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Ren M, Zheng H, Lu X, Lian W, Feng B. Expanding the genotypic and phenotypic spectrum associated with TBL1XR1 de novo variants. Gene 2023; 886:147777. [PMID: 37683765 DOI: 10.1016/j.gene.2023.147777] [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: 03/20/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND The TBL1XR1 gene encodes the protein transducin-beta-like 1 receptor1, widely distributed in the pituitary, hypothalamus, white and brown adipose tissue, muscle, and liver. Current evidence suggests that heterozygous TBL1XR1 pathogenic variants can lead to a wide spectrum of phenotypes. This study aims to reveal the clinical phenotype and genetic profiles of de novo TBL1XR1 variations and summarize the relevant clinical and genetic features. METHODS We analyzed four new cases harboring de novo TBL1XR1 variants and reviewed all reported cases. RESULTS All probands suffered from global developmental delay. Moreover, patient 1 exhibited susceptibility to startle, patient 2 had hypovitaminosis D, short stature and hyponatremia, and patients 3 and 4 both presented with ASD (Autism spectrum disorder) and short stature. They all had a de novo TBL1XR1 variant (NM_024665.7), c.1184A > G (p.Tyr395Cys), c.1108G > A (p.Asp370Asn), c.1047 + 1G > C, and c.1097C > T (p.Ser366Phe) respectively. In addition, pooled analysis of 51 cases showed that they had speech impairment (38/39), intellectual developmental disorder (28/28), global developmental delay (42/42), and hypotonia (24/27), and some of them had epilepsy (10/22), ASD (13/25), and developmental regression (4/13). CONCLUSIONS We report four new patients with de novo TBL1XR1 variants and provide a comprehensive overview of 47 previously reported individuals with TBL1XR1 variants, enriching the genotypic and phenotypic spectrum of TBL1XR1-related disease. This report further validates the pathogenicity de novo TBL1XR1 variants.
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Affiliation(s)
- Mingyue Ren
- School of Pediatrics, Henan University of Chinese Medicine, Zhengzhou, China
| | - Hong Zheng
- School of Pediatrics, Henan University of Chinese Medicine, Zhengzhou, China; The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
| | - Xiangpeng Lu
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Wenjun Lian
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Bin Feng
- School of Pediatrics, Henan University of Chinese Medicine, Zhengzhou, China; The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
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2
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Chung CCY, Hue SPY, Ng NYT, Doong PHL, Chu ATW, Chung BHY. Meta-analysis of the diagnostic and clinical utility of exome and genome sequencing in pediatric and adult patients with rare diseases across diverse populations. Genet Med 2023; 25:100896. [PMID: 37191093 DOI: 10.1016/j.gim.2023.100896] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 05/07/2023] [Accepted: 05/10/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE This meta-analysis aims to compare the diagnostic and clinical utility of exome sequencing (ES) vs genome sequencing (GS) in pediatric and adult patients with rare diseases across diverse populations. METHODS A meta-analysis was conducted to identify studies from 2011 to 2021. RESULTS One hundred sixty-one studies across 31 countries/regions were eligible, featuring 50,417 probands of diverse populations. Diagnostic rates of ES (0.38, 95% CI 0.36-0.40) and GS (0.34, 95% CI 0.30-0.38) were similar (P = .1). Within-cohort comparison illustrated 1.2-times odds of diagnosis by GS over ES (95% CI 0.79-1.83, P = .38). GS studies discovered a higher range of novel genes than ES studies; yet, the rate of variant of unknown significance did not differ (P = .78). Among high-quality studies, clinical utility of GS (0.77, 95% CI 0.64-0.90) was higher than that of ES (0.44, 95% CI 0.30-0.58) (P < .01). CONCLUSION This meta-analysis provides an important update to demonstrate the similar diagnostic rates between ES and GS and the higher clinical utility of GS over ES. With the newly published recommendations for clinical interpretation of variants found in noncoding regions of the genome and the trend of decreasing variant of unknown significance and GS cost, it is expected that GS will be more widely used in clinical settings.
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Affiliation(s)
| | - Shirley P Y Hue
- Hong Kong Genome Institute, Hong Kong Special Administrative Region
| | - Nicole Y T Ng
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Phoenix H L Doong
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Annie T W Chu
- Hong Kong Genome Institute, Hong Kong Special Administrative Region.
| | - Brian H Y Chung
- Hong Kong Genome Institute, Hong Kong Special Administrative Region; Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region.
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3
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von Hardenberg S, Wallaschek H, Du C, Schmidt G, Auber B. A holistic approach to maximise diagnostic output in trio exome sequencing. Front Pediatr 2023; 11:1183891. [PMID: 37274821 PMCID: PMC10238563 DOI: 10.3389/fped.2023.1183891] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
Introduction Rare genetic diseases are a major cause for severe illness in children. Whole exome sequencing (WES) is a powerful tool for identifying genetic causes of rare diseases. For a better and faster assessment of the vast number of variants that are identified in the index patient in WES, parental sequencing can be applied ("trio WES"). Methods We assessed the diagnostic rate of routine trio WES including analysis of copy number variants in 224 pediatric patients during an evaluation period of three years. Results Trio WES provided a diagnosis in 67 (30%) of all 224 analysed children. The turnaround time of trio WES analysis has been reduced significantly from 41 days in 2019 to 23 days in 2021. Copy number variants could be identified to be causative in 10 cases (4.5%), underlying the importance of copy number variant analysis. Variants in three genes which were previously not associated with a clinical condition (GAD1, TMEM222 and ZNFX1) were identified using the matching tool GeneMatcher and were part of the first description of a new syndrome. Discussion Trio WES has proven to have a high diagnostic yield and to shorten the process of identifying the correct diagnosis in paediatric patients. Re-evaluation of all 224 trio WES 1-3 years after initial analysis did not establish new diagnoses. Initiating (trio) WES as a first-tier diagnostics including copy number variant detection should be considered as early as possible, especially for children treated in ICU, if a monogenetic disease is suspected.
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Affiliation(s)
| | | | | | | | - Bernd Auber
- Correspondence: Sandra von Hardenberg Bernd Auber
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4
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Yauy K, Van Goethem C, Pégeot H, Baux D, Guignard T, Thèze C, Ardouin O, Roux AF, Koenig M, Bergougnoux A, Cossée M. Evaluating the Transition from Targeted to Exome Sequencing: A Guide for Clinical Laboratories. Int J Mol Sci 2023; 24:ijms24087330. [PMID: 37108493 PMCID: PMC10138641 DOI: 10.3390/ijms24087330] [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: 03/15/2023] [Revised: 04/03/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
The transition from targeted to exome or genome sequencing in clinical contexts requires quality standards, such as targeted sequencing, in order to be fully adopted. However, no clear recommendations or methodology have emerged for evaluating this technological evolution. We developed a structured method based on four run-specific sequencing metrics and seven sample-specific sequencing metrics for evaluating the performance of exome sequencing strategies to replace targeted strategies. The indicators include quality metrics and coverage performance on gene panels and OMIM morbid genes. We applied this general strategy to three different exome kits and compared them with a myopathy-targeted sequencing method. After having achieved 80 million reads, all-tested exome kits generated data suitable for clinical diagnosis. However, significant differences in the coverage and PCR duplicates were observed between the kits. These are two main criteria to consider for the initial implementation with high-quality assurance. This study aims to assist molecular diagnostic laboratories in adopting and evaluating exome sequencing kits in a diagnostic context compared to the strategy used previously. A similar strategy could be used to implement whole-genome sequencing for diagnostic purposes.
