1
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Thomson KL, Jiang C, Richardson E, Westphal DS, Burkard T, Wolf CM, Vatta M, Harrison SM, Ingles J, Bezzina CR, Kroncke BM, Vandenberg JI, Ng CA. Clinical interpretation of KCNH2 variants using a robust PS3/BS3 functional patch-clamp assay. HGG Adv 2024; 5:100270. [PMID: 38219013 PMCID: PMC10840334 DOI: 10.1016/j.xhgg.2024.100270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/07/2024] [Accepted: 01/08/2024] [Indexed: 01/15/2024] Open
Abstract
Long QT syndrome (LQTS), caused by the dysfunction of cardiac ion channels, increases the risk of sudden death in otherwise healthy young people. For many variants in LQTS genes, there is insufficient evidence to make a definitive genetic diagnosis. We have established a robust functional patch-clamp assay to facilitate classification of missense variants in KCNH2, one of the key LQTS genes. A curated set of 30 benign and 30 pathogenic missense variants were used to establish the range of normal and abnormal function. The extent to which variants reduced protein function was quantified using Z scores, the number of standard deviations from the mean of the normalized current density of the set of benign variant controls. A Z score of -2 defined the threshold for abnormal loss of function, which corresponds to 55% wild-type function. More extreme Z scores were observed for variants with a greater loss-of-function effect. We propose that the Z score for each variant can be used to inform the application and weighting of abnormal and normal functional evidence criteria (PS3 and BS3) within the American College of Medical Genetics and Genomics variant classification framework. The validity of this approach was demonstrated using a series of 18 KCNH2 missense variants detected in a childhood onset LQTS cohort, where the level of function assessed using our assay correlated to the Schwartz score (a scoring system used to quantify the probability of a clinical diagnosis of LQTS) and the length of the corrected QT (QTc) interval.
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Affiliation(s)
- Kate L Thomson
- Oxford Genetics Laboratories, Churchill Hospital, Oxford, UK
| | - Connie Jiang
- Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, Australia; Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
| | - Ebony Richardson
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Dominik S Westphal
- Institute of Human Genetics, Klinikum rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany; Department of Internal Medicine I, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany; European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart: ERN GUARD-Heart
| | - Tobias Burkard
- Department of Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Technical University of Munich, School of Medicine and Health, Munich, Germany
| | - Cordula M Wolf
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart: ERN GUARD-Heart; Department of Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Technical University of Munich, School of Medicine and Health, Munich, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | | | | | - Jodie Ingles
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Connie R Bezzina
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart: ERN GUARD-Heart; Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Brett M Kroncke
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jamie I Vandenberg
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia.
| | - Chai-Ann Ng
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia.
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2
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Hu J, Korchina V, Zouk H, Harden MV, Murdock D, Macbeth A, Harrison SM, Lennon N, Kovar C, Balasubramanian A, Zhang L, Chandanavelli G, Pasham D, Rowley R, Wiley K, Smith ME, Gordon A, Jarvik GP, Sleiman P, Kelly MA, Bland HT, Murugan M, Venner E, Boerwinkle E, Prows C, Mahanta L, Rehm HL, Gibbs RA, Muzny DM. Genetic sex validation for sample tracking in next-generation sequencing clinical testing. BMC Res Notes 2024; 17:62. [PMID: 38433186 PMCID: PMC10910835 DOI: 10.1186/s13104-024-06723-w] [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: 08/28/2023] [Accepted: 02/16/2024] [Indexed: 03/05/2024] Open
Abstract
OBJECTIVE Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. RESULTS Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors (49.09%), samples from transgender participants (3.64%) and stem cell or bone marrow transplant patients (7.27%) along with undetermined sample mix-ups (40%) for which sample swaps occurred prior to arrival at genome centers, however the exact cause of the events at the sampling sites resulting in the mix-ups were not able to be determined.
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Affiliation(s)
- Jianhong Hu
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | - Viktoriya Korchina
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | - Hana Zouk
- Laboratory for Molecular Medicine (LMM), Mass General Brigham, Cambridge, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - David Murdock
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Steven M Harrison
- Laboratory for Molecular Medicine (LMM), Mass General Brigham, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christie Kovar
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | | | - Lan Zhang
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | | | - Divya Pasham
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | - Robb Rowley
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Ken Wiley
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Maureen E Smith
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Adam Gordon
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington Medical Center, Seattle, WA, USA
| | - Patrick Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Harris T Bland
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mullai Murugan
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | - Eric Venner
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | - Eric Boerwinkle
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Cynthia Prows
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Lisa Mahanta
- Laboratory for Molecular Medicine (LMM), Mass General Brigham, Cambridge, MA, USA
| | - Heidi L Rehm
- Laboratory for Molecular Medicine (LMM), Mass General Brigham, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard A Gibbs
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA
| | - Donna M Muzny
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC), Houston, TX, USA.
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3
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Bick AG, Metcalf GA, Mayo KR, Lichtenstein L, Rura S, Carroll RJ, Musick A, Linder JE, Jordan IK, Nagar SD, Sharma S, Meller R, Basford M, Boerwinkle E, Cicek MS, Doheny KF, Eichler EE, Gabriel S, Gibbs RA, Glazer D, Harris PA, Jarvik GP, Philippakis A, Rehm HL, Roden DM, Thibodeau SN, Topper S, Blegen AL, Wirkus SJ, Wagner VA, Meyer JG, Cicek MS, Muzny DM, Venner E, Mawhinney MZ, Griffith SML, Hsu E, Ling H, Adams MK, Walker K, Hu J, Doddapaneni H, Kovar CL, Murugan M, Dugan S, Khan Z, Boerwinkle E, Lennon NJ, Austin-Tse C, Banks E, Gatzen M, Gupta N, Henricks E, Larsson K, McDonough S, Harrison SM, Kachulis C, Lebo MS, Neben CL, Steeves M, Zhou AY, Smith JD, Frazar CD, Davis CP, Patterson KE, Wheeler MM, McGee S, Lockwood CM, Shirts BH, Pritchard CC, Murray ML, Vasta V, Leistritz D, Richardson MA, Buchan JG, Radhakrishnan A, Krumm N, Ehmen BW, Schwartz S, Aster MMT, Cibulskis K, Haessly A, Asch R, Cremer A, Degatano K, Shergill A, Gauthier LD, Lee SK, Hatcher A, Grant GB, Brandt GR, Covarrubias M, Banks E, Able A, Green AE, Carroll RJ, Zhang J, Condon HR, Wang Y, Dillon MK, Albach CH, Baalawi W, Choi SH, Wang X, Rosenthal EA, Ramirez AH, Lim S, Nambiar S, Ozenberger B, Wise AL, Lunt C, Ginsburg GS, Denny JC. Genomic data in the All of Us Research Program. Nature 2024; 627:340-346. [PMID: 38374255 PMCID: PMC10937371 DOI: 10.1038/s41586-023-06957-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/08/2023] [Indexed: 02/21/2024]
Abstract
Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.
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4
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Biesecker LG, Byrne AB, Harrison SM, Pesaran T, Schäffer AA, Shirts BH, Tavtigian SV, Rehm HL. ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification. Am J Hum Genet 2024; 111:24-38. [PMID: 38103548 PMCID: PMC10806742 DOI: 10.1016/j.ajhg.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 04/14/2023] [Revised: 11/21/2023] [Accepted: 11/21/2023] [Indexed: 12/19/2023] Open
Abstract
The 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology variant classification publication established a standard employed internationally to guide laboratories in variant assessment. Those recommendations included both pathogenic (PP1) and benign (BS4) criteria for evaluating the inheritance patterns of variants, but details of how to apply those criteria at appropriate evidence levels were sparse. Several publications have since attempted to provide additional guidance, but anecdotally, this issue is still challenging. Additionally, it is not clear that those prior efforts fully distinguished disease-gene identification considerations from variant pathogenicity considerations nor did they address autosomal-recessive and X-linked inheritance. Here, we have taken a mixed inductive and deductive approach to this problem using real diseases as examples. We have developed a practical heuristic for genetic co-segregation evidence and have also determined that the specific phenotype criterion (PP4) is inseparably coupled to the co-segregation criterion. We have also determined that negative evidence at one locus constitutes positive evidence for other loci for disorders with locus heterogeneity. Finally, we provide a points-based system for evaluating phenotype and co-segregation as evidence types to support or refute a locus and show how that can be integrated into the Bayesian framework now used for variant classification and consistent with the 2015 guidelines.
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Affiliation(s)
- Leslie G Biesecker
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Alicia B Byrne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Ambry Genetics, Aliso Viejo, CA, USA
| | | | - Alejandro A Schäffer
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brian H Shirts
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Sean V Tavtigian
- Department of Oncological Sciences, University of Utah School of Medicine and Huntsman Cancer Institute at the University of Utah, Salt Lake City, UT, USA
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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5
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Rehm HL, Alaimo JT, Aradhya S, Bayrak-Toydemir P, Best H, Brandon R, Buchan JG, Chao EC, Chen E, Clifford J, Cohen ASA, Conlin LK, Das S, Davis KW, Del Gaudio D, Del Viso F, DiVincenzo C, Eisenberg M, Guidugli L, Hammer MB, Harrison SM, Hatchell KE, Dyer LH, Hoang LU, Holt JM, Jobanputra V, Karbassi ID, Kearney HM, Kelly MA, Kelly JM, Kluge ML, Komala T, Kruszka P, Lau L, Lebo MS, Marshall CR, McKnight D, McWalter K, Meng Y, Nagan N, Neckelmann CS, Neerman N, Niu Z, Paolillo VK, Paolucci SA, Perry D, Pesaran T, Radtke K, Rasmussen KJ, Retterer K, Saunders CJ, Spiteri E, Stanley C, Szuto A, Taft RJ, Thiffault I, Thomas BC, Thomas-Wilson A, Thorpe E, Tidwell TJ, Towne MC, Zouk H. The landscape of reported VUS in multi-gene panel and genomic testing: Time for a change. Genet Med 2023; 25:100947. [PMID: 37534744 PMCID: PMC10825061 DOI: 10.1016/j.gim.2023.100947] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [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: 04/24/2023] [Revised: 07/20/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023] Open
Abstract
PURPOSE Variants of uncertain significance (VUS) are a common result of diagnostic genetic testing and can be difficult to manage with potential misinterpretation and downstream costs, including time investment by clinicians. We investigated the rate of VUS reported on diagnostic testing via multi-gene panels (MGPs) and exome and genome sequencing (ES/GS) to measure the magnitude of uncertain results and explore ways to reduce their potentially detrimental impact. METHODS Rates of inconclusive results due to VUS were collected from over 1.5 million sequencing test results from 19 clinical laboratories in North America from 2020 to 2021. RESULTS We found a lower rate of inconclusive test results due to VUSs from ES/GS (22.5%) compared with MGPs (32.6%; P < .0001). For MGPs, the rate of inconclusive results correlated with panel size. The use of trios reduced inconclusive rates (18.9% vs 27.6%; P < .0001), whereas the use of GS compared with ES had no impact (22.2% vs 22.6%; P = ns). CONCLUSION The high rate of VUS observed in diagnostic MGP testing warrants examining current variant reporting practices. We propose several approaches to reduce reported VUS rates, while directing clinician resources toward important VUS follow-up.
