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Her Y, Pascual DM, Goldstone-Joubert Z, Marcogliese PC. Variant functional assessment in Drosophila by overexpression: what can we learn? Genome 2024; 67:158-167. [PMID: 38412472 DOI: 10.1139/gen-2023-0135] [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] [Indexed: 02/29/2024]
Abstract
The last decade has been highlighted by the increased use of next-generation DNA sequencing technology to identify novel human disease genes. A critical downstream part of this process is assigning function to a candidate gene variant. Functional studies in Drosophila melanogaster, the common fruit fly, have made a prominent contribution in annotating variant impact in an in vivo system. The use of patient-derived knock-in flies or rescue-based, "humanization", approaches are novel and valuable strategies in variant testing but have been recently widely reviewed. An often-overlooked strategy for determining variant impact has been GAL4/upstream activation sequence-mediated tissue-defined overexpression in Drosophila. This mini-review will summarize the recent contribution of ectopic overexpression of human reference and variant cDNA in Drosophila to assess variant function, interpret the consequence of the variant, and in some cases infer biological mechanisms.
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Affiliation(s)
- Yina Her
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Children's Hospital Research Institute of Manitoba (CHRIM), University of Manitoba, Winnipeg, MB, Canada
| | - Danielle M Pascual
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Children's Hospital Research Institute of Manitoba (CHRIM), University of Manitoba, Winnipeg, MB, Canada
| | - Zoe Goldstone-Joubert
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Children's Hospital Research Institute of Manitoba (CHRIM), University of Manitoba, Winnipeg, MB, Canada
| | - Paul C Marcogliese
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Children's Hospital Research Institute of Manitoba (CHRIM), University of Manitoba, Winnipeg, MB, Canada
- Excellence in Neurodevelopment and Rehabilitation Research in Child Health (ENRRICH) Theme, Winnipeg, MB, Canada
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2
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Iacomino M, Houerbi N, Fortuna S, Howe J, Li S, Scorrano G, Riva A, Cheng KW, Steiman M, Peltekova I, Yusuf A, Baldassari S, Tamburro S, Scudieri P, Musante I, Di Ludovico A, Guerrisi S, Balagura G, Corsello A, Efthymiou S, Murphy D, Uva P, Verrotti A, Fiorillo C, Delvecchio M, Accogli A, Elsabbagh M, Houlden H, Scherer SW, Striano P, Zara F, Chou TF, Salpietro V. Allelic heterogeneity and abnormal vesicle recycling in PLAA-related neurodevelopmental disorders. Front Mol Neurosci 2024; 17:1268013. [PMID: 38650658 PMCID: PMC11033462 DOI: 10.3389/fnmol.2024.1268013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 02/16/2024] [Indexed: 04/25/2024] Open
Abstract
The human PLAA gene encodes Phospholipase-A2-Activating-Protein (PLAA) involved in trafficking of membrane proteins. Through its PUL domain (PLAP, Ufd3p, and Lub1p), PLAA interacts with p97/VCP modulating synaptic vesicles recycling. Although few families carrying biallelic PLAA variants were reported with progressive neurodegeneration, consequences of monoallelic PLAA variants have not been elucidated. Using exome or genome sequencing we identified PLAA de-novo missense variants, affecting conserved residues within the PUL domain, in children affected with neurodevelopmental disorders (NDDs), including psychomotor regression, intellectual disability (ID) and autism spectrum disorders (ASDs). Computational and in-vitro studies of the identified variants revealed abnormal chain arrangements at C-terminal and reduced PLAA-p97/VCP interaction, respectively. These findings expand both allelic and phenotypic heterogeneity associated to PLAA-related neurological disorders, highlighting perturbed vesicle recycling as a potential disease mechanism in NDDs due to genetic defects of PLAA.
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Affiliation(s)
- Michele Iacomino
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Nadia Houerbi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Sara Fortuna
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy
| | - Jennifer Howe
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Shan Li
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Giovanna Scorrano
- Department of Pediatrics, Sant'Annunziata Hospital, University "G. D'Annunzio", Chieti, Italy
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Antonella Riva
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Kai-Wen Cheng
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Mandy Steiman
- Montreal Neurological Institute-Hospital, Azrieli Centre for Autism Research, McGill University, Montreal, QC, Canada
| | - Iskra Peltekova
- McGill University Health Centre, McGill University, Montreal, QC, Canada
| | - Afiqah Yusuf
- Montreal Neurological Institute-Hospital, Azrieli Centre for Autism Research, McGill University, Montreal, QC, Canada
| | - Simona Baldassari
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Serena Tamburro
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Paolo Scudieri
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Ilaria Musante
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Armando Di Ludovico
- Department of Pediatrics, Sant'Annunziata Hospital, University "G. D'Annunzio", Chieti, Italy
| | - Sara Guerrisi
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Ganna Balagura
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Antonio Corsello
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Stephanie Efthymiou
- Department of Neuromuscular Diseases, UCL Institute of Neurology, London, United Kingdom
| | - David Murphy
- Department of Neuromuscular Diseases, UCL Institute of Neurology, London, United Kingdom
| | - Paolo Uva
- Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Chiara Fiorillo
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Maurizio Delvecchio
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Andrea Accogli
- Division of Medical Genetics, Department of Specialized Medicine, McGill University, Montreal, QC, Canada
| | - Mayada Elsabbagh
- Montreal Neurological Institute-Hospital, Azrieli Centre for Autism Research, McGill University, Montreal, QC, Canada
| | - Henry Houlden
- Department of Neuromuscular Diseases, UCL Institute of Neurology, London, United Kingdom
| | - Stephen W Scherer
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- McLaughlin Centre, University of Toronto, Toronto, ON, Canada
| | - Pasquale Striano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Federico Zara
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Tsui-Fen Chou
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, Pasadena, CA, United States
| | - Vincenzo Salpietro
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
- Department of Neuromuscular Diseases, UCL Institute of Neurology, London, United Kingdom
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3
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Rips J, Halstuk O, Fuchs A, Lang Z, Sido T, Gershon-Naamat S, Abu-Libdeh B, Edvardson S, Salah S, Breuer O, Hadhud M, Eden S, Simon I, Slae M, Damseh NS, Abu-Libdeh A, Eskin-Schwartz M, Birk OS, Varga J, Schueler-Furman O, Rosenbluh C, Elpeleg O, Yanovsky-Dagan S, Mor-Shaked H, Harel T. Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield. Genet Med 2024; 26:101068. [PMID: 38193396 DOI: 10.1016/j.gim.2024.101068] [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: 08/11/2023] [Revised: 12/31/2023] [Accepted: 01/04/2024] [Indexed: 01/10/2024] Open
Abstract
PURPOSE Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. METHODS Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. RESULTS 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. CONCLUSION Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.
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Affiliation(s)
- Jonathan Rips
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel
| | - Orli Halstuk
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel; Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Adina Fuchs
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel; Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Ziv Lang
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel
| | - Tal Sido
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel
| | | | - Bassam Abu-Libdeh
- Department of Pediatrics & Genetics, Makassed Hospital & Al-Quds Medical School, E. Jerusalem, Palestine
| | - Simon Edvardson
- Faculty of Medicine, Hebrew University of Jerusalem, Israel; Pediatric Neurology Unit, Hadassah Medical Center, Jerusalem, Israel
| | - Somaya Salah
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel
| | - Oded Breuer
- Faculty of Medicine, Hebrew University of Jerusalem, Israel; Pediatric Pulmonology and CF Unit, Department of Pediatrics, Hadassah Medical Center, Jerusalem, Israel
| | - Mohamad Hadhud
- Faculty of Medicine, Hebrew University of Jerusalem, Israel; Pediatric Pulmonology and CF Unit, Department of Pediatrics, Hadassah Medical Center, Jerusalem, Israel
| | - Sharon Eden
- Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Itamar Simon
- Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Mordechai Slae
- Pediatric Gastroenterology Unit, Department of Pediatrics, Hadassah Medical Center, Jerusalem, Israel
| | - Nadirah S Damseh
- Department of Pediatrics & Genetics, Makassed Hospital & Al-Quds Medical School, E. Jerusalem, Palestine
| | - Abdulsalam Abu-Libdeh
- Department of Pediatrics & Genetics, Makassed Hospital & Al-Quds Medical School, E. Jerusalem, Palestine; Division of Pediatric Endocrinology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Marina Eskin-Schwartz
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel; Genetics Institute, Soroka University Medical Center, Beer-Sheva, Israel
| | - Ohad S Birk
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel; Genetics Institute, Soroka University Medical Center, Beer-Sheva, Israel
| | - Julia Varga
- Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ora Schueler-Furman
- Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | | | - Orly Elpeleg
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel; Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | | | - Hagar Mor-Shaked
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel; Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Tamar Harel
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel; Faculty of Medicine, Hebrew University of Jerusalem, Israel.
