1
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Arbesfeld JA, Da EY, Stevenson JS, Kuzma K, Paul A, Farris T, Capodanno BJ, Grindstaff SB, Riehle K, Saraiva-Agostinho N, Safer JF, Milosavljevic A, Foreman J, Firth HV, Hunt SE, Iqbal S, Cline MS, Rubin AF, Wagner AH. Mapping MAVE data for use in human genomics applications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.20.545702. [PMID: 38979347 PMCID: PMC11230167 DOI: 10.1101/2023.06.20.545702] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
The large-scale experimental measures of variant functional assays submitted to MaveDB have the potential to provide key information for resolving variants of uncertain significance, but the reporting of results relative to assayed sequence hinders their downstream utility. The Atlas of Variant Effects Alliance mapped multiplexed assays of variant effect data to human reference sequences, creating a robust set of machine-readable homology mappings. This method processed approximately 2.5 million protein and genomic variants in MaveDB, successfully mapping 98.61% of examined variants and disseminating data to resources such as the UCSC Genome Browser and Ensembl Variant Effect Predictor.
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Affiliation(s)
- Jeremy A Arbesfeld
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Estelle Y Da
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - James S Stevenson
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Kori Kuzma
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Anika Paul
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Tierra Farris
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | | | | | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Nuno Saraiva-Agostinho
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Jordan F Safer
- The Center for the Development of Therapeutics, The Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Julia Foreman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Helen V Firth
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Sarah E Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Sumaiya Iqbal
- The Center for the Development of Therapeutics, The Broad Institute of MIT and Harvard, Cambridge, MA
| | - Melissa S Cline
- BRCA Exchange, University of California Santa Cruz, Santa Cruz, CA
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Australia
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
- Department of Pediatrics and Biomedical Informatics, The Ohio State University, Columbus, OH
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2
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie ME, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D’Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. Database (Oxford) 2024; 2024:baae031. [PMID: 38713862 PMCID: PMC11184451 DOI: 10.1093/database/baae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/23/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024]
Abstract
Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.
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Affiliation(s)
- Marija Orlic-Milacic
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Lisa Matthews
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Adam Wright
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Veronica Shamovsky
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Quang Trinh
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Marc E Gillespie
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- College of Pharmacy and Health Sciences, St. John’s University, 8000 Utopia Parkway, Queens, NY 11439, USA
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ralf Stephan
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Bruce May
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Robin Haw
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Deidre Beavers
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Patrick Conley
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Lincoln D Stein
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Room 4386, Toronto, ON M5S 1A8, Canada
| | - Peter D’Eustachio
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Guanming Wu
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
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3
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Claussnitzer M, Parikh VN, Wagner AH, Arbesfeld JA, Bult CJ, Firth HV, Muffley LA, Nguyen Ba AN, Riehle K, Roth FP, Tabet D, Bolognesi B, Glazer AM, Rubin AF. Minimum information and guidelines for reporting a multiplexed assay of variant effect. Genome Biol 2024; 25:100. [PMID: 38641812 PMCID: PMC11027375 DOI: 10.1186/s13059-024-03223-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 03/25/2024] [Indexed: 04/21/2024] Open
Abstract
Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.
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Affiliation(s)
- Melina Claussnitzer
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA, 02142, USA
| | - Victoria N Parikh
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, 43210, USA
| | - Jeremy A Arbesfeld
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | - Helen V Firth
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Dept of Medical Genetics, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Lara A Muffley
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Alex N Nguyen Ba
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Daniel Tabet
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Benedetta Bolognesi
- Institute for Bioengineering of Catalunya (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Andrew M Glazer
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
- Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.
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4
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El Kassaby B, Castellanos F, Gerring M, Kunde-Ramamoorthy G, Bult CJ. MVAR: A Mouse Variation Registry. J Mol Biol 2024:168518. [PMID: 38458603 DOI: 10.1016/j.jmb.2024.168518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/01/2024] [Indexed: 03/10/2024]
Abstract
The Mouse Variation Registry (MVAR) resource is a scalable registry of mouse single nucleotide variants and small indels and variant annotation. The resource accepts data in standard Variant Call Format (VCF) and assesses the uniqueness of the submitted variants via a canonicalization process. Novel variants are assigned a unique, persistent MVAR identifier; variants that are equivalent to an existing variant in the resource are associated with the existing identifier. Annotations for variant type, molecular consequence, impact, and genomic region in the context of specific transcripts and protein sequences are generated using Ensembl's Variant Effect Predictor (VEP) and Jannovar. Access to the data and annotations in MVAR are supported via an Application Programming Interface (API) and web application. Researchers can search the resource by gene symbol, genomic region, variant (expressed in Human Genome Variation Society syntax), refSNP identifiers, or MVAR identifiers. Tabular search results can be filtered by variant annotations (variant type, molecular consequence, impact, variant region) and viewed according to variant distribution across mouse strains. The registry currently comprises more than 99 million canonical single nucleotide variants for 581 strains of mice. MVAR is accessible from https://mvar.jax.org.