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Affiliation(s)
- Kevin Yauy
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
- Service de Génétique Médicale, CHU Montpellier, 371 Avenue du Doyen G. Giraud, 34090 Montpellier, France
| | - Charles Van Goethem
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
| | - Henri Pégeot
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
| | - David Baux
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
- INM, Université de Montpellier, INSERM, Hôpital Saint Eloi-Bâtiment INM 80, rue Augustin Fliche-BP 74103, 34090 Montpellier, France
| | - Thomas Guignard
- Unité de Génétique Chromosomique, Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Hôpital Arnaud de Villeneuve, CHU de Montpellier, 371 Av. du Doyen Gaston Giraud, 34090 Montpellier, France
| | - Corinne Thèze
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
| | - Olivier Ardouin
- Plateau de Médecine Moléculaire et Génomique, Hôpital Arnaud de Villeneuve, CHU de Montpellier, 34090 Montpellier, France
| | - Anne-Françoise Roux
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
- INM, Université de Montpellier, INSERM, Hôpital Saint Eloi-Bâtiment INM 80, rue Augustin Fliche-BP 74103, 34090 Montpellier, France
| | - Michel Koenig
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
- PhyMedExp-Physiologie et Médecine Expérimentale du Cœur et des Muscles, Université de Montpellier, Inserm U1046, CNRS UMR 9214, 371 Avenue du Doyen G. Giraud, 34090 Montpellier, France
| | - Anne Bergougnoux
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
- PhyMedExp-Physiologie et Médecine Expérimentale du Cœur et des Muscles, Université de Montpellier, Inserm U1046, CNRS UMR 9214, 371 Avenue du Doyen G. Giraud, 34090 Montpellier, France
| | - Mireille Cossée
- Laboratoire de Génétique Moléculaire, LGM, Centre Hospitalier Universitaire de Montpellier, IURC-Institut Universitaire de Recherche Clinique, 641 Avenue du Doyen G. Giraud, 34090 Montpellier, France
- PhyMedExp-Physiologie et Médecine Expérimentale du Cœur et des Muscles, Université de Montpellier, Inserm U1046, CNRS UMR 9214, 371 Avenue du Doyen G. Giraud, 34090 Montpellier, France
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5
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Yang Z, Yang X, Sun Y, Wang Y, Song L, Qiao Z, Fang Z, Wang Z, Liu L, Chen Y, Yan S, Guo X, Zhang J, Fan C, Liu F, Peng Z, Peng H, Sun J, Chen W. Test development, optimization and validation of a WGS pipeline for genetic disorders. BMC Med Genomics 2023; 16:74. [PMID: 37020281 PMCID: PMC10077614 DOI: 10.1186/s12920-023-01495-x] [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: 12/08/2022] [Accepted: 03/22/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND With advances in massive parallel sequencing (MPS) technology, whole-genome sequencing (WGS) has gradually evolved into the first-tier diagnostic test for genetic disorders. However, deployment practice and pipeline testing for clinical WGS are lacking. METHODS In this study, we introduced a whole WGS pipeline for genetic disorders, which included the entire process from obtaining a sample to clinical reporting. All samples that underwent WGS were constructed using polymerase chain reaction (PCR)-free library preparation protocols and sequenced on the MGISEQ-2000 platform. Bioinformatics pipelines were developed for the simultaneous detection of various types of variants, including single nucleotide variants (SNVs), insertions and deletions (indels), copy number variants (CNVs) and balanced rearrangements, mitochondrial (MT) variants, and other complex variants such as repeat expansion, pseudogenes and absence of heterozygosity (AOH). A semiautomatic pipeline was developed for the interpretation of potential SNVs and CNVs. Forty-five samples (including 14 positive commercially available samples, 23 laboratory-held positive cell lines and 8 clinical cases) with known variants were used to validate the whole pipeline. RESULTS In this study, a whole WGS pipeline for genetic disorders was developed and optimized. Forty-five samples with known variants (6 with SNVs and Indels, 3 with MT variants, 5 with aneuploidies, 1 with triploidy, 23 with CNVs, 5 with balanced rearrangements, 2 with repeat expansions, 1 with AOHs, and 1 with exon 7-8 deletion of SMN1 gene) validated the effectiveness of our pipeline. CONCLUSIONS This study has been piloted in test development, optimization, and validation of the WGS pipeline for genetic disorders. A set of best practices were recommended using our pipeline, along with a dataset of positive samples for benchmarking.
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Affiliation(s)
- Ziying Yang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Xu Yang
- Department of Paediatrics, Pu'er People's Hospital, Pu'er, 665000, China
| | - Yan Sun
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yaoshen Wang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Lijie Song
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- DTU Bioengineering, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Zhihong Qiao
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Zhonghai Fang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Zhonghua Wang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Lipei Liu
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Yunmei Chen
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Saiying Yan
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Xueqin Guo
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Junqing Zhang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Chunna Fan
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Fengxia Liu
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Zhiyu Peng
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Huanhuan Peng
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen, 518083, China
| | - Jun Sun
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.
- BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.
| | - Wei Chen
- Pu'er People's Hospital, Pu'er, 665000, China.
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6
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Perrier S, Guerrero K, Tran LT, Michell-Robinson MA, Legault G, Brais B, Sylvain M, Dorman J, Demos M, Köhler W, Pastinen T, Thiffault I, Bernard G. Solving inherited white matter disorder etiologies in the neurology clinic: Challenges and lessons learned using next-generation sequencing. Front Neurol 2023; 14:1148377. [PMID: 37077564 PMCID: PMC10108901 DOI: 10.3389/fneur.2023.1148377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 02/23/2023] [Indexed: 04/05/2023] Open
Abstract
IntroductionRare neurodevelopmental disorders, including inherited white matter disorders or leukodystrophies, often present a diagnostic challenge on a genetic level given the large number of causal genes associated with a range of disease subtypes. This study aims to demonstrate the challenges and lessons learned in the genetic investigations of leukodystrophies through presentation of a series of cases solved using exome or genome sequencing.MethodsEach of the six patients had a leukodystrophy associated with hypomyelination or delayed myelination on MRI, and inconclusive clinical diagnostic genetic testing results. We performed next generation sequencing (case-based exome or genome sequencing) to further investigate the genetic cause of disease.ResultsFollowing different lines of investigation, molecular diagnoses were obtained for each case, with patients harboring pathogenic variants in a range of genes including TMEM106B, GJA1, AGA, POLR3A, and TUBB4A. We describe the lessons learned in reaching the genetic diagnosis, including the importance of (a) utilizing proper multi-gene panels in clinical testing, (b) assessing the reliability of biochemical assays in supporting diagnoses, and (c) understanding the limitations of exome sequencing methods in regard to CNV detection and region coverage in GC-rich areas.DiscussionThis study illustrates the importance of applying a collaborative diagnostic approach by combining detailed phenotyping data and metabolic results from the clinical environment with advanced next generation sequencing analysis techniques from the research environment to increase the diagnostic yield in patients with genetically unresolved leukodystrophies.