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4
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Clark KJ, Lubin EE, Gonzalez EM, Sangree AK, Layo-Carris DE, Durham EL, Ahrens-Nicklas RC, Nomakuchi TT, Bhoj EJ. NeuroTri2-VISDOT: An open-access tool to harness the power of second trimester human single cell data to inform models of Mendelian neurodevelopmental disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.578438. [PMID: 38352329 PMCID: PMC10862881 DOI: 10.1101/2024.02.01.578438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Whole exome and genome sequencing, coupled with refined bioinformatic pipelines, have enabled improved diagnostic yields for individuals with Mendelian conditions and have led to the rapid identification of novel syndromes. For many Mendelian neurodevelopmental disorders (NDDs), there is a lack of pre-existing model systems for mechanistic work. Thus, it is critical for translational researchers to have an accessible phenotype- and genotype-informed approach for model system selection. Single-cell RNA sequencing data can be informative in such an approach, as it can indicate which cell types express a gene of interest at the highest levels across time. For Mendelian NDDs, such data for the developing human brain is especially useful. A valuable single-cell RNA sequencing dataset of the second trimester developing human brain was produced by Bhaduri et al in 2021, but access to these data can be limited by computing power and the learning curve of single-cell data analysis. To reduce these barriers for translational research on Mendelian NDDs, we have built the web-based tool, Neurodevelopment in Trimester 2 - VIsualization of Single cell Data Online Tool (NeuroTri2-VISDOT), for exploring this single-cell dataset, and we have employed it in several different settings to demonstrate its utility for the translational research community.
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Affiliation(s)
- Kelly J. Clark
- Biomedical Graduate School, University of Pennsylvania, Perelman School of Medicine
- Children’s Hospital of Philadelphia
| | - Emily E. Lubin
- Biomedical Graduate School, University of Pennsylvania, Perelman School of Medicine
- Children’s Hospital of Philadelphia
| | - Elizabeth M. Gonzalez
- Biomedical Graduate School, University of Pennsylvania, Perelman School of Medicine
- Children’s Hospital of Philadelphia
| | - Annabel K. Sangree
- Biomedical Graduate School, University of Pennsylvania, Perelman School of Medicine
- Children’s Hospital of Philadelphia
| | | | | | - Rebecca C. Ahrens-Nicklas
- Children’s Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
| | | | - Elizabeth J. Bhoj
- Children’s Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
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5
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Boussetta A, Abida N, Jellouli M, Ziadi J, Gargah T. Delayed Graft Function in Pediatric Kidney Transplant: Risk Factors and Outcomes. EXP CLIN TRANSPLANT 2024; 22:110-117. [PMID: 38385384 DOI: 10.6002/ect.mesot2023.o20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
OBJECTIVES We aimed to identify risk factors and outcomes of delayed graft function in pediatric kidney transplant. MATERIALS AND METHODS This retrospective study included all kidney transplant recipients ≤19 years old followed up in our department for a period of 34 years, from January 1989 to December 2022. RESULTS We included 113 kidney transplant recipients. Delayed graft function occurred in 17 cases (15%). Posttransplant red blood cell transfusion was strongly associated with delayed graft function (adjusted odds ratio = 23.91; 95% CI, 2.889-197.915). Use of allografts with multiple arteries and cold ischemia time >20 hours were risk factors for delayed graft function (adjusted odds ratio = 52.51 and 49.4; 95% CI, 2.576-1070.407 and 1.833-1334.204, respectively). Sex-matched transplants and living donors were protective factors for delayed graft function (adjusted odds ratio = 0.043 and 0.027; 95% CI, 0.005-0.344 and 0.003-0.247, respectively). Total HLA mismatches <3 played a protective role for delayed graft function (adjusted odds ratio = 0.114; 95% CI, 0.020-0.662), whereas transplant within compatible but different blood types increased the risk of delayed graft function (adjusted odds ratio = 20.54; 95% CI, 1.960- 215.263). No significant correlation was shown between delayed graft function and allograft survival (P = .190). Our study suggested delayed graft function as a key factor in allograft rejection-free survival (adjusted odds ratio = 3.832; 95% CI, 1.186-12.377). Delayed graft function was a negative factor for early graft function; patients with delayed graft function had a lower estimated glomerular filtration rate at discharge (P = .024) and at 3 (P = .034), 6 (P = .019), and 12 months (P = .011) posttransplant. CONCLUSIONS Delayed graft function is a major determinant of early graft function and allograft rejection-free survival. Further research is required to establish proper preventive measures.
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Affiliation(s)
- Abir Boussetta
- From the Pediatric Nephrology Department, Charles Nicolle Hospital and the University of Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia
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Jordan DM, Vy HMT, Do R. A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.21.23300393. [PMID: 38196638 PMCID: PMC10775679 DOI: 10.1101/2023.12.21.23300393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
It is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.
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Affiliation(s)
- Daniel M. Jordan
- Center for Genomic Data Analytics, Charles Bronfman Institute for Personalized Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ha My T. Vy
- Center for Genomic Data Analytics, Charles Bronfman Institute for Personalized Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- Center for Genomic Data Analytics, Charles Bronfman Institute for Personalized Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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7
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Seaby EG, Thomas NS, Hunt D, Baralle D, Rehm HL, O’Donnell-Luria A, Ennis S. A Panel-Agnostic Strategy 'HiPPo' Improves Diagnostic Efficiency in the UK Genomic Medicine Service. Healthcare (Basel) 2023; 11:3179. [PMID: 38132069 PMCID: PMC10742528 DOI: 10.3390/healthcare11243179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Genome sequencing is available as a clinical test in the UK through the Genomic Medicine Service (GMS). The GMS analytical strategy predominantly filters genome data on preselected gene panels. Whilst this reduces variants requiring assessment by reporting laboratories, pathogenic variants outside applied panels may be missed, and variants in genes without established disease-gene relationships are largely ignored. This study compares the analysis of a research exome to a GMS clinical genome for the same patients. For the research exome, we applied a panel-agnostic approach filtering for variants with High Pathogenic Potential (HiPPo) using ClinVar, allele frequency, and in silico prediction tools. We then restricted HiPPo variants to Gene Curation Coalition (GenCC) disease genes. These results were compared with the GMS genome panel-based approach. Twenty-four participants from eight families underwent parallel research exome and GMS genome sequencing. Exome HiPPo analysis identified a similar number of variants as the GMS panel-based approach. GMS genome analysis returned two pathogenic variants and one de novo variant. Exome HiPPo analysis returned the same variants plus an additional pathogenic variant and three further de novo variants in novel genes, where case series are underway. When HiPPo was restricted to GenCC disease genes, statistically fewer variants required assessment to identify more pathogenic variants than reported by the GMS, giving a diagnostic rate per variant assessed of 20% for HiPPo versus 3% for the GMS. With UK plans to sequence 5 million genomes, strategies are needed to optimise genome analysis beyond gene panels whilst minimising the burden of variants requiring clinical assessment.
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Affiliation(s)
- Eleanor G. Seaby
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton SO16 6YD, Hampshire, UK; (D.H.); (D.B.); (S.E.)
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; (H.L.R.); (A.O.-L.)
