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Li S, Zhao S, Sinson JC, Bajic A, Rosenfeld JA, Neeley MB, Pena M, Worley KC, Burrage LC, Weisz-Hubshman M, Ketkar S, Craigen WJ, Clark GD, Lalani S, Bacino CA, Machol K, Chao HT, Potocki L, Emrick L, Sheppard J, Nguyen MTT, Khoramnia A, Hernandez PP, Nagamani SC, Liu Z, Eng CM, Lee B, Liu P. The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing. Am J Hum Genet 2024; 111:841-862. [PMID: 38593811 PMCID: PMC11080285 DOI: 10.1016/j.ajhg.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/11/2024] Open
Abstract
RNA sequencing (RNA-seq) has recently been used in translational research settings to facilitate diagnoses of Mendelian disorders. A significant obstacle for clinical laboratories in adopting RNA-seq is the low or absent expression of a significant number of disease-associated genes/transcripts in clinically accessible samples. As this is especially problematic in neurological diseases, we developed a clinical diagnostic approach that enhanced the detection and evaluation of tissue-specific genes/transcripts through fibroblast-to-neuron cell transdifferentiation. The approach is designed specifically to suit clinical implementation, emphasizing simplicity, cost effectiveness, turnaround time, and reproducibility. For clinical validation, we generated induced neurons (iNeurons) from 71 individuals with primary neurological phenotypes recruited to the Undiagnosed Diseases Network. The overall diagnostic yield was 25.4%. Over a quarter of the diagnostic findings benefited from transdifferentiation and could not be achieved by fibroblast RNA-seq alone. This iNeuron transcriptomic approach can be effectively integrated into diagnostic whole-transcriptome evaluation of individuals with genetic disorders.
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Affiliation(s)
- Shenglan Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Sen Zhao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jefferson C Sinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Aleksandar Bajic
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Advanced Technology Cores, Baylor College of Medicine, Houston, TX, USA
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Matthew B Neeley
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Mezthly Pena
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Kim C Worley
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Lindsay C Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Monika Weisz-Hubshman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Shamika Ketkar
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - William J Craigen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Gary D Clark
- Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Seema Lalani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Carlos A Bacino
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Keren Machol
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Hsiao-Tuan Chao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Cain Pediatric Research Foundation Laboratories, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; McNair Medical Institute, The Robert and Janice McNair Foundation, Houston, TX, USA
| | - Lorraine Potocki
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Lisa Emrick
- Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Jennifer Sheppard
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - My T T Nguyen
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA
| | - Anahita Khoramnia
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Sandesh Cs Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Christine M Eng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Baylor Genetics, Houston, TX, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Baylor Genetics, Houston, TX, USA.
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Hills S, Li Q, Madden JA, Genetti CA, Brownstein CA, Schmitz-Abe K, Beggs AH, Agrawal PB. High number of candidate gene variants are identified as disease-causing in a period of 4 years. Am J Med Genet A 2024; 194:e63509. [PMID: 38158391 DOI: 10.1002/ajmg.a.63509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/15/2023] [Accepted: 12/09/2023] [Indexed: 01/03/2024]
Abstract
Advances in bioinformatic tools paired with the ongoing accumulation of genetic knowledge and periodic reanalysis of genomic sequencing data have led to an improvement in genetic diagnostic rates. Candidate gene variants (CGVs) identified during sequencing or on reanalysis but not yet implicated in human disease or associated with a phenotypically distinct condition are often not revisited, leading to missed diagnostic opportunities. Here, we revisited 33 such CGVs from our previously published study and determined that 16 of them are indeed disease-causing (novel or phenotype expansion) since their identification. These results emphasize the need to focus on previously identified CGVs during sequencing or reanalysis and the importance of sharing that information with researchers around the world, including relevant functional analysis to establish disease causality.
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Affiliation(s)
- Sonia Hills
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Qifei Li
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
- Division of Neonatology, Department of Pediatrics, University of Miami Miller School of Medicine and Jackson Health System, Miami, Florida, USA
| | - Jill A Madden
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Casie A Genetti
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Catherine A Brownstein
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Klaus Schmitz-Abe
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
- Division of Neonatology, Department of Pediatrics, University of Miami Miller School of Medicine and Jackson Health System, Miami, Florida, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Alan H Beggs
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Pankaj B Agrawal
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts, USA
- Division of Neonatology, Department of Pediatrics, University of Miami Miller School of Medicine and Jackson Health System, Miami, Florida, USA
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van Slobbe M, van Haeringen A, Vissers LELM, Bijlsma EK, Rutten JW, Suerink M, Nibbeling EAR, Ruivenkamp CAL, Koene S. Reanalysis of whole-exome sequencing (WES) data of children with neurodevelopmental disorders in a standard patient care context. Eur J Pediatr 2024; 183:345-355. [PMID: 37889289 PMCID: PMC10858114 DOI: 10.1007/s00431-023-05279-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/20/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
This study aims to inform future genetic reanalysis management by evaluating the yield of whole-exome sequencing (WES) reanalysis in standard patient care in the Netherlands. Single-center data of 159 patients with a neurodevelopmental disorder (NDD), in which WES analysis and reanalysis were performed between January 1, 2014, and December 31, 2021, was retrospectively collected. Patients were included if they were under the age of 18 years at initial analysis and if this initial analysis did not result in a diagnosis. Demographic, phenotypic, and genotypic characteristics of patients were collected and analyzed. The primary outcomes of our study were (i) diagnostic yield at reanalysis, (ii) reasons for detecting a new possibly causal variant at reanalysis, (iii) unsolicited findings, and (iv) factors associated with positive result of reanalysis. In addition, we conducted a questionnaire study amongst the 7 genetic department in the Netherlands creating an overview of used techniques, yield, and organization of WES reanalysis. The single-center data show that in most cases, WES reanalysis was initiated by the clinical geneticist (65%) or treating physician (30%). The mean time between initial WES analysis and reanalysis was 3.7 years. A new (likely) pathogenic variant or VUS with a clear link to the phenotype was found in 20 initially negative cases, resulting in a diagnostic yield of 12.6%. In 75% of these patients, the diagnosis had clinical consequences, as for example, a screening plan for associated signs and symptoms could be devised. Most (32%) of the (likely) causal variants identified at WES reanalysis were discovered due to a newly described gene-disease association. In addition to the 12.6% diagnostic yield based on new diagnoses, reclassification of a variant of uncertain significance found at initial analysis led to a definite diagnosis in three patients. Diagnostic yield was higher in patients with dysmorphic features compared to patients without clear dysmorphic features (yield 27% vs. 6%; p = 0.001). CONCLUSIONS Our results show that WES reanalysis in patients with NDD in standard patient care leads to a substantial increase in genetic diagnoses. In the majority of newly diagnosed patients, the diagnosis had clinical consequences. Knowledge about the clinical impact of WES reanalysis, clinical characteristics associated with higher yield, and the yield per year after a negative WES in larger clinical cohorts is warranted to inform guidelines for genetic reanalysis. These guidelines will be of great value for pediatricians, pediatric rehabilitation specialists, and pediatric neurologists in daily care of patients with NDD. WHAT IS KNOWN • Whole exome sequencing can cost-effectively identify a genetic cause of intellectual disability in about 30-40% of patients. • WES reanalysis in a research setting can lead to a definitive diagnosis in 10-20% of previously exome negative cases. WHAT IS NEW • WES reanalysis in standard patient care resulted in a diagnostic yield of 13% in previously exome negative children with NDD. • The presence of dysmorphic features is associated with an increased diagnostic yield of WES reanalysis.
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Affiliation(s)
- Michelle van Slobbe
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Arie van Haeringen
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Lisenka E L M Vissers
- Department of Human Genetics, Donders Centre for Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Emilia K Bijlsma
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Julie W Rutten
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Manon Suerink
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Esther A R Nibbeling
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Claudia A L Ruivenkamp
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Saskia Koene
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands.
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Kong XF, Bogyo K, Kapoor S, Shea PR, Groopman EE, Thomas-Wilson A, Cocchi E, Milo Rasouly H, Zheng B, Sun S, Zhang J, Martinez M, Vittorio JM, Dove LM, Marasa M, Wang TC, Verna EC, Worman HJ, Gharavi AG, Goldstein DB, Wattacheril J. The diagnostic yield of exome sequencing in liver diseases from a curated gene panel. Sci Rep 2023; 13:21540. [PMID: 38057357 PMCID: PMC10700603 DOI: 10.1038/s41598-023-42202-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 09/06/2023] [Indexed: 12/08/2023] Open
Abstract
Exome sequencing (ES) has been used in a variety of clinical settings but there are limited data on its utility for diagnosis and/or prediction of monogenic liver diseases. We developed a curated list of 502 genes for monogenic disorders associated with liver phenotypes and analyzed ES data for these genes in 758 patients with chronic liver diseases (CLD). For comparison, we examined ES data in 7856 self-declared healthy controls (HC), and 2187 patients with chronic kidney disease (CKD). Candidate pathogenic (P) or likely pathogenic (LP) variants were initially identified in 19.9% of participants, most of which were attributable to previously reported pathogenic variants with implausibly high allele frequencies. After variant annotation and filtering based on population minor allele frequency (MAF ≤ 10-4 for dominant disorders and MAF ≤ 10-3 for recessive disorders), we detected a significant enrichment of P/LP variants in the CLD cohort compared to the HC cohort (X2 test OR 5.00, 95% CI 3.06-8.18, p value = 4.5e-12). A second-level manual annotation was necessary to capture true pathogenic variants that were removed by stringent allele frequency and quality filters. After these sequential steps, the diagnostic rate of monogenic disorders was 5.7% in the CLD cohort, attributable to P/LP variants in 25 genes. We also identified concordant liver disease phenotypes for 15/22 kidney disease patients with P/LP variants in liver genes, mostly associated with cystic liver disease phenotypes. Sequencing results had many implications for clinical management, including familial testing for early diagnosis and management, preventative screening for associated comorbidities, and in some cases for therapy. Exome sequencing provided a 5.7% diagnostic rate in CLD patients and required multiple rounds of review to reduce both false positive and false negative findings. The identification of concordant phenotypes in many patients with P/LP variants and no known liver disease also indicates a potential for predictive testing for selected monogenic liver disorders.
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Affiliation(s)
- Xiao-Fei Kong
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA.
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
- Department of Medicine, McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, 75390-9151, USA.
| | - Kelsie Bogyo
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Sheena Kapoor
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Patrick R Shea
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Emily E Groopman
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Amanda Thomas-Wilson
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, 10032, USA
- Molecular Diagnostics, New York Genome Center, New York, NY, USA
| | - Enrico Cocchi
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Hila Milo Rasouly
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Beishi Zheng
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA
| | - Siming Sun
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA
| | - Junying Zhang
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Mercedes Martinez
- Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, 622 West 168th Street, PH 14-105D, New York, NY, 10032, USA
| | - Jennifer M Vittorio
- Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, 622 West 168th Street, PH 14-105D, New York, NY, 10032, USA
- NYU Transplant Institute, NYU Langone Health, New York, NY, USA
| | - Lorna M Dove
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA
- Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, 622 West 168th Street, PH 14-105D, New York, NY, 10032, USA
| | - Maddalena Marasa
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Timothy C Wang
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA
| | - Elizabeth C Verna
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA
- Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, 622 West 168th Street, PH 14-105D, New York, NY, 10032, USA
| | - Howard J Worman
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Ali G Gharavi
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - David B Goldstein
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Julia Wattacheril
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, Hammer Health Sciences Building Rm 402, 701 W 168th St, New York, NY, 10032, USA.
- Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, 622 West 168th Street, PH 14-105D, New York, NY, 10032, USA.
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Wojcik MH, Lemire G, Zaki MS, Wissman M, Win W, White S, Weisburd B, Waddell LB, Verboon JM, VanNoy GE, Töpf A, Tan TY, Straub V, Stenton SL, Snow H, Singer-Berk M, Silver J, Shril S, Seaby EG, Schneider R, Sankaran VG, Sanchis-Juan A, Russell KA, Reinson K, Ravenscroft G, Pierce EA, Place EM, Pajusalu S, Pais L, Õunap K, Osei-Owusu I, Okur V, Oja KT, O'Leary M, O'Heir E, Morel C, Marchant RG, Mangilog BE, Madden JA, MacArthur D, Lovgren A, Lerner-Ellis JP, Lin J, Laing N, Hildebrandt F, Groopman E, Goodrich J, Gleeson JG, Ghaoui R, Genetti CA, Gazda HT, Ganesh VS, Ganapathy M, Gallacher L, Fu J, Evangelista E, England E, Donkervoort S, DiTroia S, Cooper ST, Chung WK, Christodoulou J, Chao KR, Cato LD, Bujakowska KM, Bryen SJ, Brand H, Bonnemann C, Beggs AH, Baxter SM, Agrawal PB, Talkowski M, Austin-Tse C, Rehm HL, O'Donnell-Luria A. Unique Capabilities of Genome Sequencing for Rare Disease Diagnosis. medRxiv 2023:2023.08.08.23293829. [PMID: 38328047 PMCID: PMC10849673 DOI: 10.1101/2023.08.08.23293829] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Background Causal variants underlying rare disorders may remain elusive even after expansive gene panels or exome sequencing (ES). Clinicians and researchers may then turn to genome sequencing (GS), though the added value of this technique and its optimal use remain poorly defined. We therefore investigated the advantages of GS within a phenotypically diverse cohort. Methods GS was performed for 744 individuals with rare disease who were genetically undiagnosed. Analysis included review of single nucleotide, indel, structural, and mitochondrial variants. Results We successfully solved 218/744 (29.3%) cases using GS, with most solves involving established disease genes (157/218, 72.0%). Of all solved cases, 148 (67.9%) had previously had non-diagnostic ES. We systematically evaluated the 218 causal variants for features requiring GS to identify and 61/218 (28.0%) met these criteria, representing 8.2% of the entire cohort. These included small structural variants (13), copy neutral inversions and complex rearrangements (8), tandem repeat expansions (6), deep intronic variants (15), and coding variants that may be more easily found using GS related to uniformity of coverage (19). Conclusion We describe the diagnostic yield of GS in a large and diverse cohort, illustrating several types of pathogenic variation eluding ES or other techniques. Our results reveal a higher diagnostic yield of GS, supporting the utility of a genome-first approach, with consideration of GS as a secondary or tertiary test when higher-resolution structural variant analysis is needed or there is a strong clinical suspicion for a condition and prior targeted genetic testing has been negative.
