moslerová
- Department of Biology and Medical Genetics, 2nd Faculty of Medicine, Charles University and University Hospital, Prague, Czechia
| | - Juan M. Politei
- Laboratorio Chamoles, Errores Congénitos del Metabolismo, Buenos Aires, Argentina
| | - Cathryn C. Poulton
- Department of Paediatrics and Neonates, Fiona Stanley Hospital, Perth, WA, Australia
| | - F Lucy Raymond
- CIMR (Wellcome Trust/MRC Building), Cambridge, United Kingdom
| | - Margot R.F. Reijnders
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | | | | | - Catherine M. Rose
- Victorian Clinical Genetics Service and Murdoch Childrens Research Institute, The Royal Children’s Hospital, Parkville, VIC, Australia
| | - David C.G. Sainsbury
- Northern & Yorkshire Cleft Lip and Palate Service, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Lyn Schofield
- Genetic Services of Western Australia, King Edward Memorial Hospital, Subiaco, WA, Australia
| | - Vernon R. Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Marek Turnovec
- Department of Biology and Medical Genetics, 2nd Faculty of Medicine, Charles University and University Hospital, Prague, Czechia
| | - Anke Van Dijck
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | - Hilde Van Esch
- Center for Human Genetics, University Hospitals Leuven, University of Leuven, Leuven, Belgium
| | - Andrew O.M. Wilkie
- Clinical Genetics Group, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, United Kingdom
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45
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Thompson R, Papakonstantinou Ntalis A, Beltran S, Töpf A, de Paula Estephan E, Polavarapu K, 't Hoen PAC, Missier P, Lochmüller H. Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder. Hum Mutat 2019; 40:1797-1812. [PMID: 31231902 DOI: 10.1002/humu.23792] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/22/2019] [Accepted: 05/08/2019] [Indexed: 12/12/2022]
Abstract
Phenotype-based filtering and prioritization contribute to the interpretation of genetic variants detected in exome sequencing. However, it is currently unclear how extensive this phenotypic annotation should be. In this study, we compare methods for incorporating phenotype into the interpretation process and assess the extent to which phenotypic annotation aids prioritization of the correct variant. Using a cohort of 29 patients with congenital myasthenic syndromes with causative variants in known or newly discovered disease genes, exome data and the Human Phenotype Ontology (HPO)-coded phenotypic profiles, we show that gene-list filters created from phenotypic annotations perform similarly to curated disease-gene virtual panels. We use Exomiser, a prioritization tool incorporating phenotypic comparisons, to rank candidate variants while varying phenotypic annotation. Analyzing 3,712 combinations, we show that increasing phenotypic annotation improved prioritization of the causative variant, from 62% ranked first on variant alone to 90% with seven HPO annotations. We conclude that any HPO-based phenotypic annotation aids variant discovery and that annotation with over five terms is recommended in our context. Although focused on a constrained cohort, this provides real-world validation of the utility of phenotypic annotation for variant prioritization. Further research is needed to extend this concept to other diseases and more diverse cohorts.