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Paediatric Infectious Diseases, Imperial College London, London W2 1NY, UK
| | - N. Simon Thomas
- Wessex Regional Genomics Laboratory, Salisbury NHS Foundation Trust, Salisbury SP2 8BJ, UK;
| | - David Hunt
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton SO16 6YD, Hampshire, UK; (D.H.); (D.B.); (S.E.)
| | - Diana Baralle
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton SO16 6YD, Hampshire, UK; (D.H.); (D.B.); (S.E.)
| | - Heidi L. Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; (H.L.R.); (A.O.-L.)
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anne O’Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; (H.L.R.); (A.O.-L.)
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Sarah Ennis
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton SO16 6YD, Hampshire, UK; (D.H.); (D.B.); (S.E.)
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Curic E, Ewans L, Pysar R, Taylan F, Botto LD, Nordgren A, Gahl W, Palmer EE. International Undiagnosed Diseases Programs (UDPs): components and outcomes. Orphanet J Rare Dis 2023; 18:348. [PMID: 37946247 PMCID: PMC10633944 DOI: 10.1186/s13023-023-02966-1] [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: 04/27/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023] Open
Abstract
Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.
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Affiliation(s)
- Ela Curic
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia
| | - Lisa Ewans
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia
- Centre for Clinical Genetics, Sydney Children's Hospital, Randwick, NSW, Australia
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Ryan Pysar
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia
- Centre for Clinical Genetics, Sydney Children's Hospital, Randwick, NSW, Australia
- Department of Clinical Genetics, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Fulya Taylan
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
| | - Lorenzo D Botto
- Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Ann Nordgren
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - William Gahl
- Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Elizabeth Emma Palmer
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia.
- Centre for Clinical Genetics, Sydney Children's Hospital, Randwick, NSW, Australia.
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9
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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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10
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Accogli A, Lu S, Musante I, Scudieri P, Rosenfeld JA, Severino M, Baldassari S, Iacomino M, Riva A, Balagura G, Piccolo G, Minetti C, Roberto D, Xia F, Razak R, Lawrence E, Hussein M, Chang EYH, Holick M, Calì E, Aliberto E, De-Sarro R, Gambardella A, Network UD, Group SYNS, Emrick L, McCaffery PJA, Clagett-Dame M, Marcogliese PC, Bellen HJ, Lalani SR, Zara F, Striano P, Salpietro V. Loss of Neuron Navigator 2 Impairs Brain and Cerebellar Development. CEREBELLUM (LONDON, ENGLAND) 2023; 22:206-222. [PMID: 35218524 PMCID: PMC9985553 DOI: 10.1007/s12311-022-01379-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 10/19/2022]
Abstract
Cerebellar hypoplasia and dysplasia encompass a group of clinically and genetically heterogeneous disorders frequently associated with neurodevelopmental impairment. The Neuron Navigator 2 (NAV2) gene (MIM: 607,026) encodes a member of the Neuron Navigator protein family, widely expressed within the central nervous system (CNS), and particularly abundant in the developing cerebellum. Evidence across different species supports a pivotal function of NAV2 in cytoskeletal dynamics and neurite outgrowth. Specifically, deficiency of Nav2 in mice leads to cerebellar hypoplasia with abnormal foliation due to impaired axonal outgrowth. However, little is known about the involvement of the NAV2 gene in human disease phenotypes. In this study, we identified a female affected with neurodevelopmental impairment and a complex brain and cardiac malformations in which clinical exome sequencing led to the identification of NAV2 biallelic truncating variants. Through protein expression analysis and cell migration assay in patient-derived fibroblasts, we provide evidence linking NAV2 deficiency to cellular migration deficits. In model organisms, the overall CNS histopathology of the Nav2 hypomorphic mouse revealed developmental anomalies including cerebellar hypoplasia and dysplasia, corpus callosum hypo-dysgenesis, and agenesis of the olfactory bulbs. Lastly, we show that the NAV2 ortholog in Drosophila, sickie (sick) is widely expressed in the fly brain, and sick mutants are mostly lethal with surviving escapers showing neurobehavioral phenotypes. In summary, our results unveil a novel human neurodevelopmental disorder due to genetic loss of NAV2, highlighting a critical conserved role of the NAV2 gene in brain and cerebellar development across species.
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Affiliation(s)
- Andrea Accogli
- Division of Medical Genetics, Department of Specialized Medicine, McGill University, Montreal, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Shenzhao Lu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Houston, TX, 77030, USA
- Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Ilaria Musante
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Paolo Scudieri
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | | | - Simona Baldassari
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Michele Iacomino
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Antonella Riva
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Ganna Balagura
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Gianluca Piccolo
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS Giannina Gaslini Institute, Genoa, Italy
| | - Carlo Minetti
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS Giannina Gaslini Institute, Genoa, Italy
| | - Denis Roberto
- Child Neurology and Psychiatry Unit, System Medicine Department, Tor Vergata University of Rome, 00133, Rome, Italy
| | - Fan Xia
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Baylor Genetics Laboratories, Houston, TX, USA
| | | | - Emily Lawrence
- Department of Cardiology, Texas Childrens Hospital, Houston, USA
| | - Mohamed Hussein
- Department of Ophthalmology, Texas Childrens Hospital, Houston, USA
| | | | - Michelle Holick
- Texas Childrens Hospital, Houston, TX, USA
- Department of Pediatrics, Division of Neurology and Developmental Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Elisa Calì
- Department of Neuromuscular Diseases, University College London, Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | | | - Rosalba De-Sarro
- Department of Clinical and Experimental Medicine, Policlinic "G. Martino", University of Messina, 98100, Messina, Italy
| | - Antonio Gambardella
- Department of Medical and Surgical Sciences, Universita' Degli Studi "Magna Graecia" Viale Europa, 88100, CATANZARO, Italy
| | | | | | - Lisa Emrick
- Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Houston, TX, 77030, USA
- Texas Childrens Hospital, Houston, TX, USA
- Department of Pediatrics, Division of Neurology and Developmental Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Peter J A McCaffery
- Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, UK
| | - Margaret Clagett-Dame
- Department of Biochemistry, College of Agricultural and Life Sciences, University of Wisconsin, Madison, WI, 53706, USA
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI, 53706, USA
| | - Paul C Marcogliese
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Houston, TX, 77030, USA
| | - Hugo J Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Houston, TX, 77030, USA
- Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Seema R Lalani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Texas Childrens Hospital, Houston, TX, USA
| | - Federico Zara
- Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Pasquale Striano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS Giannina Gaslini Institute, Genoa, Italy
| | - Vincenzo Salpietro
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.
- Pediatric Neurology and Muscular Diseases Unit, IRCCS Giannina Gaslini Institute, Genoa, Italy.
- Department of Neuromuscular Diseases, University College London, Queen Square Institute of Neurology, London, WC1N 3BG, UK.
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11
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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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12
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Seaby EG, Thomas NS, Hunt D, Baralle D, Rehm HL, O’Donnell-Luria A, Ennis S. A panel-agnostic strategy 'HiPPo' improves diagnostic efficiency in the UK Genome Medicine Service. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.31.23285025. [PMID: 36778464 PMCID: PMC9915838 DOI: 10.1101/2023.01.31.23285025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Genome sequencing is now available as a clinical test on the National Health Service (NHS) through the Genome Medicine Service (GMS). The GMS have set out an analytical strategy that predominantly filters genome data on a pre-selected gene panel(s). Whilst this approach reduces the number of variants requiring assessment by reporting laboratories, pathogenic variants outside of the gene panel applied may be missed, and candidate variants in novel genes are largely ignored. This study sought to compare a research exome analysis to an independent clinical genome analysis performed through the NHS for the same group of patients. When analysing the exome data, we applied a panel agnostic approach filtering for variants with High Pathogenic Potential (HiPPo) using ClinVar, allele frequency, and in silico prediction tools. We then compared this gene agnostic analysis to the panel-based approach as applied by the GMS to genome data. Later we restricted HiPPo variants to a panel of the Gene Curation Coalition (GenCC) morbid genes and compared the diagnostic yield with the variants filtered using the GMS strategy. 24 patients from 8 families underwent parallel research exome sequencing and GMS genome sequencing. HiPPo analysis applied to research exome data identified a similar number of variants as the gene panel-based approach applied by the GMS. GMS clinical genome analysis identified and returned 2 pathogenic variants and 3 variants of uncertain significance. HiPPo research exome analysis identified the same variants plus an additional pathogenic variant and a further 3 de novo variants of uncertain significance in novel genes, where case series and functional studies are underway. When HiPPo was restricted to GenCC disease genes (strong or definitive), the same pathogenic variants were identified yet statistically fewer variants required assessment to identify more diagnostic variants than reported by the GMS genome strategy. This gave a diagnostic rate per variant assessed of 20% for HiPPo restricted to GenCC versus 3% for the GMS panel-based approach. With plans to sequence 5 million more NHS patients, strategies are needed to optimise the full potential of genome data beyond gene panels whilst minimising the burden of variants that require clinical assessment.