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Rosenfeld LE, LeBlanc K, Nagy A, Ego BK, McCray AT. Participation in a national diagnostic research study: assessing the patient experience. Orphanet J Rare Dis 2023; 18:73. [PMID: 37032333 PMCID: PMC10084693 DOI: 10.1186/s13023-023-02695-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/02/2023] [Indexed: 04/11/2023] Open
Abstract
INTRODUCTION The Undiagnosed Diseases Network (UDN), a clinical research study funded by the National Institutes of Health, aims to provide answers for patients with undiagnosed conditions and generate knowledge about underlying disease mechanisms. UDN evaluations involve collaboration between clinicians and researchers and go beyond what is possible in clinical settings. While medical and research outcomes of UDN evaluations have been explored, this is the first formal assessment of the patient and caregiver experience. METHODS We invited UDN participants and caregivers to participate in focus groups via email, newsletter, and a private participant Facebook group. We developed focus group questions based on research team expertise, literature focused on patients with rare and undiagnosed conditions, and UDN participant and family member feedback. In March 2021, we conducted, recorded, and transcribed four 60-min focus groups via Zoom. Transcripts were evaluated using a thematic analysis approach. RESULTS The adult undiagnosed focus group described the UDN evaluation as validating and an avenue for access to medical providers. They also noted that the experience impacted professional choices and helped them rely on others for support. The adult diagnosed focus group described the healthcare system as not set up for rare disease. In the pediatric undiagnosed focus group, caregivers discussed a continued desire for information and gratitude for the UDN evaluation. They also described an ability to rule out information and coming to terms with not having answers. The pediatric diagnosed focus group discussed how the experience helped them focus on management and improved communication. Across focus groups, adults (undiagnosed/diagnosed) noted the comprehensiveness of the evaluation. Undiagnosed focus groups (adult/pediatric) discussed a desire for ongoing communication and care with the UDN. Diagnosed focus groups (adult/pediatric) highlighted the importance of the diagnosis they received in the UDN. The majority of the focus groups noted a positive future orientation after participation. CONCLUSION Our findings are consistent with prior literature focused on the patient experience of rare and undiagnosed conditions and highlight benefits from comprehensive evaluations, regardless of whether a diagnosis is obtained. Focus group themes also suggest areas for improvement and future research related to the diagnostic odyssey.
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Affiliation(s)
- Lindsay E Rosenfeld
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
- Heller School for Social Policy and Management, Institute for Child, Youth, and Family Policy, Brandeis University, 415 South St., Waltham, MA, 02453, USA
| | - Kimberly LeBlanc
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Anna Nagy
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Braeden K Ego
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA, 94305, USA
| | - Alexa T McCray
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.
- Division of Clinical Informatics, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02215, USA.
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7
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Spillmann RC, Tan QKG, Reuter C, Schoch K, Kohler J, Bonner D, Zastrow D, Alkelai A, Baugh E, Cope H, Marwaha S, Wheeler MT, Bernstein JA, Shashi V. A concurrent dual analysis of genomic data augments diagnoses: Experiences of 2 clinical sites in the Undiagnosed Diseases Network. Genet Med 2023; 25:100353. [PMID: 36481303 PMCID: PMC10506157 DOI: 10.1016/j.gim.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/28/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Next-generation sequencing (NGS) has revolutionized the diagnostic process for rare/ultrarare conditions. However, diagnosis rates differ between analytical pipelines. In the National Institutes of Health-Undiagnosed Diseases Network (UDN) study, each individual's NGS data are concurrently analyzed by the UDN sequencing core laboratory and the clinical sites. We examined the outcomes of this practice. METHODS A retrospective review was performed at 2 UDN clinical sites to compare the variants and diagnoses/candidate genes identified with the dual analyses of the NGS data. RESULTS In total, 95 individuals had 100 diagnoses/candidate genes. There was 59% concordance between the UDN sequencing core laboratories and the clinical sites in identifying diagnoses/candidate genes. The core laboratory provided more diagnoses, whereas the clinical sites prioritized more research variants/candidate genes (P < .001). The clinical sites solely identified 15% of the diagnoses/candidate genes. The differences between the 2 pipelines were more often because of variant prioritization disparities than variant detection. CONCLUSION The unique dual analysis of NGS data in the UDN synergistically enhances outcomes. The core laboratory provided a clinical analysis with more diagnoses and the clinical sites prioritized more research variants/candidate genes. Implementing such concurrent dual analyses in other genomic research studies and clinical settings can improve both variant detection and prioritization.
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Affiliation(s)
- Rebecca C Spillmann
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, NC
| | - Queenie K-G Tan
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, NC
| | - Chloe Reuter
- Stanford Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA; Stanford Center for Undiagnosed Diseases, Stanford University, and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, NC
| | - Jennefer Kohler
- Stanford Center for Undiagnosed Diseases, Stanford University, and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Devon Bonner
- Stanford Center for Undiagnosed Diseases, Stanford University, and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Diane Zastrow
- Stanford Center for Undiagnosed Diseases, Stanford University, and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Anna Alkelai
- Institute for Genome Medicine, Columbia University Medical Center, New York, NY
| | - Evan Baugh
- Institute for Genome Medicine, Columbia University Medical Center, New York, NY
| | - Heidi Cope
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, NC
| | - Shruti Marwaha
- Stanford Center for Undiagnosed Diseases, Stanford University, and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Matthew T Wheeler
- Stanford Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA; Stanford Center for Undiagnosed Diseases, Stanford University, and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Jonathan A Bernstein
- Stanford Center for Undiagnosed Diseases, Stanford University, and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, NC.
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8
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Hartley T, Soubry É, Acker M, Osmond M, Couse M, Gillespie MK, Ito Y, Marshall AE, Lemire G, Huang L, Chisholm C, Eaton AJ, Price EM, Dowling JJ, Ramani AK, Mendoza-Londono R, Costain G, Axford MM, Szuto A, McNiven V, Damseh N, Jobling R, de Kock L, Mojarad BA, Young T, Shao Z, Hayeems RZ, Graham ID, Tarnopolsky M, Brady L, Armour CM, Geraghty M, Richer J, Sawyer S, Lines M, Mercimek-Andrews S, Carter MT, Graham G, Kannu P, Lazier J, Li C, Aul RB, Balci TB, Dlamini N, Badalato L, Guerin A, Walia J, Chitayat D, Cohn R, Faghfoury H, Forster-Gibson C, Gonorazky H, Grunebaum E, Inbar-Feigenberg M, Karp N, Morel C, Rusnak A, Sondheimer N, Warman-Chardon J, Bhola PT, Bourque DK, Chacon IJ, Chad L, Chakraborty P, Chong K, Doja A, Goh ESY, Saleh M, Potter BK, Marshall CR, Dyment DA, Kernohan K, Boycott KM. Bridging clinical care and research in Ontario, Canada: Maximizing diagnoses from reanalysis of clinical exome sequencing data. Clin Genet 2023; 103:288-300. [PMID: 36353900 DOI: 10.1111/cge.14262] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 10/28/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
We examined the utility of clinical and research processes in the reanalysis of publicly-funded clinical exome sequencing data in Ontario, Canada. In partnership with eight sites, we recruited 287 families with suspected rare genetic diseases tested between 2014 and 2020. Data from seven laboratories was reanalyzed with the referring clinicians. Reanalysis of clinically relevant genes identified diagnoses in 4% (13/287); four were missed by clinical testing. Translational research methods, including analysis of novel candidate genes, identified candidates in 21% (61/287). Of these, 24 families have additional evidence through data sharing to support likely diagnoses (8% of cohort). This study indicates few diagnoses are missed by clinical laboratories, the incremental gain from reanalysis of clinically-relevant genes is modest, and the highest yield comes from validation of novel disease-gene associations. Future implementation of translational research methods, including continued reporting of compelling genes of uncertain significance by clinical laboratories, should be considered to maximize diagnoses.
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Affiliation(s)
- Taila Hartley
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
| | - Élisabeth Soubry
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Meryl Acker
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | - Matthew Osmond
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | | | - Meredith K Gillespie
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Yoko Ito
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
| | - Aren E Marshall
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
| | - Gabrielle Lemire
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
| | - Lijia Huang
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | | | - Alison J Eaton
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
- University of Alberta, Edmonton, Canada
| | - E Magda Price
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - James J Dowling
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | | | | | - Gregory Costain
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | - Michelle M Axford
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | - Anna Szuto
- Hospital for Sick Children, Toronto, Canada
| | - Vanda McNiven
- Hospital for Sick Children, Toronto, Canada
- University Health Network, Toronto, Canada
| | | | | | - Leanne de Kock
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | | | - Ted Young
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | - Zhuo Shao
- University of Toronto, Toronto, Canada
- North York General Hospital, Toronto, Canada
| | | | - Ian D Graham
- University of Ottawa, Ottawa, Canada
- Ottawa Hospital Research Institute, Ottawa, Canada
| | | | | | - Christine M Armour
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | | | - Julie Richer
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Sarah Sawyer
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Matthew Lines
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | | | - Melissa T Carter
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Gail Graham
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Peter Kannu
- Hospital for Sick Children, Toronto, Canada
- University of Alberta, Edmonton, Canada
| | - Joanna Lazier
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Chumei Li
- McMaster Children's Hospital, Hamilton, Canada
| | - Ritu B Aul
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | - Tugce B Balci
- London Health Sciences Center, Western University, London, Canada
| | | | - Lauren Badalato
- Kingston Health Sciences Center, Queen's University, Kingston, Canada
| | - Andrea Guerin
- Kingston Health Sciences Center, Queen's University, Kingston, Canada
| | - Jagdeep Walia
- Kingston Health Sciences Center, Queen's University, Kingston, Canada
| | - David Chitayat
- Hospital for Sick Children, Toronto, Canada
- Mount Sinai Hospital, Toronto, Canada
| | | | | | | | | | | | | | - Natalya Karp
- London Health Sciences Center, Western University, London, Canada
| | | | - Alison Rusnak
- Children's Hospital of Eastern Ontario, Ottawa, Canada
- Kingston Health Sciences Center, Queen's University, Kingston, Canada
| | | | - Jodi Warman-Chardon
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
- The Ottawa Hospital, Ottawa, Canada
| | - Priya T Bhola
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Danielle K Bourque
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | | | - Lauren Chad
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | - Pranesh Chakraborty
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | | | - Asif Doja
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | | | - Maha Saleh
- London Health Sciences Center, Western University, London, Canada
| | | | - Beth K Potter
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
| | - Christian R Marshall
- Hospital for Sick Children, Toronto, Canada
- University of Toronto, Toronto, Canada
| | - David A Dyment
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Kristin Kernohan
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- University of Ottawa, Ottawa, Canada
- Children's Hospital of Eastern Ontario, Ottawa, Canada
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9
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Abstract
Exome sequencing (ES) and genome sequencing (GS) have radically transformed the diagnostic approach to undiagnosed rare/ultrarare Mendelian diseases. Next-generation sequencing (NGS), the technology integral for ES, GS, and most large (100+) gene panels, has enabled previously unimaginable diagnoses, changes in medical management, new treatments, and accurate reproductive risk assessments for patients, as well as new disease gene discoveries. Yet, challenges remain, as most individuals remain undiagnosed with current NGS. Improved NGS technology has resulted in long-read sequencing, which may resolve diagnoses in some patients who do not obtain a diagnosis with current short-read ES and GS, but its effectiveness is unclear, and it is expensive. Other challenges that persist include the resolution of variants of uncertain significance, the urgent need for patients with ultrarare disorders to have access to therapeutics, the need for equity in patient access to NGS-based testing, and the study of ethical concerns. However, the outlook for undiagnosed disease resolution is bright, due to continual advancements in the field.
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Affiliation(s)
- Jennifer A Sullivan
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
| | - Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
| | - Rebecca C Spillmann
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
| | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA;
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10
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Baker EK, Ulm EA, Belonis A, Brightman DS, Hallinan BE, Leslie ND, Miethke AG, Vawter-Lee M, Wu Y, Pena LDM. Clinically available testing options resulting in diagnosis in post-exome clinic at one medical center. Front Genet 2022; 13:887698. [PMID: 35937981 PMCID: PMC9355124 DOI: 10.3389/fgene.2022.887698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/28/2022] [Indexed: 11/27/2022] Open
Abstract
Exome sequencing (ES) became clinically available in 2011 and promised an agnostic, unbiased next-generation sequencing (NGS) platform for patients with symptoms believed to have a genetic etiology. The diagnostic yield of ES has been estimated to be between 25–40% and may be higher in specific clinical scenarios. Those who remain undiagnosed may have no molecular findings of interest on ES, variants of uncertain significance in genes that are linked to human disease, or variants of uncertain significance in candidate genes that are not definitively tied to human disease. Recent evidence suggests that a post-exome evaluation consisting of clinical re-phenotyping, functional studies of candidate variants in known genes, and variant reevaluation can lead to a diagnosis in 5–15% of additional cases. In this brief research study, we present our experience on post-exome evaluations in a cohort of patients who are believed to have a genetic etiology for their symptoms. We have reached a full or partial diagnosis in approximately 18% (6/33) of cases that have completed evaluations to date. We accomplished this by utilizing NGS-based methods that are available on a clinical basis. A sample of these cases highlights the utility of ES reanalysis with updated phenotyping allowing for the discovery of new genes, re-adjudication of known variants, incorporating updated phenotypic information, utilizing functional testing such as targeted RNA sequencing, and deploying other NGS-based testing methods such as gene panels and genome sequencing to reach a diagnosis.
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Affiliation(s)
- Elizabeth K. Baker
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Elizabeth A. Ulm
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Alyce Belonis
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Diana S. Brightman
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Barbara E. Hallinan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Nancy D. Leslie
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Alexander G. Miethke
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Marissa Vawter-Lee
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Yaning Wu
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Loren D. M. Pena
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- *Correspondence: Loren D. M. Pena,
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11
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Mørup SB, Nazaryan-Petersen L, Gabrielaite M, Reekie J, Marquart HV, Hartling HJ, Marvig RL, Katzenstein TL, Masmas TN, Lundgren J, Murray DD, Helleberg M, Borgwardt L. Added Value of Reanalysis of Whole Exome- and Whole Genome Sequencing Data From Patients Suspected of Primary Immune Deficiency Using an Extended Gene Panel and Structural Variation Calling. Front Immunol 2022; 13:906328. [PMID: 35874679 PMCID: PMC9302041 DOI: 10.3389/fimmu.2022.906328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Knowledge of the genetic variation underlying Primary Immune Deficiency (PID) is increasing. Reanalysis of genome-wide sequencing data from undiagnosed patients with suspected PID may improve the diagnostic rate. Methods We included patients monitored at the Department of Infectious Diseases or the Child and Adolescent Department, Rigshospitalet, Denmark, for a suspected PID, who had been analysed previously using a targeted PID gene panel (457 PID-related genes) on whole exome- (WES) or whole genome sequencing (WGS) data. A literature review was performed to extend the PID gene panel used for reanalysis of single nucleotide variation (SNV) and small indels. Structural variant (SV) calling was added on WGS data. Results Genetic data from 94 patients (86 adults) including 36 WES and 58 WGS was reanalysed a median of 23 months after the initial analysis. The extended gene panel included 208 additional PID-related genes. Genetic reanalysis led to a small increase in the proportion of patients with new suspicious PID related variants of uncertain significance (VUS). The proportion of patients with a causal genetic diagnosis was constant. In total, five patients (5%, including three WES and two WGS) had a new suspicious PID VUS identified due to reanalysis. Among these, two patients had a variant added due to the expansion of the PID gene panel, and three patients had a variant reclassified to a VUS in a gene included in the initial PID gene panel. The total proportion of patients with PID related VUS, likely pathogenic, and pathogenic variants increased from 43 (46%) to 47 (50%), as one patient had a VUS detected in both initial- and reanalysis. In addition, we detected new suspicious SNVs and SVs of uncertain significance in PID candidate genes with unknown inheritance and/or as heterozygous variants in genes with autosomal recessive inheritance in 8 patients. Conclusion These data indicate a possible diagnostic gain of reassessing WES/WGS data from patients with suspected PID. Reasons for the possible gain included improved knowledge of genotype-phenotype correlation, expanding the gene panel, and adding SV analyses. Future studies of genotype-phenotype correlations may provide additional knowledge on the impact of the new suspicious VUSs.