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Affiliation(s)
- Heidi L Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA; Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Pathology, Harvard Medical School, Boston, MA.
| | - Joseph T Alaimo
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO; Department of Pediatrics, School of Medicine, University of Missouri, Kansas City, MO; Genomic Medicine Center, Children's Mercy Hospital, Kansas City, MO
| | - Swaroop Aradhya
- Invitae, San Francisco, CA; Department of Pathology, Stanford University School of Medicine, Palo Alto, CA
| | - Pinar Bayrak-Toydemir
- ARUP Laboratories, Salt Lake City, UT; Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT
| | - Hunter Best
- ARUP Laboratories, Salt Lake City, UT; Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT
| | | | - Jillian G Buchan
- Genetics Division, Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | | | | | | | - Ana S A Cohen
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO; Department of Pediatrics, School of Medicine, University of Missouri, Kansas City, MO; Genomic Medicine Center, Children's Mercy Hospital, Kansas City, MO
| | - Laura K Conlin
- Division of Genomic Diagnostics, Children's Hospital of Philadelphia, Philadelphia, PA; Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA
| | - Soma Das
- Human Genetics, University of Chicago, Chicago, IL
| | | | | | - Florencia Del Viso
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO
| | | | - Marcia Eisenberg
- Women's Health and Genetics, Labcorp, Research Triangle Park, NC
| | - Lucia Guidugli
- Rady Children's Institute for Genomic Medicine, San Diego, CA
| | - Monia B Hammer
- Rady Children's Institute for Genomic Medicine, San Diego, CA
| | | | | | | | | | - James M Holt
- HudsonAlpha Clinical Services Lab, LLC, Huntsville, AL
| | - Vaidehi Jobanputra
- Molecular Diagnostics, New York Genome Center, New York, NY; Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY
| | | | - Hutton M Kearney
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Jacob M Kelly
- HudsonAlpha Clinical Services Lab, LLC, Huntsville, AL
| | - Michelle L Kluge
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | | | - Lynette Lau
- Division of Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Matthew S Lebo
- Pathology, Harvard Medical School, Boston, MA; Laboratory for Molecular Medicine, Mass General Brigham, Cambridge, MA
| | - Christian R Marshall
- Division of Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | | | | | - Yan Meng
- Fulgent Genetics, Temple City, CA
| | | | | | | | - Zhiyv Niu
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Vitoria K Paolillo
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO
| | - Sarah A Paolucci
- Genetics Division, Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | | | | | | | - Kristen J Rasmussen
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Carol J Saunders
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO; Genomic Medicine Center, Children's Mercy Hospital, Kansas City, MO; Department of Pediatrics and Pathology, School of Medicine, University of Missouri, Kansas City, MO
| | | | | | - Anna Szuto
- Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Isabelle Thiffault
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO; Department of Pediatrics, School of Medicine, University of Missouri, Kansas City, MO; Genomic Medicine Center, Children's Mercy Hospital, Kansas City, MO
| | | | | | | | | | | | - Hana Zouk
- Pathology, Harvard Medical School, Boston, MA; Laboratory for Molecular Medicine, Mass General Brigham, Cambridge, MA
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6
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Hu J, Korchina V, Zouk H, Harden MV, Murdock D, Macbeth A, Harrison SM, Lennon N, Kovar C, Balasubramanian A, Zhang L, Chandanavelli G, Pasham D, Rowley R, Wiley K, Smith ME, Gordon A, Jarvik GP, Sleiman P, Kelly MA, Bland HT, Murugan M, Venner E, Boerwinkle E, Prows C, Mahanta L, Rehm HL, Gibbs RA, Muzny DM. Genetic Sex Validation for Sample Tracking in Clinical Testing. Res Sq 2023:rs.3.rs-3304685. [PMID: 37790445 PMCID: PMC10543510 DOI: 10.21203/rs.3.rs-3304685/v1] [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] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Objective Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. Results Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors, samples from transgender participants and stem cell or bone marrow transplant patients along with undetermined sample mix-ups.
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Affiliation(s)
- Jianhong Hu
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | | | - Hana Zouk
- Laboratory for Molecular Medicine (LMM), Mass General Brigham
| | | | - David Murdock
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | | | | | | | - Christie Kovar
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | | | - Lan Zhang
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | | | - Divya Pasham
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | | | - Ken Wiley
- National Human Genome Research Institute
| | | | - Adam Gordon
- Northwestern University Feinberg School of Medicine
| | | | | | | | | | - Mullai Murugan
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | - Eric Venner
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | - Eric Boerwinkle
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | | | - Lisa Mahanta
- Laboratory for Molecular Medicine (LMM), Mass General Brigham
| | | | - Richard A Gibbs
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
| | - Donna M Muzny
- Baylor College of Medicine, Human Genome Sequencing Center (HGSC)
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7
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Singer-Berk M, Gudmundsson S, Baxter S, Seaby EG, England E, Wood JC, Son RG, Watts NA, Karczewski KJ, Harrison SM, MacArthur DG, Rehm HL, O'Donnell-Luria A. Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data. Am J Hum Genet 2023; 110:1496-1508. [PMID: 37633279 PMCID: PMC10502856 DOI: 10.1016/j.ajhg.2023.08.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/28/2023] Open
Abstract
Predicted loss of function (pLoF) variants are often highly deleterious and play an important role in disease biology, but many pLoF variants may not result in loss of function (LoF). Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines' PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 genes associated with autosomal-recessive disease from the Genome Aggregation Database (gnomAD v.2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in a low proportion expressed across transcripts (pext) scored region, or the presence of cryptic in-frame splice rescues. Variants predicted to evade LoF or to be potential artifacts were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of pLoF variants predicted as likely not LoF/not LoF, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.
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Affiliation(s)
- Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Sanna Gudmundsson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eleanor G Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Genomic Informatics Group, University Hospital Southampton, Southampton, UK
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan C Wood
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel G Son
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Ambry Genetics, Aliso Viejo, CA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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8
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Miller DT, Lee K, Abul-Husn NS, Amendola LM, Brothers K, Chung WK, Gollob MH, Gordon AS, Harrison SM, Hershberger RE, Klein TE, Richards CS, Stewart DR, Martin CL. ACMG SF v3.2 list for reporting of secondary findings in clinical exome and genome sequencing: A policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2023; 25:100866. [PMID: 37347242 PMCID: PMC10524344 DOI: 10.1016/j.gim.2023.100866] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [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: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 06/23/2023] Open
Abstract
Clinicians are encouraged to document the reasons for the use of a particular procedure or test, whether or not it is in conformance with this statement. Clinicians also are advised to take notice of the date this statement was adopted, and to consider other medical and scientific information that becomes available after that date. It also would be prudent to consider whether intellectual property interests may restrict the performance of certain tests and other procedures. Where individual authors are listed, the views expressed may not reflect those of authors’ employers or affiliated institutions.
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Affiliation(s)
- David T Miller
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
| | - Kristy Lee
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Noura S Abul-Husn
- Department of Medicine, Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY; 23andMe, Inc., Sunnyvale, CA
| | | | - Kyle Brothers
- Department of Pediatrics, University of Louisville, Louisville, KY
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY
| | - Michael H Gollob
- Division of Cardiology, Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Adam S Gordon
- Department of Pharmacology, Center for Genetic Medicine, Northwestern University, Chicago, IL
| | | | - Ray E Hershberger
- Divisions of Human Genetics and Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH
| | - Teri E Klein
- Departments of Biomedical Data Science and Medicine, Stanford University, Stanford, CA
| | - C Sue Richards
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR
| | - Douglas R Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
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9
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Walker LC, Hoya MDL, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A, Zimmermann H, Byrne AB, Pesaran T, Karam R, Harrison SM, Spurdle AB. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet 2023; 110:1046-1067. [PMID: 37352859 PMCID: PMC10357475 DOI: 10.1016/j.ajhg.2023.06.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [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: 02/20/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 06/25/2023] Open
Abstract
The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1, PS3, PP3, BS3, BP4, and BP7. However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. We utilized empirically derived splicing evidence to (1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, (2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and (3) exemplify methodology to calibrate splice prediction tools. We propose repurposing the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely, BP7 may be used to capture RNA results demonstrating no splicing impact for intronic and synonymous variants. We propose that the PS3/BS3 codes are applied only for well-established assays that measure functional impact not directly captured by RNA-splicing assays. We recommend the application of PS1 based on similarity of predicted RNA-splicing effects for a variant under assessment in comparison with a known pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA-assay evidence described aim to help standardize variant pathogenicity classification processes when interpreting splicing-based evidence.
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Affiliation(s)
- Logan C Walker
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Miguel de la Hoya
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - George A R Wiggins
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | | | | | - Michael T Parsons
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Daffodil M Canson
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | | | | | | | - Alicia B Byrne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Steven M Harrison
- Ambry Genetics, Aliso Viejo, CA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Amanda B Spurdle
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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10
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Singer-Berk M, Gudmundsson S, Baxter S, Seaby EG, England E, Wood JC, Son RG, Watts NA, Karczewski KJ, Harrison SM, MacArthur DG, Rehm HL, O'Donnell-Luria A. Advanced variant classification framework reduces the false positive rate of predicted loss of function (pLoF) variants in population sequencing data. medRxiv 2023:2023.03.08.23286955. [PMID: 36945502 PMCID: PMC10029069 DOI: 10.1101/2023.03.08.23286955] [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] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Predicted loss of function (pLoF) variants are highly deleterious and play an important role in disease biology, but many of these variants may not actually result in loss-of-function. Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines's PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 autosomal recessive disease-genes from the Genome Aggregation Database (gnomAD, v2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in low per-base expression (pext) score regions, or the presence of cryptic splice rescues. Variants predicted to be potential artifacts or to evade LoF were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of LoF evading variants assessed, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines, and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.
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Affiliation(s)
- Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Sanna Gudmundsson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eleanor G Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Genomic Informatics Group, University Hospital Southampton, Southampton, United Kingdom
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan C Wood
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel G Son
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ambry Genetics, Aliso Viejo, CA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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11
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Walker LC, de la Hoya M, Wiggins GA, Lindy A, Vincent LM, Parsons M, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A, Zimmermann H, Byrne AB, Pesaran T, Karam R, Harrison SM, Spurdle AB. APPLICATION OF THE ACMG/AMP FRAMEWORK TO CAPTURE EVIDENCE RELEVANT TO PREDICTED AND OBSERVED IMPACT ON SPLICING: RECOMMENDATIONS FROM THE CLINGEN SVI SPLICING SUBGROUP. medRxiv 2023:2023.02.24.23286431. [PMID: 36865205 PMCID: PMC9980257 DOI: 10.1101/2023.02.24.23286431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1 (null variant in a gene where loss-of-function is the mechanism of disease), PS3 (functional assays show damaging effect on splicing), PP3 (computational evidence supports a splicing effect), BS3 (functional assays show no damaging effect on splicing), BP4 (computational evidence suggests no splicing impact), and BP7 (silent change with no predicted impact on splicing). However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation (SVI) Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. Our study utilised empirically derived splicing evidence to: 1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, 2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and 3) exemplify methodology to calibrate bioinformatic splice prediction tools. We propose repurposing of the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely BP7 may be used to capture RNA results demonstrating no impact on splicing for both intronic and synonymous variants, and for missense variants if protein functional impact has been excluded. Furthermore, we propose that the PS3 and BS3 codes are applied only for well-established assays that measure functional impact that is not directly captured by RNA splicing assays. We recommend the application of PS1 based on similarity of predicted RNA splicing effects for a variant under assessment in comparison to a known Pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA assay evidence described aim to help standardise variant pathogenicity classification processes and result in greater consistency when interpreting splicing-based evidence.
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12
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Pejaver V, Byrne AB, Feng BJ, Pagel KA, Mooney SD, Karchin R, O'Donnell-Luria A, Harrison SM, Tavtigian SV, Greenblatt MS, Biesecker LG, Radivojac P, Brenner SE. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet 2022; 109:2163-2177. [PMID: 36413997 PMCID: PMC9748256 DOI: 10.1016/j.ajhg.2022.10.013] [Citation(s) in RCA: 107] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022] Open
Abstract
Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.
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Affiliation(s)
- Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - Alicia B Byrne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bing-Jian Feng
- Department of Dermatology, University of Utah, Salt Lake City, UT 84132, USA; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Kymberleigh A Pagel
- The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - Rachel Karchin
- The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Departments of Biomedical Engineering, Oncology, and Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Ambry Genetics, Aliso Viejo, CA 92656, USA
| | - Sean V Tavtigian
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Marc S Greenblatt
- Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA
| | - Leslie G Biesecker
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
| | - Steven E Brenner
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
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13
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Ellingford JM, Ahn JW, Bagnall RD, Baralle D, Barton S, Campbell C, Downes K, Ellard S, Duff-Farrier C, FitzPatrick DR, Greally JM, Ingles J, Krishnan N, Lord J, Martin HC, Newman WG, O’Donnell-Luria A, Ramsden SC, Rehm HL, Richardson E, Singer-Berk M, Taylor JC, Williams M, Wood JC, Wright CF, Harrison SM, Whiffin N. Recommendations for clinical interpretation of variants found in non-coding regions of the genome. Genome Med 2022; 14:73. [PMID: 35850704 PMCID: PMC9295495 DOI: 10.1186/s13073-022-01073-3] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [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: 02/09/2022] [Accepted: 06/16/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The majority of clinical genetic testing focuses almost exclusively on regions of the genome that directly encode proteins. The important role of variants in non-coding regions in penetrant disease is, however, increasingly being demonstrated, and the use of whole genome sequencing in clinical diagnostic settings is rising across a large range of genetic disorders. Despite this, there is no existing guidance on how current guidelines designed primarily for variants in protein-coding regions should be adapted for variants identified in other genomic contexts. METHODS We convened a panel of nine clinical and research scientists with wide-ranging expertise in clinical variant interpretation, with specific experience in variants within non-coding regions. This panel discussed and refined an initial draft of the guidelines which were then extensively tested and reviewed by external groups. RESULTS We discuss considerations specifically for variants in non-coding regions of the genome. We outline how to define candidate regulatory elements, highlight examples of mechanisms through which non-coding region variants can lead to penetrant monogenic disease, and outline how existing guidelines can be adapted for the interpretation of these variants. CONCLUSIONS These recommendations aim to increase the number and range of non-coding region variants that can be clinically interpreted, which, together with a compatible phenotype, can lead to new diagnoses and catalyse the discovery of novel disease mechanisms.