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Affiliation(s)
- Eleanor G. Seaby
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton, Hampshire, SO16 6YD, UK
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Paediatric Infectious Diseases, Imperial College London, London, W2 1NY, UK
| | - N. Simon Thomas
- Wessex Regional Genomics Laboratory, Salisbury NHS Foundation Trust, Salisbury, SP2 8BJ, UK
| | - David Hunt
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton, Hampshire, SO16 6YD, UK
| | - Diana Baralle
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton, Hampshire, SO16 6YD, UK
| | - Heidi L. Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anne O’Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Sarah Ennis
- Human Development and Health, Faculty of Medicine, University Hospital Southampton, Southampton, Hampshire, SO16 6YD, UK
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13
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Abstract
Hundreds of different genetic causes of chronic kidney disease are now recognized, and while individually rare, taken together they are significant contributors to both adult and pediatric diseases. Traditional genetics approaches relied heavily on the identification of large families with multiple affected members and have been fundamental to the identification of genetic kidney diseases. With the increased utilization of massively parallel sequencing and improvements to genotype imputation, we can analyze rare variants in large cohorts of unrelated individuals, leading to personalized care for patients and significant research advancements. This review evaluates the contribution of rare disorders to patient care and the study of genetic kidney diseases and highlights key advancements that utilize new techniques to improve our ability to identify new gene-disease associations.
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Affiliation(s)
- Mark D Elliott
- Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA;
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Institute for Genomic Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Hila Milo Rasouly
- Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA;
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA;
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Institute for Genomic Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
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14
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Abstract
Exome sequencing (ES) and genome sequencing (GS) have radically transformed the diagnostic approach to undiagnosed rare/ultrarare Mendelian diseases. Next-generation sequencing (NGS), the technology integral for ES, GS, and most large (100+) gene panels, has enabled previously unimaginable diagnoses, changes in medical management, new treatments, and accurate reproductive risk assessments for patients, as well as new disease gene discoveries. Yet, challenges remain, as most individuals remain undiagnosed with current NGS. Improved NGS technology has resulted in long-read sequencing, which may resolve diagnoses in some patients who do not obtain a diagnosis with current short-read ES and GS, but its effectiveness is unclear, and it is expensive. Other challenges that persist include the resolution of variants of uncertain significance, the urgent need for patients with ultrarare disorders to have access to therapeutics, the need for equity in patient access to NGS-based testing, and the study of ethical concerns. However, the outlook for undiagnosed disease resolution is bright, due to continual advancements in the field.
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Affiliation(s)
- Jennifer A Sullivan
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
| | - Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
| | - Rebecca C Spillmann
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
| | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
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15
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Phenotype-aware prioritisation of rare Mendelian disease variants. Trends Genet 2022; 38:1271-1283. [PMID: 35934592 PMCID: PMC9950798 DOI: 10.1016/j.tig.2022.07.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/06/2022] [Accepted: 07/05/2022] [Indexed: 01/24/2023]
Abstract
A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.
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16
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Knowles JK, Helbig I, Metcalf CS, Lubbers LS, Isom LL, Demarest S, Goldberg EM, George AL, Lerche H, Weckhuysen S, Whittemore V, Berkovic SF, Lowenstein DH. Precision medicine for genetic epilepsy on the horizon: Recent advances, present challenges, and suggestions for continued progress. Epilepsia 2022; 63:2461-2475. [PMID: 35716052 PMCID: PMC9561034 DOI: 10.1111/epi.17332] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 01/18/2023]
Abstract
The genetic basis of many epilepsies is increasingly understood, giving rise to the possibility of precision treatments tailored to specific genetic etiologies. Despite this, current medical therapy for most epilepsies remains imprecise, aimed primarily at empirical seizure reduction rather than targeting specific disease processes. Intellectual and technological leaps in diagnosis over the past 10 years have not yet translated to routine changes in clinical practice. However, the epilepsy community is poised to make impressive gains in precision therapy, with continued innovation in gene discovery, diagnostic ability, and bioinformatics; increased access to genetic testing and counseling; fuller understanding of natural histories; agility and rigor in preclinical research, including strategic use of emerging model systems; and engagement of an evolving group of stakeholders (including patient advocates, governmental resources, and clinicians and scientists in academia and industry). In each of these areas, we highlight notable examples of recent progress, new or persistent challenges, and future directions. The future of precision medicine for genetic epilepsy looks bright if key opportunities on the horizon can be pursued with strategic and coordinated effort.
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Affiliation(s)
- Juliet K. Knowles
- Department of Neurology, Division of Child Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
- Department of Neuropediatrics, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Cameron S. Metcalf
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, Utah, USA
| | - Laura S. Lubbers
- Citizens United for Research in Epilepsy, Chicago, Illinois, USA
| | - Lori L. Isom
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Scott Demarest
- Department of Pediatrics and Neurology, University of Colorado, School of Medicine, Aurora, Colorado, USA
| | - Ethan M. Goldberg
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Alfred L. George
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Sarah Weckhuysen
- Division of Neurology, University Hospital Antwerp, Antwerp, Belgium
- Applied and Translational Neurogenomics Group, Vlaams Instituut voor Biotechnologie Center for Molecular Neurology, Antwerp, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Vicky Whittemore
- Division of Neuroscience, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, Maryland, USA
| | - Samuel F. Berkovic
- Epilepsy Research Centre, Department of Medicine, Austin Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Daniel H. Lowenstein
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
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17
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Stutterd CA, Vanderver A, Lockhart PJ, Helman G, Pope K, Uebergang E, Love C, Delatycki MB, Thorburn D, Mackay MT, Peters H, Kornberg AJ, Patel C, Rodriguez-Casero V, Waak M, Silberstein J, Sinclair A, Nolan M, Field M, Davis MR, Fahey M, Scheffer IE, Freeman JL, Wolf NI, Taft RJ, van der Knaap MS, Simons C, Leventer RJ. Unclassified white matter disorders: A diagnostic journey requiring close collaboration between clinical and laboratory services. Eur J Med Genet 2022; 65:104551. [PMID: 35803560 DOI: 10.1016/j.ejmg.2022.104551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/27/2022] [Accepted: 06/18/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Next generation sequencing studies have revealed an ever-increasing number of causes for genetic disorders of central nervous system white matter. A substantial number of disorders are identifiable from their specific pattern of biochemical and/or imaging findings for which single gene testing may be indicated. Beyond this group, the causes of genetic white matter disorders are unclear and a broader approach to genomic testing is recommended. AIM This study aimed to identify the genetic causes for a group of individuals with unclassified white matter disorders with suspected genetic aetiology and highlight the investigations required when the initial testing is non-diagnostic. METHODS Twenty-six individuals from 22 families with unclassified white matter disorders underwent deep phenotyping and genome sequencing performed on trio, or larger, family groups. Functional studies and transcriptomics were used to resolve variants of uncertain significance with potential clinical relevance. RESULTS Causative or candidate variants were identified in 15/22 (68.2%) families. Six of the 15 implicated genes had been previously associated with white matter disease (COL4A1, NDUFV1, SLC17A5, TUBB4A, BOLA3, DARS2). Patients with variants in the latter two presented with an atypical phenotype. The other nine genes had not been specifically associated with white matter disease at the time of diagnosis and included genes associated with monogenic syndromes, developmental disorders, and developmental and epileptic encephalopathies (STAG2, LSS, FIG4, GLS, PMPCA, SPTBN1, AGO2, SCN2A, SCN8A). Consequently, only 46% of the diagnoses would have been made via a current leukodystrophy gene panel test. DISCUSSION These results confirm the importance of broad genomic testing for patients with white matter disorders. The high diagnostic yield reflects the integration of deep phenotyping, whole genome sequencing, trio analysis, functional studies, and transcriptomic analyses. CONCLUSIONS Genetic white matter disorders are genetically and phenotypically heterogeneous. Deep phenotyping together with a range of genomic technologies underpin the identification of causes of unclassified white matter disease. A molecular diagnosis is essential for prognostication, appropriate management, and accurate reproductive counseling.