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Affiliation(s)
- Stefanie Perrier
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Kether Guerrero
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Luan T. Tran
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Mackenzie A. Michell-Robinson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Geneviève Legault
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Pediatrics, McGill University, Montreal, QC, Canada
| | - Bernard Brais
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Michel Sylvain
- Division of Pediatric Neurology, Centre Mère-Enfant Soleil du CHU de Québec - Université Laval, Québec City, QC, Canada
| | - James Dorman
- John H. Stroger Jr. Hospital of Cook County, Chicago, IL, United States
- Department of Neurological Sciences, Rush Medical College, Chicago, IL, United States
| | - Michelle Demos
- Division of Neurology, Department of Pediatrics, University of British Columbia, BC Children's Hospital, Vancouver, BC, Canada
| | - Wolfgang Köhler
- Leukodystrophy Center, University of Leipzig Medical Center, Leipzig, Germany
| | - Tomi Pastinen
- Genomic Medicine Center, Children's Mercy Hospital, Kansas City, MO, United States
- University of Missouri Kansas City School of Medicine, Kansas City, MO, United States
| | - Isabelle Thiffault
- Genomic Medicine Center, Children's Mercy Hospital, Kansas City, MO, United States
- University of Missouri Kansas City School of Medicine, Kansas City, MO, United States
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO, United States
- Isabelle Thiffault
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Pediatrics, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Center, Montreal, QC, Canada
- *Correspondence: Geneviève Bernard
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7
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Cohen ASA, Farrow EG, Abdelmoity AT, Alaimo JT, Amudhavalli SM, Anderson JT, Bansal L, Bartik L, Baybayan P, Belden B, Berrios CD, Biswell RL, Buczkowicz P, Buske O, Chakraborty S, Cheung WA, Coffman KA, Cooper AM, Cross LA, Curran T, Dang TTT, Elfrink MM, Engleman KL, Fecske ED, Fieser C, Fitzgerald K, Fleming EA, Gadea RN, Gannon JL, Gelineau-Morel RN, Gibson M, Goldstein J, Grundberg E, Halpin K, Harvey BS, Heese BA, Hein W, Herd SM, Hughes SS, Ilyas M, Jacobson J, Jenkins JL, Jiang S, Johnston JJ, Keeler K, Korlach J, Kussmann J, Lambert C, Lawson C, Le Pichon JB, Leeder JS, Little VC, Louiselle DA, Lypka M, McDonald BD, Miller N, Modrcin A, Nair A, Neal SH, Oermann CM, Pacicca DM, Pawar K, Posey NL, Price N, Puckett LMB, Quezada JF, Raje N, Rowell WJ, Rush ET, Sampath V, Saunders CJ, Schwager C, Schwend RM, Shaffer E, Smail C, Soden S, Strenk ME, Sullivan BR, Sweeney BR, Tam-Williams JB, Walter AM, Welsh H, Wenger AM, Willig LK, Yan Y, Younger ST, Zhou D, Zion TN, Thiffault I, Pastinen T. Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes. Genet Med 2022; 24:1336-1348. [PMID: 35305867 DOI: 10.1016/j.gim.2022.02.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/05/2022] [Accepted: 02/07/2022] [Indexed: 12/17/2022] Open
Abstract
PURPOSE This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program. METHODS Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes. RESULTS Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases). CONCLUSION Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.
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Affiliation(s)
- Ana S A Cohen
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Emily G Farrow
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Joseph T Alaimo
- Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Shivarajan M Amudhavalli
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - John T Anderson
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Lalit Bansal
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Lauren Bartik
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Bradley Belden
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Rebecca L Biswell
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | | | | | - Warren A Cheung
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Keith A Coffman
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Ashley M Cooper
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Laura A Cross
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Tom Curran
- Children's Mercy Research Institute, Kansas City, MO
| | - Thuy Tien T Dang
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Mary M Elfrink
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Erin D Fecske
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Cynthia Fieser
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Keely Fitzgerald
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Emily A Fleming
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Randi N Gadea
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Rose N Gelineau-Morel
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Margaret Gibson
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Jeffrey Goldstein
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Elin Grundberg
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Kelsee Halpin
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Brian S Harvey
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Bryce A Heese
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Wendy Hein
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Suzanne M Herd
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Susan S Hughes
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Mohammed Ilyas
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Jill Jacobson
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Janda L Jenkins
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | - Kathryn Keeler
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Jonas Korlach
- Pacific Biosciences of California, Inc, Menlo Park, CA
| | | | | | - Caitlin Lawson
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | - Vicki C Little
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | | | - Neil Miller
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Allergy Immunology Pulmonary and Sleep Medicine, Children's Mercy Kansas City, Kansas City, MO
| | - Ann Modrcin
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Annapoorna Nair
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Shelby H Neal
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Donna M Pacicca
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Kailash Pawar
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Nyshele L Posey
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Nigel Price
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Laura M B Puckett
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Julio F Quezada
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Nikita Raje
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Neonatology, Children's Mercy Kansas City, Kansas City, MO
| | | | - Eric T Rush
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO; Department of Internal Medicine, University of Kansas School of Medicine, Kansas City, MO
| | - Venkatesh Sampath
- Division of Neonatology, Children's Mercy Hospital Kansas City, Kansas City, MO
| | - Carol J Saunders
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Caitlin Schwager
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Richard M Schwend
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Elizabeth Shaffer
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Craig Smail
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Sarah Soden
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Meghan E Strenk
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Brooke R Sweeney
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Adam M Walter
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Holly Welsh
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Laurel K Willig
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Yun Yan
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Scott T Younger
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Dihong Zhou
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Tricia N Zion
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Isabelle Thiffault
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO.
| | - Tomi Pastinen
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Children's Mercy Research Institute, Kansas City, MO.
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8
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Kingsmore SF. 2022: a pivotal year for diagnosis and treatment of rare genetic diseases. Cold Spring Harb Mol Case Stud 2022; 8:mcs.a006204. [PMID: 35217563 PMCID: PMC8958907 DOI: 10.1101/mcs.a006204] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The start of 2022 is an inflection point in the development of diagnostics and treatments for rare genetic diseases in prenatal, pediatric, and adult individuals-the theme of this special issue. Here I briefly review recent developments in two pivotal aspects of genetic disease diagnostics and treatments: education and equitable implementation.
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9
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De Novo ACTG1 Variant Expands the Phenotype and Genotype of Partial Deafness and Baraitser-Winter Syndrome. Int J Mol Sci 2022; 23:ijms23020692. [PMID: 35054877 PMCID: PMC8776155 DOI: 10.3390/ijms23020692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/02/2022] [Accepted: 01/05/2022] [Indexed: 02/04/2023] Open
Abstract
Actin molecules are fundamental for embryonic structural and functional differentiation; γ-actin is specifically required for the maintenance and function of cytoskeletal structures in the ear, resulting in hearing. Baraitser–Winter Syndrome (B-WS, OMIM #243310, #614583) is a rare, multiple-anomaly genetic disorder caused by mutations in either cytoplasmically expressed actin gene, ACTB (β-actin) or ACTG1 (γ-actin). The resulting actinopathies cause characteristic cerebrofrontofacial and developmental traits, including progressive sensorineural deafness. Both ACTG1-related non-syndromic A20/A26 deafness and B-WS diagnoses are characterized by hypervariable penetrance in phenotype. Here, we identify a 28th patient worldwide carrying a mutated γ-actin ACTG1 allele, with mildly manifested cerebrofrontofacial B-WS traits, hypervariable penetrance of developmental traits and sensorineural hearing loss. This patient also displays brachycephaly and a complete absence of speech faculty, previously unreported for ACTG1-related B-WS or DFNA20/26 deafness, representing phenotypic expansion. The patient’s exome sequence analyses (ES) confirms a de novo ACTG1 variant previously unlinked to the pathology. Additional microarray analysis uncover no further mutational basis for dual molecular diagnosis in our patient. We conclude that γ-actin c.542C > T, p.Ala181Val is a dominant pathogenic variant, associated with mildly manifested facial and cerebral traits typical of B-WS, hypervariable penetrance of developmental traits and sensorineural deafness. We further posit and present argument and evidence suggesting ACTG1-related non-syndromic DFNA20/A26 deafness is a manifestation of undiagnosed ACTG1-related B-WS.
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10
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DFNA20/26 and Other ACTG1-Associated Phenotypes: A Case Report and Review of the Literature. Audiol Res 2021; 11:582-593. [PMID: 34698053 PMCID: PMC8544197 DOI: 10.3390/audiolres11040052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/10/2021] [Accepted: 10/13/2021] [Indexed: 02/08/2023] Open
Abstract
Since the early 2000s, an ever-increasing subset of missense pathogenic variants in the ACTG1 gene has been associated with an autosomal-dominant, progressive, typically post-lingual non-syndromic hearing loss (NSHL) condition designed as DFNA20/26. ACTG1 gene encodes gamma actin, the predominant actin protein in the cytoskeleton of auditory hair cells; its normal expression and function are essential for the stereocilia maintenance. Different gain-of-function pathogenic variants of ACTG1 have been associated with two major phenotypes: DFNA20/26 and Baraitser-Winter syndrome, a multiple congenital anomaly disorder. Here, we report a novel ACTG1 variant [c.625G>A (p. Val209Met)] in an adult patient with moderate-severe NSHL characterized by a downsloping audiogram. The patient, who had a clinical history of slowly progressive NSHL and tinnitus, was referred to our laboratory for the analysis of a large panel of NSHL-associated genes by next generation sequencing. An extensive review of previously reported ACTG1 variants and their associated phenotypes was also performed.