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Affiliation(s)
- Rachel Thompson
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Newcastle upon Tyne, UK
| | - Anastasios Papakonstantinou Ntalis
- Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Sergi Beltran
- Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.,Universitat Pompeu Fabra-UPF, Barcelona, Spain
| | - Ana Töpf
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Newcastle upon Tyne, UK
| | - Eduardo de Paula Estephan
- Departamento de Neurologia, Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil
| | - Kiran Polavarapu
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | - Peter A C 't Hoen
- Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Paolo Missier
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Hanns Lochmüller
- Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.,Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Canada.,Department of Medicine, Division of Neurology, The Ottawa Hospital, Ottawa, Canada.,Department of Neuropediatrics and Muscle Disorders, Faculty of Medicine, Medical Center-University of Freiburg, Freiburg, Germany
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46
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Jones EG, Mazaheri N, Maroofian R, Zamani M, Seifi T, Sedaghat A, Shariati G, Jamshidi Y, Allen HD, Wehrens XHT, Galehdari H, Landstrom AP. Analysis of enriched rare variants in JPH2-encoded junctophilin-2 among Greater Middle Eastern individuals reveals a novel homozygous variant associated with neonatal dilated cardiomyopathy. Sci Rep 2019; 9:9038. [PMID: 31227780 PMCID: PMC6588559 DOI: 10.1038/s41598-019-44987-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 05/24/2019] [Indexed: 02/07/2023] Open
Abstract
Junctophilin-2 (JPH2) is a part of the junctional membrane complex that facilitates calcium-handling in the cardiomyocyte. Previously, missense variants in JPH2 have been linked to hypertrophic cardiomyopathy; however, pathogenic "loss of function" (LOF) variants have not been described. Family-based genetic analysis of GME individuals with cardiomyopathic disease identified an Iranian patient with dilated cardiomyopathy (DCM) as a carrier of a novel, homozygous single nucleotide insertion in JPH2 resulting in a stop codon (JPH2-p.E641*). A second Iranian family with consanguineous parents hosting an identical heterozygous variant had 2 children die in childhood from cardiac failure. To characterize ethnicity-dependent genetic variability in JPH2 and to identify homozygous JPH2 variants associated with cardiac disease, we identified variants in JPH2 in a worldwide control cohort (gnomAD) and 2 similar cohorts from the Greater Middle East (GME Variome, Iranome). These were compared against ethnicity-matched clinical whole exome sequencing (WES) referral tests and a case cohort of individuals with hypertrophic cardiomyopathy (HCM) based on comprehensive review of the literature. Worldwide, 1.45% of healthy individuals hosted a rare JPH2 variant with a significantly higher proportion among GME individuals (4.45%); LOF variants were rare overall (0.04%) yet were most prevalent in GME (0.21%). The increased prevalence of LOF variants in GME individuals was corroborated among region-specific, clinical WES cohorts. In conclusion, we report ethnic-specific differences in JPH2 rare variants, with GME individuals being at higher risk of hosting homozygous LOF variants. This conclusion is supported by the identification of a novel JPH2 LOF variant confirmed by segregation analysis resulting in autosomal recessive pediatric DCM due to presumptive JPH2 truncation.
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Affiliation(s)
- Edward G Jones
- Department of Pediatrics, Section of Pediatric Cardiology, Baylor College of Medicine, Houston, Texas, United States
| | - Neda Mazaheri
- Department of Genetics, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran.,Narges Medical Genetics and Prenatal Diagnosis Laboratory, Kianpars, Ahvaz, Iran
| | - Reza Maroofian
- Molecular and Clinical Sciences Institute, St George's University of London, London, United Kingdom
| | - Mina Zamani
- Department of Genetics, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran.,Narges Medical Genetics and Prenatal Diagnosis Laboratory, Kianpars, Ahvaz, Iran
| | - Tahereh Seifi
- Department of Genetics, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran.,Narges Medical Genetics and Prenatal Diagnosis Laboratory, Kianpars, Ahvaz, Iran
| | - Alireza Sedaghat
- Diabetes Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Gholamreza Shariati
- Narges Medical Genetics and Prenatal Diagnosis Laboratory, Kianpars, Ahvaz, Iran
| | - Yalda Jamshidi
- Molecular and Clinical Sciences Institute, St George's University of London, London, United Kingdom
| | - Hugh D Allen
- Department of Pediatrics, Section of Pediatric Cardiology, Baylor College of Medicine, Houston, Texas, United States.,Cardiovascular Research Institute, Baylor College of Medicine, Houston, Texas, United States
| | - Xander H T Wehrens
- Cardiovascular Research Institute, Baylor College of Medicine, Houston, Texas, United States.,Department of Molecular Physiology and Biophysics, Department of Medicine, Section of Cardiology, Center for Space Medicine, Baylor College of Medicine, Houston, Texas, United States
| | - Hamid Galehdari
- Department of Genetics, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
| | - Andrew P Landstrom
- Department of Pediatrics, Section of Pediatric Cardiology, Baylor College of Medicine, Houston, Texas, United States. .,Cardiovascular Research Institute, Baylor College of Medicine, Houston, Texas, United States. .,Department of Pediatrics, Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, United States.