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Affiliation(s)
- C A Stutterd
- Murdoch Children's Research Institute, Victoria, Australia; Department of Neurology, Royal Children's Hospital, Victoria, Australia; Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - A Vanderver
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - P J Lockhart
- Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - G Helman
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, Queensland, Australia
| | - K Pope
- Murdoch Children's Research Institute, Victoria, Australia
| | - E Uebergang
- Murdoch Children's Research Institute, Victoria, Australia
| | - C Love
- Murdoch Children's Research Institute, Victoria, Australia
| | - M B Delatycki
- Murdoch Children's Research Institute, Victoria, Australia; Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - D Thorburn
- Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - M T Mackay
- Murdoch Children's Research Institute, Victoria, Australia; Department of Neurology, Royal Children's Hospital, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - H Peters
- Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia; Department of Metabolic Medicine, Royal Children's Hospital, Victoria, Australia
| | - A J Kornberg
- Murdoch Children's Research Institute, Victoria, Australia; Department of Neurology, Royal Children's Hospital, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - C Patel
- Genetic Health Queensland, Royal Brisbane and Women's Children's Hospital, South Brisbane Queensland, Australia; Centre for Children's Health Research, The University of Queensland, Queensland, Australia
| | - V Rodriguez-Casero
- Murdoch Children's Research Institute, Victoria, Australia; Department of Neurology, Royal Children's Hospital, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - M Waak
- Centre for Children's Health Research, The University of Queensland, Queensland, Australia; Department of Neurosciences, Queensland Children's Hospital, Brisbane, Queensland, Australia
| | - J Silberstein
- Princess Margaret Hospital, Perth, Western Australia, Australia
| | - A Sinclair
- Department of Neurosciences, Queensland Children's Hospital, Brisbane, Queensland, Australia
| | - M Nolan
- Department of Paediatric Neurology, Starship Children's Health, Auckland, New Zealand
| | - M Field
- Genetics of Learning Disability (GOLD) Service, Hunter Genetics, Newcastle, New South Wales, Australia
| | - M R Davis
- Department of Diagnostic Genomics, Path West Laboratory Medicine, QEII Medical Centre, Hospital Avenue, Nedlands, WA, Australia
| | - M Fahey
- Department of Paediatrics, Monash University, Victoria, Australia
| | - I E Scheffer
- Murdoch Children's Research Institute, Victoria, Australia; Department of Neurology, Royal Children's Hospital, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia; Department of Medicine, The University of Melbourne, Austin Health, Heidelberg, Victoria, 3084, Australia; The Florey Institute of Neuroscience and Mental Health and Murdoch Children's Research Institute, Parkville, Victoria, 3052, Australia
| | - J L Freeman
- Murdoch Children's Research Institute, Victoria, Australia; Department of Neurology, Royal Children's Hospital, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - N I Wolf
- Amsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, VU University, Amsterdam Neuroscience, Amsterdam, the Netherlands; Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands
| | - R J Taft
- Illumina Inc, San Diego, CA, USA
| | - M S van der Knaap
- Amsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, VU University, Amsterdam Neuroscience, Amsterdam, the Netherlands; Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands
| | - C Simons
- Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia.
| | - R J Leventer
- Murdoch Children's Research Institute, Victoria, Australia; Department of Neurology, Royal Children's Hospital, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia.
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18
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Seaby EG, Smedley D, Taylor Tavares AL, Brittain H, van Jaarsveld RH, Baralle D, Rehm HL, O'Donnell-Luria A, Ennis S. A gene-to-patient approach uplifts novel disease gene discovery and identifies 18 putative novel disease genes. Genet Med 2022; 24:1697-1707. [PMID: 35532742 DOI: 10.1016/j.gim.2022.04.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 04/14/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Exome and genome sequencing have drastically accelerated novel disease gene discoveries. However, discovery is still hindered by myriad variants of uncertain significance found in genes of undetermined biological function. This necessitates intensive functional experiments on genes of equal predicted causality, leading to a major bottleneck. METHODS We apply the loss-of-function observed/expected upper-bound fraction metric of intolerance to gene inactivation to curate a list of predicted haploinsufficient disease genes. Using data from the 100,000 Genomes Project, we adopt a gene-to-patient approach that matches de novo loss-of-function variants in constrained genes to patients with rare disease. Through large-scale aggregation of data, we reduce excess analytical noise currently hindering novel discoveries. RESULTS Results from 13,949 trios revealed 643 rare, de novo predicted loss-of-function events filtered from 1044 loss-of-function observed/expected upper-bound fraction-constrained genes. A total of 168 variants occurred within 126 genes without a known disease-gene relationship. Of these, 27 genes had >1 kindred affected, and for 18 of these genes, multiple kindreds had overlapping phenotypes. Two years after initial analysis, 11 of 18 (61%) of these genes have been independently published as novel disease gene discoveries. CONCLUSION Using large cohorts and adopting gene-based approaches can rapidly and objectively accelerate dominantly inherited novel gene discovery by targeting the most appropriate genes for functional validation.
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Affiliation(s)
- Eleanor G Seaby
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Program in Medical and Population Genetics, Broad institute of MIT and Harvard, Boston, MA; Center for Genomic Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA.
| | - Damian Smedley
- Genomics England, Dawson Hall, Charterhouse Square, London, EC1M 6BQ, United Kingdom
| | | | - Helen Brittain
- Genomics England, Dawson Hall, Charterhouse Square, London, EC1M 6BQ, United Kingdom
| | | | - Diana Baralle
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad institute of MIT and Harvard, Boston, MA; Center for Genomic Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad institute of MIT and Harvard, Boston, MA; Center for Genomic Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
| | - Sarah Ennis
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
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19
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Rodrigues EDS, Griffith S, Martin R, Antonescu C, Posey JE, Coban‐Akdemir Z, Jhangiani SN, Doheny KF, Lupski JR, Valle D, Bamshad MJ, Hamosh A, Sheffer A, Chong JX, Einhorn Y, Cupak M, Sobreira N. Variant-level matching for diagnosis and discovery: Challenges and opportunities. Hum Mutat 2022; 43:782-790. [PMID: 35191117 PMCID: PMC9133151 DOI: 10.1002/humu.24359] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 11/30/2022]
Abstract
Here we describe MyGene2, Geno2MP, VariantMatcher, and Franklin; databases that provide variant-level information and phenotypic features to researchers, clinicians, healthcare providers and patients. Following the footsteps of the Matchmaker Exchange project that connects exome, genome, and phenotype databases at the gene level, these databases have as one goal to facilitate connection to one another using Data Connect, a standard for discovery and search of biomedical data from the Global Alliance for Genomics and Health (GA4GH).