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11
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Sharma S, Repnikova E, Noel-MacDonnell JR, LePichon JB. Diagnostic yield of genetic testing in 324 infants with hypotonia. Clin Genet 2021; 100:752-757. [PMID: 34480364 PMCID: PMC9291145 DOI: 10.1111/cge.14057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/12/2021] [Accepted: 08/31/2021] [Indexed: 11/05/2022]
Abstract
This retrospective cohort study was designed to determine the yield of genetic tests in hypotonic infants and develop a diagnostic algorithm. Out of 496 patients identified by International Classification of Diseases (ICD) 9/10 coding, 324 patients met the inclusion criteria. Diagnostic yields were 32% for karyotype, 19% for microarray, 30% for targeted genetic tests, 38% for gene panels, and 31% for exome sequencing. In addition, we considered the diagnostic contribution of ancillary tests, including neuroimaging, metabolic tests, and so forth. The combination of microarray and exome sequencing gave the highest diagnostic yield. None of the other tests added significant value in arriving at a diagnosis. Based on these results we propose that the vast majority of infants with congenital hypotonia should start with a microarray and proceed with exome sequencing, with the notable exception of infants with clearly syndromic features in whom karyotyping or targeted testing may be more appropriate.
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Affiliation(s)
- Sonal Sharma
- Division of Neurology, Children's Mercy Hospital, Kansas City, Missouri, USA.,Mitochondrial Medicine Frontier Program, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia 19104, Pennsylvania, USA
| | - Elena Repnikova
- Department of Pathology and Laboratory Medicine, Cytogenetics and Molecular Genetics Laboratories, Children's Mercy Hospital, Kansas City, Missouri, USA.,UMKC School of Medicine, Kansas City, Missouri, USA
| | - Janelle R Noel-MacDonnell
- UMKC School of Medicine, Kansas City, Missouri, USA.,Department of Health Services and Outcomes Research, Children's Mercy Hospital, Kansas City, Missouri, USA
| | - Jean-Baptiste LePichon
- Division of Neurology, Children's Mercy Hospital, Kansas City, Missouri, USA.,UMKC School of Medicine, Kansas City, Missouri, USA
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12
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Brockman DG, Austin-Tse CA, Pelletier RC, Harley C, Patterson C, Head H, Leonard CE, O'Brien K, Mahanta LM, Lebo MS, Lu CY, Natarajan P, Khera AV, Aragam KG, Kathiresan S, Rehm HL, Udler MS. Randomized prospective evaluation of genome sequencing versus standard-of-care as a first molecular diagnostic test. Genet Med 2021; 23:1689-1696. [PMID: 33976420 PMCID: PMC8488861 DOI: 10.1038/s41436-021-01193-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To evaluate the diagnostic yield and clinical relevance of clinical genome sequencing (cGS) as a first genetic test for patients with suspected monogenic disorders. METHODS We conducted a prospective randomized study with pediatric and adult patients recruited from genetics clinics at Massachusetts General Hospital who were undergoing planned genetic testing. Participants were randomized into two groups: standard-of-care genetic testing (SOC) only or SOC and cGS. RESULTS Two hundred four participants were enrolled, 202 were randomized to one of the intervention arms, and 99 received cGS. In total, cGS returned 16 molecular diagnoses that fully or partially explained the indication for testing in 16 individuals (16.2% of the cohort, 95% confidence interval [CI] 8.9-23.4%), which was not significantly different from SOC (18.2%, 95% CI 10.6-25.8%, P = 0.71). An additional eight molecular diagnoses reported by cGS had uncertain relevance to the participant's phenotype. Nevertheless, referring providers considered 20/24 total cGS molecular diagnoses (83%) to be explanatory for clinical features or worthy of additional workup. CONCLUSION cGS is technically suitable as a first genetic test. In our cohort, diagnostic yield was not significantly different from SOC. Further studies addressing other variant types and implementation challenges are needed to support feasibility and utility of broad-scale cGS adoption.
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Affiliation(s)
- Deanna G Brockman
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
| | - Christina A Austin-Tse
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Laboratory for Molecular Medicine, Partners Personalized Medicine, Cambridge, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Renée C Pelletier
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Caroline Harley
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Candace Patterson
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Holly Head
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Courtney Elizabeth Leonard
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Kimberly O'Brien
- Laboratory for Molecular Medicine, Partners Personalized Medicine, Cambridge, MA, USA
| | - Lisa M Mahanta
- Laboratory for Molecular Medicine, Partners Personalized Medicine, Cambridge, MA, USA
| | - Matthew S Lebo
- Laboratory for Molecular Medicine, Partners Personalized Medicine, Cambridge, MA, USA
| | - Christine Y Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Pradeep Natarajan
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Krishna G Aragam
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Sekar Kathiresan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Heidi L Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Miriam S Udler
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
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13
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Exome and genome sequencing for pediatric patients with congenital anomalies or intellectual disability: an evidence-based clinical guideline of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021; 23:2029-2037. [PMID: 34211152 DOI: 10.1038/s41436-021-01242-6] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 05/26/2021] [Accepted: 05/26/2021] [Indexed: 01/22/2023] Open
Abstract
PURPOSE To develop an evidence-based clinical practice guideline for the use of exome and genome sequencing (ES/GS) in the care of pediatric patients with one or more congenital anomalies (CA) with onset prior to age 1 year or developmental delay (DD) or intellectual disability (ID) with onset prior to age 18 years. METHODS The Pediatric Exome/Genome Sequencing Evidence-Based Guideline Work Group (n = 10) used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) evidence to decision (EtD) framework based on the recent American College of Medical Genetics and Genomics (ACMG) systematic review, and an Ontario Health Technology Assessment to develop and present evidence summaries and health-care recommendations. The document underwent extensive internal and external peer review, and public comment, before approval by the ACMG Board of Directors. RESULTS The literature supports the clinical utility and desirable effects of ES/GS on active and long-term clinical management of patients with CA/DD/ID, and on family-focused and reproductive outcomes with relatively few harms. Compared with standard genetic testing, ES/GS has a higher diagnostic yield and may be more cost-effective when ordered early in the diagnostic evaluation. CONCLUSION We strongly recommend that ES/GS be considered as a first- or second-tier test for patients with CA/DD/ID.
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14
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Harvey S, King MD, Gorman KM. Paroxysmal Movement Disorders. Front Neurol 2021; 12:659064. [PMID: 34177764 PMCID: PMC8232056 DOI: 10.3389/fneur.2021.659064] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/20/2021] [Indexed: 11/13/2022] Open
Abstract
Paroxysmal movement disorders (PxMDs) are a clinical and genetically heterogeneous group of movement disorders characterized by episodic involuntary movements (dystonia, dyskinesia, chorea and/or ataxia). Historically, PxMDs were classified clinically (triggers and characteristics of the movements) and this directed single-gene testing. With the advent of next-generation sequencing (NGS), how we classify and investigate PxMDs has been transformed. Next-generation sequencing has enabled new gene discovery (RHOBTB2, TBC1D24), expansion of phenotypes in known PxMDs genes and a better understanding of disease mechanisms. However, PxMDs exhibit phenotypic pleiotropy and genetic heterogeneity, making it challenging to predict genotype based on the clinical phenotype. For example, paroxysmal kinesigenic dyskinesia is most commonly associated with variants in PRRT2 but also variants identified in PNKD, SCN8A, and SCL2A1. There are no radiological or biochemical biomarkers to differentiate genetic causes. Even with NGS, diagnosis rates are variable, ranging from 11 to 51% depending on the cohort studied and technology employed. Thus, a large proportion of patients remain undiagnosed compared to other neurological disorders such as epilepsy, highlighting the need for further genomic research in PxMDs. Whole-genome sequencing, deep-sequencing, copy number variant analysis, detection of deep-intronic variants, mosaicism and repeat expansions, will improve diagnostic rates. Identifying the underlying genetic cause has a significant impact on patient care, modification of treatment, long-term prognostication and genetic counseling. This paper provides an update on the genetics of PxMDs, description of PxMDs classified according to causative gene rather than clinical phenotype, highlighting key clinical features and providing an algorithm for genetic testing of PxMDs.