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Affiliation(s)
- Jamie M. Ellingford
- grid.5379.80000000121662407Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester, M13 9PT UK ,grid.498924.a0000 0004 0430 9101Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL UK ,grid.498322.6Genomics England, London, UK
| | - Joo Wook Ahn
- grid.24029.3d0000 0004 0383 8386Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, UK
| | - Richard D. Bagnall
- grid.1013.30000 0004 1936 834XAgnes Ginges Centre for Molecular Cardiology at Centenary Institute, University of Sydney, Sydney, Australia
| | - Diana Baralle
- grid.5491.90000 0004 1936 9297School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK ,grid.430506.40000 0004 0465 4079Wessex Clinical Genetics Service, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Stephanie Barton
- grid.498924.a0000 0004 0430 9101Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL UK
| | - Chris Campbell
- grid.498924.a0000 0004 0430 9101Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL UK
| | - Kate Downes
- grid.24029.3d0000 0004 0383 8386Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, UK
| | - Sian Ellard
- grid.8391.30000 0004 1936 8024Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK ,grid.419309.60000 0004 0495 6261South West Genomic Laboratory Hub, Exeter Genomic Laboratory, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Celia Duff-Farrier
- grid.418484.50000 0004 0380 7221South West NHS Genomic Laboratory Hub, Bristol Genetics Laboratory, North Bristol NHS Trust, Bristol, UK
| | - David R. FitzPatrick
- grid.417068.c0000 0004 0624 9907MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - John M. Greally
- grid.251993.50000000121791997Department of Pediatrics, Division of Pediatric Genetic, Medicine, Children’s Hospital at Montefiore/Montefiore Medical Center/Albert, Einstein College of Medicine, Bronx, NY USA
| | - Jodie Ingles
- grid.1005.40000 0004 4902 0432Centre for Population Genomics, Garvan Institute of Medical Research, and UNSW Sydney, Sydney, Australia ,grid.1058.c0000 0000 9442 535XCentre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Neesha Krishnan
- grid.1005.40000 0004 4902 0432Centre for Population Genomics, Garvan Institute of Medical Research, and UNSW Sydney, Sydney, Australia ,grid.1058.c0000 0000 9442 535XCentre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Jenny Lord
- grid.5491.90000 0004 1936 9297School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Hilary C. Martin
- grid.10306.340000 0004 0606 5382Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - William G. Newman
- grid.5379.80000000121662407Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester, M13 9PT UK ,grid.498924.a0000 0004 0430 9101Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL UK
| | - Anne O’Donnell-Luria
- grid.66859.340000 0004 0546 1623Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.2515.30000 0004 0378 8438Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA USA ,grid.32224.350000 0004 0386 9924Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA USA
| | - Simon C. Ramsden
- grid.498924.a0000 0004 0430 9101Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL UK
| | - Heidi L. Rehm
- grid.66859.340000 0004 0546 1623Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.32224.350000 0004 0386 9924Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA USA
| | - Ebony Richardson
- grid.1005.40000 0004 4902 0432Centre for Population Genomics, Garvan Institute of Medical Research, and UNSW Sydney, Sydney, Australia ,grid.1058.c0000 0000 9442 535XCentre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Moriel Singer-Berk
- grid.66859.340000 0004 0546 1623Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Jenny C. Taylor
- grid.4991.50000 0004 1936 8948National Institute for Health Research Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN UK ,grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN UK
| | - Maggie Williams
- grid.418484.50000 0004 0380 7221South West NHS Genomic Laboratory Hub, Bristol Genetics Laboratory, North Bristol NHS Trust, Bristol, UK
| | - Jordan C. Wood
- grid.66859.340000 0004 0546 1623Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Caroline F. Wright
- grid.8391.30000 0004 1936 8024Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Steven M. Harrison
- grid.66859.340000 0004 0546 1623Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.465138.d0000 0004 0455 211XAmbry Genetics, Aliso Viejo, CA USA
| | - Nicola Whiffin
- grid.66859.340000 0004 0546 1623Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN UK
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14
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Jiang C, Richardson E, Farr J, Hill AP, Ullah R, Kroncke BM, Harrison SM, Thomson KL, Ingles J, Vandenberg JI, Ng CA. A calibrated functional patch-clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome. Am J Hum Genet 2022; 109:1199-1207. [PMID: 35688147 PMCID: PMC9300752 DOI: 10.1016/j.ajhg.2022.05.002] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 05/03/2022] [Indexed: 01/09/2023] Open
Abstract
Modern sequencing technologies have revolutionized our detection of gene variants. However, in most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUSs). The aim of this study was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in KCNH2. The assay was designed according to recommendations proposed by the Clinical Genome Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variants on current density and channel gating in order to determine the sensitivity and specificity of the assay. All 17 pathogenic variant controls had reduced current density, and 13 of 14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying evidence of moderate or supporting strength for VUS reclassification. Inclusion of functional assay evidence enabled us to reclassify 6 out of 44 KCNH2 VUSs as likely pathogenic. The high-throughput patch-clamp assay can provide moderate-strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value of developing automated patch-clamp assays for functional characterization of ion channel gene variants.
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Affiliation(s)
- Connie Jiang
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, Australia
| | - Ebony Richardson
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Jessica Farr
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, Australia
| | - Adam P Hill
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia
| | - Rizwan Ullah
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brett M Kroncke
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Kate L Thomson
- Oxford Medical Genetics Laboratories, Churchill Hospital, Oxford, UK
| | - Jodie Ingles
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Jamie I Vandenberg
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia.
| | - Chai-Ann Ng
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia.
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15
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Miller DT, Lee K, Abul-Husn NS, Amendola LM, Brothers K, Chung WK, Gollob MH, Gordon AS, Harrison SM, Hershberger RE, Klein TE, Richards CS, Stewart DR, Martin CL. ACMG SF v3.1 list for reporting of secondary findings in clinical exome and genome sequencing: A policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2022; 24:1407-1414. [DOI: 10.1016/j.gim.2022.04.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/01/2022] [Indexed: 11/25/2022] Open
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16
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Preston CG, Wright MW, Madhavrao R, Harrison SM, Goldstein JL, Luo X, Wand H, Wulf B, Cheung G, Mandell ME, Tong H, Cheng S, Iacocca MA, Pineda AL, Popejoy AB, Dalton K, Zhen J, Dwight SS, Babb L, DiStefano M, O’Daniel JM, Lee K, Riggs ER, Zastrow DB, Mester JL, Ritter DI, Patel RY, Subramanian SL, Milosavljevic A, Berg JS, Rehm HL, Plon SE, Cherry JM, Bustamante CD, Costa HA. ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines. Genome Med 2022; 14:6. [PMID: 35039090 PMCID: PMC8764818 DOI: 10.1186/s13073-021-01004-8] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 11/12/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Identification of clinically significant genetic alterations involved in human disease has been dramatically accelerated by developments in next-generation sequencing technologies. However, the infrastructure and accessible comprehensive curation tools necessary for analyzing an individual patient genome and interpreting genetic variants to inform healthcare management have been lacking. RESULTS Here we present the ClinGen Variant Curation Interface (VCI), a global open-source variant classification platform for supporting the application of evidence criteria and classification of variants based on the ACMG/AMP variant classification guidelines. The VCI is among a suite of tools developed by the NIH-funded Clinical Genome Resource (ClinGen) Consortium and supports an FDA-recognized human variant curation process. Essential to this is the ability to enable collaboration and peer review across ClinGen Expert Panels supporting users in comprehensively identifying, annotating, and sharing relevant evidence while making variant pathogenicity assertions. To facilitate evidence-based improvements in human variant classification, the VCI is publicly available to the genomics community. Navigation workflows support users providing guidance to comprehensively apply the ACMG/AMP evidence criteria and document provenance for asserting variant classifications. CONCLUSIONS The VCI offers a central platform for clinical variant classification that fills a gap in the learning healthcare system, facilitates widespread adoption of standards for clinical curation, and is available at https://curation.clinicalgenome.org.
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Affiliation(s)
- Christine G. Preston
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Matt W. Wright
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Rao Madhavrao
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Steven M. Harrison
- grid.66859.340000 0004 0546 1623Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Jennifer L. Goldstein
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Xi Luo
- grid.39382.330000 0001 2160 926XDepartment of Pediatrics/Hematology-Oncology, Baylor College of Medicine, Houston, TX 77030 USA
| | - Hannah Wand
- grid.490568.60000 0004 5997 482XCenter for Inherited Cardiovascular Disease, Stanford Health Care, Stanford, CA 94305 USA
| | - Bryan Wulf
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Gloria Cheung
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Mark E. Mandell
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Howard Tong
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Shaung Cheng
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Michael A. Iacocca
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA
| | - Arturo Lopez Pineda
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Alice B. Popejoy
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Karen Dalton
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Jimmy Zhen
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305 USA
| | | | - Lawrence Babb
- grid.66859.340000 0004 0546 1623Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Marina DiStefano
- grid.66859.340000 0004 0546 1623Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Julianne M. O’Daniel
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Kristy Lee
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Erin R. Riggs
- grid.280776.c0000 0004 0394 1447Autism & Developmental Medicine Institute, Geisinger Health System, Lewisburg, PA 17837 USA
| | - Diane B. Zastrow
- grid.416759.80000 0004 0460 3124Sutter Health, Mountain View, CA 94040 USA
| | - Jessica L. Mester
- grid.428467.b0000 0004 0409 2707GeneDx Inc., Gaithersburg, MD 20877 USA
| | - Deborah I. Ritter
- grid.39382.330000 0001 2160 926XDepartment of Pediatrics/Hematology-Oncology, Baylor College of Medicine, Houston, TX 77030 USA
| | - Ronak Y. Patel
- grid.39382.330000 0001 2160 926XDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Sai Lakshmi Subramanian
- grid.39382.330000 0001 2160 926XDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Aleksander Milosavljevic
- grid.39382.330000 0001 2160 926XDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Jonathan S. Berg
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Heidi L. Rehm
- grid.66859.340000 0004 0546 1623Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA ,grid.32224.350000 0004 0386 9924Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114 USA
| | - Sharon E. Plon
- grid.39382.330000 0001 2160 926XDepartment of Pediatrics/Hematology-Oncology, Baylor College of Medicine, Houston, TX 77030 USA ,grid.39382.330000 0001 2160 926XDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - J. Michael Cherry
- grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Carlos D. Bustamante
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305 USA ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Helio A. Costa
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, MSOB x313, Stanford, CA 94305 USA ,grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305 USA
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17
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Gold NB, Harrison SM, Rowe JH, Gold J, Furutani E, Biffi A, Duncan CN, Shimamura A, Lehmann LE, Green RC. Low frequency of treatable pediatric disease alleles in gnomAD: An opportunity for future genomic screening of newborns. HGG Adv 2022; 3:100059. [PMID: 35047849 PMCID: PMC8756496 DOI: 10.1016/j.xhgg.2021.100059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 04/13/2021] [Accepted: 09/20/2021] [Indexed: 01/18/2023] Open
Abstract
Hematopoietic stem cell transplant (HSCT) can prevent progression of several genetic disorders. Although a subset of these disorders are identified on newborn screening panels, others are not identified until irreversible symptoms develop. Genetic testing is an efficient methodology to ascertain pre-symptomatic children, but the penetrance of risk-associated variants in the general population is not well understood. We developed a list of 127 genes associated with disorders treatable with HSCT. We identified likely pathogenic or pathogenic (LP/P) and loss-of-function (LoF) variants in these genes in the Genome Aggregation Database (gnomAD), a dataset containing exome and genome sequencing data from 141,456 healthy adults. Within gnomAD, we identified 59 individuals with a LP/P or LoF variant in 15 genes. Genes were associated with bone marrow failure syndromes, bleeding disorders, primary immunodeficiencies, osteopetrosis, metabolic disorders, and epidermolysis bullosa. In conclusion, few ostensibly healthy adults had genotypes associated with pediatric disorders treatable with HSCTs. Given that most of these disorders do not have biomarkers that could be cheaply and universally assessed on a standard newborn screen, our data suggest that genetic testing may be a complementary approach to traditional newborn screening methodology that has the potential to improve mortality and is not expected to lead to a high burden of false-positive results.