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Affiliation(s)
- Sara Bohnstedt Mørup
- Centre of Excellence for Health, Immunity, and Infections, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Lusine Nazaryan-Petersen
- Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Migle Gabrielaite
- Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Joanne Reekie
- Centre of Excellence for Health, Immunity, and Infections, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Hanne V. Marquart
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Hans Jakob Hartling
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rasmus L. Marvig
- Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Terese L. Katzenstein
- Department of Infectious Diseases, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Tania N. Masmas
- The Child and Adolescent Department, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jens Lundgren
- Centre of Excellence for Health, Immunity, and Infections, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Infectious Diseases, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Daniel D. Murray
- Centre of Excellence for Health, Immunity, and Infections, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Marie Helleberg
- Centre of Excellence for Health, Immunity, and Infections, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Infectious Diseases, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Line Borgwardt
- Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
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12
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Dai P, Honda A, Ewans L, McGaughran J, Burnett L, Law M, Phan TG. Recommendations for next generation sequencing data reanalysis of unsolved cases with suspected Mendelian disorders: A systematic review and meta-analysis. Genet Med 2022; 24:1618-1629. [PMID: 35550369 DOI: 10.1016/j.gim.2022.04.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE The study aimed to determine the diagnostic yield, optimal timing, and methodology of next generation sequencing data reanalysis in suspected Mendelian disorders. METHODS We conducted a systematic review and meta-analysis of studies that conducted data reanalysis in patients with suspected Mendelian disorders. Random effects model was used to pool the estimated outcome with subgroup analysis stratified by timing, sequencing methodology, sample size, segregation, use of research validation, and artificial intelligence (AI) variant curation tools. RESULTS A search of PubMed, Embase, Scopus, and Web of Science between 2007 and 2021 yielded 9327 articles, of which 29 were selected. Significant heterogeneity was noted between studies. Reanalysis had an overall diagnostic yield of 0.10 (95% CI = 0.06-0.13). Literature updates accounted for most new diagnoses. Diagnostic yield was higher after 24 months, although this was not statistically significant. Increased diagnoses were obtained with research validation and data sharing. AI-based tools did not adversely affect reanalysis diagnostic rate. CONCLUSION Next generation sequencing data reanalysis can improve diagnostic yield. Owing to the heterogeneity of the studies, the optimal time to reanalysis and the impact of AI-based tools could not be determined with confidence. We propose standardized guidelines for future studies to reduce heterogeneity and improve the quality of the conclusions.
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Affiliation(s)
- Pei Dai
- Precision Immunology Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia; Clinical Immunogenomics Research Consortium Australasia (CIRCA), Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Andrew Honda
- The Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Lisa Ewans
- Department of Clinical Genetics, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Julie McGaughran
- Genetic Health Queensland, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Leslie Burnett
- Precision Immunology Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia; Clinical Immunogenomics Research Consortium Australasia (CIRCA), Garvan Institute of Medical Research, Sydney, New South Wales, Australia; The Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; Genetic Medicine Program, Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Matthew Law
- Biostatistics and Databases Program, The Kirby Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Tri Giang Phan
- Precision Immunology Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia; Clinical Immunogenomics Research Consortium Australasia (CIRCA), Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
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13
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Bullich G, Matalonga L, Pujadas M, Papakonstantinou A, Piscia D, Tonda R, Artuch R, Gallano P, Garrabou G, González JR, Grinberg D, Guitart M, Laurie S, Lázaro C, Luengo C, Martí R, Milà M, Ovelleiro D, Parra G, Pujol A, Tizzano E, Macaya A, Palau F, Ribes A, Pérez-Jurado LA, Beltran S. Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases. J Mol Diagn 2022; 24:529-542. [PMID: 35569879 DOI: 10.1016/j.jmoldx.2022.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 12/16/2021] [Accepted: 02/03/2022] [Indexed: 11/26/2022] Open
Abstract
Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%).
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Affiliation(s)
- Gemma Bullich
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Leslie Matalonga
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Montserrat Pujadas
- Genetics Unit, University Pompeu Fabra, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - Anastasios Papakonstantinou
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Davide Piscia
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Raúl Tonda
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Rafael Artuch
- Clinical Biochemistry Department, Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain; Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Pia Gallano
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Genetics Department, Institut d'Investigacions Biomèdiques (IIB) Sant Pau, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Glòria Garrabou
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Muscle Research and Mitochondrial Function Laboratory, CELLEX-Institut d'Investigació Biomèdica August Pi i Sunyer (IDIBAPS), Internal Medicine Department, Hospital Clinic of Barcelona, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Juan R González
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Centro de Investigaciones Biomédicas en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Daniel Grinberg
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Institute of Biomedicine of the University of Barcelona (IBUB), Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain
| | - Míriam Guitart
- Genetics Laboratory, Paediatric Unit, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Steven Laurie
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Conxi Lázaro
- Molecular Diagnostic Unit, Hereditary Cancer Program, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Catalan Institute of Oncology, Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain
| | - Cristina Luengo
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ramon Martí
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Research Group on Neuromuscular and Mitochondrial Diseases, Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Montserrat Milà
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Biochemistry and Molecular Genetics Department, Hospital Clínic de Barcelona, Institut d'Investigació Biomèdica August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - David Ovelleiro
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Genís Parra
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Aurora Pujol
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Neurometabolic Diseases Laboratory, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL)-Hospital Duran i Reynals, Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Eduardo Tizzano
- Department of Clinical and Molecular Genetics, Medicine Genetics Group Vall d'Hebron Institut de Recerca (VHIR), European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability ERN-ITHACA, Universitat Autònoma de Barcelona, Hospital Vall d´Hebron, Barcelona, Spain
| | - Alfons Macaya
- Pediatric Neurology Research Group, Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Francesc Palau
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Department of Genetic and Molecular Medicine, Pediatric Institute of Rare Diseases (IPER), Hospital Sant Joan de Déu, Clinic Institute of Medicine and Dermatology, Hospital Clínic de Barcelona and Division of Pediatrics, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Antònia Ribes
- Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Secció d'Errors Congènits del Metabolisme-Institute of Clinical Biochemistry (IBC), Servei de Bioquímica i Genètìca Molecular, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Luis A Pérez-Jurado
- Genetics Unit, University Pompeu Fabra, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain; Centro de Investigaciones Biomédicas en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Women's and Children's Hospital, South Australian Health and Medical Research Institute and The University of Adelaide, Adelaide, South Australia, Australia
| | - Sergi Beltran
- Centro Nacional Análisis Genómico (CNAG)-Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain.
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14
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Pan X, Liu S, Liu L, Zhang X, Yao H, Tan B. Case Report: Exome and RNA Sequencing Identify a Novel de novo Missense Variant in HNRNPK in a Chinese Patient With Au-Kline Syndrome. Front Genet 2022; 13:853028. [PMID: 35422839 PMCID: PMC9001983 DOI: 10.3389/fgene.2022.853028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/14/2022] [Indexed: 02/05/2023] Open
Abstract
Au-Kline syndrome is a severe multisystemic syndrome characterized by several congenital defects, including intellectual disability. Loss-of-function and missense variants in the HNRNPK gene are associated with a range of dysmorphic features. This report describes an eleven-year-old Chinese boy with intellectual disability and developmental delays. Family-based whole-exome and Sanger sequencing identified a de novo missense variant in HNRNPK (NM_002140.3: c.143T > A, p. Leu48Val). In silico analysis predicted that this variant would be damaged in a highly conserved residue in the K homology 1 (KH1) domain. Bioinformatic analysis showed that the affinity change (ΔΔG) caused by this variant was -0.033 kcal/mol, indicating that it would have reduced affinity for RNA binding. Transcript analysis of the peripheral blood from this case found 42 aberrantly expressed and 86 aberrantly spliced genes (p-value <0.01). Functional enrichment analysis confirmed that the biological functions of these genes, including protein binding and transcriptional regulation, are associated with HNRNPK. In summary, this study identifies the first Chinese patient with a novel de novo heterozygous HNRNPK gene variant that contributes to Au-Kline syndrome and expands current knowledge of the clinical spectrum of HNRNPK variants.
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Affiliation(s)
- Xin Pan
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Sihan Liu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, China
| | - Li Liu
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xu Zhang
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hong Yao
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bo Tan
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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15
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Robertson AJ, Tan NB, Spurdle AB, Metke-Jimenez A, Sullivan C, Waddell N. Re-analysis of genomic data: An overview of the mechanisms and complexities of clinical adoption. Genet Med 2022; 24:798-810. [PMID: 35065883 DOI: 10.1016/j.gim.2021.12.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 12/20/2022] Open
Abstract
Re-analyzing genomic information from a patient suspected of having an underlying genetic condition can improve the diagnostic yield of sequencing tests, potentially providing significant benefits to the patient and to the health care system. Although a significant number of studies have shown the clinical potential of re-analysis, less work has been performed to characterize the mechanisms responsible for driving the increases in diagnostic yield. Complexities surrounding re-analysis have also emerged. The terminology itself represents a challenge because "re-analysis" can refer to a range of different concepts. Other challenges include the increased workload that re-analysis demands of curators, adequate reimbursement pathways for clinical and diagnostic services, and the development of systems to handle large volumes of data. Re-analysis also raises ethical implications for patients and families, most notably when re-classification of a variant alters diagnosis, treatment, and prognosis. This review highlights the possibilities and complexities associated with the re-analysis of existing clinical genomic data. We propose a terminology that builds on the foundation presented in a recent statement from the American College of Medical Genetics and Genomics and describes each re-analysis process. We identify mechanisms for increasing diagnostic yield and provide perspectives on the range of challenges that must be addressed by health care systems and individual patients.
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Affiliation(s)
- Alan J Robertson
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Queensland Digital Health Research Network, Global Change Institute, The University of Queensland, Brisbane, Queensland, Australia; The Genomic Institute, Department of Health, Queensland Government, Brisbane, Queensland, Australia
| | - Natalie B Tan
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia; Department of Paediatrics, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Amanda B Spurdle
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Clair Sullivan
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia; Queensland Digital Health Research Network, Global Change Institute, The University of Queensland, Brisbane, Queensland, Australia; Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia; Metro North Hospital and Health Service, Department of Health, Queensland Government, Brisbane, Queensland, Australia
| | - Nicola Waddell
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
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16
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Kadlubowska MK, Schrauwen I. Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology. Genes (Basel) 2022; 13:333. [PMID: 35205378 PMCID: PMC8871714 DOI: 10.3390/genes13020333] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/06/2022] [Accepted: 02/07/2022] [Indexed: 02/04/2023] Open
Abstract
During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.
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17
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Leung ML, Ji J, Baker S, Buchan JG, Sivakumaran TA, Krock BL, Hutchins R, Bayrak-Toydemir P, Pfeifer J, Cremona ML, Funke B, Santani AB. A Framework of Critical Considerations in Clinical Exome Reanalyses by Clinical and Laboratory Standards Institute. J Mol Diagn 2022; 24:177-188. [PMID: 35074075 DOI: 10.1016/j.jmoldx.2021.11.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 10/20/2021] [Accepted: 11/02/2021] [Indexed: 11/28/2022] Open
Abstract
Exome reanalysis is useful for providing molecular diagnoses for previously uninformative samples. However, challenges exist in implementing a practical solution for clinicians and laboratories. This study complements the current literature by providing practical considerations for patient-level and cohort-level reanalyses. The Clinical and Laboratory Standards Institute assembled the Document Development Committee and an interpretation working group that developed the framework for reevaluation of exome-based data. We describe two distinct but complementary approaches toward exome reanalyses: clinician-initiated patient-level reanalysis, and laboratory-initiated cohort-level reanalysis. We highlight the advantages and constraints for both approaches, and provide a high-level conceptual guide for ordering clinicians and laboratories through the critical decision pathways. Because clinical exome sequencing continues to be the standard of care in genetics, exome reanalysis would be critical in increasing the overall diagnostic yield. A systematic guide will facilitate the efficient adoption of reevaluation of exome data for laboratories, health care professionals, genetic counselors, and clinicians.
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Affiliation(s)
- Marco L Leung
- Departments of Pathology and Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio; The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio; Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, Columbus, Ohio
| | - Jianling Ji
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California; Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California
| | - Samuel Baker
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jillian G Buchan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington
| | - Theru A Sivakumaran
- Division of Pathology and Laboratory Medicine, Phoenix Children's Hospital, Phoenix, Arizona
| | | | | | - Pinar Bayrak-Toydemir
- Department of Pathology, The University of Utah, Salt Lake City, Utah; ARUP Laboratories, Salt Lake City, Utah
| | - John Pfeifer
- Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | | | | | - Avni B Santani
- Center for Applied Genomics, Children's Hospital of Philadelphia, Pennsylvania; Veritas Genetics, Boston, Massachusetts.
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18
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Abstract
Genetic testing has undergone a revolution in the last decade, particularly with the advent of next-generation sequencing and its associated reductions in costs and increases in efficiencies. The Undiagnosed Diseases Network (UDN) has been a leader in the application of such genomic testing for rare disease diagnosis. This review discusses the current state of genomic testing performed within the UDN, with a focus on the strengths and limitations of whole-exome and whole-genome sequencing in clinical diagnostics and the importance of ongoing data reanalysis. The role of emerging technologies such as RNA and long-read sequencing to further improve diagnostic rates in the UDN is also described. This review concludes with a discussion of the challenges faced in insurance coverage of comprehensive genomic testing as well as the opportunities for a larger role of testing in clinical medicine.