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Affiliation(s)
- Susan Harvey
- Department of Paediatric Neurology and Clinical Neurophysiology, Children's Health Ireland at Temple Street, Dublin, Ireland
| | - Mary D King
- Department of Paediatric Neurology and Clinical Neurophysiology, Children's Health Ireland at Temple Street, Dublin, Ireland.,School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
| | - Kathleen M Gorman
- Department of Paediatric Neurology and Clinical Neurophysiology, Children's Health Ireland at Temple Street, Dublin, Ireland.,School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
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15
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Sun Y, Liu F, Fan C, Wang Y, Song L, Fang Z, Han R, Wang Z, Wang X, Yang Z, Xu Z, Peng J, Shi C, Zhang H, Dong W, Huang H, Li Y, Le Y, Sun J, Peng Z. Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders. BMC Med Genomics 2021; 14:102. [PMID: 33849535 PMCID: PMC8045368 DOI: 10.1186/s12920-021-00948-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 03/31/2021] [Indexed: 12/30/2022] Open
Abstract
Background Due to its reduced cost and incomparable advantages, WGS is likely to lead to changes in clinical diagnosis of rare and undiagnosed diseases. However, the sensitivity and breadth of coverage of clinical WGS as a diagnostic test for genetic disorders has not been fully evaluated. Methods Here, the performance of WGS in NA12878, the YH cell line, and the Chinese trios were measured by assessing their sensitivity, PPV, depth and breadth of coverage using MGISEQ-2000. We also compared the performance of WES and WGS using NA12878. The sensitivity and PPV were tested using the family-based trio design for the Chinese trios. We further developed a systematic WGS pipeline for the analysis of 8 clinical cases. Results In general, the sensitivity and PPV for SNV/indel detection increased with mean depth and reached a plateau at an ~ 40X mean depth using down-sampling samples of NA12878. With a mean depth of 40X, the sensitivity of homozygous and heterozygous SNPs of NA12878 was > 99.25% and > 99.50%, respectively, and the PPV was 99.97% and 98.96%. Homozygous and heterozygous indels showed lower sensitivity and PPV. The sensitivity and PPV were still not 100% even with a mean depth of ~ 150X. We also observed a substantial variation in the sensitivity of CNV detection across different tools, especially in CNVs with a size less than 1 kb. In general, the breadth of coverage for disease-associated genes and CNVs increased with mean depth. The sensitivity and coverage of WGS (~ 40X) was better than WES (~ 120X). Among the Chinese trios with an ~ 40X mean depth, the sensitivity among offspring was > 99.48% and > 96.36% for SNP and indel detection, and the PPVs were 99.86% and 97.93%. All 12 previously validated variants in the 8 clinical cases were successfully detected using our WGS pipeline. Conclusions The current standard of a mean depth of 40X may be sufficient for SNV/indel detection and identification of most CNVs. It would be advisable for clinical scientists to determine the range of sensitivity and PPV for different classes of variants for a particular WGS pipeline, which would be useful when interpreting and delivering clinical reports. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-021-00948-5.
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Affiliation(s)
- Yan Sun
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Fengxia Liu
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Chunna Fan
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Yaoshen Wang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Lijie Song
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Zhonghai Fang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Rui Han
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Zhonghua Wang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Xiaodan Wang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Ziying Yang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Zhenpeng Xu
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Jiguang Peng
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Chaonan Shi
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | | | - Wei Dong
- BGI-Beijing Clinical Laboratories, BGI-Shenzhen, Beijing, 101300, China
| | - Hui Huang
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yun Li
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yanqun Le
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Jun Sun
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China. .,Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China.
| | - Zhiyu Peng
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
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16
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Focused Revision: ACMG practice resource: Genetic evaluation of short stature. Genet Med 2021; 23:813-815. [PMID: 33514815 DOI: 10.1038/s41436-020-01046-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 11/12/2020] [Accepted: 11/16/2020] [Indexed: 01/31/2023] Open
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17
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Chen J, Madireddi S, Nagarkar D, Migdal M, Vander Heiden J, Chang D, Mukhyala K, Selvaraj S, Kadel EE, Brauer MJ, Mariathasan S, Hunkapiller J, Jhunjhunwala S, Albert ML, Hammer C. In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales. Brief Bioinform 2020; 22:5906908. [PMID: 32940337 PMCID: PMC8138874 DOI: 10.1093/bib/bbaa223] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/30/2020] [Accepted: 08/19/2020] [Indexed: 12/13/2022] Open
Abstract
Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of natural killer (NK) and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for four-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.
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Affiliation(s)
- Jieming Chen
- Department of Bioinformatics and Computational Biology
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18
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Hakonen AH, Lehtonen J, Kivirikko S, Keski-Filppula R, Moilanen J, Kivisaari R, Almusa H, Jakkula E, Saarela J, Avela K, Aittomäki K. Recessive MYH3 variants cause "Contractures, pterygia, and variable skeletal fusions syndrome 1B" mimicking Escobar variant multiple pterygium syndrome. Am J Med Genet A 2020; 182:2605-2610. [PMID: 32902138 DOI: 10.1002/ajmg.a.61836] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 07/06/2020] [Accepted: 08/01/2020] [Indexed: 11/09/2022]
Abstract
The multiple pterygium syndromes (MPS) are rare disorders with disease severity ranging from lethal to milder forms. The nonlethal Escobar variant MPS (EVMPS) is characterized by multiple pterygia and arthrogryposis, as well as various additional features including congenital anomalies. The genetic etiology of EVMPS is heterogeneous and the diagnosis has been based either on the detection of pathogenic CHRNG variants (~23% of patients), or suggestive clinical features. We describe four patients with a clinical suspicion of EVMPS who manifested with multiple pterygia, mild flexion contractures of several joints, and vertebral anomalies. We revealed recessively inherited MYH3 variants as the underlying cause in all patients: two novel variants, c.1053C>G, p.(Tyr351Ter) and c.3102+5G>C, as compound heterozygous with the hypomorphic MYH3 variant c.-9+1G>A. Recessive MYH3 variants have been previously associated with spondylocarpotarsal synostosis syndrome. Our findings now highlight multiple pterygia as an important feature in patients with recessive MYH3 variants. Based on all patients with recessive MYH3 variants reported up to date, we consider that this disease entity should be designated as "Contractures, pterygia, and variable skeletal fusions syndrome 1B," as recently suggested by OMIM. Our findings underline the importance of analyzing MYH3 in the differential diagnosis of EVMPS, particularly as the hypomorphic MYH3 variant might remain undetected by routine exome sequencing.
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Affiliation(s)
- Anna H Hakonen
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Johanna Lehtonen
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.,Folkhälsan Research Center, Helsinki, Finland
| | - Sirpa Kivirikko
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Riikka Keski-Filppula
- Department of Clinical Genetics, Oulu University Hospital, Medical Research Center Oulu and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Jukka Moilanen
- Department of Clinical Genetics, Oulu University Hospital, Medical Research Center Oulu and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Reetta Kivisaari
- HUS Medical Imaging Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Henrikki Almusa
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Eveliina Jakkula
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Janna Saarela
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.,Centre for Molecular Medicine Norway (NCMM), University of Oslo, Oslo, Norway.,HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kristiina Avela
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kristiina Aittomäki
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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19
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Li S, van der Velde KJ, de Ridder D, van Dijk ADJ, Soudis D, Zwerwer LR, Deelen P, Hendriksen D, Charbon B, van Gijn ME, Abbott K, Sikkema-Raddatz B, van Diemen CC, Kerstjens-Frederikse WS, Sinke RJ, Swertz MA. CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations. Genome Med 2020; 12:75. [PMID: 32831124 PMCID: PMC7446154 DOI: 10.1186/s13073-020-00775-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 08/11/2020] [Indexed: 12/20/2022] Open
Abstract
Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .