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Affiliation(s)
- Nina B. Gold
- Massachusetts General Hospital for Children, Division of Medical Genetics and Metabolism, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Jared H. Rowe
- Harvard Medical School, Boston, MA, USA
- Boston Children’s Hospital, Division of Hematology and Oncology, Boston, MA, USA
- Dana-Farber Cancer Institute Division of Pediatric Oncology, Boston, MA, USA
| | - Jessica Gold
- Department of Pediatrics, Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elissa Furutani
- Boston Children’s Hospital, Division of Hematology and Oncology, Boston, MA, USA
| | - Alessandra Biffi
- Harvard Medical School, Boston, MA, USA
- Boston Children’s Hospital, Division of Hematology and Oncology, Boston, MA, USA
- Dana-Farber Cancer Institute Division of Pediatric Oncology, Boston, MA, USA
| | - Christine N. Duncan
- Harvard Medical School, Boston, MA, USA
- Boston Children’s Hospital, Division of Hematology and Oncology, Boston, MA, USA
- Dana-Farber Cancer Institute Division of Pediatric Oncology, Boston, MA, USA
| | - Akiko Shimamura
- Harvard Medical School, Boston, MA, USA
- Boston Children’s Hospital, Division of Hematology and Oncology, Boston, MA, USA
- Dana-Farber Cancer Institute Division of Pediatric Oncology, Boston, MA, USA
| | - Leslie E. Lehmann
- Harvard Medical School, Boston, MA, USA
- Boston Children’s Hospital, Division of Hematology and Oncology, Boston, MA, USA
- Dana-Farber Cancer Institute Division of Pediatric Oncology, Boston, MA, USA
| | - Robert C. Green
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
- Ariadne Labs, Boston, MA, USA
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18
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Harrison SM, Austin-Tse CA, Kim S, Lebo M, Leon A, Murdock D, Radhakrishnan A, Shirts BH, Steeves M, Venner E, Gibbs RA, Jarvik GP, Rehm HL. Harmonizing variant classification for return of results in the All of Us Research Program. Hum Mutat 2021; 43:1114-1121. [PMID: 34923710 PMCID: PMC9206690 DOI: 10.1002/humu.24317] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 11/24/2021] [Accepted: 12/15/2021] [Indexed: 11/18/2022]
Abstract
The All of Us Research Program (AoURP) is a historic effort to accelerate research and improve healthcare by generating and collating data from one million people in the United States. Participants will have the option to receive results from their genome analysis, including actionable findings in 59 gene‐disorder pairs for which disorder‐associated variants are recommended for return by the American College of Medical Genetics and Genomics. To ensure consistent reporting across the AoURP, in a prelaunch study the four participating clinical laboratories shared all variant classifications in the 59 genes of interest from their internal databases. Of the 11,813 unique variants classified by at least two of the four laboratories, classifications were concordant with regard to reportability for 99.1% (11,711), with only 0.9% (102) having reportability differences. Through variant reassessment, data sharing, and discussion of rationale, participating laboratories resolved all 102 reportable differences. These approaches will be maintained during routine AoU reporting to ensure continuous classification harmonization and consistent reporting within AoURP.
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Affiliation(s)
| | - Christina A Austin-Tse
- Laboratory for Molecular Medicine, Mass General Brigham, Boston, Massachusetts, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Serra Kim
- Color Health, Burlingame, California, USA
| | - Matthew Lebo
- Laboratory for Molecular Medicine, Mass General Brigham, Boston, Massachusetts, USA
| | | | - David Murdock
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | | | - Brian H Shirts
- University of Washington Medical Center, Seattle, Washington, USA
| | - Marcie Steeves
- Laboratory for Molecular Medicine, Mass General Brigham, Boston, Massachusetts, USA
| | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - Gail P Jarvik
- University of Washington Medical Center, Seattle, Washington, USA
| | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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19
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Gollob MH, Hershberger RE, Gordon AS, Harrison SM, Lee K, Martin CL, Miller DT. Response to McGurk et al. Genet Med 2021; 24:747-748. [PMID: 34906521 DOI: 10.1016/j.gim.2021.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 10/13/2021] [Indexed: 11/17/2022] Open
Affiliation(s)
- Michael H Gollob
- Division of Cardiology, Department of Medicine, Faculty of Medicine, and Department of Physiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ray E Hershberger
- Divisions of Human Genetics and Cardiovascular Medicine, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH
| | - Adam S Gordon
- Department of Pharmacology and Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | | | - Kristy Lee
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - David T Miller
- Division of Genetics & Genomics, Boston Children's Hospital, Boston, MA.
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20
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Wilcox E, Harrison SM, Lockhart E, Voelkerding K, Lubin IM, Rehm HL, Kalman LV, Funke B. Creation of an Expert Curated Variant List for Clinical Genomic Test Development and Validation: A ClinGen and GeT-RM Collaborative Project. J Mol Diagn 2021; 23:1500-1505. [PMID: 34384894 PMCID: PMC8647424 DOI: 10.1016/j.jmoldx.2021.07.018] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/09/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022] Open
Abstract
Modern genomic sequencing tests often interrogate large numbers of genes. Identification of appropriate reference materials for development, validation studies, and quality assurance of these tests poses a significant challenge for laboratories. It is difficult to develop and maintain expert knowledge to identify all variants that must be validated to ensure analytic and clinical validity. Additionally, it is usually not possible to procure appropriate and characterized genomic DNA reference materials containing the number and scope of variants required. To address these challenges, the Centers for Disease Control and Prevention's Genetic Testing Reference Material Program (GeT-RM) has partnered with the Clinical Genome Resource (ClinGen) to develop a publicly available list of expert curated, clinically important variants. ClinGen Variant Curation Expert Panels nominated 546 variants found in 84 disease-associated genes, including common pathogenic and difficult-to-detect variants. Variant types nominated included 346 single nucleotide variants, 104 deletions, 37 copy number variants, 25 duplications, 18 deletion-insertions, 5 inversions, 4 insertions, 2 complex rearrangements, 3 difficult-to-sequence regions, and 2 fusions. This expert-curated variant list is a resource that provides a foundation for designing comprehensive validation studies and for creating in silico reference materials for clinical genomic test development and validation.
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Affiliation(s)
- Emma Wilcox
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Edward Lockhart
- Informatics and Data Science Branch, Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Ira M Lubin
- Quality and Safety Systems Branch, Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Lisa V Kalman
- Informatics and Data Science Branch, Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia.
| | - Birgit Funke
- Division of Genomic Health, Sema4, Stamford, Connecticut
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21
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Miller DT, Lee K, Chung WK, Gordon AS, Herman GE, Klein TE, Stewart DR, Amendola LM, Adelman K, Bale SJ, Gollob MH, Harrison SM, Hershberger RE, McKelvey K, Richards CS, Vlangos CN, Watson MS, Martin CL. Correction to: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021; 23:1582-1584. [PMID: 34345026 PMCID: PMC10764012 DOI: 10.1038/s41436-021-01278-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- David T Miller
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Kristy Lee
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Adam S Gordon
- Department of Pharmacology & Center for Genetic Medicine, Northwestern University, Chicago, IL, USA
| | - Gail E Herman
- Professor Emeritus, Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Teri E Klein
- Departments of Biomedical Data Science & Medicine, Stanford University, Stanford, CA, USA
| | - Douglas R Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Laura M Amendola
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | | | | | - Michael H Gollob
- Division of Cardiology and Department of Physiology, University of Toronto, Toronto, ON, Canada
| | | | - Ray E Hershberger
- Divisions of Human Genetics and Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Kent McKelvey
- Departments of Genetics and Family Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - C Sue Richards
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
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22
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Miller DT, Lee K, Chung WK, Gordon AS, Herman GE, Klein TE, Stewart DR, Amendola LM, Adelman K, Bale SJ, Gollob MH, Harrison SM, Hershberger RE, McKelvey K, Richards CS, Vlangos CN, Watson MS, Martin CL. ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021; 23:1381-1390. [PMID: 34012068 DOI: 10.1038/s41436-021-01172-3] [Citation(s) in RCA: 268] [Impact Index Per Article: 89.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 03/26/2021] [Accepted: 03/26/2021] [Indexed: 01/17/2023] Open
Affiliation(s)
- David T Miller
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Kristy Lee
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Adam S Gordon
- Department of Pharmacology & Center for Genetic Medicine, Northwestern University, Chicago, IL, USA
| | - Gail E Herman
- Professor Emeritus, Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Teri E Klein
- Departments of Biomedical Data Science & Medicine, Stanford University, Stanford, CA, USA
| | - Douglas R Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Laura M Amendola
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | | | | | - Michael H Gollob
- Division of Cardiology and Department of Physiology, University of Toronto, Toronto, ON, Canada
| | | | - Ray E Hershberger
- Divisions of Human Genetics and Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Kent McKelvey
- Departments of Genetics and Family Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - C Sue Richards
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
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23
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Biesecker LG, Harrison SM, Rehm HL. Correspondence on "The role of clinical response to treatment in determining pathogenicity of genomic variants" by Shen et al. Genet Med 2020; 23:586. [PMID: 33154565 DOI: 10.1038/s41436-020-01032-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 02/06/2023] Open
Affiliation(s)
- Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | | | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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24
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Marshall CR, Chowdhury S, Taft RJ, Lebo MS, Buchan JG, Harrison SM, Rowsey R, Klee EW, Liu P, Worthey EA, Jobanputra V, Dimmock D, Kearney HM, Bick D, Kulkarni S, Taylor SL, Belmont JW, Stavropoulos DJ, Lennon NJ. Best practices for the analytical validation of clinical whole-genome sequencing intended for the diagnosis of germline disease. NPJ Genom Med 2020; 5:47. [PMID: 33110627 PMCID: PMC7585436 DOI: 10.1038/s41525-020-00154-9] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [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: 01/27/2020] [Accepted: 09/25/2020] [Indexed: 12/16/2022] Open
Abstract
Whole-genome sequencing (WGS) has shown promise in becoming a first-tier diagnostic test for patients with rare genetic disorders; however, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading healthcare and research organizations in the US and Canada, was formed to expand access to high-quality clinical WGS by publishing best practices. Here, we present consensus recommendations on clinical WGS analytical validation for the diagnosis of individuals with suspected germline disease with a focus on test development, upfront considerations for test design, test validation practices, and metrics to monitor test performance. This work also provides insight into the current state of WGS testing at each member institution, including the utilization of reference and other standards across sites. Importantly, members of this initiative strongly believe that clinical WGS is an appropriate first-tier test for patients with rare genetic disorders, and at minimum is ready to replace chromosomal microarray analysis and whole-exome sequencing. The recommendations presented here should reduce the burden on laboratories introducing WGS into clinical practice, and support safe and effective WGS testing for diagnosis of germline disease.
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Affiliation(s)
- Christian R Marshall
- Department of Paediatric Laboratory Medicine, Genome Diagnostics, The Hospital for Sick Children, Toronto, ON Canada
| | - Shimul Chowdhury
- Rady Children's Institute for Genomic Medicine, San Diego, CA USA
| | | | - Mathew S Lebo
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA USA.,Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Jillian G Buchan
- Stanford Medicine Clinical Genomics Program, Stanford Health Care, Stanford, CA USA.,Present Address: Department of Laboratory Medicine, University of Washington, Seattle, WA USA
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA USA.,Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Ross Rowsey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN USA
| | - Eric W Klee
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN USA.,Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Pengfei Liu
- Baylor Genetics and Baylor College of Medicine, Houston, TX USA
| | - Elizabeth A Worthey
- HudsonAlpha Institute for Biotechnology, Huntsville, AL USA.,Present Address: Center for Genomic Data Sciences, University of Alabama at Birmingham, Birmingham, AL USA
| | - Vaidehi Jobanputra
- Molecular Diagnostics, New York Genome Center, New York, NY USA.,Department of Pathology and Cell Biology, Columbia University Irving Medical Center (CUIMC), New York, NY USA
| | - David Dimmock
- Rady Children's Institute for Genomic Medicine, San Diego, CA USA
| | - Hutton M Kearney
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN USA
| | - David Bick
- HudsonAlpha Institute for Biotechnology, Huntsville, AL USA
| | - Shashikant Kulkarni
- Baylor Genetics and Baylor College of Medicine, Houston, TX USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX USA
| | | | | | - Dimitri J Stavropoulos
- Department of Paediatric Laboratory Medicine, Genome Diagnostics, The Hospital for Sick Children, Toronto, ON Canada
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Harrison SM, Funke B. Use of “Coldspot” Regions in Variant Classification. Clin Chem 2020; 66:1263-1265. [DOI: 10.1093/clinchem/hvaa133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 05/27/2020] [Indexed: 11/13/2022]
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Tavtigian SV, Harrison SM, Boucher KM, Biesecker LG. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat 2020; 41:1734-1737. [PMID: 32720330 PMCID: PMC8011844 DOI: 10.1002/humu.24088] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [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: 06/11/2020] [Revised: 07/18/2020] [Accepted: 07/23/2020] [Indexed: 12/29/2022]
Abstract
Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP "strength of evidence categories" can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points-based system has the practical attribute of user-friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.