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Affiliation(s)
- Eliete da S. Rodrigues
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Sean Griffith
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Renan Martin
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Corina Antonescu
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Jennifer E. Posey
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Zeynep Coban‐Akdemir
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Shalini N. Jhangiani
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
- Human Genome Sequencing CenterBaylor College of MedicineHoustonTexasUSA
| | - Kimberly F. Doheny
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - James R. Lupski
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
- Human Genome Sequencing CenterBaylor College of MedicineHoustonTexasUSA
- Department of PediatricsBaylor College of MedicineHoustonTexasUSA
- Texas Children's HospitalHoustonTexasUSA
| | - David Valle
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Michael J. Bamshad
- Division of Genetic Medicine, Department of PediatricsUniversity of WashingtonSeattleWashingtonUSA
- Brotman Baty Institute for Precision MedicineSeattleWashingtonUSA
| | - Ada Hamosh
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | | | - Jessica X. Chong
- Division of Genetic Medicine, Department of PediatricsUniversity of WashingtonSeattleWashingtonUSA
- Brotman Baty Institute for Precision MedicineSeattleWashingtonUSA
| | | | | | - Nara Sobreira
- McKusick‐Nathans Department of Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
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20
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Austin-Tse CA, Jobanputra V, Perry DL, Bick D, Taft RJ, Venner E, Gibbs RA, Young T, Barnett S, Belmont JW, Boczek N, Chowdhury S, Ellsworth KA, Guha S, Kulkarni S, Marcou C, Meng L, Murdock DR, Rehman AU, Spiteri E, Thomas-Wilson A, Kearney HM, Rehm HL. Best practices for the interpretation and reporting of clinical whole genome sequencing. NPJ Genom Med 2022; 7:27. [PMID: 35395838 PMCID: PMC8993917 DOI: 10.1038/s41525-022-00295-z] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/17/2022] [Indexed: 01/19/2023] Open
Abstract
Whole genome sequencing (WGS) shows promise as a first-tier diagnostic test for patients with rare genetic disorders. However, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading health care and research organizations in the US and Canada, was formed to expand access to high quality clinical WGS by convening experts and publishing best practices. Here, we present best practice recommendations for the interpretation and reporting of clinical diagnostic WGS, including discussion of challenges and emerging approaches that will be critical to harness the full potential of this comprehensive test.
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Affiliation(s)
- Christina A Austin-Tse
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Vaidehi Jobanputra
- Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | | | - David Bick
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | | | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ted Young
- Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Sarah Barnett
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | - Nicole Boczek
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
- Center for Individualized Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Shimul Chowdhury
- Rady Children's Institute for Genomic Medicine, San Diego, CA, USA
| | | | - Saurav Guha
- Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA
| | - Shashikant Kulkarni
- Baylor Genetics and Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Cherisse Marcou
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Linyan Meng
- Baylor Genetics and Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - David R Murdock
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Atteeq U Rehman
- Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA
| | - Elizabeth Spiteri
- Department of Pathology, Stanford Medicine, Stanford University, Stanford, CA, USA
| | | | - Hutton M Kearney
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Heidi L Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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21
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Baxter SM, Posey JE, Lake NJ, Sobreira N, Chong JX, Buyske S, Blue EE, Chadwick LH, Coban-Akdemir ZH, Doheny KF, Davis CP, Lek M, Wellington C, Jhangiani SN, Gerstein M, Gibbs RA, Lifton RP, MacArthur DG, Matise TC, Lupski JR, Valle D, Bamshad MJ, Hamosh A, Mane S, Nickerson DA, Rehm HL, O'Donnell-Luria A. Centers for Mendelian Genomics: A decade of facilitating gene discovery. Genet Med 2022; 24:784-797. [PMID: 35148959 PMCID: PMC9119004 DOI: 10.1016/j.gim.2021.12.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/08/2021] [Accepted: 12/12/2021] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Mendelian disease genomic research has undergone a massive transformation over the past decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration. METHODS Over the past 10 years, the National Institutes of Health-supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution. RESULTS We highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Beyond generating a list of gene-phenotype relationships and participating in widespread data sharing, the CMGs have created resources, tools, and training for the larger community to foster understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into the Analysis, Visualization, and Informatics Lab-space (AnVIL), sharing candidate genes through the Matchmaker Exchange and the CMG website, and sharing variants in Genotypes to Mendelian Phenotypes (Geno2MP) and VariantMatcher. CONCLUSION The work is far from complete; strengthening communication between research and clinical realms, continued development and sharing of knowledge and tools, and improving access to richly characterized data sets are all required to diagnose the remaining molecularly undiagnosed patients.
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Affiliation(s)
- Samantha M Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA.
| | - Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Nicole J Lake
- Department of Genetics, Yale School of Medicine, New Haven, CT; Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Nara Sobreira
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jessica X Chong
- Department of Pediatrics, Division of Genetic Medicine, University of Washington and Seattle Children's Hospital, Seattle, WA; Brotman Baty Institute for Precision Medicine, Seattle, WA
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ; Department of Genetics, Rutgers University, Piscataway, NJ
| | - Elizabeth E Blue
- Brotman Baty Institute for Precision Medicine, Seattle, WA; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA
| | - Lisa H Chadwick
- Division of Genome Sciences, National Human Genome Research Institute, Bethesda, MD
| | - Zeynep H Coban-Akdemir
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Kimberly F Doheny
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Colleen P Davis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA
| | - Monkol Lek
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Department of Genetics, Yale School of Medicine, New Haven, CT
| | | | | | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Richard P Lifton
- Department of Genetics, Yale School of Medicine, New Haven, CT; Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Tara C Matise
- Department of Genetics, Rutgers University, Piscataway, NJ
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX; Department of Pediatrics, Baylor College of Medicine, Houston, TX
| | - David Valle
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Michael J Bamshad
- Department of Pediatrics, Division of Genetic Medicine, University of Washington and Seattle Children's Hospital, Seattle, WA; Brotman Baty Institute for Precision Medicine, Seattle, WA; Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Shrikant Mane
- Department of Genetics, Yale School of Medicine, New Haven, CT
| | - Deborah A Nickerson
- Brotman Baty Institute for Precision Medicine, Seattle, WA; Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA.
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA; Department of Pediatrics, Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA.
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22
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Schuler BA, Nelson ET, Koziura M, Cogan JD, Hamid R, Phillips JA. Lessons learned: next-generation sequencing applied to undiagnosed genetic diseases. J Clin Invest 2022; 132:e154942. [PMID: 35362483 PMCID: PMC8970663 DOI: 10.1172/jci154942] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Rare genetic disorders, when considered together, are relatively common. Despite advancements in genetics and genomics technologies as well as increased understanding of genomic function and dysfunction, many genetic diseases continue to be difficult to diagnose. The goal of this Review is to increase the familiarity of genetic testing strategies for non-genetics providers. As genetic testing is increasingly used in primary care, many subspecialty clinics, and various inpatient settings, it is important that non-genetics providers have a fundamental understanding of the strengths and weaknesses of various genetic testing strategies as well as develop an ability to interpret genetic testing results. We provide background on commonly used genetic testing approaches, give examples of phenotypes in which the various genetic testing approaches are used, describe types of genetic and genomic variations, cover challenges in variant identification, provide examples in which next-generation sequencing (NGS) failed to uncover the variant responsible for a disease, and discuss opportunities for continued improvement in the application of NGS clinically. As genetic testing becomes increasingly a part of all areas of medicine, familiarity with genetic testing approaches and result interpretation is vital to decrease the burden of undiagnosed disease.