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Affiliation(s)
- Shuang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - K Joeri van der Velde
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Aalt D J van Dijk
- Bioinformatics Group, Wageningen University & Research, Wageningen, the Netherlands
- Biometris, Wageningen University & Research, Wageningen, the Netherlands
| | - Dimitrios Soudis
- Donald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands
| | - Leslie R Zwerwer
- Donald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands
| | - Patrick Deelen
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dennis Hendriksen
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bart Charbon
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marielle E van Gijn
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Kristin Abbott
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Birgit Sikkema-Raddatz
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Cleo C van Diemen
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Richard J Sinke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Morris A Swertz
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
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20
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Pacio Miguez M, Santos-Simarro F, García-Miñaúr S, Velázquez Fragua R, Del Pozo Á, Solís M, Jiménez Rodríguez C, Rufo-Rabadán V, Fernandez VE, Rueda I, Gomez Del Pozo MV, Gallego N, Lapunzina P, Palomares-Bralo M. Pathogenic variants in KPTN, a rare cause of macrocephaly and intellectual disability. Am J Med Genet A 2020; 182:2222-2225. [PMID: 32808430 DOI: 10.1002/ajmg.a.61778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/16/2020] [Accepted: 06/21/2020] [Indexed: 12/20/2022]
Affiliation(s)
- Marta Pacio Miguez
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain
| | - Fernando Santos-Simarro
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain.,CIBERER, Centro de Investigación Biomédica en Red de Enfermedades Raras, ISCIII, Madrid, Spain.,ITHACA-European Reference Network, Madrid, Spain
| | - Sixto García-Miñaúr
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain.,CIBERER, Centro de Investigación Biomédica en Red de Enfermedades Raras, ISCIII, Madrid, Spain.,ITHACA-European Reference Network, Madrid, Spain
| | | | - Ángela Del Pozo
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain.,CIBERER, Centro de Investigación Biomédica en Red de Enfermedades Raras, ISCIII, Madrid, Spain
| | - Mario Solís
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain
| | - Carmen Jiménez Rodríguez
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain
| | - Virginia Rufo-Rabadán
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain
| | | | - Inmaculada Rueda
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain
| | | | - Natividad Gallego
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain
| | - Pablo Lapunzina
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain.,CIBERER, Centro de Investigación Biomédica en Red de Enfermedades Raras, ISCIII, Madrid, Spain.,ITHACA-European Reference Network, Madrid, Spain
| | - María Palomares-Bralo
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz, IdiPaz, Madrid, Spain.,CIBERER, Centro de Investigación Biomédica en Red de Enfermedades Raras, ISCIII, Madrid, Spain.,ITHACA-European Reference Network, Madrid, Spain
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21
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Lalonde E, Rentas S, Lin F, Dulik MC, Skraban CM, Spinner NB. Genomic Diagnosis for Pediatric Disorders: Revolution and Evolution. Front Pediatr 2020; 8:373. [PMID: 32733828 PMCID: PMC7360789 DOI: 10.3389/fped.2020.00373] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 06/02/2020] [Indexed: 12/14/2022] Open
Abstract
Powerful, recent advances in technologies to analyze the genome have had a profound impact on the practice of medical genetics, both in the laboratory and in the clinic. Increasing utilization of genome-wide testing such as chromosomal microarray analysis and exome sequencing have lead a shift toward a "genotype-first" approach. Numerous techniques are now available to diagnose a particular syndrome or phenotype, and while traditional techniques remain efficient tools in certain situations, higher-throughput technologies have become the de facto laboratory tool for diagnosis of most conditions. However, selecting the right assay or technology is challenging, and the wrong choice may lead to prolonged time to diagnosis, or even a missed diagnosis. In this review, we will discuss current core technologies for the diagnosis of classic genetic disorders to shed light on the benefits and disadvantages of these strategies, including diagnostic efficiency, variant interpretation, and secondary findings. Finally, we review upcoming technologies posed to impart further changes in the field of genetic diagnostics as we move toward "genome-first" practice.
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Affiliation(s)
- Emilie Lalonde
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Stefan Rentas
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Fumin Lin
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Matthew C. Dulik
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Cara M. Skraban
- Division of Human Genetics, Department of Pediatrics, School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Nancy B. Spinner
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
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22
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Thiffault I, Atherton A, Heese BA, T Abdelmoity A, Pawar K, Farrow E, Zellmer L, Miller N, Soden S, Saunders C. Pathogenic variants in KPTN gene identified by clinical whole-genome sequencing. Cold Spring Harb Mol Case Stud 2020; 6:mcs.a003970. [PMID: 32358097 PMCID: PMC7304362 DOI: 10.1101/mcs.a003970] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 04/16/2020] [Indexed: 11/24/2022] Open
Abstract
Status epilepticus is not rare in critically ill intensive care unit patients, but its diagnosis is often delayed or missed. The mortality for convulsive status epilepticus is dependent on the underlying aetiologies and the age of the patients and thus varies from study to study. In this context, effective molecular diagnosis in a pediatric patient with a genetically heterogeneous phenotype is essential. Homozygous or compound heterozygous variants in KPTN have been recently associated with a syndrome typified by macrocephaly, neurodevelopmental delay, and seizures. We describe a comprehensive investigation of a 9-yr-old male patient who was admitted to the intensive care unit, with focal epilepsy, static encephalopathy, autism spectrum disorder, and macrocephaly of unknown etiology, who died of status epilepticus. Clinical whole-genome sequencing revealed compound heterozygous variants in the KPTN gene. The first variant is a previously characterized 18-bp in-frame duplication (c.714_731dup) in exon 8, resulting in the protein change p.Met241_Gln246dup. The second variant, c.394 + 1G > A, affects the splice junction of exon 3. These results are consistent with a diagnosis of autosomal recessive KPTN-related disease. This is the fourth clinical report for KPTN deficiency, providing further evidence of a wider range of severity.
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Affiliation(s)
- Isabelle Thiffault
- Center for Pediatric Genomic Medicine, Children's Mercy Hospital, Kansas City, Missouri 64108, USA.,Department of Pathology and Laboratory Medicine, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA.,University of Missouri-Kansas City School of Medicine, Kansas City, Missouri 64108, USA
| | - Andrea Atherton
- Department of Pediatrics, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA
| | - Bryce A Heese
- Department of Pediatrics, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA
| | - Ahmed T Abdelmoity
- Department of Pediatrics, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA
| | - Kailash Pawar
- Department of Pediatrics, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA
| | - Emily Farrow
- Center for Pediatric Genomic Medicine, Children's Mercy Hospital, Kansas City, Missouri 64108, USA.,University of Missouri-Kansas City School of Medicine, Kansas City, Missouri 64108, USA.,Department of Pediatrics, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA
| | - Lee Zellmer
- Center for Pediatric Genomic Medicine, Children's Mercy Hospital, Kansas City, Missouri 64108, USA
| | - Neil Miller
- Center for Pediatric Genomic Medicine, Children's Mercy Hospital, Kansas City, Missouri 64108, USA
| | - Sarah Soden
- Center for Pediatric Genomic Medicine, Children's Mercy Hospital, Kansas City, Missouri 64108, USA.,University of Missouri-Kansas City School of Medicine, Kansas City, Missouri 64108, USA.,Department of Pediatrics, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA
| | - Carol Saunders
- Center for Pediatric Genomic Medicine, Children's Mercy Hospital, Kansas City, Missouri 64108, USA.,Department of Pathology and Laboratory Medicine, Children's Mercy Hospitals, Kansas City, Missouri 64108, USA.,University of Missouri-Kansas City School of Medicine, Kansas City, Missouri 64108, USA
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23
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Köhler S, Carmody L, Vasilevsky N, Jacobsen JOB, Danis D, Gourdine JP, Gargano M, Harris NL, Matentzoglu N, McMurry JA, Osumi-Sutherland D, Cipriani V, Balhoff JP, Conlin T, Blau H, Baynam G, Palmer R, Gratian D, Dawkins H, Segal M, Jansen AC, Muaz A, Chang WH, Bergerson J, Laulederkind SJF, Yüksel Z, Beltran S, Freeman AF, Sergouniotis PI, Durkin D, Storm AL, Hanauer M, Brudno M, Bello SM, Sincan M, Rageth K, Wheeler MT, Oegema R, Lourghi H, Della Rocca MG, Thompson R, Castellanos F, Priest J, Cunningham-Rundles C, Hegde A, Lovering RC, Hajek C, Olry A, Notarangelo L, Similuk M, Zhang XA, Gómez-Andrés D, Lochmüller H, Dollfus H, Rosenzweig S, Marwaha S, Rath A, Sullivan K, Smith C, Milner JD, Leroux D, Boerkoel CF, Klion A, Carter MC, Groza T, Smedley D, Haendel MA, Mungall C, Robinson PN. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res 2020; 47:D1018-D1027. [PMID: 30476213 PMCID: PMC6324074 DOI: 10.1093/nar/gky1105] [Citation(s) in RCA: 406] [Impact Index Per Article: 101.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 10/24/2018] [Indexed: 12/12/2022] Open
Abstract
The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.