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Affiliation(s)
- David R Murdock
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA;
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA;
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA;
- Texas Children's Hospital, Houston, Texas 77030, USA
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19
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Li Q, Madden JA, Lin J, Shi J, Rosen SM, Schmitz-Abe K, Agrawal PB. Reanalysis of Exome Data Identifies Novel SLC25A46 Variants Associated with Leigh Syndrome. J Pers Med 2021; 11:jpm11121277. [PMID: 34945750 PMCID: PMC8703603 DOI: 10.3390/jpm11121277] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/27/2021] [Accepted: 11/29/2021] [Indexed: 11/21/2022] Open
Abstract
SLC25A46 (solute carrier family 25 member 46) mutations have been linked to various neurological diseases with recessive inheritance, including Leigh syndrome, optic atrophy, and lethal congenital pontocerebellar hypoplasia. SLC25A46 is expressed in the outer membrane of mitochondria, where it plays a critical role in mitochondrial dynamics. A deceased 7-month-old female infant was suspected to have Leigh syndrome. Clinical exome sequencing was non-diagnostic, but research reanalysis of the sequencing data identified two novel variants in SLC25A46: a missense (c.1039C>T, p.Arg347Cys; NM_138773, hg19) and a donor splice region variant (c.283+5G>A) in intron 1. Both variants were predicted to be damaging. Sanger sequencing of cDNA detected a single missense allele in the patient compared to control, and the SLC25A46 transcript levels were also reduced due to the splice region variant. Additionally, Western blot analysis of whole-cell lysate showed a decrease of SLC25A46 expression in proband fibroblasts, relative to control cells. Further, analysis of mitochondrial morphology revealed evidence of increased fragmentation of the mitochondrial network in proband fibroblasts, compared to control cells. Collectively, our findings suggest that these novel variants in SLC24A46, the donor splice one and the missense variant, are the cause of the neurological phenotype in this proband.
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Affiliation(s)
- Qifei Li
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.L.); (J.L.); (S.M.R.); (K.S.-A.)
- The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jill A. Madden
- The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jasmine Lin
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.L.); (J.L.); (S.M.R.); (K.S.-A.)
- The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jiahai Shi
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong, China;
| | - Samantha M. Rosen
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.L.); (J.L.); (S.M.R.); (K.S.-A.)
- The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Klaus Schmitz-Abe
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.L.); (J.L.); (S.M.R.); (K.S.-A.)
- The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Pankaj B. Agrawal
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.L.); (J.L.); (S.M.R.); (K.S.-A.)
- The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Correspondence: ; Tel.: +1-6179192153
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20
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Incerti D, Xu XM, Chou JW, Gonzaludo N, Belmont JW, Schroeder BE. Cost-effectiveness of genome sequencing for diagnosing patients with undiagnosed rare genetic diseases. Genet Med 2021:S1098-3600(21)01129-1. [PMID: 34906478 DOI: 10.1016/j.gim.2021.08.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 06/26/2020] [Accepted: 08/25/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To estimate the cost-effectiveness of genome sequencing (GS) for diagnosing critically ill infants and noncritically ill pediatric patients (children) with suspected rare genetic diseases from a United States health sector perspective. METHODS A decision-analytic model was developed to simulate the diagnostic trajectory of patients. Parameter estimates were derived from a targeted literature review and meta-analysis. The model simulated clinical and economic outcomes associated with 3 diagnostic pathways: (1) standard diagnostic care, (2) GS, and (3) standard diagnostic care followed by GS. RESULTS For children, costs of GS ($7284) were similar to that of standard care ($7355) and lower than that of standard care followed by GS pathways ($12,030). In critically ill infants, when cost estimates were based on the length of stay in the neonatal intensive care unit, the lowest cost pathway was GS ($209,472). When only diagnostic test costs were included, the cost per diagnosis was $17,940 for standard, $17,019 for GS, and $20,255 for standard care followed by GS. CONCLUSION The results of this economic model suggest that GS may be cost neutral or possibly cost saving as a first line diagnostic tool for children and critically ill infants.
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21
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Abstract
Background Pediatric-onset cardiomyopathies are rare yet cause significant morbidity and mortality in affected children. Genetic testing has a major role in the clinical evaluation of pediatric-onset cardiomyopathies, and identification of a variant in an associated gene can be used to confirm the clinical diagnosis and exclude syndromic causes that may warrant different treatment strategies. Further, risk-predictive testing of first-degree relatives can assess who is at-risk of disease and requires continued clinical follow-up. Aim of Review In this review, we seek to describe the current role of genetic testing in the clinical diagnosis and management of patients and families with the five major cardiomyopathies. Further, we highlight the ongoing development of precision-based approaches to diagnosis, prognosis, and treatment. Key Scientific Concepts of Review Emerging application of genotype-phenotype correlations opens the door for genetics to guide a precision medicine-based approach to prognosis and potentially for therapies. Despite advances in our understanding of the genetic etiology of cardiomyopathy and increased accessibility of clinical genetic testing, not all pediatric cardiomyopathy patients have a clear genetic explanation for their disease. Expanded genomic studies are needed to understand the cause of disease in these patients, improve variant classification and genotype-driven prognostic predictions, and ultimately develop truly disease preventing treatment.
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Affiliation(s)
- Lauren E Parker
- Department of Pediatrics, Division of Cardiology, Duke University School of Medicine, Durham, NC, United States
| | - Andrew P Landstrom
- Department of Pediatrics, Division of Cardiology, Duke University School of Medicine, Durham, NC, United States.,Department of Cell Biology, Duke University School of Medicine, Durham, NC, United States
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22
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Cloney T, Gallacher L, Pais LS, Tan NB, Yeung A, Stark Z, Brown NJ, McGillivray G, Delatycki MB, de Silva MG, Downie L, Stutterd CA, Elliott J, Compton AG, Lovgren A, Oertel R, Francis D, Bell KM, Sadedin S, Lim SC, Helman G, Simons C, Macarthur DG, Thorburn DR, O'Donnell-Luria AH, Christodoulou J, White SM, Tan TY. Lessons learnt from multifaceted diagnostic approaches to the first 150 families in Victoria's Undiagnosed Diseases Program. J Med Genet 2021; 59:748-758. [PMID: 34740920 DOI: 10.1136/jmedgenet-2021-107902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 09/14/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND Clinical exome sequencing typically achieves diagnostic yields of 30%-57.5% in individuals with monogenic rare diseases. Undiagnosed diseases programmes implement strategies to improve diagnostic outcomes for these individuals. AIM We share the lessons learnt from the first 3 years of the Undiagnosed Diseases Program-Victoria, an Australian programme embedded within a clinical genetics service in the state of Victoria with a focus on paediatric rare diseases. METHODS We enrolled families who remained without a diagnosis after clinical genomic (panel, exome or genome) sequencing between 2016 and 2018. We used family-based exome sequencing (family ES), family-based genome sequencing (family GS), RNA sequencing (RNA-seq) and high-resolution chromosomal microarray (CMA) with research-based analysis. RESULTS In 150 families, we achieved a diagnosis or strong candidate in 64 (42.7%) (37 in known genes with a consistent phenotype, 3 in known genes with a novel phenotype and 24 in novel disease genes). Fifty-four diagnoses or strong candidates were made by family ES, six by family GS with RNA-seq, two by high-resolution CMA and two by data reanalysis. CONCLUSION We share our lessons learnt from the programme. Flexible implementation of multiple strategies allowed for scalability and response to the availability of new technologies. Broad implementation of family ES with research-based analysis showed promising yields post a negative clinical singleton ES. RNA-seq offered multiple benefits in family ES-negative populations. International data sharing strategies were critical in facilitating collaborations to establish novel disease-gene associations. Finally, the integrated approach of a multiskilled, multidisciplinary team was fundamental to having diverse perspectives and strategic decision-making.
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Affiliation(s)
- Thomas Cloney
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Lyndon Gallacher
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Lynn S Pais
- Center for Mendelian Genomics, Eli and Edythe L Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.,Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Natalie B Tan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alison Yeung
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Zornitza Stark
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Natasha J Brown
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - George McGillivray
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Martin B Delatycki
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Michelle G de Silva
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Lilian Downie
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Chloe A Stutterd
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Justine Elliott
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Alison G Compton
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.,Brain and Mitochondrial Research Group, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Alysia Lovgren
- Center for Mendelian Genomics, Eli and Edythe L Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.,Analytic and Translational Genomics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Ralph Oertel
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - David Francis
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Katrina M Bell
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Bioinformatics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Simon Sadedin
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Sze Chern Lim
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Guy Helman
- Brain and Mitochondrial Research Group, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Cas Simons
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Translational Bioinformatics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Daniel G Macarthur
- Center for Mendelian Genomics, Eli and Edythe L Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.,Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.,Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - David R Thorburn
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.,Brain and Mitochondrial Research Group, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Anne H O'Donnell-Luria
- Center for Mendelian Genomics, Eli and Edythe L Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.,Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - John Christodoulou
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.,Neurodevelopmental Genomics Research Group, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Susan M White
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Tiong Yang Tan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia .,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
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23
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Tosur M, Soler-Alfonso C, Chan KM, Khayat MM, Jhangiani SN, Meng Q, Refaey A, Muzny D, Gibbs RA, Murdock DR, Posey JE, Balasubramanyam A, Redondo MJ, Sabo A. Exome sequencing in children with clinically suspected maturity-onset diabetes of the young. Pediatr Diabetes 2021; 22:960-968. [PMID: 34387403 PMCID: PMC8530905 DOI: 10.1111/pedi.13257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 08/09/2021] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE Commercial gene panels identify pathogenic variants in as low as 27% of patients suspected to have MODY, suggesting the role of yet unidentified pathogenic variants. We sought to identify novel gene variants associated with MODY. RESEARCH DESIGN AND METHODS We recruited 10 children with a clinical suspicion of MODY but non-diagnostic commercial MODY gene panels. We performed exome sequencing (ES) in them and their parents. RESULTS Mean age at diabetes diagnosis was 10 (± 3.8) years. Six were females; 4 were non-Hispanic white, 5 Hispanic, and 1 Asian. Our variant prioritization analysis identified a pathogenic, de novo variant in INS (c.94G > A, p.Gly32Ser), confirmed by Sanger sequencing, in a proband who was previously diagnosed with "autoantibody-negative type 1 diabetes (T1D)" at 3 y/o. This rare variant, absent in the general population (gnomAD database), has been reported previously in neonatal diabetes. We also identified a frameshift deletion (c.2650delC, p.Gln884AsnfsTer57) in RFX6 in a child with a previous diagnosis of "autoantibody-negative T1D" at 12 y/o. The variant was inherited from the mother, who was diagnosed with "thin type 2 diabetes" at 25 y/o. Heterozygous protein-truncating variants in RFX6 gene have been recently reported in individuals with MODY. CONCLUSIONS We diagnosed two patients with MODY using ES in children initially classified as "T1D". One has a likely pathogenic novel gene variant not previously associated with MODY. We demonstrate the clinical utility of ES in patients with clinical suspicion of MODY.
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Affiliation(s)
- Mustafa Tosur
- Department of Pediatrics, The Section of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA
| | - Claudia Soler-Alfonso
- Department of Molecular and Human Genetics, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA
| | - Katie M Chan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA
| | - Michael M Khayat
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Shalini N Jhangiani
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Qingchang Meng
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | | | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - David R Murdock
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ashok Balasubramanyam
- Baylor College of Medicine, Division of Diabetes, Endocrinology and Metabolism, Houston, TX, USA
| | - Maria J Redondo
- Department of Pediatrics, The Section of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA
| | - Aniko Sabo
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
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24
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Mensah NE, Sabir AH, Bond A, Roworth W, Irving M, Davies AC, Ahn JW. Automated reanalysis application to assist in detecting novel gene–disease associations after genome sequencing. Genet Med 2021; 24:811-820. [DOI: 10.1016/j.gim.2021.11.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 08/31/2021] [Accepted: 11/24/2021] [Indexed: 02/02/2023] Open
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25
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Parikh JR, Genetti CA, Aykanat A, Brownstein CA, Schmitz-Abe K, Danowski M, Quitadomo A, Madden JA, Yacoubian C, Gain R, Williams T, Meskell M, Brown A, Frith A, Rockowitz S, Sliz P, Agrawal PB, Defay T, McDonagh P, Reynders J, Lefebvre S, Beggs AH. A data-driven architecture using natural language processing to improve phenotyping efficiency and accelerate genetic diagnoses of rare disorders. HGG Adv 2021; 2:100035. [PMID: 34514437 DOI: 10.1016/j.xhgg.2021.100035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Effective genetic diagnosis requires the correlation of genetic variant data with detailed phenotypic information. However, manual encoding of clinical data into machine-readable forms is laborious and subject to observer bias. Natural language processing (NLP) of electronic health records has great potential to enhance reproducibility at scale but suffers from idiosyncrasies in physician notes and other medical records. We developed methods to optimize NLP outputs for automated diagnosis. We filtered NLP-extracted Human Phenotype Ontology (HPO) terms to more closely resemble manually extracted terms and identified filter parameters across a three-dimensional space for optimal gene prioritization. We then developed a tiered pipeline that reduces manual effort by prioritizing smaller subsets of genes to consider for genetic diagnosis. Our filtering pipeline enabled NLP-based extraction of HPO terms to serve as a sufficient replacement for manual extraction in 92% of prospectively evaluated cases. In 75% of cases, the correct causal gene was ranked higher with our applied filters than without any filters. We describe a framework that can maximize the utility of NLP-based phenotype extraction for gene prioritization and diagnosis. The framework is implemented within a cloud-based modular architecture that can be deployed across health and research institutions.
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26
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Helman G, Mendes MI, Nicita F, Darbelli L, Sherbini O, Moore T, Derksen A, Amy Pizzino, Carrozzo R, Torraco A, Catteruccia M, Aiello C, Goffrini P, Figuccia S, Smith DEC, Hadzsiev K, Hahn A, Biskup S, Brösse I, Kotzaeridou U, Gauck D, Grebe TA, Elmslie F, Stals K, Gupta R, Bertini E, Thiffault I, Taft RJ, Schiffmann R, Brandl U, Haack TB, Salomons GS, Simons C, Bernard G, van der Knaap MS, Vanderver A, Husain RA. Expanded phenotype of AARS1-related white matter disease. Genet Med 2021; 23:2352-2359. [PMID: 34446925 DOI: 10.1038/s41436-021-01286-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Recent reports of individuals with cytoplasmic transfer RNA (tRNA) synthetase-related disorders have identified cases with phenotypic variability from the index presentations. We sought to assess phenotypic variability in individuals with AARS1-related disease. METHODS A cross-sectional survey was performed on individuals with biallelic variants in AARS1. Clinical data, neuroimaging, and genetic testing results were reviewed. Alanyl tRNA synthetase (AlaRS) activity was measured in available fibroblasts. RESULTS We identified 11 affected individuals. Two phenotypic presentations emerged, one with early infantile-onset disease resembling the index cases of AARS1-related epileptic encephalopathy with deficient myelination (n = 7). The second (n = 4) was a later-onset disorder, where disease onset occurred after the first year of life and was characterized on neuroimaging by a progressive posterior predominant leukoencephalopathy evolving to include the frontal white matter. AlaRS activity was significantly reduced in five affected individuals with both early infantile-onset and late-onset phenotypes. CONCLUSION We suggest that variants in AARS1 result in a broader clinical spectrum than previously appreciated. The predominant form results in early infantile-onset disease with epileptic encephalopathy and deficient myelination. However, a subgroup of affected individuals manifests with late-onset disease and similarly rapid progressive clinical decline. Longitudinal imaging and clinical follow-up will be valuable in understanding factors affecting disease progression and outcome.