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Affiliation(s)
- Bryce A. Schuler
- Division of Medical Genetics and Genomics and
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Erica T. Nelson
- Division of Medical Genetics and Genomics and
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mary Koziura
- Division of Medical Genetics and Genomics and
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Joy D. Cogan
- Division of Medical Genetics and Genomics and
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Rizwan Hamid
- Division of Medical Genetics and Genomics and
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John A. Phillips
- Division of Medical Genetics and Genomics and
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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23
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Takahashi Y, Date H, Oi H, Adachi T, Imanishi N, Kimura E, Takizawa H, Kosugi S, Matsumoto N, Kosaki K, Matsubara Y, Mizusawa H. Six years' accomplishment of the Initiative on Rare and Undiagnosed Diseases: nationwide project in Japan to discover causes, mechanisms, and cures. J Hum Genet 2022; 67:505-513. [PMID: 35318459 PMCID: PMC9402437 DOI: 10.1038/s10038-022-01025-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/07/2022] [Accepted: 02/07/2022] [Indexed: 11/09/2022]
Abstract
The identification of causative genetic variants for hereditary diseases has revolutionized clinical medicine and an extensive collaborative framework with international cooperation has become a global trend to understand rare disorders. The Initiative on Rare and Undiagnosed Diseases (IRUD) was established in Japan to provide accurate diagnosis, discover causes, and ultimately provide cures for rare and undiagnosed diseases. The fundamental IRUD system consists of three pillars: IRUD diagnostic coordination, analysis centers (IRUD-ACs), and a data center (IRUD-DC). IRUD diagnostic coordination consists of clinical centers (IRUD-CLs) and clinical specialty subgroups (IRUD-CSSs). In addition, the IRUD coordinating center (IRUD-CC) manages the entire IRUD system and temporarily operates the IRUD resource center (IRUD-RC). By the end of March 2021, 6301 pedigrees consisting of 18,136 individuals were registered in the IRUD. The whole-exome sequencing method was completed in 5136 pedigrees, and a final diagnosis was established in 2247 pedigrees (43.8%). The total number of aberrated genes and pathogenic variants was 657 and 1718, among which 1113 (64.8%) were novel. In addition, 39 novel disease entities or phenotypes with 41 aberrated genes were identified. The 6-year endeavor of IRUD has been an overwhelming success, establishing an all-Japan comprehensive diagnostic and research system covering all geographic areas and clinical specialties/subspecialties. IRUD has accurately diagnosed diseases, identified novel aberrated genes or disease entities, discovered many candidate genes, and enriched phenotypic and pathogenic variant databases. Further promotion of the IRUD is essential for determining causes and developing cures for rare and undiagnosed diseases.
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Affiliation(s)
- Yuji Takahashi
- Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hidetoshi Date
- Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hideki Oi
- Department of Clinical Data Science, Clinical Research and Education Promotion Division, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takeya Adachi
- Keio Frontier Research & Education Collaborative Square (K-FRECS) at Tonomachi, Keio University, Kawasaki, Japan.,Department of Medical Regulatory Science, Kyoto Prefectural University of Medicine, Graduate School of Medical Science, Kyoto, Japan.,Japan Agency for Medical Research and Development (AMED), Tokyo, Japan
| | - Noriaki Imanishi
- Japan Agency for Medical Research and Development (AMED), Tokyo, Japan.,Department of Research Promotion and Management, National Cerebral and Cardiovascular Center, Suita, Japan
| | - En Kimura
- Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan.,Japan Agency for Medical Research and Development (AMED), Tokyo, Japan.,Astellas Pharma Incorporated, Tokyo, Japan
| | - Hotake Takizawa
- Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan.,Japan Agency for Medical Research and Development (AMED), Tokyo, Japan
| | - Shinji Kosugi
- Department of Medical Ethics/Medical Genetics, Kyoto University School of Public Health, Kyoto, Japan
| | - Naomichi Matsumoto
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | | | | | - Hidehiro Mizusawa
- Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan.
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24
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Palmer EE. Commentary on: Establishing the phenotypic spectrum of ZTTK syndrome by analysis of 52 individuals with variants in SON. Eur J Hum Genet 2022; 30:258-259. [PMID: 34840333 PMCID: PMC8904575 DOI: 10.1038/s41431-021-01007-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/08/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Elizabeth Emma Palmer
- School of Women's and Children's Health, UNSW, Sydney, NSW, Australia. .,Sydney Childrens' Hospitals Network, Sydney, NSW, Australia.
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25
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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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26
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Rasi C, Nilsson D, Magnusson M, Lesko N, Lagerstedt-Robinson K, Wedell A, Lindstrand A, Wirta V, Stranneheim H. PatientMatcher: a customizable Python-based open-source tool for matching undiagnosed rare disease patients via the Matchmaker Exchange network. Hum Mutat 2022; 43:708-716. [PMID: 35192731 PMCID: PMC9311682 DOI: 10.1002/humu.24358] [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: 09/29/2021] [Revised: 02/14/2022] [Accepted: 02/18/2022] [Indexed: 11/06/2022]
Abstract
The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype–phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the Matchmaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by “matching” the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open‐source Python and MongoDB‐based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical routine in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom‐fit similarity score algorithm and adjustable matching results notifications.
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Affiliation(s)
- Chiara Rasi
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - Daniel Nilsson
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.,Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Måns Magnusson
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, School of engineering sciences in Chemistry, Biotechnology and Health, KTH Royal Institutet of Technology, Stockholm, Sweden
| | - Nicole Lesko
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Kristina Lagerstedt-Robinson
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.,Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Anna Wedell
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.,Science for Life Laboratory, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Anna Lindstrand
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.,Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Valtteri Wirta
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden.,Science for Life Laboratory, School of engineering sciences in Chemistry, Biotechnology and Health, KTH Royal Institutet of Technology, Stockholm, Sweden.,Genomic Medicine Center Karolinska, Karolinska University Hospital, Stockholm, Sweden
| | - Henrik Stranneheim
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden.,Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.,Genomic Medicine Center Karolinska, Karolinska University Hospital, Stockholm, Sweden
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27
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Taylor JP, Malhotra A, Burns NJ, Clause AR, Brown CM, Burns BT, Chandrasekhar A, Schlachetzki Z, Bennett M, Thorpe E, Taft RJ, Perry DL, Coffey AJ. A clinical laboratory's experience using GeneMatcher - building stronger gene-disease relationships. Hum Mutat 2022; 43:765-771. [PMID: 35181961 DOI: 10.1002/humu.24356] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/11/2022] [Accepted: 02/16/2022] [Indexed: 11/08/2022]
Abstract
The use of whole-genome sequencing (WGS) has accelerated the pace of gene discovery and highlighted the need for open and collaborative data sharing in the search for novel disease genes and variants. GeneMatcher (GM) is designed to facilitate connections between researchers, clinicians, health-care providers and others to help in the identification of additional patients with variants in the same candidate disease genes. The Illumina Clinical Services Laboratory offers a WGS test for patients with suspected rare and undiagnosed genetic disease and regularly submits potential candidate genes to GM to strengthen gene-disease relationships. We describe our experience with GM, including criteria for evaluation of candidate genes, and our workflow for the submission and review process. We have made 69 submissions, 36 of which are currently active. Ten per cent of submissions have resulted in publications, with an additional 14 submissions part of ongoing collaborations and expected to result in a publication. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Julie P Taylor
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Alka Malhotra
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Nicole J Burns
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Amanda R Clause
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Carolyn M Brown
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Brendan T Burns
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Anjana Chandrasekhar
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Zina Schlachetzki
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Maren Bennett
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Erin Thorpe
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Ryan J Taft
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Denise L Perry
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Alison J Coffey
- Medical Genomics Research, Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
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28
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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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29
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Lupski JR. Clan genomics: From OMIM phenotypic traits to genes and biology. Am J Med Genet A 2021; 185:3294-3313. [PMID: 34405553 PMCID: PMC8530976 DOI: 10.1002/ajmg.a.62434] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 12/20/2022]
Abstract
Clinical characterization of a patient phenotype has been the quintessential approach for elucidating a differential diagnosis and a hypothesis to explore a potential clinical diagnosis. This has resulted in a language of medicine and a semantic ontology, with both specialty- and subspecialty-specific lexicons, that can be challenging to translate and interpret. There is no 'Rosetta Stone' of clinical medicine such as the genetic code that can assist translation and interpretation of the language of genetics. Nevertheless, the information content embodied within a clinical diagnosis can guide management, therapeutic intervention, and potentially prognostic outlook of disease enabling anticipatory guidance for patients and families. Clinical genomics is now established firmly in medical practice. The granularity and informative content of a personal genome is immense. Yet, we are limited in our utility of much of that personal genome information by the lack of functional characterization of the overwhelming majority of computationally annotated genes in the haploid human reference genome sequence. Whereas DNA and the genetic code have provided a 'Rosetta Stone' to translate genetic variant information, clinical medicine, and clinical genomics provide the context to understand human biology and disease. A path forward will integrate deep phenotyping, such as available in a clinical synopsis in the Online Mendelian Inheritance in Man (OMIM) entries, with personal genome analyses.