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47
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Fredrich B, Schmöhl M, Junge O, Gundlach S, Ellinghaus D, Pfeufer A, Bettecken T, Siddiqui R, Franke A, Wienker TF, Hoeppner MP, Krawczak M. VarWatch-A stand-alone software tool for variant matching. PLoS One 2019; 14:e0215618. [PMID: 31022234 PMCID: PMC6483337 DOI: 10.1371/journal.pone.0215618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 04/04/2019] [Indexed: 11/19/2022] Open
Abstract
Massively parallel DNA sequencing of clinical samples holds great promise for the gene-based diagnosis of human inherited diseases because it allows rapid detection of putatively causative mutations at genome-wide level. Without additional evidence complementing their initial bioinformatics evaluation, however, the clinical relevance of such candidate genetic variants often remains unclear. In consequence, dedicated 'matching' services have been established in recent years that aim at the discovery of other, comparable case reports to facilitate individual diagnoses. However, legal concerns have been raised about the global sharing of genetic data, particularly in Europe where the recently enacted General Data Protection Regulation EU-2016/679 classifies genetic data as highly sensitive. Hence, unrestricted sharing of genetic data from clinical cases on platforms outside the national jurisdiction increasingly may be perceived as problematic. To allow collaborative data producers, particularly large consortia of diagnostic laboratories, to acknowledge these concerns while still practicing efficient case matching internally, novel tools are required. To this end, we developed VarWatch, an easy-to-deploy and highly scalable case matching software that provides users with comprehensive programmatic tools and a user-friendly interface to fulfil said purpose.
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Affiliation(s)
- Broder Fredrich
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Marcus Schmöhl
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Olaf Junge
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Sven Gundlach
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Arne Pfeufer
- Humangenetische Praxis PD Dr. Pfeufer, München, Germany
- MVZ für Molekulardiagnostik GmbH, München, Germany
- Myriad GmbH, Martinsried, Germany
| | | | - Roman Siddiqui
- TMF – Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V., Berlin, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | | | - Marc P. Hoeppner
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
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48
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Maroilley T, Tarailo-Graovac M. Uncovering Missing Heritability in Rare Diseases. Genes (Basel) 2019; 10:E275. [PMID: 30987386 PMCID: PMC6523881 DOI: 10.3390/genes10040275] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/29/2019] [Accepted: 04/01/2019] [Indexed: 12/14/2022] Open
Abstract
The problem of 'missing heritability' affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The 'missing heritability' concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases-the majority of RDs are also facing the 'missing heritability' problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources.
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Affiliation(s)
- Tatiana Maroilley
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
| | - Maja Tarailo-Graovac
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
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49
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Fiume M, Cupak M, Keenan S, Rambla J, de la Torre S, Dyke SOM, Brookes AJ, Carey K, Lloyd D, Goodhand P, Haeussler M, Baudis M, Stockinger H, Dolman L, Lappalainen I, Törnroos J, Linden M, Spalding JD, Ur-Rehman S, Page A, Flicek P, Sherry S, Haussler D, Varma S, Saunders G, Scollen S. Federated discovery and sharing of genomic data using Beacons. Nat Biotechnol 2019; 37:220-224. [PMID: 30833764 PMCID: PMC6728157 DOI: 10.1038/s41587-019-0046-x] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
| | | | - Stephen Keenan
- Global Alliance for Genomics and Health, Toronto, Ontario, Canada
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | | | - Stephanie O M Dyke
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | | | | | - David Lloyd
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Peter Goodhand
- Global Alliance for Genomics and Health, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Michael Baudis
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Lena Dolman
- Global Alliance for Genomics and Health, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Ilkka Lappalainen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- CSC - IT Center for Science Ltd, Espoo, Finland
| | | | | | - J Dylan Spalding
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Saif Ur-Rehman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Angela Page
- Global Alliance for Genomics and Health, Toronto, Ontario, Canada
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Stephen Sherry
- National Center for Biotechnology Information, US National Library of Medicine, Bethesda, MD, USA
| | - David Haussler
- Genomics Institute, University of California at Santa Cruz, Santa Cruz, CA, USA
| | - Susheel Varma
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Gary Saunders
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Serena Scollen
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
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50
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2.5 years’ experience of GeneMatcher data-sharing: a powerful tool for identifying new genes responsible for rare diseases. Genet Med 2018; 21:1657-1661. [DOI: 10.1038/s41436-018-0383-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 11/15/2018] [Indexed: 02/07/2023] Open
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