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Affiliation(s)
- Guy Helman
- Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville, VIC, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Marisa I Mendes
- Metabolic Unit, Department of Clinical Chemistry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Francesco Nicita
- Department of Neurosciences, Unit of Muscular and Neurodegenerative Disorders, Laboratory of Molecular Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Lama Darbelli
- 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
| | - Omar Sherbini
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Travis Moore
- 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
| | - Alexa Derksen
- 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
| | - Amy Pizzino
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Rosalba Carrozzo
- Department of Neurosciences, Unit of Muscular and Neurodegenerative Disorders, Laboratory of Molecular Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Alessandra Torraco
- Department of Neurosciences, Unit of Muscular and Neurodegenerative Disorders, Laboratory of Molecular Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Michela Catteruccia
- Department of Neurosciences, Unit of Muscular and Neurodegenerative Disorders, Laboratory of Molecular Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Chiara Aiello
- Department of Neurosciences, Unit of Muscular and Neurodegenerative Disorders, Laboratory of Molecular Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Paola Goffrini
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - Sonia Figuccia
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - Desiree E C Smith
- Metabolic Unit, Department of Clinical Chemistry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kinga Hadzsiev
- Department of Medical Genetics, University of Pécs, Pécs, Hungary
| | - Andreas Hahn
- Department of Child Neurology, Justus-Liebig-University, Giessen, Germany
| | - Saskia Biskup
- Praxis fuer Humangenetik and CeGaT GmbH, Tuebingen, Germany
| | - Ines Brösse
- Division of Child Neurology and Inherited Metabolic Diseases, Centre for Paediatrics and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Urania Kotzaeridou
- Division of Child Neurology and Inherited Metabolic Diseases, Centre for Paediatrics and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Darja Gauck
- Institute of Medical Genetics and Applied Genomics, University of Tuebingen, Tuebingen, Germany
| | - Theresa A Grebe
- Division of Genetics and Metabolism, Department of Child Health, Phoenix Children's Hospital, University of Arizona College of Medicine, Phoenix, AZ, USA
| | - Frances Elmslie
- South West Thames Regional Genetics Service, St George's University Hospital, London, UK
| | - Karen Stals
- Molecular Genetics Department, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Rajat Gupta
- Department of Neurology, Birmingham Children's Hospital, Birmingham, UK
| | - Enrico Bertini
- Department of Neurosciences, Unit of Muscular and Neurodegenerative Disorders, Laboratory of Molecular Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Isabelle Thiffault
- Children's Mercy Kansas City, Center for Pediatric Genomic Medicine, Kansas City, MO, USA.,Department of Pathology and Laboratory Medicine, Children's Mercy Hospitals, Kansas City, MO, USA.,School of Medicine, University of Missouri-Kansas City, Kansas City, MO, USA
| | | | | | - Ulrich Brandl
- Department of Neuropediatrics, Jena University Hospital, Jena, Germany
| | - Tobias B Haack
- Institute of Medical Genetics and Applied Genomics, University of Tuebingen, Tuebingen, Germany
| | - Gajja S Salomons
- Metabolic Unit, Department of Clinical Chemistry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Cas Simons
- Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville, VIC, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - 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
| | - Marjo S van der Knaap
- Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam and Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Adeline Vanderver
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA. .,Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
| | - Ralf A Husain
- Department of Neuropediatrics, Jena University Hospital, Jena, Germany.
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27
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Cope H, Barseghyan H, Bhattacharya S, Fu Y, Hoppman N, Marcou C, Walley N, Rehder C, Deak K, Alkelai A, Vilain E, Shashi V. Detection of a mosaic CDKL5 deletion and inversion by optical genome mapping ends an exhaustive diagnostic odyssey. Mol Genet Genomic Med 2021; 9:e1665. [PMID: 33955715 PMCID: PMC8372083 DOI: 10.1002/mgg3.1665] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 02/23/2021] [Accepted: 03/01/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Currently available structural variant (SV) detection methods do not span the complete spectrum of disease-causing SVs. Optical genome mapping (OGM), an emerging technology with the potential to resolve diagnostic dilemmas, was performed to investigate clinically-relevant SVs in a 4-year-old male with an epileptic encephalopathy of undiagnosed molecular origin. METHODS OGM was utilized to image long, megabase-size DNA molecules, fluorescently labeled at specific sequence motifs throughout the genome with high sensitivity for detection of SVs greater than 500 bp in size. OGM results were confirmed in a CLIA-certified laboratory via mate-pair sequencing. RESULTS OGM identified a mosaic, de novo 90 kb deletion and inversion on the X chromosome disrupting the CDKL5 gene. Detection of the mosaic deletion, which had been previously undetected by chromosomal microarray, an infantile epilepsy panel including exon-level microarray for CDKL5, exome sequencing as well as genome sequencing, resulted in a diagnosis of X-linked dominant early infantile epileptic encephalopathy-2. CONCLUSION OGM affords an effective technology for the detection of SVs, especially those that are mosaic, since these remain difficult to detect with current NGS technologies and with conventional chromosomal microarrays. Further research in undiagnosed populations with OGM is warranted.
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Affiliation(s)
- Heidi Cope
- Division of Medical GeneticsDepartment of PediatricsDuke University Medical CenterDurhamNCUSA
| | - Hayk Barseghyan
- Center for Genetic Medicine ResearchChildren’s National HospitalWashingtonDCUSA
- Department of genomics and Precision MedicineSchool of Medicine and Health SciencesGeorge Washington UniversityWashingtonDCUSA
- Bionano Genomics IncSan DiegoCAUSA
| | | | - Yulong Fu
- Center for Genetic Medicine ResearchChildren’s National HospitalWashingtonDCUSA
| | - Nicole Hoppman
- Division of Laboratory Genetics and GenomicsDepartment of Laboratory Medicine and PathologyMayo ClinicRochesterMNUSA
| | - Cherisse Marcou
- Division of Laboratory Genetics and GenomicsDepartment of Laboratory Medicine and PathologyMayo ClinicRochesterMNUSA
| | - Nicole Walley
- Division of Medical GeneticsDepartment of PediatricsDuke University Medical CenterDurhamNCUSA
| | - Catherine Rehder
- Department of PathologyDuke University Medical CenterDurhamNCUSA
| | - Kristen Deak
- Department of PathologyDuke University Medical CenterDurhamNCUSA
| | - Anna Alkelai
- Institute for Genomic MedicineColumbia University Medical CenterNew YorkNYUSA
| | - Eric Vilain
- Center for Genetic Medicine ResearchChildren’s National HospitalWashingtonDCUSA
- Department of genomics and Precision MedicineSchool of Medicine and Health SciencesGeorge Washington UniversityWashingtonDCUSA
| | - Vandana Shashi
- Division of Medical GeneticsDepartment of PediatricsDuke University Medical CenterDurhamNCUSA
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28
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Luo X, Schoch K, Jangam SV, Bhavana VH, Graves HK, Kansagra S, Jasien JM, Stong N, Keren B, Mignot C, Ravelli C, Bellen HJ, Wangler MF, Shashi V, Yamamoto S. Rare deleterious de novo missense variants in Rnf2/Ring2 are associated with a neurodevelopmental disorder with unique clinical features. Hum Mol Genet 2021; 30:1283-1292. [PMID: 33864376 PMCID: PMC8255132 DOI: 10.1093/hmg/ddab110] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/09/2021] [Accepted: 04/14/2021] [Indexed: 01/16/2023] Open
Abstract
The Polycomb group (PcG) gene RNF2 (RING2) encodes a catalytic subunit of the Polycomb repressive complex 1 (PRC1), an evolutionarily conserved machinery that post-translationally modifies chromatin to maintain epigenetic transcriptional repressive states of target genes including Hox genes. Here, we describe two individuals, each with rare de novo missense variants in RNF2. Their phenotypes include intrauterine growth retardation, severe intellectual disabilities, behavioral problems, seizures, feeding difficulties and dysmorphic features. Population genomics data suggest that RNF2 is highly constrained for loss-of-function (LoF) and missense variants, and both p.R70H and p.S82R variants have not been reported to date. Structural analyses of the two alleles indicate that these changes likely impact the interaction between RNF2 and BMI1, another PRC1 subunit or its substrate Histone H2A, respectively. Finally, we provide functional data in Drosophila that these two missense variants behave as LoF alleles in vivo. The evidence provide support for deleterious alleles in RNF2 being associated with a new and recognizable genetic disorder. This tentative gene-disease association in addition to the 12 previously identified disorders caused by PcG genes attests to the importance of these chromatin regulators in Mendelian disorders.
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Affiliation(s)
- Xi Luo
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC 27710, USA
| | - Sharayu V Jangam
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Venkata Hemanjani Bhavana
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Hillary K Graves
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Sujay Kansagra
- Division of Pediatric Neurology, Department of Pediatrics, Duke Health, Durham, NC 27710, USA
| | - Joan M Jasien
- Division of Pediatric Neurology, Department of Pediatrics, Duke Health, Durham, NC 27710, USA
| | - Nicholas Stong
- Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA
| | - Boris Keren
- Département de Génétique, Hospitalier Pitié-Salpêtrière, APHP, Paris 75013, France
- Sorbonne Université, Paris 75006, France
| | - Cyril Mignot
- Sorbonne Université, Paris 75006, France
- APHP, Sorbonne Université, Département de Génétique et Centre de Référence Déficiences Intellectuelles de Causes Rares, Groupe Hospitalier Pitié-Salpêtrière et Hôpital Trousseau, Paris 75013, France
| | - Claudia Ravelli
- Sorbonne Université, Paris 75006, France
- Département de Neuropédiatrie, Hôpital Armand Trousseau, APHP, Paris 75012, France
| | | | - Hugo J Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
- Department of Neuroscience, BCM, Houston, TX 77030, USA
- Howard Hughes Medical Institute, Houston, TX 77030, USA
| | - Michael F Wangler
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC 27710, USA
| | - Shinya Yamamoto
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
- Department of Neuroscience, BCM, Houston, TX 77030, USA
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29
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Zhang L, Pan L, Teng Y, Liang D, Li Z, Wu L. Molecular diagnosis for 55 fetuses with skeletal dysplasias by whole-exome sequencing: A retrospective cohort study. Clin Genet 2021; 100:219-226. [PMID: 33942288 DOI: 10.1111/cge.13976] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 11/29/2022]
Abstract
Skeletal dysplasias (SDs) are common birth defects, but they are difficult to diagnose accurately according to only the limited phenotypic information available from ultrasound during the pregnancy. To evaluate the application of whole-exome sequencing (WES) and expand the data in the prenatal molecular diagnosis of fetuses with SDs, we collected 55 fetuses with SDs based on ultrasonographic features. WES of the fetuses or parent-fetus trio were subjected to sequential tests and produced a diagnostic yield of 64% (35/55). 65% (11/17) of families with a history of adverse pregnancies were diagnosed, 16 genes were involved and 37 different pathogenic or likely pathogenic variants were identified, including 14 novel variants, which were first reported in this study. De novo variants were identified in 21 cases (60%, 21/35) among the fetuses with a genetic diagnosis. The pathogenicity of two novel splice-site variants was confirmed by constructing minigene in vitro. Our results revealed that WES can provide new evidence for the relationship between the genotype and phenotype of fetuses with SDs, as well as broaden the mutation spectrum of detected genes, which is significant for prenatal diagnosis and genetic counseling.
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Affiliation(s)
- Li Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of life sciences, Central South University, Changsha, Hunan, China
| | - Lijuan Pan
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of life sciences, Central South University, Changsha, Hunan, China
| | - Yanling Teng
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of life sciences, Central South University, Changsha, Hunan, China
| | - Desheng Liang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of life sciences, Central South University, Changsha, Hunan, China.,Hunan Jiahui Genetics Hospital, Changsha, Hunan, China
| | - Zhuo Li
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of life sciences, Central South University, Changsha, Hunan, China
| | - Lingqian Wu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of life sciences, Central South University, Changsha, Hunan, China.,Hunan Jiahui Genetics Hospital, Changsha, Hunan, China
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30
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Kobren SN, Baldridge D, Velinder M, Krier JB, LeBlanc K, Esteves C, Pusey BN, Züchner S, Blue E, Lee H, Huang A, Bastarache L, Bican A, Cogan J, Marwaha S, Alkelai A, Murdock DR, Liu P, Wegner DJ, Paul AJ, Sunyaev SR, Kohane IS. Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases. Genet Med 2021; 23:1075-1085. [PMID: 33580225 PMCID: PMC8187147 DOI: 10.1038/s41436-020-01084-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. METHODS We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. RESULTS We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. CONCLUSION The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.
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Affiliation(s)
| | - Dustin Baldridge
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Matt Velinder
- Center for Genomic Discovery, University of Utah, Salt Lake City, UT, USA
| | - Joel B Krier
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kimberly LeBlanc
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Cecilia Esteves
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Barbara N Pusey
- National Human Genome Research Institute (NHGRI) at the National Institutes of Health (NIH), Bethesda, MD, USA
| | - Stephan Züchner
- Department of Human Genetics and Hussman Institute for Human Genomics, University of Miami Health System, Miami, FL, USA
| | - Elizabeth Blue
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Hane Lee
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Alden Huang
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anna Bican
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joy Cogan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shruti Marwaha
- Stanford Center for Undiagnosed Diseases, Stanford, CA, USA
| | - Anna Alkelai
- Institute for Genomic Medicine, Columbia University Medical Center, New York City, NY, USA
| | - David R Murdock
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Baylor Genetics, Houston, TX, USA
| | - Daniel J Wegner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander J Paul
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Rehder C, Bean LJH, Bick D, Chao E, Chung W, Das S, O'Daniel J, Rehm H, Shashi V, Vincent LM. Next-generation sequencing for constitutional variants in the clinical laboratory, 2021 revision: a technical standard of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021; 23:1399-1415. [PMID: 33927380 DOI: 10.1038/s41436-021-01139-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 12/17/2022] Open
Abstract
Next-generation sequencing (NGS) technologies are now established in clinical laboratories as a primary testing modality in genomic medicine. These technologies have reduced the cost of large-scale sequencing by several orders of magnitude. It is now cost-effective to analyze an individual with disease-targeted gene panels, exome sequencing, or genome sequencing to assist in the diagnosis of a wide array of clinical scenarios. While clinical validation and use of NGS in many settings is established, there are continuing challenges as technologies and the associated informatics evolve. To assist clinical laboratories with the validation of NGS methods and platforms, the ongoing monitoring of NGS testing to ensure quality results, and the interpretation and reporting of variants found using these technologies, the American College of Medical Genetics and Genomics (ACMG) has developed the following technical standards.