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Affiliation(s)
- Sean V. Tavtigian
- Department of Oncological Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA.,Huntsman Cancer Institute at the University of Utah, Salt Lake City, UT, USA
| | | | - Kenneth M. Boucher
- Huntsman Cancer Institute at the University of Utah, Salt Lake City, UT, USA.,Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Leslie G. Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, USA
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Abstract
The 2015 ACMG/AMP guidelines established a classification system for sequence variants; however, the broad scope of these guidelines necessitates specification of evidence types for specific genes or diseases of interest. Since publication of the guidelines, both general use and disease-focused specifications have emerged to aid in accurate application of ACMG/AMP evidence types. This article summarizes the approaches to, and rationale for, specifying three evidence categories (population frequency data, variant type and location, and case-level data), including available resources and a quantitative framework that can inform the specification process. © 2019 by John Wiley & Sons, Inc.
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Affiliation(s)
- Steven M Harrison
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Department of Pathology, Harvard Medical School, Boston, Massachusetts
| | - Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Department of Pathology, Harvard Medical School, Boston, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
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28
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Iqbal NS, Jascur TA, Harrison SM, Edwards AB, Smith LT, Choi ES, Arevalo MK, Chen C, Zhang S, Kern AJ, Scheuerle AE, Sanchez EJ, Xing C, Baker LA. Prune belly syndrome in surviving males can be caused by Hemizygous missense mutations in the X-linked Filamin A gene. BMC Med Genet 2020; 21:38. [PMID: 32085749 PMCID: PMC7035669 DOI: 10.1186/s12881-020-0973-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 02/12/2020] [Indexed: 12/12/2022]
Abstract
Background Prune belly syndrome (PBS) is a rare, multi-system congenital myopathy primarily affecting males that is poorly described genetically. Phenotypically, its morbidity spans from mild to lethal, however, all isolated PBS cases manifest three cardinal pathological features: 1) wrinkled flaccid ventral abdominal wall with skeletal muscle deficiency, 2) urinary tract dilation with poorly contractile smooth muscle, and 3) intra-abdominal undescended testes. Despite evidence for a genetic basis, previously reported PBS autosomal candidate genes only account for one consanguineous family and single cases. Methods We performed whole exome sequencing (WES) of two maternal adult half-brothers with syndromic PBS (PBS + Otopalatodigital spectrum disorder [OPDSD]) and two unrelated sporadic individuals with isolated PBS and further functionally validated the identified mutations. Results We identified three unreported hemizygous missense point mutations in the X-chromosome gene Filamin A (FLNA) (c.4952 C > T (p.A1448V), c.6727C > T (p.C2160R), c.5966 G > A (p.G2236E)) in two related cases and two unrelated sporadic individuals. Two of the three PBS mutations map to the highly regulatory, stretch-sensing Ig19–21 region of FLNA and enhance binding to intracellular tails of the transmembrane receptor β-integrin 1 (ITGβ1). Conclusions FLNA is a regulatory actin-crosslinking protein that functions in smooth muscle cells as a mechanosensing molecular scaffold, transmitting force signals from the actin-myosin motor units and cytoskeleton via binding partners to the extracellular matrix. This is the first evidence for an X-linked cause of PBS in multiple unrelated individuals and expands the phenotypic spectrum associated with FLNA in males surviving even into adulthood.
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Affiliation(s)
- Nida S Iqbal
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
| | - Thomas A Jascur
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Steven M Harrison
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Angelena B Edwards
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Luke T Smith
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Erin S Choi
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Michelle K Arevalo
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Catherine Chen
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Shaohua Zhang
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Adam J Kern
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Angela E Scheuerle
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.,McDermott Center for Human Growth and Development, Department of Bioinformatics, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Emma J Sanchez
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.,Children's Health Dallas, 2350 N. Stemmons Freeway, Suite F4300, Dallas, TX, 75207, USA
| | - Chao Xing
- McDermott Center for Human Growth and Development, Department of Bioinformatics, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Linda A Baker
- Department of Urology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA. .,Children's Health Dallas, 2350 N. Stemmons Freeway, Suite F4300, Dallas, TX, 75207, USA.
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29
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Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS, Heinen CD, Kanavy DM, Luo X, McNulty SM, Starita LM, Tavtigian SV, Wright MW, Harrison SM, Biesecker LG, Berg JS. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med 2019; 12:3. [PMID: 31892348 PMCID: PMC6938631 DOI: 10.1186/s13073-019-0690-2] [Citation(s) in RCA: 266] [Impact Index Per Article: 53.2] [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: 07/01/2019] [Accepted: 11/13/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria for different types of evidence. This includes the strong evidence codes PS3 and BS3 for "well-established" functional assays demonstrating a variant has abnormal or normal gene/protein function, respectively. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes are a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation. METHODS The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated the odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development. RESULTS The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are as follows: (1) define the disease mechanism, (2) evaluate the applicability of general classes of assays used in the field, (3) evaluate the validity of specific instances of assays, and (4) apply evidence to individual variant interpretation. We found that a minimum of 11 total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis. CONCLUSIONS The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.
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Affiliation(s)
- Sarah E Brnich
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, 120 Mason Farm Rd., Chapel Hill, NC, 27599-7264, USA
| | - Ahmad N Abou Tayoun
- Genomics Department, Al Jalila Children's Specialty Hospital, Dubai, United Arab Emirates
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Garry R Cutting
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Marc S Greenblatt
- Department of Medicine and University of Vermont Cancer Center, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | | | - Dona M Kanavy
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, 120 Mason Farm Rd., Chapel Hill, NC, 27599-7264, USA
| | - Xi Luo
- Department of Pediatrics/Hematology-Oncology, Baylor College of Medicine, Houston, TX, USA
| | - Shannon M McNulty
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, 120 Mason Farm Rd., Chapel Hill, NC, 27599-7264, USA
| | - Lea M Starita
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Sean V Tavtigian
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Matt W Wright
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - Jonathan S Berg
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, 120 Mason Farm Rd., Chapel Hill, NC, 27599-7264, USA.
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30
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Abstract
In 2015, professional guidelines defined the term 'likely pathogenic' to mean with a 90% chance of pathogenicity. To determine whether current practice reflects this definition, ClinVar classifications were tracked from 2016 to 2019. During that period, between 83.8 and 99.1% of likely pathogenic classifications were reclassified as pathogenic, depending on whether LP to VUS reclassifications are included and on how these classifications are categorized.
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Affiliation(s)
- Steven M Harrison
- Medical & Population Genetics Program and Genomics Platform, Broad Institute of MIT and Harvard, Main Street, Cambridge, MA, 02142, USA. .,Department of Pathology, Harvard Medical School, Shattuck Street, Boston, MA, 02115, USA.
| | - Heidi L Rehm
- Medical & Population Genetics Program and Genomics Platform, Broad Institute of MIT and Harvard, Main Street, Cambridge, MA, 02142, USA.,Department of Pathology, Harvard Medical School, Shattuck Street, Boston, MA, 02115, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Fruit Street, Boston, MA, 02114, USA
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31
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Harrison SM, Dolinksy JS, Chen W, Collins CD, Das S, Deignan JL, Garber KB, Garcia J, Jarinova O, Knight Johnson AE, Koskenvuo JW, Lee H, Mao R, Mar-Heyming R, McFaddin AS, Moyer K, Nagan N, Rentas S, Santani AB, Seppälä EH, Shirts BH, Tidwell T, Topper S, Vincent LM, Vinette K, Rehm HL. Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach. Hum Mutat 2019; 39:1641-1649. [PMID: 30311378 DOI: 10.1002/humu.23643] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [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: 05/02/2018] [Revised: 08/27/2018] [Accepted: 08/30/2018] [Indexed: 11/09/2022]
Abstract
ClinVar provides open access to variant classifications shared from many clinical laboratories. Although most classifications are consistent across laboratories, classification differences exist. To facilitate resolution of classification differences on a large scale, clinical laboratories were encouraged to reassess outlier classifications of variants with medically significant differences (MSDs). Outliers were identified by first comparing ClinVar submissions from 41 clinical laboratories to detect variants with MSDs between the laboratories (650 variants). Next, MSDs were filtered for variants with ≥3 classifications (244 variants), of which 87.6% (213 variants) had a majority consensus in ClinVar, thus allowing for identification of outlier classifications in need of reassessment. Laboratories with outlier classifications were sent a custom report and encouraged to reassess variants. Results were returned for 204 (96%) variants, of which 62.3% (127) were resolved. Of those 127, 64.6% (82) were resolved due to reassessment prompted by this study and 35.4% (45) resolved by a previously completed reassessment. This study demonstrates a scalable approach to classification resolution and capitalizes on the value of data sharing within ClinVar. These activities will help the community move toward more consistent variant classifications, which will improve the care of patients with, or at risk for, genetic disorders.
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Affiliation(s)
- Steven M Harrison
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts.,Department of Pathology, Harvard Medical School, Boston, Massachusetts.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | | | - Wenjie Chen
- Integrated Genetics, Laboratory Corporation of America Holdings, Westborough, Massachusetts
| | - Christin D Collins
- EGL Genetics, Tucker, Georgia.,Global Laboratory Services, PerkinElmer Genomics, Branford, Connecticut
| | - Soma Das
- Department of Human Genetics, The University of Chicago, Chicago, Illinois
| | - Joshua L Deignan
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | | | - John Garcia
- Invitae Corporation, San Francisco, California
| | - Olga Jarinova
- Department of Genetics, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | | | | | - Hane Lee
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Rong Mao
- ARUP Laboratories, Salt Lake City, Utah.,Department of Pathology, University of Utah, Salt Lake City, Utah
| | | | - Andrew S McFaddin
- Department of Laboratory Medicine, University of Washington, Seattle, Washington
| | | | - Narasimhan Nagan
- Integrated Genetics, Laboratory Corporation of America Holdings, Westborough, Massachusetts
| | - Stefan Rentas
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Avni B Santani
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Seattle, Washington
| | | | - Scott Topper
- Invitae Corporation, San Francisco, California.,Color Genomics, South San Francisco, California
| | | | - Kathy Vinette
- Molecular Diagnostics Laboratory, A. I. duPont Hospital for Children, Wilmington, Delaware
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts.,Department of Pathology, Harvard Medical School, Boston, Massachusetts.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston
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Rivera-Muñoz EA, Milko LV, Harrison SM, Azzariti DR, Kurtz CL, Lee K, Mester JL, Weaver MA, Currey E, Craigen W, Eng C, Funke B, Hegde M, Hershberger RE, Mao R, Steiner RD, Vincent LM, Martin CL, Plon SE, Ramos E, Rehm HL, Watson M, Berg JS. ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation. Hum Mutat 2019; 39:1614-1622. [PMID: 30311389 DOI: 10.1002/humu.23645] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [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: 04/30/2018] [Revised: 08/09/2018] [Accepted: 08/30/2018] [Indexed: 01/09/2023]
Abstract
Genome-scale sequencing creates vast amounts of genomic data, increasing the challenge of clinical sequence variant interpretation. The demand for high-quality interpretation requires multiple specialties to join forces to accelerate the interpretation of sequence variant pathogenicity. With over 600 international members including clinicians, researchers, and laboratory diagnosticians, the Clinical Genome Resource (ClinGen), funded by the National Institutes of Health, is forming expert groups to systematically evaluate variants in clinically relevant genes. Here, we describe the first ClinGen variant curation expert panels (VCEPs), development of consistent and streamlined processes for establishing new VCEPs, and creation of standard operating procedures for VCEPs to define application of the ACMG/AMP guidelines for sequence variant interpretation in specific genes or diseases. Additionally, ClinGen has created user interfaces to enhance reliability of curation and a Sequence Variant Interpretation Working Group (SVI WG) to harmonize guideline specifications and ensure consistency between groups. The expansion of VCEPs represents the primary mechanism by which curation of a substantial fraction of genomic variants can be accelerated and ultimately undertaken systematically and comprehensively. We welcome groups to utilize our resources and become involved in our effort to create a publicly accessible, centralized resource for clinically relevant genes and variants.