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Affiliation(s)
- James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
- Texas Children's Hospital, Houston, Texas, USA
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30
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Seaby EG, Rehm HL, O’Donnell-Luria A. Strategies to Uplift Novel Mendelian Gene Discovery for Improved Clinical Outcomes. Front Genet 2021; 12:674295. [PMID: 34220947 PMCID: PMC8248347 DOI: 10.3389/fgene.2021.674295] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/12/2021] [Indexed: 01/31/2023] Open
Abstract
Rare genetic disorders, while individually rare, are collectively common. They represent some of the most severe disorders affecting patients worldwide with significant morbidity and mortality. Over the last decade, advances in genomic methods have significantly uplifted diagnostic rates for patients and facilitated novel and targeted therapies. However, many patients with rare genetic disorders still remain undiagnosed as the genetic etiology of only a proportion of Mendelian conditions has been discovered to date. This article explores existing strategies to identify novel Mendelian genes and how these discoveries impact clinical care and therapeutics. We discuss the importance of data sharing, phenotype-driven approaches, patient-led approaches, utilization of large-scale genomic sequencing projects, constraint-based methods, integration of multi-omics data, and gene-to-patient methods. We further consider the health economic advantages of novel gene discovery and speculate on potential future methods for improved clinical outcomes.
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Affiliation(s)
- Eleanor G. Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Genomic Informatics Group, University Hospital Southampton, Southampton, United Kingdom
- Center for Genomic Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, United States
| | - Heidi L. Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Center for Genomic Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
| | - Anne O’Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Center for Genomic Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, United States
- Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA, United States
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31
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LeBlanc K, Glanton E, Nagy A, Bater J, Berro T, McGuinness MA, Studwell C, Might M. Rare disease patient matchmaking: development and outcomes of an internet case-finding strategy in the Undiagnosed Diseases Network. Orphanet J Rare Dis 2021; 16:210. [PMID: 33971915 PMCID: PMC8108446 DOI: 10.1186/s13023-021-01825-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/20/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Although clinician, researcher, and patient resources for matchmaking exist, finding similar patients remains an obstacle for rare disease diagnosis. The goals of this study were to develop and test the effectiveness of an Internet case-finding strategy and identify factors associated with increased matching within a rare disease population. METHODS Public web pages were created for consented participants. Matches made, time to each inquiry and match, and outcomes were recorded and analyzed using descriptive statistics. A Poisson regression model was run to identify characteristics associated with matches. RESULTS 385 participants were referred to the project and 158 had pages posted. 579 inquiries were received; 89.0% were from the general public and 24.7% resulted in a match. 81.6% of pages received at least one inquiry and 15.0% had at least one patient match. Primary symptom category of neurology, diagnosis, gene page, and photo were associated with increased matches (p ≤ 0.05). CONCLUSIONS This Internet case-finding strategy was of interest to patients, families, and clinicians, and similar patients were identified using this approach. Extending matchmaking efforts to the general public resulted in matches and suggests including this population in matchmaking activities can improve identification of similar patients.
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Affiliation(s)
- Kimberly LeBlanc
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Emily Glanton
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Anna Nagy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jorick Bater
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tala Berro
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Molly A McGuinness
- Bass Center for Childhood Cancer and Blood Diseases, Stanford Children's Health, Palo Alto, CA, USA
| | - Courtney Studwell
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew Might
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, AL, USA
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32
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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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33
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Good JM, Atallah I, Castro Jimenez M, Benninger D, Kuntzer T, Superti-Furga A, Tran C. NGS-Based Diagnosis of Treatable Neurogenetic Disorders in Adults: Opportunities and Challenges. Genes (Basel) 2021; 12:genes12050695. [PMID: 34066437 PMCID: PMC8148126 DOI: 10.3390/genes12050695] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 12/28/2022] Open
Abstract
The identification of neurological disorders by next-generation sequencing (NGS)-based gene panels has helped clinicians understand the underlying physiopathology, resulting in personalized treatment for some rare diseases. While the phenotype of distinct neurogenetic disorders is generally well-known in childhood, in adulthood, the phenotype can be unspecific and make the standard diagnostic approach more complex. Here we present three unrelated adults with various neurological manifestations who were successfully diagnosed using NGS, allowing for the initiation of potentially life-changing treatments. A 63-year-old woman with progressive cognitive decline, pyramidal signs, and bilateral cataract was treated by chenodeoxycholic acid following the diagnosis of cerebrotendinous xanthomatosis due to a homozygous variant in CYP27A1. A 32-year-old man with adult-onset spastic paraplegia, in whom a variant in ABCD1 confirmed an X-linked adrenoleukodystrophy, was treated with corticoids for adrenal insufficiency. The third patient, a 28-year-old woman with early-onset developmental delay, epilepsy, and movement disorders was treated with a ketogenic diet following the identification of a variant in SLC2A1, confirming a glucose transporter type 1 deficiency syndrome. This case study illustrates the challenges in the timely diagnosis of medically actionable neurogenetic conditions, but also the considerable potential for improving patient health through modern sequencing technologies.
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Affiliation(s)
- Jean-Marc Good
- Division of Genetic Medicine, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland; (J.-M.G.); (I.A.); (A.S.-F.)
| | - Isis Atallah
- Division of Genetic Medicine, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland; (J.-M.G.); (I.A.); (A.S.-F.)
| | - Mayte Castro Jimenez
- Division of Neurology, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland; (M.C.J.); (D.B.); (T.K.)
| | - David Benninger
- Division of Neurology, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland; (M.C.J.); (D.B.); (T.K.)
| | - Thierry Kuntzer
- Division of Neurology, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland; (M.C.J.); (D.B.); (T.K.)
| | - Andrea Superti-Furga
- Division of Genetic Medicine, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland; (J.-M.G.); (I.A.); (A.S.-F.)
| | - Christel Tran
- Division of Genetic Medicine, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland; (J.-M.G.); (I.A.); (A.S.-F.)
- Correspondence: ; Tel.: +41-79-556-5325
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34
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Lupski JR, Liu P, Stankiewicz P, Carvalho CMB, Posey JE. Clinical genomics and contextualizing genome variation in the diagnostic laboratory. Expert Rev Mol Diagn 2020; 20:995-1002. [PMID: 32954863 DOI: 10.1080/14737159.2020.1826312] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION The human genome contains the instructions for the development and biological homeostasis of the human organism and the genetic transmission of traits. Genome variation in human populations is the basis of evolution; individual or personal genomes vary tremendously, making each of us truly unique. AREAS COVERED Assaying this individual variation using genomic technologies has many applications in clinical medicine, from elucidating the biology of disease to designing strategies to ameliorate perturbations from homeostasis. Detecting pathogenic rare variation in a genome may provide a molecular diagnosis that can be informative for patient management and family healthcare. EXPERT OPINION Despite the increasing clinical use of unbiased genomic testing, including chromosome microarray analysis (CMA) with array comparative genomic hybridization (aCGH) or SNP arrays, clinical exome sequencing (cES), and whole-genome sequencing (WGS), to survey genome-wide for molecular aberrations, clinical acumen paired with an understanding of the limitations of each testing type will be needed to achieve molecular diagnoses. Potential opportunities for improving case solved rates, functionally annotating the majority of genes in the human genome, and further understanding genetic contributions to disease will empower clinical genomics and the precision medicine initiative.
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Affiliation(s)
- James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine , Houston, TX, USA.,Department of Pediatrics, Baylor College of Medicine , Houston, TX, USA.,Human Genome Sequencing Center, Baylor College of Medicine , Houston, TX, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine , Houston, TX, USA.,Baylor Genetics, Baylor College of Medicine , Houston, TX, USA
| | - Pawel Stankiewicz
- Department of Molecular and Human Genetics, Baylor College of Medicine , Houston, TX, USA
| | - Claudia M B Carvalho
- Department of Molecular and Human Genetics, Baylor College of Medicine , Houston, TX, USA
| | - Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine , Houston, TX, USA
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