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Affiliation(s)
| | - Lora J H Bean
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - David Bick
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Elizabeth Chao
- Division of Genetics and Genomics, Department of Pediatrics, University of California, Irvine, CA, USA
| | - Wendy Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Soma Das
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Julianne O'Daniel
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Heidi Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vandana Shashi
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Lisa M Vincent
- Division of Pathology & Laboratory Medicine, Children's National Health System, Washington, DC, USA.,Departments of Pathology and Pediatrics, George Washington University, Washington, DC, USA
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Schoch K, Esteves C, Bican A, Spillmann R, Cope H, McConkie-Rosell A, Walley N, Fernandez L, Kohler JN, Bonner D, Reuter C, Stong N, Mulvihill JJ, Novacic D, Wolfe L, Abdelbaki A, Toro C, Tifft C, Malicdan M, Gahl W, Liu P, Newman J, Goldstein DB, Hom J, Sampson J, Wheeler MT, Cogan J, Bernstein JA, Adams DR, McCray AT, Shashi V. Clinical sites of the Undiagnosed Diseases Network: unique contributions to genomic medicine and science. Genet Med 2021; 23:259-271. [PMID: 33093671 PMCID: PMC7867619 DOI: 10.1038/s41436-020-00984-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The NIH Undiagnosed Diseases Network (UDN) evaluates participants with disorders that have defied diagnosis, applying personalized clinical and genomic evaluations and innovative research. The clinical sites of the UDN are essential to advancing the UDN mission; this study assesses their contributions relative to standard clinical practices. METHODS We analyzed retrospective data from four UDN clinical sites, from July 2015 to September 2019, for diagnoses, new disease gene discoveries and the underlying investigative methods. RESULTS Of 791 evaluated individuals, 231 received 240 diagnoses and 17 new disease-gene associations were recognized. Straightforward diagnoses on UDN exome and genome sequencing occurred in 35% (84/240). We considered these tractable in standard clinical practice, although genome sequencing is not yet widely available clinically. The majority (156/240, 65%) required additional UDN-driven investigations, including 90 diagnoses that occurred after prior nondiagnostic exome sequencing and 45 diagnoses (19%) that were nongenetic. The UDN-driven investigations included complementary/supplementary phenotyping, innovative analyses of genomic variants, and collaborative science for functional assays and animal modeling. CONCLUSION Investigations driven by the clinical sites identified diagnostic and research paradigms that surpass standard diagnostic processes. The new diagnoses, disease gene discoveries, and delineation of novel disorders represent a model for genomic medicine and science.
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Affiliation(s)
- Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC, USA
| | - Cecilia Esteves
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Anna Bican
- Vanderbilt Center for Undiagnosed Disease, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rebecca Spillmann
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC, USA
| | - Heidi Cope
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC, USA
| | - Allyn McConkie-Rosell
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC, USA
| | - Nicole Walley
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC, USA
| | - Liliana Fernandez
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Jennefer N Kohler
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Devon Bonner
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Chloe Reuter
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Nicholas Stong
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
| | - John J Mulvihill
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
| | - Donna Novacic
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
| | - Lynne Wolfe
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
| | - Ayat Abdelbaki
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
| | - Camilo Toro
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
| | - Cyndi Tifft
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
- Office of the Clinical Director, NHGRI, NIH, Bethesda, MD, USA
| | - May Malicdan
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
- Medical Genetics Branch, NHGRI, NIH, Bethesda, MD, USA
| | - William Gahl
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
- Medical Genetics Branch, NHGRI, NIH, Bethesda, MD, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Baylor Genetics, Houston, TX, USA
| | - John Newman
- Vanderbilt Center for Undiagnosed Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David B Goldstein
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
| | - Jason Hom
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Jacinda Sampson
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
- Department of Neurology, Stanford School of Medicine, Stanford, CA, USA
| | - Matthew T Wheeler
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Joy Cogan
- Vanderbilt Center for Undiagnosed Disease, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan A Bernstein
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
| | - David R Adams
- Undiagnosed Diseases Program, Common Fund, NIH Office of the Director, NIH, Bethesda, MD, USA
- Office of the Clinical Director, NHGRI, NIH, Bethesda, MD, USA
| | - Alexa T McCray
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke Health, Durham, NC, USA.
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Abstract
Congenital heart disease is the most common congenital defect observed in newborns. Within the spectrum of congenital heart disease are left‐sided obstructive lesions (LSOLs), which include hypoplastic left heart syndrome, aortic stenosis, bicuspid aortic valve, coarctation of the aorta, and interrupted aortic arch. These defects can arise in isolation or as a component of a defined syndrome; however, nonsyndromic defects are often observed in multiple family members and associated with high sibling recurrence risk. This clear evidence for a heritable basis has driven a lengthy search for disease‐causing variants that has uncovered both rare and common variants in genes that, when perturbed in cardiac development, can result in LSOLs. Despite advancements in genetic sequencing platforms and broadening use of exome sequencing, the currently accepted LSOL‐associated genes explain only 10% to 20% of patients. Further, the combinatorial effects of common and rare variants as a cause of LSOLs are emerging. In this review, we highlight the genes and variants associated with the different LSOLs and discuss the strengths and weaknesses of the present genetic associations. Furthermore, we discuss the research avenues needed to bridge the gaps in our current understanding of the genetic basis of nonsyndromic congenital heart disease.
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Affiliation(s)
- Lauren E Parker
- Division of Cardiology Department of Pediatrics Duke University School of Medicine Durham NC
| | - Andrew P Landstrom
- Division of Cardiology Department of Pediatrics Duke University School of Medicine Durham NC.,Department of Cell Biology Duke University School of Medicine Durham NC
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Wei H, Lai A, Tan ES, Koh MJA, Ng I, Ting TW, Thomas T, Cham B, Lim JY, Kam S, Goh CYJ, Lin G, Brett M, Chan D, Jamuar SS, Tan EC. Genetic landscape of congenital disorders in patients from Southeast Asia: results from sequencing using a gene panel for Mendelian phenotypes. Arch Dis Child 2021; 106:38-43. [PMID: 32978145 DOI: 10.1136/archdischild-2020-319177] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/24/2020] [Accepted: 08/30/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To test the utility and diagnostic yield of a medical-exome gene panel for identifying pathogenic variants in Mendelian disorders. METHODS Next-generation sequencing was performed with the TruSight One gene panel (targeting 4813 genes) followed by MiSeq sequencing on 216 patients who presented with suspected genetic disorders as assessed by their attending physicians. RESULTS There were 56 pathogenic and 36 likely pathogenic variants across 57 genes identified in 87 patients. Causal mutations were more likely to be truncating and from patients with a prior clinical diagnosis. Another 18 promising variants need further evaluation for more evidence to meet the requirement for potential upgrade to pathogenic. Forty-five of the 92 clinically significant variants were novel. CONCLUSION The 40.3% positive yield compares favourably with similar studies using either this panel or whole exome sequencing, demonstrating that large gene panels could be a good alternative to whole exome sequencing for quick genetic confirmation of Mendelian disorders.
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Affiliation(s)
- Heming Wei
- KK Research Centre, KK Women's & Children's Hospital, Singapore
| | - Angeline Lai
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Genetics Service, KK Women's & Children's Hospital, Singapore
| | - Ee Shien Tan
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Genetics Service, KK Women's & Children's Hospital, Singapore
| | - Mark Jean Aan Koh
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Dermatology Service, KK Women's & Children's Hospital, Singapore
| | - Ivy Ng
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Genetics Service, KK Women's and Children's Hospital, Singapore
| | - Teck Wah Ting
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Genetics Service, KK Women's and Children's Hospital, Singapore
| | - Terrence Thomas
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Neurology Service, KK Women's & Children's Hospital, Singapore
| | - Breana Cham
- Genetics Service, KK Women's & Children's Hospital, Singapore
| | - Jiin Ying Lim
- Genetics Service, KK Women's & Children's Hospital, Singapore
| | - Sylvia Kam
- Genetics Service, KK Women's & Children's Hospital, Singapore
| | | | - Grace Lin
- KK Research Centre, KK Women's & Children's Hospital, Singapore
| | - Maggie Brett
- KK Research Centre, KK Women's & Children's Hospital, Singapore
| | - Derrick Chan
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Neurology Service, KK Women's & Children's Hospital, Singapore
| | - Saumya Shekhar Jamuar
- Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore.,Genetics Service, KK Women's & Children's Hospital, Singapore
| | - Ene-Choo Tan
- KK Research Centre, KK Women's & Children's Hospital, Singapore .,Paediatric Academic Clinical Programme, SingHealth Duke-NUS Graduate Medical School, Singapore
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Abraham RS. How to evaluate for immunodeficiency in patients with autoimmune cytopenias: laboratory evaluation for the diagnosis of inborn errors of immunity associated with immune dysregulation. Hematology Am Soc Hematol Educ Program 2020; 2020:661-672. [PMID: 33275711 PMCID: PMC7727558 DOI: 10.1182/hematology.2020000173] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The identification of genetic disorders associated with dysregulated immunity has upended the notion that germline pathogenic variants in immune genes universally result in susceptibility to infection. Immune dysregulation (autoimmunity, autoinflammation, lymphoproliferation, and malignancy) and immunodeficiency (susceptibility to infection) represent 2 sides of the same coin and are not mutually exclusive. Also, although autoimmunity implies dysregulation within the adaptive immune system and autoinflammation indicates disordered innate immunity, these lines may be blurred, depending on the genetic defect and diversity in clinical and immunological phenotypes. Patients with immune dysregulatory disorders may present to a variety of clinical specialties, depending on the dominant clinical features. Therefore, awareness of these disorders, which may manifest at any age, is essential to avoid a protracted diagnostic evaluation and associated complications. Availability of and access to expanded immunological testing has altered the diagnostic landscape for immunological diseases. Nonetheless, there are constraints in using these resources due to a lack of awareness, challenges in systematic and logical evaluation, interpretation of results, and using results to justify additional advanced testing, when needed. The ability to molecularly characterize immune defects and develop "bespoke" therapy and management mandates a new paradigm for diagnostic evaluation of these patients. The immunological tests run the gamut from triage to confirmation and can be used for both diagnosis and refinement of treatment or management strategies. However, the complexity of testing and interpretation of results often necessitates dialogue between laboratory immunologists and specialty physicians to ensure timely and appropriate use of testing and delivery of care.
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Affiliation(s)
- Roshini S Abraham
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH
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Abstract
PURPOSE OF REVIEW This article reviews the current understanding and limitations in knowledge of the effect genetics and genetic diagnoses have on perioperative and postoperative surgical outcomes in patients with congenital heart disease (CHD). RECENT FINDINGS Presence of a known genetic diagnosis seems to effect multiple significant outcome metrics in CHD surgery including length of stay, need for extracorporeal membrane oxygenation, mortality, bleeding, and heart failure. Data regarding the effects of genetics in CHD is complicated by lack of standard genetic assessment resulting in inaccurate risk stratification of patients when analyzing data. Only 30% of variation in CHD surgical outcomes are explained by currently measured variables, with 2.5% being attributed to diagnosed genetic disorders, it is thought a significant amount of the remaining outcome variation is because of unmeasured genetic factors. SUMMARY Genetic diagnoses clearly have a significant effect on surgical outcomes in patients with CHD. Our current understanding is limited by lack of consistent genetic evaluation and assessment as well as evolving knowledge and discovery regarding the genetics of CHD. Standardizing genetic assessment of patients with CHD will allow for the best risk stratification and ultimate understanding of these effects.
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Becher N, Andreasen L, Sandager P, Lou S, Petersen OB, Christensen R, Vogel I. Implementation of exome sequencing in fetal diagnostics-Data and experiences from a tertiary center in Denmark. Acta Obstet Gynecol Scand 2020; 99:783-790. [PMID: 32304219 DOI: 10.1111/aogs.13871] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Applying whole-exome sequencing (WES) for the diagnosis of diseases in children has shown significant diagnostic strength compared with chromosomal microarray. WES may also have the potential of adding clinically relevant prenatal information in cases where a fetus is found to have structural anomalies. We present results from the first fetal exomes performed in a tertiary center in Denmark. MATERIAL AND METHODS Couples/expectant parents were included in Central Denmark Region from July 2016 to March 2019. Inclusion was not systematic, but where one or more fetal malformations or severe fetal hydrops were detected, and a specific diagnosis had not been obtained by chromosomal microarray. WES was performed in ongoing pregnancies (N = 11), after intrauterine demise (N = 5), or after termination of pregnancy based on ultrasound findings (N = 19). In most cases, a trio format was applied comprising fetal and parental DNA. RESULTS WES was performed in 35 highly selected fetal cases. Pathogenic variants, or variants likely to explain the phenotype, were detected in 9/35 (26%). Variants of uncertain significance were detected in 7/35 (20%) and there was one secondary finding (3%). Out of the 11 ongoing pregnancies, four reached a genetic diagnosis (36%). Detection rate was highest in cases of multisystem anomalies (7/13, 54%). WES was completed in all three trimesters and both autosomal dominant, autosomal recessive and X-linked inheritance were revealed. CONCLUSIONS We present data from 35 cases of exome sequencing applied in a setting of fetal malformations. Importantly, though, we wish to share our personal experiences with implementing WES into a prenatal setting. As a medical society, we must continue to share what we do not understand, what went wrong, what is difficult, and what we do not agree upon. A common understanding and language are warranted. We also advocate that more research is needed concerning the clinical value, as well as costs and patient perspectives, of using WES in pregnancy. We believe that WES will lead to improved prenatal and perinatal care.