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Affiliation(s)
- Edgar A Rivera-Muñoz
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
| | - Laura V Milko
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
| | - Steven M Harrison
- Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Danielle R Azzariti
- Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - C Lisa Kurtz
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
| | - Kristy Lee
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
| | | | - Meredith A Weaver
- American College of Medical Genetics and Genomics, Bethesda, Maryland
| | - Erin Currey
- Division of Genomic Medicine, National Human Genome Research Institute (NHGRI), NIH, Bethesda, Maryland
| | - William Craigen
- Baylor College of Medicine, Departments of Molecular and Human Genetics, and Pediatrics, Houston, Texas
| | - Charis Eng
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio
| | - Birgit Funke
- Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts.,Veritas Genetics, Danvers, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Madhuri Hegde
- PerkinElmer, Global Laboratory Services, Waltham, Massachusetts.,Emory University, Department of Human Genetics, Atlanta, Georgia
| | - Ray E Hershberger
- Divisions of Human Genetics and Cardiovascular Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Rong Mao
- Department of Pathology, University of Utah, Salt Lake City, Utah.,Department of Molecular Genetics and Genomics, ARUP Laboratories, Salt Lake City, Utah
| | - Robert D Steiner
- Departments of Pediatrics and Genetics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Prevention Genetics, Marshfield, Wisconsin
| | | | - Christa L Martin
- Autism & Developmental Medicine Institute, Geisinger, Danville, PA
| | - Sharon E Plon
- Baylor College of Medicine, Departments of Molecular and Human Genetics, and Pediatrics, Houston, Texas
| | - Erin Ramos
- Division of Genomic Medicine, National Human Genome Research Institute (NHGRI), NIH, Bethesda, Maryland
| | - Heidi L Rehm
- Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Michael Watson
- American College of Medical Genetics and Genomics, Bethesda, Maryland
| | - Jonathan S Berg
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
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33
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Ghosh R, Harrison SM, Rehm HL, Plon SE, Biesecker LG. Updated recommendation for the benign stand-alone ACMG/AMP criterion. Hum Mutat 2019; 39:1525-1530. [PMID: 30311383 DOI: 10.1002/humu.23642] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [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: 05/04/2018] [Revised: 08/03/2018] [Accepted: 08/28/2018] [Indexed: 11/11/2022]
Abstract
The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation Working Group set out to refine the American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG/AMP) variant pathogenicity recommendations for stand-alone rule BA1 (a variant with minor allele frequency [MAF] > 0.05 is benign), by clarifying how it should be used and specifying a set of variants that should be exempted from this rule. We cross-referenced ClinVar and Exome Aggregation Consortium data to identify variants for which there was a plausible argument for pathogenicity and the variant exists in one or more population data sets at MAF > 0.05. We identified nine such variants that were present in these data sets that may not be benign. The ACMG/AMP criteria were applied to these variants that resulted in four pathogenic and five variants of uncertain significance. We have refined benign rule BA1 by clarifying terms used to describe its use, which databases we recommend using, and assumptions made about this rule. We also recognized an initial list of nine variants for which there was some evidence of pathogenicity even though the MAF was high for these variants. We specify processes whereby individuals can petition ClinGen for amendments to our variant-specific assertions and the criteria experts should use when setting a numerically lower threshold for BA1 for specific genes.
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Affiliation(s)
- Rajarshi Ghosh
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts.,Department of Pathology, Harvard Medical School, Boston, Massachusetts
| | - Heidi L Rehm
- Department of Pathology, Harvard Medical School, Boston, Massachusetts.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Sharon E Plon
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
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34
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Affiliation(s)
- Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Steven M Harrison
- Partners HealthCare Laboratory for Molecular Medicine and Harvard Medical School, Boston, Massachusetts, USA.
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35
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Ingles J, Goldstein J, Thaxton C, Caleshu C, Corty EW, Crowley SB, Dougherty K, Harrison SM, McGlaughon J, Milko LV, Morales A, Seifert BA, Strande N, Thomson K, Peter van Tintelen J, Wallace K, Walsh R, Wells Q, Whiffin N, Witkowski L, Semsarian C, Ware JS, Hershberger RE, Funke B. Evaluating the Clinical Validity of Hypertrophic Cardiomyopathy Genes. Circ Genom Precis Med 2019; 12:e002460. [PMID: 30681346 PMCID: PMC6410971 DOI: 10.1161/circgen.119.002460] [Citation(s) in RCA: 214] [Impact Index Per Article: 42.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 01/18/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND Genetic testing for families with hypertrophic cardiomyopathy (HCM) provides a significant opportunity to improve care. Recent trends to increase gene panel sizes often mean variants in genes with questionable association are reported to patients. Classification of HCM genes and variants is critical, as misclassification can lead to genetic misdiagnosis. We show the validity of previously reported HCM genes using an established method for evaluating gene-disease associations. METHODS A systematic approach was used to assess the validity of reported gene-disease associations, including associations with isolated HCM and syndromes including left ventricular hypertrophy. Genes were categorized as having definitive, strong, moderate, limited, or no evidence of disease causation. We also reviewed current variant classifications for HCM in ClinVar, a publicly available variant resource. RESULTS Fifty-seven genes were selected for curation based on their frequent inclusion in HCM testing and prior association reports. Of 33 HCM genes, only 8 (24%) were categorized as definitive ( MYBPC3, MYH7, TNNT2, TNNI3, TPM1, ACTC1, MYL2, and MYL3); 3 had moderate evidence ( CSRP3, TNNC1, and JPH2; 33%); and 22 (66%) had limited (n=16) or no evidence (n=6). There were 12 of 24 syndromic genes definitively associated with isolated left ventricular hypertrophy. Of 4191 HCM variants in ClinVar, 31% were in genes with limited or no evidence of disease association. CONCLUSIONS The majority of genes previously reported as causative of HCM and commonly included in diagnostic tests have limited or no evidence of disease association. Systematically curated HCM genes are essential to guide appropriate reporting of variants and ensure the best possible outcomes for HCM families.
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Affiliation(s)
- Jodie Ingles
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute and Faculty of Medicine and Health, The University of Sydney, University of Sydney, Australia (J.I., C.S.)
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia (J.I., C.S.)
| | - Jennifer Goldstein
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Courtney Thaxton
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Colleen Caleshu
- Stanford Center for Inherited Cardiovascular Disease, Stanford University, CA (C.C.)
| | - Edward W. Corty
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Stephanie B. Crowley
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | | | - Steven M. Harrison
- Laboratory for Molecular Medicine, Partners Healthcare, Harvard Medical School, Cambridge, MA (S.M.H.)
| | - Jennifer McGlaughon
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Laura V. Milko
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Ana Morales
- Division of Human Genetics, Davis Heart and Lung Research Institute (A.M., R.E.H.)
| | - Bryce A. Seifert
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Natasha Strande
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Kate Thomson
- Oxford Medical Genetics Laboratory, United Kingdom (K.T.)
| | - J. Peter van Tintelen
- Department of Clinical Genetics, Amsterdam University Medical Centers, University of Amsterdam, Cardiovascular Sciences, The Netherlands (J.P.v.T.)
| | - Kathleen Wallace
- Department of Genetics, UNC Chapel Hill, NC (J.G., C.T., E.W.C., S.B.C., J.M., L.V.M., B.A.S., N.S., K.W.)
| | - Roddy Walsh
- National Heart and Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (R.W., N.W., J.S.W.)
- Cardiovascular Research Centre at Royal Brompton & Harefield Hospitals NHS Trust, London, United Kingdom (R.W., N.W., J.S.W.)
| | - Quinn Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN (Q.W.)
| | - Nicola Whiffin
- National Heart and Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (R.W., N.W., J.S.W.)
- Cardiovascular Research Centre at Royal Brompton & Harefield Hospitals NHS Trust, London, United Kingdom (R.W., N.W., J.S.W.)
| | - Leora Witkowski
- Department of Pathology, Harvard Medical School/Massachusetts General Hospital, Boston (L.W., B.F.)
| | - Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute and Faculty of Medicine and Health, The University of Sydney, University of Sydney, Australia (J.I., C.S.)
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia (J.I., C.S.)
| | - James S. Ware
- National Heart and Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (R.W., N.W., J.S.W.)
- Cardiovascular Research Centre at Royal Brompton & Harefield Hospitals NHS Trust, London, United Kingdom (R.W., N.W., J.S.W.)
| | - Ray E. Hershberger
- Division of Human Genetics, Davis Heart and Lung Research Institute (A.M., R.E.H.)
- Division of Cardiovascular Medicine, The Ohio State University, Columbus (R.E.H.)
| | - Birgit Funke
- Department of Pathology, Harvard Medical School/Massachusetts General Hospital, Boston (L.W., B.F.)
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Niehaus A, Azzariti DR, Harrison SM, DiStefano MT, Hemphill SE, Senol-Cosar O, Rehm HL. A survey assessing adoption of the ACMG-AMP guidelines for interpreting sequence variants and identification of areas for continued improvement. Genet Med 2019; 21:1699-1701. [PMID: 30670879 PMCID: PMC7233466 DOI: 10.1038/s41436-018-0432-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/21/2018] [Indexed: 11/09/2022] Open
Affiliation(s)
- Annie Niehaus
- College of Medicine, Medical University of South Carolina, Charleston, SC, USA.,Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA
| | - Danielle R Azzariti
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA.,Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA.,Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marina T DiStefano
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA
| | - Sarah E Hemphill
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA
| | - Ozlem Senol-Cosar
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA. .,Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
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37
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Whiffin N, Roberts AM, Minikel E, Zappala Z, Walsh R, O’Donnell-Luria AH, Karczewski KJ, Harrison SM, Thomson KL, Sage H, Ing AY, Barton PJ, Funke B, Cook SA, MacArthur DG, Ware JS. Using High-Resolution Variant Frequencies Empowers Clinical Genome Interpretation and Enables Investigation of Genetic Architecture. Am J Hum Genet 2019; 104:187-190. [PMID: 30609406 DOI: 10.1016/j.ajhg.2018.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 11/16/2018] [Indexed: 01/23/2023] Open
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Harrison SM, Bush NC, Wang Y, Mucher ZR, Lorenzo AJ, Grimsby GM, Schlomer BJ, Büllesbach EE, Baker LA. Insulin-Like Peptide 3 (INSL3) Serum Concentration During Human Male Fetal Life. Front Endocrinol (Lausanne) 2019; 10:596. [PMID: 31611843 PMCID: PMC6737488 DOI: 10.3389/fendo.2019.00596] [Citation(s) in RCA: 5] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 08/13/2019] [Indexed: 12/28/2022] Open
Abstract
Context: Insulin-like peptide 3 (INSL3), a protein hormone produced by Leydig cells, may play a crucial role in testicular descent as male INSL3 knockout mice have bilateral cryptorchidism. Previous studies have measured human fetal INSL3 levels in amniotic fluid only. Objective: To measure INSL3 serum levels and mRNA in fetal umbilical cord blood and fetal testes, respectively. Design: INSL3 concentrations were assayed on 50 μl of serum from male human fetal umbilical cord blood by a non-commercial highly sensitive and specific radioimmunoassay. For secondary confirmation, quantitative real-time PCR was used to measure INSL3 relative mRNA expression in 7 age-matched human fetal testes. Setting: UT Southwestern Medical Center, Dallas, TX and Medical University of South Carolina, Charleston, SC. Patients or other Participants: Twelve human male umbilical cord blood samples and 7 human male testes were obtained from fetuses 14-21 weeks gestation. Male sex was verified by leukocyte genomic DNA SRY PCR. Interventions: None. Main Outcome Measures: Human male fetal INSL3 cord blood serum concentrations and testicular relative mRNA expression. Results: INSL3 serum concentrations during human male gestational weeks 15-20 were 2-4 times higher than published prepubertal male levels and were 5-100 times higher than previous reports of INSL3 concentrations obtained from amniotic fluid. Testicular fetal INSL3 mRNA relative expression was low from weeks 14-16, rose significantly weeks 17 and 18, and returned to low levels at week 21. Conclusions: These findings further support the role of INSL3 in human testicular descent and could prove relevant in uncovering the pathophysiology of cryptorchidism.