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Affiliation(s)
- Sebastian Köhler
- Charité Centrum für Therapieforschung, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany.,Einstein Center Digital Future, Berlin 10117, Germany.,Monarch Initiative, monarchinitiative.org
| | - Leigh Carmody
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Nicole Vasilevsky
- Monarch Initiative, monarchinitiative.org.,Oregon Health & Science University, Portland, OR 97217, USA
| | - Julius O B Jacobsen
- Monarch Initiative, monarchinitiative.org.,Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Daniel Danis
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Jean-Philippe Gourdine
- Monarch Initiative, monarchinitiative.org.,Oregon Health & Science University, Portland, OR 97217, USA
| | - Michael Gargano
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Nomi L Harris
- Monarch Initiative, monarchinitiative.org.,Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Nicolas Matentzoglu
- Monarch Initiative, monarchinitiative.org.,European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Julie A McMurry
- Monarch Initiative, monarchinitiative.org.,Linus Pauling institute, Oregon State University, Corvallis, OR, USA
| | - David Osumi-Sutherland
- Monarch Initiative, monarchinitiative.org.,European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Valentina Cipriani
- Monarch Initiative, monarchinitiative.org.,William Harvey Research Institute, Queen Mary University College of London.,UCL Genetics Institute, University College of London.,UCL Institute of Ophthalmology, University College of London
| | - James P Balhoff
- Monarch Initiative, monarchinitiative.org.,Renaissance Computing Institute, University of North Carolina at Chapel Hill
| | - Tom Conlin
- Monarch Initiative, monarchinitiative.org.,Linus Pauling institute, Oregon State University, Corvallis, OR, USA
| | - Hannah Blau
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies and Genetic Services of Western Australia, Department of Health, Government of Western Australia, WA, Australia.,School of Paediatrics and Telethon Kids Institute, University of Western Australia, Perth, WA, Australia.,Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, Australia.,Spatial Sciences, Department of Science and Engineering, Curtin University, Perth, WA, Australia.,The Office of Population Health Genomics, Department of Health, Government of Western Australia, Perth, WA, Australia
| | - Richard Palmer
- Spatial Sciences, Department of Science and Engineering, Curtin University, Perth, WA, Australia
| | - Dylan Gratian
- Western Australian Register of Developmental Anomalies and Genetic Services of Western Australia, Department of Health, Government of Western Australia, WA, Australia
| | - Hugh Dawkins
- The Office of Population Health Genomics, Department of Health, Government of Western Australia, Perth, WA, Australia
| | | | - Anna C Jansen
- Neurogenetics Research Group, Vrije Universiteit Brussel, Brussels, Belgium.,Pediatric Neurology Unit, Department of Pediatrics, UZ Brussel, Brussels, Belgium
| | - Ahmed Muaz
- Monarch Initiative, monarchinitiative.org.,Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia
| | - Willie H Chang
- Centre for Computational Medicine, Hospital for Sick Children and Department of Computer Science, University of Toronto, Toronto, Canada
| | - Jenna Bergerson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Stanley J F Laulederkind
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin & Marquette University, 8701 Watertown Plank Road Milwaukee, WI 53226, USA
| | | | - Sergi Beltran
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Alexandra F Freeman
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | - Daniel Durkin
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Andrea L Storm
- ICF, Rockville, MD, USA.,National Center for Advancing Translational Sciences, Office of Rare Diseases Research, National Institutes of Health, Bethesda, MD, USA
| | - Marc Hanauer
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Michael Brudno
- Centre for Computational Medicine, Hospital for Sick Children and Department of Computer Science, University of Toronto, Toronto, Canada
| | | | - Murat Sincan
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, USA
| | - Kayli Rageth
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, USA
| | - Matthew T Wheeler
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
| | - Renske Oegema
- Department of Genetics, University Medical Center Utrecht, the Netherlands
| | - Halima Lourghi
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Maria G Della Rocca
- ICF, Rockville, MD, USA.,National Center for Advancing Translational Sciences, Office of Rare Diseases Research, National Institutes of Health, Bethesda, MD, USA
| | - Rachel Thompson
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
| | | | - James Priest
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ayushi Hegde
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Ruth C Lovering
- Institute of Cardiovascular Science, University College London, UK
| | | | - Annie Olry
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Luigi Notarangelo
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Morgan Similuk
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Xingmin A Zhang
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - David Gómez-Andrés
- Child Neurology Unit. Hospital Universitari Vall d'Hebron, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
| | - Hanns Lochmüller
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain.,Department of Neuropediatrics and Muscle Disorders, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany.,Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Canada.,Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, Canada
| | - Hélène Dollfus
- Centre for Rare Eye Diseases CARGO, SENSGENE FSMR Network, Strasbourg University Hospital, Strasbourg, France
| | - Sergio Rosenzweig
- Immunology Service, Department of Laboratory Medicine, NIH Clinical Center, Bethesda, MD, USA
| | - Shruti Marwaha
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
| | - Ana Rath
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Kathleen Sullivan
- Department of Pediatrics, Division of Allergy Immunology, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, 3615 Civic Center Boulevard, Philadelphia, PA 19104, USA
| | | | - Joshua D Milner
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Dorothée Leroux
- Centre for Rare Eye Diseases CARGO, SENSGENE FSMR Network, Strasbourg University Hospital, Strasbourg, France
| | | | - Amy Klion
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Melody C Carter
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Tudor Groza
- Monarch Initiative, monarchinitiative.org.,Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia
| | - Damian Smedley
- Monarch Initiative, monarchinitiative.org.,Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Melissa A Haendel
- Monarch Initiative, monarchinitiative.org.,Oregon Health & Science University, Portland, OR 97217, USA.,Linus Pauling institute, Oregon State University, Corvallis, OR, USA
| | - Chris Mungall
- Monarch Initiative, monarchinitiative.org.,Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Peter N Robinson
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.,Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
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24
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Malinowski J, Miller DT, Demmer L, Gannon J, Pereira EM, Schroeder MC, Scheuner MT, Tsai ACH, Hickey SE, Shen J. Systematic evidence-based review: outcomes from exome and genome sequencing for pediatric patients with congenital anomalies or intellectual disability. Genet Med 2020; 22:986-1004. [PMID: 32203227 PMCID: PMC7222126 DOI: 10.1038/s41436-020-0771-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 02/24/2020] [Accepted: 02/25/2020] [Indexed: 12/31/2022] Open
Abstract
Purpose Exome and genome sequencing (ES/GS) are performed frequently in patients with congenital anomalies, developmental delay, or intellectual disability (CA/DD/ID), but the impact of results from ES/GS on clinical management and patient outcomes is not well characterized. A systematic evidence review (SER) can support future evidence-based guideline development for use of ES/GS in this patient population. Methods We undertook an SER to identify primary literature from January 2007 to March 2019 describing health, clinical, reproductive, and psychosocial outcomes resulting from ES/GS in patients with CA/DD/ID. A narrative synthesis of results was performed. Results We retrieved 2654 publications for full-text review from 7178 articles. Only 167 articles met our inclusion criteria, and these were primarily case reports or small case series of fewer than 20 patients. The most frequently reported outcomes from ES/GS were changes to clinical management or reproductive decision-making. Two studies reported on the reduction of mortality or morbidity or impact on quality of life following ES/GS. Conclusion There is evidence that ES/GS for patients with CA/DD/ID informs clinical and reproductive decision-making, which could lead to improved outcomes for patients and their family members. Further research is needed to generate evidence regarding health outcomes to inform robust guidelines regarding ES/GS in the care of patients with CA/DD/ID.