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Affiliation(s)
- Naja Becher
- Center for Fetal Diagnostics, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark.,Department of Biomedicine, Health, Aarhus University, Aarhus, Denmark
| | - Lotte Andreasen
- Department of Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark
| | - Puk Sandager
- Center for Fetal Diagnostics, Aarhus University Hospital, Aarhus, Denmark.,Department of Obstetrics and Gynecology, Aarhus University Hospital, Aarhus, Denmark
| | - Stina Lou
- Center for Fetal Diagnostics, Aarhus University Hospital, Aarhus, Denmark.,DEFACTUM-Public Health & Health Services Research, Central Denmark Region, Aarhus, Denmark
| | - Olav Bjørn Petersen
- Center for Fetal Diagnostics, Aarhus University Hospital, Aarhus, Denmark.,Department of Obstetrics and Gynecology, Aarhus University Hospital, Aarhus, Denmark
| | - Rikke Christensen
- Department of Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark
| | - Ida Vogel
- Center for Fetal Diagnostics, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Li D, Shen KM, Zackai EH, Bhoj EJ. Clinical variability of TUBB-associated disorders: Diagnosis through reanalysis. Am J Med Genet A 2020; 182:3035-3039. [PMID: 33016642 DOI: 10.1002/ajmg.a.61897] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/14/2020] [Accepted: 09/14/2020] [Indexed: 11/06/2022]
Abstract
A range of clinical findings have been associated with heterozygous mutations in the Beta Tubulin (TUBB) gene, including microcephaly, structural brain abnormalities, intellectual disability, and skin creases. We report a 5-year-old male who presented for evaluation of cleft palate, cardiac defects, growth retardation, hemivertebrae causing scoliosis, and preauricular skin tags. Previous clinical exome sequencing of this patient was nondiagnostic, but reanalysis in the research setting identified a de novo missense c. 925C>G p.(Arg309Gly) mutation in TUBB. This mutation was not found in population allele frequency databases, and was classified to be likely pathogenic. This patient shares some phenotypic characteristics with previous reported patients of TUBB mutations of the two TUBB-related phenotypes: "Cortical dysplasia, complex, with other brain malformations 6" [MIM 615771] and "Circumferential Skin Creases Kunze type (CSC-KT)" [MIM 156610], but has no excess skin creases or structural brain anomalies. We also report previously undescribed features, including transposition of the great arteries and vertebral fusion, thus representing phenotype expansion of TUBB-associated disorders.
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Affiliation(s)
- Dong Li
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Kaitlyn M Shen
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Elaine H Zackai
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Elizabeth J Bhoj
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Tan NB, Stapleton R, Stark Z, Delatycki MB, Yeung A, Hunter MF, Amor DJ, Brown NJ, Stutterd CA, McGillivray G, Yap P, Regan M, Chong B, Fanjul Fernandez M, Marum J, Phelan D, Pais LS, White SM, Lunke S, Tan TY. Evaluating systematic reanalysis of clinical genomic data in rare disease from single center experience and literature review. Mol Genet Genomic Med 2020; 8:e1508. [PMID: 32969205 PMCID: PMC7667328 DOI: 10.1002/mgg3.1508] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/15/2020] [Accepted: 08/30/2020] [Indexed: 12/12/2022] Open
Abstract
Background Our primary aim was to evaluate the systematic reanalysis of singleton exome sequencing (ES) data for unsolved cases referred for any indication. A secondary objective was to undertake a literature review of studies examining the reanalysis of genomic data from unsolved cases. Methods We examined data from 58 unsolved cases referred between June 2016 and March 2017. First reanalysis at 4–13 months after the initial report considered genes newly associated with disease since the original analysis; second reanalysis at 9–18 months considered all disease‐associated genes. At 25–34 months we reviewed all cases and the strategies which solved them. Results Reanalysis of existing ES data alone at two timepoints did not yield new diagnoses. Over the same timeframe, 10 new diagnoses were obtained (17%) from additional strategies, such as microarray detection of copy number variation, repeat sequencing to improve coverage, and trio sequencing. Twenty‐seven peer‐reviewed articles were identified on the literature review, with a median new diagnosis rate via reanalysis of 15% and median reanalysis timeframe of 22 months. Conclusion Our findings suggest that an interval of greater than 18 months from the original report may be optimal for reanalysis. We also recommend a multi‐faceted strategy for cases remaining unsolved after singleton ES.
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Affiliation(s)
- Natalie B Tan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia.,Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Rachel Stapleton
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Zornitza Stark
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Martin B Delatycki
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Alison Yeung
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Monash Genetics, Monash Health, Clayton, VIC, Australia.,Department of Paediatrics, Monash University, Clayton, VIC, Australia
| | - Matthew F Hunter
- Monash Genetics, Monash Health, Clayton, VIC, Australia.,Department of Paediatrics, Monash University, Clayton, VIC, Australia
| | - David J Amor
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia.,Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Royal Children's Hospital, Parkville, VIC, Australia
| | - Natasha J Brown
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia.,Royal Children's Hospital, Parkville, VIC, Australia.,Austin Health Clinical Genetics Service, Heidelberg, VIC, Australia
| | - Chloe A Stutterd
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Austin Health Clinical Genetics Service, Heidelberg, VIC, Australia
| | - George McGillivray
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Patrick Yap
- Genetic Health Service NZ, Auckland, New Zealand.,Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Matthew Regan
- Monash Genetics, Monash Health, Clayton, VIC, Australia.,Department of Paediatrics, Monash University, Clayton, VIC, Australia
| | - Belinda Chong
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Miriam Fanjul Fernandez
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Justine Marum
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Dean Phelan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Lynn S Pais
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Susan M White
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Sebastian Lunke
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Tiong Y Tan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
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Fung JLF, Yu MHC, Huang S, Chung CCY, Chan MCY, Pajusalu S, Mak CCY, Hui VCC, Tsang MHY, Yeung KS, Lek M, Chung BHY. A three-year follow-up study evaluating clinical utility of exome sequencing and diagnostic potential of reanalysis. NPJ Genom Med 2020; 5:37. [PMID: 32963807 DOI: 10.1038/s41525-020-00144-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 08/14/2020] [Indexed: 01/05/2023] Open
Abstract
Exome sequencing (ES) has become one of the important diagnostic tools in clinical genetics with a reported diagnostic rate of 25–58%. Many studies have illustrated the diagnostic and immediate clinical impact of ES. However, up to 75% of individuals remain undiagnosed and there is scarce evidence supporting clinical utility beyond a follow-up period of >1 year. This is a 3-year follow-up analysis to our previous publication by Mak et al. (NPJ Genom. Med. 3:19, 2018), to evaluate the long-term clinical utility of ES and the diagnostic potential of exome reanalysis. The diagnostic yield of the initial study was 41% (43/104). Exome reanalysis in 46 undiagnosed individuals has achieved 12 new diagnoses. The additional yield compared with the initial analysis was at least 12% (increased from 41% to at least 53%). After a median follow-up period of 3.4 years, change in clinical management was observed in 72.2% of the individuals (26/36), leading to positive change in clinical outcome in four individuals (11%). There was a minimum healthcare cost saving of HKD$152,078 (USD$19,497; €17,282) annually for these four individuals. There were a total of six pregnancies from five families within the period. Prenatal diagnosis was performed in four pregnancies; one fetus was affected and resulted in termination. None of the parents underwent preimplantation genetic diagnosis. This 3-year follow-up study demonstrated the long-term clinical utility of ES at individual, familial and health system level, and the promising diagnostic potential of subsequent reanalysis. This highlights the benefits of implementing ES and regular reanalysis in the clinical setting.
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Costain G, Walker S, Marano M, Veenma D, Snell M, Curtis M, Luca S, Buera J, Arje D, Reuter MS, Thiruvahindrapuram B, Trost B, Sung WWL, Yuen RKC, Chitayat D, Mendoza-Londono R, Stavropoulos DJ, Scherer SW, Marshall CR, Cohn RD, Cohen E, Orkin J, Meyn MS, Hayeems RZ. Genome Sequencing as a Diagnostic Test in Children With Unexplained Medical Complexity. JAMA Netw Open 2020; 3:e2018109. [PMID: 32960281 PMCID: PMC7509619 DOI: 10.1001/jamanetworkopen.2020.18109] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 07/12/2020] [Indexed: 12/16/2022] Open
Abstract
Importance Children with medical complexity (CMC) represent a growing population in the pediatric health care system, with high resource use and associated health care costs. A genetic diagnosis can inform prognosis, anticipatory care, management, and reproductive planning. Conventional genetic testing strategies for CMC are often costly, time consuming, and ultimately unsuccessful. Objective To evaluate the analytical and clinical validity of genome sequencing as a comprehensive diagnostic genetic test for CMC. Design, Setting, and Participants In this cohort study of the prospective use of genome sequencing and comparison with standard-of-care genetic testing, CMC were recruited from May 1, 2017, to November 30, 2018, from a structured complex care program based at a tertiary care pediatric hospital in Toronto, Canada. Recruited CMC had at least 1 chronic condition, technology dependence (child is dependent at least part of each day on mechanical ventilators, and/or child requires prolonged intravenous administration of nutritional substances or drugs, and/or child is expected to have prolonged dependence on other device-based support), multiple subspecialist involvement, and substantial health care use. Review of the care plans for 545 CMC identified 143 suspected of having an undiagnosed genetic condition. Fifty-four families met inclusion criteria and were interested in participating, and 49 completed the study. Probands, similarly affected siblings, and biological parents were eligible for genome sequencing. Exposures Genome sequencing was performed using blood-derived DNA from probands and family members using established methods and a bioinformatics pipeline for clinical genome annotation. Main Outcomes and Measures The primary study outcome was the diagnostic yield of genome sequencing (proportion of CMC for whom the test result yielded a new diagnosis). Results Genome sequencing was performed for 138 individuals from 49 families of CMC (29 male and 20 female probands; mean [SD] age, 7.0 [4.5] years). Genome sequencing detected all genomic variation previously identified by conventional genetic testing. A total of 15 probands (30.6%; 95% CI 19.5%-44.6%) received a new primary molecular genetic diagnosis after genome sequencing. Three individuals had novel diseases and an additional 9 had either ultrarare genetic conditions or rare genetic conditions with atypical features. At least 11 families received diagnostic information that had clinical management implications beyond genetic and reproductive counseling. Conclusions and Relevance This study suggests that genome sequencing has high analytical and clinical validity and can result in new diagnoses in CMC even in the setting of extensive prior investigations. This clinical population may be enriched for ultrarare and novel genetic disorders. Genome sequencing is a potentially first-tier genetic test for CMC.
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Affiliation(s)
- Gregory Costain
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Susan Walker
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Maria Marano
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Danielle Veenma
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Meaghan Snell
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Meredith Curtis
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Stephanie Luca
- Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jason Buera
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Danielle Arje
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Miriam S. Reuter
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | | | - Brett Trost
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Wilson W. L. Sung
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ryan K. C. Yuen
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - David Chitayat
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
- The Prenatal Diagnosis and Medical Genetics Program, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Roberto Mendoza-Londono
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - D. James Stavropoulos
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Stephen W. Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Christian R. Marshall
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Ronald D. Cohn
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Eyal Cohen
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Julia Orkin
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - M. Stephen Meyn
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
- Center for Human Genomics and Precision Medicine, University of Wisconsin, Madison
| | - Robin Z. Hayeems
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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Sabo A, Murdock D, Dugan S, Meng Q, Gingras MC, Hu J, Muzny D, Gibbs R. Community-based recruitment and exome sequencing indicates high diagnostic yield in adults with intellectual disability. Mol Genet Genomic Med 2020; 8:e1439. [PMID: 32767738 PMCID: PMC7549560 DOI: 10.1002/mgg3.1439] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/12/2020] [Accepted: 07/01/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Establishing a genetic diagnosis for individuals with intellectual disability (ID) benefits patients and their families as it may inform the prognosis, lead to appropriate therapy, and facilitate access to medical and supportive services. Exome sequencing has been successfully applied in a diagnostic setting, but most clinical exome referrals are pediatric patients, with many adults with ID lacking a comprehensive genetic evaluation. METHODS Our unique recruitment strategy involved partnering with service and education providers for individuals with ID. We performed exome sequencing and analysis, and clinical variant interpretation for each recruited family. RESULTS All five families enrolled in the study opted-in for the return of genetic results. In three out of five families exome sequencing analysis identified pathogenic or likely pathogenic variants in KANSL1, TUSC3, and MED13L genes. Families discussed the results and any potential medical follow-up in an appointment with a board certified clinical geneticist. CONCLUSION Our study suggests high yield of exome sequencing as a diagnostic tool in adult patients with ID who have not undergone comprehensive sequencing-based genetic testing. Research studies including an option of return of results through a genetic clinic could help minimize the disparity in exome diagnostic testing between pediatric and adult patients with ID.
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Affiliation(s)
- Aniko Sabo
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - David Murdock
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Shannon Dugan
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Qingchang Meng
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | | | - Jianhong Hu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
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Cope H, Spillmann R, Rosenfeld JA, Brokamp E, Signer R, Schoch K, Kelley EG, Sullivan JA, Macnamara E, Lincoln S, Golden-Grant K, Orengo JP, Clark G, Burrage LC, Posey JE, Punetha J, Robertson A, Cogan J, Phillips JA, Martinez-Agosto J, Shashi V. Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network. Mol Genet Genomic Med 2020; 8:e1397. [PMID: 32730690 PMCID: PMC7549585 DOI: 10.1002/mgg3.1397] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/04/2020] [Accepted: 06/08/2020] [Indexed: 12/12/2022] Open
Abstract
Background Resources within the Undiagnosed Diseases Network (UDN), such as genome sequencing (GS) and model organisms aid in diagnosis and identification of new disease genes, but are currently difficult to access by clinical providers. While these resources do contribute to diagnoses in many cases, they are not always necessary to reach diagnostic resolution. The UDN experience has been that participants can also receive diagnoses through the thoughtful and customized application of approaches and resources that are readily available in clinical settings. Methods The UDN Genetic Counseling and Testing Working Group collected case vignettes that illustrated how clinically available methods resulted in diagnoses. The case vignettes were classified into three themes; phenotypic considerations, selection of genetic testing, and evaluating exome/GS variants and data. Results We present 12 participants that illustrate how clinical practices such as phenotype‐driven genomic investigations, consideration of variable expressivity, selecting the relevant tissue of interest for testing, utilizing updated testing platforms, and recognition of alternate transcript nomenclature resulted in diagnoses. Conclusion These examples demonstrate that when a diagnosis is elusive, an iterative patient‐specific approach utilizing assessment options available to clinical providers may solve a portion of cases. However, this does require increased provider time commitment, a particular challenge in the current practice of genomics.