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Affiliation(s)
- Steven M. Harrison
- Clinical R&D Sequencing Platform, Broad Institute, MIT and Harvard, Cambridge, MA, United States
| | | | - Yi Wang
- Endocrinology Division, Department of Internal Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zachary R. Mucher
- Department of Urology, Memorial Hermann Health System, Houston, TX, United States
| | - Armando J. Lorenzo
- Department of Pediatric Urology, Hospital for Sick Children, Toronto, ON, Canada
| | | | - Bruce J. Schlomer
- Division of Pediatric Urology, Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Erika E. Büllesbach
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, United States
| | - Linda A. Baker
- John W. Duckett MD Laboratory in Pediatric Urology, Division of Pediatric Urology, Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, United States
- *Correspondence: Linda A. Baker
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39
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Abou Tayoun AN, Pesaran T, DiStefano MT, Oza A, Rehm HL, Biesecker LG, Harrison SM. Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion. Hum Mutat 2018; 39:1517-1524. [PMID: 30192042 DOI: 10.1002/humu.23626] [Citation(s) in RCA: 427] [Impact Index Per Article: 71.2] [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: 05/01/2018] [Revised: 07/15/2018] [Accepted: 08/23/2018] [Indexed: 12/24/2022]
Abstract
The 2015 ACMG/AMP sequence variant interpretation guideline provided a framework for classifying variants based on several benign and pathogenic evidence criteria, including a pathogenic criterion (PVS1) for predicted loss of function variants. However, the guideline did not elaborate on specific considerations for the different types of loss of function variants, nor did it provide decision-making pathways assimilating information about variant type, its location, or any additional evidence for the likelihood of a true null effect. Furthermore, this guideline did not take into account the relative strengths for each evidence type and the final outcome of their combinations with respect to PVS1 strength. Finally, criteria specifying the genes for which PVS1 can be applied are still missing. Here, as part of the ClinGen Sequence Variant Interpretation (SVI) Workgroup's goal of refining ACMG/AMP criteria, we provide recommendations for applying the PVS1 criterion using detailed guidance addressing the above-mentioned gaps. Evaluation of the refined criterion by seven disease-specific groups using heterogeneous types of loss of function variants (n = 56) showed 89% agreement with the new recommendation, while discrepancies in six variants (11%) were appropriately due to disease-specific refinements. Our recommendations will facilitate consistent and accurate interpretation of predicted loss of function variants.
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Affiliation(s)
- Ahmad N Abou Tayoun
- The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | | | - Marina T DiStefano
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Andrea Oza
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
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40
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Henrie A, Hemphill SE, Ruiz-Schultz N, Cushman B, DiStefano MT, Azzariti D, Harrison SM, Rehm HL, Eilbeck K. ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data. Hum Mutat 2018; 39:1051-1060. [PMID: 29790234 DOI: 10.1002/humu.23555] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [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: 02/08/2018] [Revised: 04/20/2018] [Accepted: 05/19/2018] [Indexed: 11/12/2022]
Abstract
ClinVar Miner is a Web-based suite that utilizes the data held in the National Center for Biotechnology Information's ClinVar archive. The goal is to render the data more accessible to processes pertaining to conflict resolution of variant interpretation as well as tracking details of data submission and data management for detailed variant curation. Here, we establish the use of these tools to address three separate use cases and to perform analyses across submissions. We demonstrate that the ClinVar Miner tools are an effective means to browse and consolidate data for variant submitters, curation groups, and general oversight. These tools are also relevant to the variant interpretation community in general.
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Affiliation(s)
- Alex Henrie
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah
| | - Sarah E Hemphill
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Nicole Ruiz-Schultz
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah
| | - Brandon Cushman
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Marina T DiStefano
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Danielle Azzariti
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts.,Department of Pathology, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah
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41
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Kelly MA, Caleshu C, Morales A, Buchan J, Wolf Z, Harrison SM, Cook S, Dillon MW, Garcia J, Haverfield E, Jongbloed JDH, Macaya D, Manrai A, Orland K, Richard G, Spoonamore K, Thomas M, Thomson K, Vincent LM, Walsh R, Watkins H, Whiffin N, Ingles J, van Tintelen JP, Semsarian C, Ware JS, Hershberger R, Funke B. Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel. Genet Med 2018; 20:351-359. [PMID: 29300372 PMCID: PMC5876064 DOI: 10.1038/gim.2017.218] [Citation(s) in RCA: 226] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 10/24/2017] [Indexed: 01/20/2023] Open
Abstract
Purpose Integrating genomic sequencing in clinical care requires standardization of variant interpretation practices. The Clinical Genome Resource has established expert panels to adapt the American College of Medical Genetics and Genomics/Association for Molecular Pathology classification framework for specific genes and diseases. The Cardiomyopathy Expert Panel selected MYH7, a key contributor to inherited cardiomyopathies, as a pilot gene to develop a broadly applicable approach. Methods Expert revisions were tested with 60 variants using a structured double review by pairs of clinical and diagnostic laboratory experts. Final consensus rules were established via iterative discussions. Results Adjustments represented disease-/gene-informed specifications (12) or strength adjustments of existing rules (5). Nine rules were deemed not applicable. Key specifications included quantitative frameworks for minor allele frequency thresholds, the use of segregation data, and a semiquantitative approach to counting multiple independent variant occurrences where fully controlled case-control studies are lacking. Initial inter-expert classification concordance was 93%. Internal data from participating diagnostic laboratories changed the classification of 20% of the variants (n = 12), highlighting the critical importance of data sharing. Conclusion These adapted rules provide increased specificity for use in MYH7-associated disorders in combination with expert review and clinical judgment and serve as a stepping stone for genes and disorders with similar genetic and clinical characteristics.
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Affiliation(s)
- Melissa A Kelly
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Boston, Massachusetts, USA
| | - Colleen Caleshu
- Stanford Center for Inherited Cardiovascular Disease, Stanford University, Stanford, California, USA
| | - Ana Morales
- Division of Human Genetics, Department of Internal Medicine, Ohio State University, Columbus, Ohio, USA
| | - Jillian Buchan
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Boston, Massachusetts, USA
| | - Zena Wolf
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Boston, Massachusetts, USA
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Boston, Massachusetts, USA
| | - Stuart Cook
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Mitchell W Dillon
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Boston, Massachusetts, USA
| | - John Garcia
- Invitae Inc., San Francisco, California, USA
| | | | - Jan D H Jongbloed
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | | | - Arjun Manrai
- Harvard School of Public Health, Boston, Massachusetts, USA
| | - Kate Orland
- Clinical Science Center, University of Wisconsin, Madison, Wisconsin, USA
| | | | - Katherine Spoonamore
- Krannert Institute of Cardiology, Indiana University, Indianapolis, Indiana, USA
| | - Matthew Thomas
- Division of Genetics, Department of Pediatrics, University of Virginia, Charlottesville, Virginia, USA
| | - Kate Thomson
- Oxford Medical Genetics Laboratory, Oxford University Hospitals NHS Foundation Trust, The Churchill Hospital, Oxford, UK.,Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | | | - Roddy Walsh
- National Heart and Lung Institute, Imperial College London, London, UK.,Royal Brompton & Harefield Hospitals NHS Trust, London, UK
| | - Hugh Watkins
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Nicola Whiffin
- National Heart and Lung Institute, Imperial College London, London, UK.,Royal Brompton & Harefield Hospitals NHS Trust, London, UK
| | - Jodie Ingles
- Agnes Ginges Centre for Molecular Cardiology, Centenary Institute and University of Sydney, Sydney, Australia
| | - J Peter van Tintelen
- Department of Clinical Genetics, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology, Centenary Institute and University of Sydney, Sydney, Australia
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, UK.,Royal Brompton & Harefield Hospitals NHS Trust, London, UK
| | - Ray Hershberger
- Division of Human Genetics, Department of Internal Medicine, Ohio State University, Columbus, Ohio, USA
| | - Birgit Funke
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Boston, Massachusetts, USA.,Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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Abstract
In this Letter to the Editor, potentially flawed conclusions of a recent study are discussed.
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Affiliation(s)
- Heidi L Rehm
- Partners Healthcare and Harvard Medical School, Boston, Massachusetts, USA
| | - Steven M Harrison
- Partners Healthcare and Harvard Medical School, Boston, Massachusetts, USA
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Patel RY, Shah N, Jackson AR, Ghosh R, Pawliczek P, Paithankar S, Baker A, Riehle K, Chen H, Milosavljevic S, Bizon C, Rynearson S, Nelson T, Jarvik GP, Rehm HL, Harrison SM, Azzariti D, Powell B, Babb L, Plon SE, Milosavljevic A. ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants. Genome Med 2017; 9:3. [PMID: 28081714 PMCID: PMC5228115 DOI: 10.1186/s13073-016-0391-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [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: 08/17/2016] [Accepted: 12/07/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The success of the clinical use of sequencing based tests (from single gene to genomes) depends on the accuracy and consistency of variant interpretation. Aiming to improve the interpretation process through practice guidelines, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have published standards and guidelines for the interpretation of sequence variants. However, manual application of the guidelines is tedious and prone to human error. Web-based tools and software systems may not only address this problem but also document reasoning and supporting evidence, thus enabling transparency of evidence-based reasoning and resolution of discordant interpretations. RESULTS In this report, we describe the design, implementation, and initial testing of the Clinical Genome Resource (ClinGen) Pathogenicity Calculator, a configurable system and web service for the assessment of pathogenicity of Mendelian germline sequence variants. The system allows users to enter the applicable ACMG/AMP-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. Through automation and comprehensive documentation of evidence codes, the system facilitates more accurate application of the ACMG/AMP guidelines, improves standardization in variant classification, and facilitates collaborative resolution of discordances. The rules of reasoning are configurable with gene-specific or disease-specific guideline variations (e.g. cardiomyopathy-specific frequency thresholds and functional assays). The software is modular, equipped with robust application program interfaces (APIs), and available under a free open source license and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems. The Pathogenicity Calculator is accessible at http://calculator.clinicalgenome.org . CONCLUSIONS By enabling evidence-based reasoning about the pathogenicity of genetic variants and by documenting supporting evidence, the Calculator contributes toward the creation of a knowledge commons and more accurate interpretation of sequence variants in research and clinical care.
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Affiliation(s)
- Ronak Y Patel
- Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Neethu Shah
- Baylor College of Medicine, Houston, TX, 77030, USA
| | | | | | | | | | - Aaron Baker
- Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kevin Riehle
- Baylor College of Medicine, Houston, TX, 77030, USA
| | - Hailin Chen
- Baylor College of Medicine, Houston, TX, 77030, USA
| | | | - Chris Bizon
- The Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA
| | - Shawn Rynearson
- University of Utah Hospitals and Clinics, University of Utah, Salt Lake City, UT, 84112, USA
| | - Tristan Nelson
- Geisinger autism and developmental medicine, Lewisburg, PA, 17837, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, 02139, USA.,Brigham & Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, 02139, USA
| | - Danielle Azzariti
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, 02139, USA
| | - Bradford Powell
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Larry Babb
- GeneInsight, Sunquest Information System, Boston, MA, 02210, USA
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44
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Grasso S, Brunton NP, Lyng JG, Harrison SM, Monahan FJ. Quality of deli-style turkey enriched with plant sterols. FOOD SCI TECHNOL INT 2016; 22:743-751. [DOI: 10.1177/1082013216646496] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 04/03/2016] [Indexed: 11/16/2022]
Abstract
Low-fat meat products could be excellent carriers for plant sterols, known for their cholesterol-lowering properties. In this study, we developed a protocol for the manufacture of a deli-style turkey enriched with plant sterols (S) at a level sufficient to deliver the maximum plant sterols amount recommended for cholesterol reduction by the European Food Safety Authority (3 g of plant sterols per day) in a 70 g portion. We investigated the stability of the plant sterols and the effects of their addition on the product quality. Plant sterols remained stable during the seven-day storage period. The addition of plant sterols significantly affected some texture parameters, shear force, lipid oxidation, L values and water-holding capacity compared with control (C). Sensory analysis was carried out by an untrained panel (32) using the difference-from-control test between C and S samples to evaluate first the extent of the overall sensory difference and then the extent of sensory difference on colour, texture and flavour. Results indicated that panellists considered the intensity of the difference between C and S samples to be ‘small’. Plant sterols could be used as a potential health-promoting meat ingredient with no effect on plant sterol stability but with some effects on texture and sensory characteristics.