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Affiliation(s)
| | - David T Miller
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Laurie Demmer
- Atrium Health's Levine Children's Hospital, Charlotte, NC, USA
| | - Jennifer Gannon
- Division of Clinical Genetics, Children's Mercy Hospital, Kansas City, MO, USA.,Department of Pediatrics, University of Missouri, Kansas City, MO, USA
| | - Elaine Maria Pereira
- Division of Clinical Genetics, Department of Pediatrics, Columbia University Medical Center, New York, NY, USA
| | - Molly C Schroeder
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Maren T Scheuner
- Division of Medical Genetics, Department of Pediatrics and Division of Hematology-Oncology, Department of Medicine, University of California, San Francisco, CA, USA.,San Francisco VA Healthcare System, San Francisco, CA, USA
| | - Anne Chun-Hui Tsai
- Section of Clinical Genetics and Metabolism, Department of Pediatrics, University of Colorado, Aurora, CO, USA
| | - Scott E Hickey
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Jun Shen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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25
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Abstract
PURPOSE OF REVIEW Identifying pathogenic variation underlying pediatric developmental disease is critical for medical management, therapeutic development, and family planning. This review summarizes current genetic testing options along with their potential benefits and limitations. We also describe results from large-scale genomic sequencing projects in pediatric and neonatal populations with a focus on clinical utility. RECENT FINDINGS Recent advances in DNA sequencing technology have made genomic sequencing a feasible and effective testing option in a variety of clinical settings. These cutting-edge tests offer much promise to both medical providers and patients as it has been demonstrated to detect causal genetic variation in ∼25% or more of previously unresolved cases. Efforts aimed at promoting data sharing across clinical genetics laboratories and systematic reanalysis of existing genomic sequencing data have further improved diagnostic rates and reduced the number of unsolved cases. SUMMARY Genomic sequencing is a powerful and increasingly cost-effective alternative to current genetic tests and will continue to grow in clinical utility as more of the genome is understood and as analytical methods are improved. The evolution of genomic sequencing is changing the landscape of clinical testing and requires medical professionals who are adept at understanding and returning genomic results to patients.
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Affiliation(s)
- Matthew B. Neu
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Department of Genetics, University of Alabama at Birmingham, Birmingham, AL, USA
- University of Alabama at Birmingham Medical Scientist Training Program, Birmingham, AL, USA
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26
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French CE, Delon I, Dolling H, Sanchis-Juan A, Shamardina O, Mégy K, Abbs S, Austin T, Bowdin S, Branco RG, Firth H, Rowitch DH, Raymond FL. Whole genome sequencing reveals that genetic conditions are frequent in intensively ill children. Intensive Care Med 2019; 45:627-636. [PMID: 30847515 PMCID: PMC6483967 DOI: 10.1007/s00134-019-05552-x] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 01/28/2019] [Indexed: 12/13/2022]
Abstract
PURPOSE With growing evidence that rare single gene disorders present in the neonatal period, there is a need for rapid, systematic, and comprehensive genomic diagnoses in ICUs to assist acute and long-term clinical decisions. This study aimed to identify genetic conditions in neonatal (NICU) and paediatric (PICU) intensive care populations. METHODS We performed trio whole genome sequence (WGS) analysis on a prospective cohort of families recruited in NICU and PICU at a single site in the UK. We developed a research pipeline in collaboration with the National Health Service to deliver validated pertinent pathogenic findings within 2-3 weeks of recruitment. RESULTS A total of 195 families had whole genome analysis performed (567 samples) and 21% received a molecular diagnosis for the underlying genetic condition in the child. The phenotypic description of the child was a poor predictor of the gene identified in 90% of cases, arguing for gene agnostic testing in NICU/PICU. The diagnosis affected clinical management in more than 65% of cases (83% in neonates) including modification of treatments and care pathways and/or informing palliative care decisions. A 2-3 week turnaround was sufficient to impact most clinical decision-making. CONCLUSIONS The use of WGS in intensively ill children is acceptable and trio analysis facilitates diagnoses. A gene agnostic approach was effective in identifying an underlying genetic condition, with phenotypes and symptomatology being primarily used for data interpretation rather than gene selection. WGS analysis has the potential to be a first-line diagnostic tool for a subset of intensively ill children.
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Affiliation(s)
- Courtney E French
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK
| | - Isabelle Delon
- Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Helen Dolling
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK
| | - Alba Sanchis-Juan
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK
| | - Olga Shamardina
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK
| | - Karyn Mégy
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK
| | - Stephen Abbs
- Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Topun Austin
- Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Sarah Bowdin
- Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Ricardo G Branco
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK.,Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.,Sidra Medicine, Doha, Qatar
| | - Helen Firth
- Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | | | | | - David H Rowitch
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK
| | - F Lucy Raymond
- School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SP, UK. .,Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
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27
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Zastrow DB, Kohler JN, Bonner D, Reuter CM, Fernandez L, Grove ME, Fisk DG, Yang Y, Eng CM, Ward PA, Bick D, Worthey EA, Fisher PG, Ashley EA, Bernstein JA, Wheeler MT. A toolkit for genetics providers in follow-up of patients with non-diagnostic exome sequencing. J Genet Couns 2019; 28:213-228. [PMID: 30964584 PMCID: PMC7385984 DOI: 10.1002/jgc4.1119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 03/01/2019] [Accepted: 03/04/2019] [Indexed: 12/11/2022]
Abstract
There are approximately 7,000 rare diseases affecting 25-30 million Americans, with 80% estimated to have a genetic basis. This presents a challenge for genetics practitioners to determine appropriate testing, make accurate diagnoses, and conduct up-to-date patient management. Exome sequencing (ES) is a comprehensive diagnostic approach, but only 25%-41% of the patients receive a molecular diagnosis. The remaining three-fifths to three-quarters of patients undergoing ES remain undiagnosed. The Stanford Center for Undiagnosed Diseases (CUD), a clinical site of the Undiagnosed Diseases Network, evaluates patients with undiagnosed and rare diseases using a combination of methods including ES. Frequently these patients have non-diagnostic ES results, but strategic follow-up techniques identify diagnoses in a subset. We present techniques used at the CUD that can be adopted by genetics providers in clinical follow-up of cases where ES is non-diagnostic. Solved case examples illustrate different types of non-diagnostic results and the additional techniques that led to a diagnosis. Frequent approaches include segregation analysis, data reanalysis, genome sequencing, additional variant identification, careful phenotype-disease correlation, confirmatory testing, and case matching. We also discuss prioritization of cases for additional analyses.
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Affiliation(s)
- Diane B Zastrow
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
| | - Jennefer N Kohler
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
| | - Devon Bonner
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
| | - Chloe M Reuter
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
| | - Liliana Fernandez
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
| | - Megan E Grove
- Clinical Genomics Program, Stanford Health Care, Stanford, California
| | - Dianna G Fisk
- Clinical Genomics Program, Stanford Health Care, Stanford, California
| | | | | | | | - David Bick
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama
| | | | - Paul G Fisher
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
- Department of Neurology, Stanford University School of Medicine, Stanford, California
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Euan A Ashley
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
- Clinical Genomics Program, Stanford Health Care, Stanford, California
- Department of Genetics, Stanford University School of Medicine, Stanford, California
- Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Jonathan A Bernstein
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Matthew T Wheeler
- Center for Undiagnosed Diseases, Stanford University, Stanford, California
- Department of Medicine, Stanford University School of Medicine, Stanford, California
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