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Affiliation(s)
- Heidi Cope
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Rebecca Spillmann
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Elly Brokamp
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rebecca Signer
- Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Emily G Kelley
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jennifer A Sullivan
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Ellen Macnamara
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, NIH, Bethesda, MD, USA
| | - Sharyn Lincoln
- Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
| | - Katie Golden-Grant
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
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- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, NIH, Bethesda, MD, USA
| | - James P Orengo
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Gary Clark
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Lindsay C Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jaya Punetha
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Amy Robertson
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joy Cogan
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John A Phillips
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
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Lee H, Nelson SF. The frontiers of sequencing in undiagnosed neurodevelopmental diseases. Curr Opin Genet Dev 2020; 65:76-83. [PMID: 32599523 DOI: 10.1016/j.gde.2020.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 04/07/2020] [Accepted: 05/01/2020] [Indexed: 12/24/2022]
Abstract
Characterized by impairments in brain and central nervous system development, neurodevelopmental diseases causes are highly heterogeneous. Although many of these diseases are individually rare, collectively more than 3% of the children are reported to be affected with a type of neurodevelopmental diseases worldwide, and many remain undiagnosed even with current genomic tools. Identifying the genetic causes of these diseases allows better clinical management and expands our understanding of human neurodevelopment. Over the past decade, expansion of genomic sequencing and some methodologic improvements have improved molecular diagnostic yield as well as the discovery of novel genetic causes for wide spectrum of neurodevelopmental diseases. Here we review the current diagnostic workflow and propose ways of improving the diagnostic yield.
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Affiliation(s)
- Hane Lee
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Stanley F Nelson
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
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Perez Maturo J, Zavala L, Vega P, González-Morón D, Medina N, Salinas V, Rosales J, Córdoba M, Arakaki T, Garretto N, Rodríguez-Quiroga S, Kauffman MA. Overwhelming genetic heterogeneity and exhausting molecular diagnostic process in chronic and progressive ataxias: facing it up with an algorithm, a gene, a panel at a time. J Hum Genet 2020; 65:895-902. [DOI: 10.1038/s10038-020-0785-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/19/2020] [Accepted: 05/20/2020] [Indexed: 12/18/2022]
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Schoch K, Tan QKG, Stong N, Deak KL, McConkie-Rosell A, McDonald MT, Goldstein DB, Jiang YH, Shashi V. Alternative transcripts in variant interpretation: the potential for missed diagnoses and misdiagnoses. Genet Med 2020; 22:1269-1275. [PMID: 32366967 PMCID: PMC7335342 DOI: 10.1038/s41436-020-0781-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/04/2020] [Accepted: 03/10/2020] [Indexed: 12/30/2022] Open
Abstract
PURPOSE Guidelines by professional organizations for assessing variant pathogenicity include the recommendation to utilize biologically relevant transcripts; however, there is variability in transcript selection by laboratories. METHODS We describe three patients whose genomic results were incorrect, because alternative transcripts and tissue expression patterns were not considered by the commercial laboratories. RESULTS In individual 1, a pathogenic coding variant in a brain-expressed isoform of CKDL5 was missed twice on sequencing, because the variant was intronic in the transcripts considered in analysis. In individual 2, a microdeletion affecting KMT2C was not reported on microarray, since deletions of proximal exons in this gene are seen in healthy individuals; however, this individual had a more distal deletion involving the brain-expressed KMT2C isoform, giving her a diagnosis of Kleefstra syndrome. Individual 3 was reported to have a pathogenic variant in exon 10 of OFD1 on exome, but had no typical features of the OFD1-related disorders. Since exon 10 is spliced from the more biologically relevant transcripts of OFD1, it was determined that he did not have an OFD1 disorder. CONCLUSION These examples illustrate the importance of considering alternative transcripts as a potential confounder when genetic results are negative or discordant with the phenotype.
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Affiliation(s)
- Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Queenie K-G Tan
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Nicholas Stong
- Institute of Genomic Medicine, Columbia University, New York, NY, USA
| | - Kristen L Deak
- Department of Pathology, Duke University Medical Center, Durham, NC, USA
| | - Allyn McConkie-Rosell
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Marie T McDonald
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | | | - David B Goldstein
- Institute of Genomic Medicine, Columbia University, New York, NY, USA
| | - Yong-Hui Jiang
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, USA.
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Surl D, Shin S, Lee ST, Choi JR, Lee J, Byeon SH, Han SH, Lim HT, Han J. Copy number variations and multiallelic variants in Korean patients with Leber congenital amaurosis. Mol Vis 2020; 26:26-35. [PMID: 32165824 PMCID: PMC7043639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 02/21/2020] [Indexed: 11/02/2022] Open
Abstract
Purpose We comprehensively evaluated the mutational spectrum of Leber congenital amaurosis (LCA) and investigated the molecular diagnostic rate and genotype-phenotype correlation in a Korean cohort. Methods This single-center retrospective case series included 50 Korean patients with LCA between June 2015 and March 2019. Molecular analysis was conducted using targeted panel-based next-generation sequencing, including deep intronic and regulatory variants or whole exome sequencing. The molecular diagnosis was made based on the inheritance pattern, zygosity, and pathogenicity. Results Among the 50 patients, 27 patients (54%) were male, and 11 (22%) showed systemic features. Genetic variants highly likely to be causative were identified in 78% (39/50) of cases and segregated into families. We detected two pathogenic or likely pathogenic variants in a gene linked to a recessive trait without segregation analysis in three cases (6.0%). GUCY2D (20%), NMNAT1 (18%), and CEP290 (16%) were the most frequently mutated genes in Korean LCA. Copy number variations were found in three patients, which accounted for 6% of LCA cases. A possible dual molecular diagnosis (Senior-Løken syndrome along with Leigh syndrome, and Joubert syndrome with transposition of the great arteries) was made in two patients (4%). Three of 50 patients were medically or surgically actionable: one patient for RPE65 gene therapy and two patients with WDR19 Senior-Løken syndrome for early preparation for kidney and liver transplantations. Conclusions This study demonstrated that approximately 4% of patients may have dual molecular diagnoses, and 6% were surgically or medically actionable in LCA. Therefore, accurate molecular diagnosis and careful interpretation of next-generation sequencing results can be of great help in patients with LCA.
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Affiliation(s)
- Dongheon Surl
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea
| | - Saeam Shin
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Tae Lee
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Rak Choi
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Junwon Lee
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea
| | - Suk Ho Byeon
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea
| | - Sueng-Han Han
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea
| | - Hyun Taek Lim
- Department of Ophthalmology, Asian Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jinu Han
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea,Department of Ophthalmology, Gangnam Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea
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Kingsmore SF, Cakici JA, Clark MM, Gaughran M, Feddock M, Batalov S, Bainbridge MN, Carroll J, Caylor SA, Clarke C, Ding Y, Ellsworth K, Farnaes L, Hildreth A, Hobbs C, James K, Kint CI, Lenberg J, Nahas S, Prince L, Reyes I, Salz L, Sanford E, Schols P, Sweeney N, Tokita M, Veeraraghavan N, Watkins K, Wigby K, Wong T, Chowdhury S, Wright MS, Dimmock D, Bezares Z, Bloss C, Braun JJ, Diaz C, Mashburn D, Tamang D, Orendain D, Friedman J, Gleeson J, Barea J, Chiang G, Cohenmeyer C, Coufal NG, Evans M, Honold J, Hovey RL, Kimball A, Lane B, Le C, Le J, Leibel S, Moyer L, Mulrooney P, Oh D, Ordonez P, Oriol A, Ortiz-Arechiga M, Puckett L, Speziale M, Suttner D, Van Der Kraan L, Knight G, Sauer C, Song R, White S, Wise A, Yamada C. A Randomized, Controlled Trial of the Analytic and Diagnostic Performance of Singleton and Trio, Rapid Genome and Exome Sequencing in Ill Infants. Am J Hum Genet 2019; 105:719-733. [PMID: 31564432 DOI: 10.1016/j.ajhg.2019.08.009] [Citation(s) in RCA: 213] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 08/23/2019] [Indexed: 12/21/2022] Open
Abstract
The second Newborn Sequencing in Genomic Medicine and Public Health study was a randomized, controlled trial of the effectiveness of rapid whole-genome or -exome sequencing (rWGS or rWES, respectively) in seriously ill infants with diseases of unknown etiology. Here we report comparisons of analytic and diagnostic performance. Of 1,248 ill inpatient infants, 578 (46%) had diseases of unknown etiology. 213 infants (37% of those eligible) were enrolled within 96 h of admission. 24 infants (11%) were very ill and received ultra-rapid whole-genome sequencing (urWGS). The remaining infants were randomized, 95 to rWES and 94 to rWGS. The analytic performance of rWGS was superior to rWES, including variants likely to affect protein function, and ClinVar pathogenic/likely pathogenic variants (p < 0.0001). The diagnostic performance of rWGS and rWES were similar (18 diagnoses in 94 infants [19%] versus 19 diagnoses in 95 infants [20%], respectively), as was time to result (median 11.0 versus 11.2 days, respectively). However, the proportion diagnosed by urWGS (11 of 24 [46%]) was higher than rWES/rWGS (p = 0.004) and time to result was less (median 4.6 days, p < 0.0001). The incremental diagnostic yield of reflexing to trio after negative proband analysis was 0.7% (1 of 147). In conclusion, rapid genomic sequencing can be performed as a first-tier diagnostic test in inpatient infants. urWGS had the shortest time to result, which was important in unstable infants, and those in whom a genetic diagnosis was likely to impact immediate management. Further comparison of urWGS and rWES is warranted because genomic technologies and knowledge of variant pathogenicity are evolving rapidly.
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Schmitz-Abe K, Li Q, Rosen SM, Nori N, Madden JA, Genetti CA, Wojcik MH, Ponnaluri S, Gubbels CS, Picker JD, O'Donnell-Luria AH, Yu TW, Bodamer O, Brownstein CA, Beggs AH, Agrawal PB. Unique bioinformatic approach and comprehensive reanalysis improve diagnostic yield of clinical exomes. Eur J Hum Genet 2019; 27:1398-1405. [PMID: 30979967 PMCID: PMC6777619 DOI: 10.1038/s41431-019-0401-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 03/11/2019] [Accepted: 03/26/2019] [Indexed: 01/30/2023] Open
Abstract
Clinical exome sequencing (CES) is increasingly being utilized; however, a large proportion of patients remain undiagnosed, creating a need for a systematic approach to increase the diagnostic yield. We have reanalyzed CES data for a clinically heterogeneous cohort of 102 probands with likely Mendelian conditions, including 74 negative cases and 28 cases with candidate variants, but reanalysis requested by clinicians. Reanalysis was performed by an interdisciplinary team using a validated custom-built pipeline, "Variant Explorer Pipeline" (VExP). This reanalysis approach and results were compared with existing literature. Reanalysis of candidate variants from CES in 28 cases revealed 1 interpretation that needed to be reclassified. A confirmed or potential genetic diagnosis was identified in 24 of 75 CES-negative/reclassified cases (32.0%), including variants in known disease-causing genes (n = 6) or candidate genes (n = 18). This yield was higher compared with similar studies demonstrating the utility of this approach. In summary, reanalysis of negative CES in a research setting enhances diagnostic yield by about a third. This study suggests the need for comprehensive, continued reanalysis of exome data when molecular diagnosis is elusive.
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Affiliation(s)
- Klaus Schmitz-Abe
- Division of Newborn Medicine and Neonatal Genomics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Qifei Li
- Division of Newborn Medicine and Neonatal Genomics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Samantha M Rosen
- Division of Newborn Medicine and Neonatal Genomics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Neeharika Nori
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jill A Madden
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Casie A Genetti
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Monica H Wojcik
- Division of Newborn Medicine and Neonatal Genomics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Sadhana Ponnaluri
- Division of Newborn Medicine and Neonatal Genomics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Cynthia S Gubbels
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonathan D Picker
- Division of Newborn Medicine and Neonatal Genomics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Anne H O'Donnell-Luria
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Timothy W Yu
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Olaf Bodamer
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Catherine A Brownstein
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Alan H Beggs
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Pankaj B Agrawal
- Division of Newborn Medicine and Neonatal Genomics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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Liu G, Yang Z, Chen W, Xu J, Mao L, Yu Q, Guo J, Xu H, Liu F, Sun Y, Huang H, Peng Z, Sun J, Li W, Yang P. Novel missense variant in TTN cosegregating with familial atrioventricular block. Eur J Med Genet 2019; 63:103752. [PMID: 31470098 DOI: 10.1016/j.ejmg.2019.103752] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 06/21/2019] [Accepted: 08/24/2019] [Indexed: 01/24/2023]
Abstract
BACKGROUND Cardiovascular diseases are the most common cause of death globally. In which atrioventricular block (AVB) is a common disorder with genetic causes, but the responsible genes have not been fully identified yet. To determine the underlying causative genes involved in cardiac AVB, here we report a three-generation Chinese family with severe autosomal dominant cardiac AVB that has been ruled out as being caused by known genes mutations. METHODS Whole-exome sequencing was performed in five affected family members across three generations, and co-segregation analysis was validated on other members of this family. RESULTS Whole-exome sequencing and subsequent co-segregation validation identified a novel germline heterozygous point missense mutation, c.49287C > A (p.N16429K), in the titin (TTN, NM_001267550.2) gene in all 5 affected family members but not in the unaffected family members, neither in the large population according to the Genome Aggregation Database (https://gnomad.broadinstitute.org/). The point mutation is predicted to be functionally deleterious by in-silico software tools. Our finding was further supported by the conservative analysis across species. CONCLUSION Based on this study, TTN was identified as a potential novel candidate gene for autosomal dominant AVB; this study expands the mutational spectrum of TTN gene and is the first to implicate TTN mutations as AVB disease causing in a Chinese pedigree.
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Affiliation(s)
- Guohui Liu
- Department of Cardiology, China-Japan Union Hospital, Jilin University, Changchun, 100029, Jilin Province, China; Jilin Provincial Key Laboratory for Genetic Diagnosis of Cardiovascular Disease, USA
| | - Ziying Yang
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China; Binhai Genomics Institute, BGI-Tianjin, BGI Shenzhen, Tianjin, 300308, China; James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Weiwei Chen
- Department of Cardiology, China-Japan Union Hospital, Jilin University, Changchun, 100029, Jilin Province, China; Jilin Provincial Key Laboratory for Genetic Diagnosis of Cardiovascular Disease, USA
| | - Junguang Xu
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Liangwei Mao
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Qinlin Yu
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China; Department of Molecular Cell Biology, UC Berkeley, Berkeley, CA, 94704, USA
| | - Jian Guo
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Hui Xu
- 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
| | - Yan Sun
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Hui Huang
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Zhiyu Peng
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | - Jun Sun
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China; Binhai Genomics Institute, BGI-Tianjin, BGI Shenzhen, Tianjin, 300308, China; James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Wei Li
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Ping Yang
- Department of Cardiology, China-Japan Union Hospital, Jilin University, Changchun, 100029, Jilin Province, China; Jilin Provincial Key Laboratory for Genetic Diagnosis of Cardiovascular Disease, USA.
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