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Affiliation(s)
- S Grasso
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - NP Brunton
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - JG Lyng
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - SM Harrison
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - FJ Monahan
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
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Harrison SM, Riggs ER, Maglott DR, Lee JM, Azzariti DR, Niehaus A, Ramos EM, Martin CL, Landrum MJ, Rehm HL. Using ClinVar as a Resource to Support Variant Interpretation. ACTA ACUST UNITED AC 2016; 89:8.16.1-8.16.23. [PMID: 27037489 DOI: 10.1002/0471142905.hg0816s89] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
ClinVar is a freely accessible, public archive of reports of the relationships among genomic variants and phenotypes. To facilitate evaluation of the clinical significance of each variant, ClinVar aggregates submissions of the same variant, displays supporting data from each submission, and determines if the submitted clinical interpretations are conflicting or concordant. The unit describes how to (1) identify sequence and structural variants of interest in ClinVar by multiple searching approaches, including Variation Viewer and (2) understand the display of submissions to ClinVar and the evidence supporting each interpretation. By following this protocol, ClinVar users will be able to learn how to incorporate the wealth of resources and knowledge in ClinVar into variant curation and interpretation.
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Affiliation(s)
- Steven M Harrison
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | | | - Donna R Maglott
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Rockville, Maryland
| | - Jennifer M Lee
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Rockville, Maryland
| | - Danielle R Azzariti
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts
| | - Annie Niehaus
- National Human Genome Research Institute, National Institutes of Health, Rockville, Maryland
| | - Erin M Ramos
- National Human Genome Research Institute, National Institutes of Health, Rockville, Maryland
| | | | - Melissa J Landrum
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Rockville, Maryland.,These authors contributed equally to this work
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts.,Harvard Medical School, Boston, Massachusetts.,These authors contributed equally to this work
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Harrison SM, Granberg CF, Keays M, Hill M, Grimsby GM, Baker LA. DNA copy number variations in patients with 46,XY disorders of sex development. J Urol 2014; 192:1801-6. [PMID: 24946221 DOI: 10.1016/j.juro.2014.06.040] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2014] [Indexed: 11/17/2022]
Abstract
PURPOSE Less than 50% of cases of 46,XY disorders of sex development are genetically defined after karyotyping and/or sequencing of known causal genes. Since copy number variations are often missed by karyotyping and sequencing, we assessed patients with unexplained 46,XY disorders of sex development using array comparative genomic hybridization for possible disease causing genomic variants. MATERIALS AND METHODS DNA from unexplained cases of 46,XY disorders of sex development were tested by whole genome array comparative genomic hybridization. In cases where novel copy number variations were detected parental testing was performed to identify whether copy number variations were de novo or inherited. RESULTS Of the 12 patients who underwent array comparative genomic hybridization testing 2 had possible copy number variations causing disorders of sex development, both maternally inherited microdeletions. One case, with a maternal history of premature ovarian failure, had a cosegregating microdeletion on 9q33.3 involving NR5A1. The other case, with a maternal family history of congenital heart disease, had a cosegregating microdeletion on 8p23.1 upstream of GATA4. CONCLUSIONS In this cohort copy number variations involving or adjacent to known causal genes led to 46,XY disorders of sex development in 2 of 12 previously unexplained cases (17%). Copy number variation testing is clinically indicated for unexplained cases of 46,XY disorders of sex development to aid in genetic counseling for family planning.
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Affiliation(s)
- Steven M Harrison
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas
| | | | - Melise Keays
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Martinez Hill
- University of Texas Southwestern Medical Center, Dallas, Texas
| | - Gwen M Grimsby
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Linda A Baker
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas; McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, Texas.
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Abstract
PURPOSE Persistent cloaca is a devastating female anomaly associated with renal insufficiency/failure, urinary and fecal incontinence and müllerian dysfunction. Genetically engineered murine models of persistent cloaca suggest that this anomaly could have a genetic component in humans. Genomic copy number variations account for previously unexplained genetic diseases by identifying candidate genes in various disorders. We assessed whether novel copy number variations are present in patients with persistent cloaca. MATERIALS AND METHODS With institutional review board approval we performed a retrospective chart review to identify patients with persistent cloaca. Lymphocyte DNA was prospectively tested by whole genome array comparative genomic hybridization. HHAT was Sanger sequenced from genomic DNA. RESULTS At study recruitment mean age was 12 years (range 0.5 to 23) in 17 females with cloaca. Seven females (41%) had a solitary functioning kidney and 2 each had renal insufficiency and renal replacement therapy. The common cloaca channel was 1.5 to 6 cm long in 6 newborns. Six patients (35%) had vaginal duplication and 4 had spinal anomalies. Array comparative genomic hybridization revealed copy number variations in 7 patients (41%), including 5 gains and 2 losses. Two copy number variations were novel, including a paternally inherited duplication on 16p13.2 and a de novo deletion on 1q32.1q32.3. Subsequent sequencing of the candidate gene HHAT identified no causal mutations. CONCLUSIONS Persistent cloaca is a rare but morbid birth defect. Copy number variations are common in these females but HHAT mutations are not common. Further investigation of these genomic rearrangements may lead to the identification of genetic causes of persistent cloaca.
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Affiliation(s)
- Steven M Harrison
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Casey Seideman
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Linda A Baker
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas; McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, Texas.
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Harrison SM, Campbell IM, Keays M, Granberg CF, Villanueva C, Tannin G, Zinn AR, Castrillon DH, Shaw CA, Stankiewicz P, Baker LA. Screening and familial characterization of copy-number variations in NR5A1 in 46,XY disorders of sex development and premature ovarian failure. Am J Med Genet A 2013; 161A:2487-94. [PMID: 23918653 DOI: 10.1002/ajmg.a.36084] [Citation(s) in RCA: 5] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2012] [Accepted: 05/12/2013] [Indexed: 11/08/2022]
Abstract
The NR5A1 gene encodes for steroidogenic factor 1, a nuclear receptor that regulates proper adrenal and gonadal development and function. Mutations identified by NR5A1 sequencing have been associated with disorders of sex development (DSD), ranging from sex reversal to severe hypospadias in 46,XY patients and premature ovarian failure (POF) in 46,XX patients. Previous reports have identified four families with a history of both 46,XY DSD and 46,XX POF carrying segregating NR5A1 sequence mutations. Recently, three 46,XY DSD sporadic cases with NR5A1 microdeletions have been reported. Here, we identify the first NR5A1 microdeletion transmitted in a pedigree with both 46,XY DSD and 46,XX POF. A 46,XY individual with DSD due to gonadal dysgenesis was born to a young mother who developed POF. Array CGH analysis revealed a maternally inherited 0.23 Mb microdeletion of chromosome 9q33.3, including the NR5A1 gene. Based on this finding, we screened patients with unexplained 46,XY DSD (n = 11), proximal hypospadias (n = 21) and 46,XX POF (n = 36) for possible NR5A1 copy-number variations (CNVs) via multiplex ligation-dependent probe amplification (MLPA), but did not identify any additional CNVs involving NR5A1. These data suggest that NR5A1 CNVs are an infrequent cause of these disorders but that array CGH and MLPA are useful genomic screening tools to uncover the genetic basis of such unexplained cases. This case is the first report of a familial NR5A1 CNV transmitting in a pedigree, causing both the male and female phenotypes associated with NR5A1 mutations, and the first report of a NR5A1 CNV associated with POF.
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Affiliation(s)
- Steven M Harrison
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas
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Chang SW, Mislankar M, Misra C, Huang N, Dajusta DG, Harrison SM, McBride KL, Baker LA, Garg V. Genetic abnormalities in FOXP1 are associated with congenital heart defects. Hum Mutat 2013; 34:1226-30. [PMID: 23766104 DOI: 10.1002/humu.22366] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [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: 12/06/2012] [Accepted: 06/03/2013] [Indexed: 12/20/2022]
Abstract
The etiology for the majority of congenital heart defects (CHD) is unknown. We identified a patient with unbalanced atrioventricular septal defect (AVSD) and hypoplastic left ventricle who harbored an ~0.3 Mb monoallelic deletion on chromosome 3p14.1. The deletion encompassed the first four exons of FOXP1, a gene critical for normal heart development that represses cardiomyocyte proliferation and expression of Nkx2.5. To determine whether FOXP1 mutations are found in patients with CHD, we sequenced FOXP1 in 82 patients with AVSD or hypoplastic left heart syndrome. We discovered two patients who harbored a heterozygous c.1702C>T variant in FOXP1 that predicted a potentially deleterious substitution of a highly conserved proline (p.Pro568Ser). This variant was not found in 287 controls but is present in dbSNP at a 0.2% frequency. The orthologous murine Foxp1 p.Pro596Ser mutant protein displayed deficits in luciferase reporter assays and resulted in increased proliferation and Nkx2.5 expression in cardiomyoblasts. Our data suggest that haploinsufficiency of FOXP1 is associated with human CHD.
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Affiliation(s)
- Sheng-Wei Chang
- Center for Cardiovascular and Pulmonary Research and The Heart Center, Nationwide Children's Hospital, Columbus, Ohio, USA
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Granberg CF, Harrison SM, Dajusta D, Zhang S, Hajarnis S, Igarashi P, Baker LA. Genetic basis of prune belly syndrome: screening for HNF1β gene. J Urol 2012; 187:272-8. [PMID: 22114815 PMCID: PMC3399512 DOI: 10.1016/j.juro.2011.09.036] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [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: 08/28/2011] [Indexed: 11/21/2022]
Abstract
PURPOSE Although the cause of prune belly syndrome is unknown, familial evidence suggests a genetic component. Recently 2 nonfamilial cases of prune belly syndrome with chromosome 17q12 deletions encompassing the HNF1β gene have made this a candidate gene for prune belly syndrome. To date, there has been no large-scale screening of patients with prune belly syndrome for HNF1β mutations. We assessed the role of HNF1β in prune belly syndrome by screening for genomic mutations with functional characterization of any detected mutations. MATERIALS AND METHODS We studied patients with prune belly syndrome who were prospectively enrolled in our Pediatric Genitourinary DNA Repository since 2001. DNA from patient samples was amplified by polymerase chain reaction, sequenced for coding and splice regions of the HNF1β gene, and compared to control databases. We performed functional assay testing of the ability of mutant HNF1β to activate a luciferase construct with an HNF1β DNA binding site. RESULTS From 32 prune belly syndrome probands (30 males, 2 females) HNF1β sequencing detected a missense mutation (V61G) in 1 child with prune belly syndrome. Absent in control databases, V61G was previously reported in 2 patients without prune belly syndrome who had congenital genitourinary anomalies. Functional testing showed similar luciferase activity compared to wild-type HNF1β, suggesting the V61G substitution does not disturb HNF1β function. CONCLUSIONS One genomic HNF1β mutation was detected in 3% of patients with prune belly syndrome but found to be functionally normal. Thus, functionally significant HNF1β mutations are uncommon in prune belly syndrome, despite case reports of HNF1β deletions. Further genetic study is necessary, as identification of the genetic basis of prune belly syndrome may ultimately lead to prevention and improved treatments for this rare but severe syndrome.
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Affiliation(s)
| | | | - Daniel Dajusta
- Department of Urology (CFG, SMH, DD, SZ, LAB) and Department of Internal Medicine (SH, PI), University of Texas Southwestern, Dallas, Texas
| | - Shaohua Zhang
- Department of Urology (CFG, SMH, DD, SZ, LAB) and Department of Internal Medicine (SH, PI), University of Texas Southwestern, Dallas, Texas
| | - Sachin Hajarnis
- Department of Urology (CFG, SMH, DD, SZ, LAB) and Department of Internal Medicine (SH, PI), University of Texas Southwestern, Dallas, Texas
| | - Peter Igarashi
- Department of Urology (CFG, SMH, DD, SZ, LAB) and Department of Internal Medicine (SH, PI), University of Texas Southwestern, Dallas, Texas
| | - Linda A. Baker
- Department of Urology (CFG, SMH, DD, SZ, LAB) and Department of Internal Medicine (SH, PI), University of Texas Southwestern, Dallas, Texas
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