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5
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Walton NA, Nagarajan R, Wang C, Sincan M, Freimuth RR, Everman DB, Walton DC, McGrath SP, Lemas DJ, Benos PV, Alekseyenko AV, Song Q, Gamsiz Uzun E, Taylor CO, Uzun A, Person TN, Rappoport N, Zhao Z, Williams MS. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup. J Am Med Inform Assoc 2024; 31:536-541. [PMID: 38037121 PMCID: PMC10797281 DOI: 10.1093/jamia/ocad211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 10/09/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
OBJECTIVE Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.
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Affiliation(s)
- Nephi A Walton
- Division of Medical Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112 ,United States
| | - Radha Nagarajan
- Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA 90025, United States
- Information Services Department, Children’s Hospital of Orange County, Orange, CA 92868, United States
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Murat Sincan
- Flatiron Health, New York, NY 10013, United States
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57107, United States
| | - Robert R Freimuth
- Department of Artificial Intelligence and Informatics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - David B Everman
- EverMed Genetics and Genomics Consulting LLC, Greenville, SC 29607, United States
| | | | - Scott P McGrath
- CITRIS Health, CITRIS and Banatao Institute, University of California Berkeley, Berkeley, CA 94720, United States
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, United States
| | - Alexander V Alekseyenko
- Department of Public Health Sciences, Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29403, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Ece Gamsiz Uzun
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI 02915, United States
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
| | - Casey Overby Taylor
- Departments of Medicine and Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States
| | - Alper Uzun
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
- Legorreta Cancer Center, Brown University, Providence, RI 02915, United States
| | - Thomas Nate Person
- Department of Bioinformatics and Genomics, Huck Institutes of the Life Sciences, Penn State University, Bloomsburg, PA 16802, United States
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, United States
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6
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Pitz V, Makarious MB, Bandres-Ciga S, Iwaki H, Singleton AB, Nalls M, Heilbron K, Blauwendraat C. Analysis of rare Parkinson's disease variants in millions of people. NPJ Parkinsons Dis 2024; 10:11. [PMID: 38191580 PMCID: PMC10774311 DOI: 10.1038/s41531-023-00608-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/24/2023] [Indexed: 01/10/2024] Open
Abstract
Although many rare variants have been reportedly associated with Parkinson's disease (PD), many have not been replicated or have failed to replicate. Here, we conduct a large-scale replication of rare PD variants. We assessed a total of 27,590 PD cases, 6701 PD proxies, and 3,106,080 controls from three data sets: 23andMe, Inc., UK Biobank, and AMP-PD. Based on well-known PD genes, 834 variants of interest were selected from the ClinVar annotated 23andMe dataset. We performed a meta-analysis using summary statistics of all three studies. The meta-analysis resulted in five significant variants after Bonferroni correction, including variants in GBA1 and LRRK2. Another eight variants are strong candidate variants for their association with PD. Here, we provide the largest rare variant meta-analysis to date, providing information on confirmed and newly identified variants for their association with PD using several large databases. Additionally we also show the complexities of studying rare variants in large-scale cohorts.
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Affiliation(s)
- Vanessa Pitz
- Integrative Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
| | - Mary B Makarious
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Hirotaka Iwaki
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Washington, DC, USA
| | - Andrew B Singleton
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Mike Nalls
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Washington, DC, USA
| | | | - Cornelis Blauwendraat
- Integrative Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
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7
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie M, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D'Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.18.562964. [PMID: 37904913 PMCID: PMC10614924 DOI: 10.1101/2023.10.18.562964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.
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8
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Pitz V, Makarious M, Bandrés-Ciga S, Iwaki H, Singleton A, Nalls M, Heilbron K, Blauwendraat C. Analysis of rare Parkinson's disease variants in millions of people. RESEARCH SQUARE 2023:rs.3.rs-2743857. [PMID: 37090536 PMCID: PMC10120789 DOI: 10.21203/rs.3.rs-2743857/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Objective Although many rare variants have been reportedly associated with Parkinson's disease (PD), many have not been replicated or have failed to replicate. Here, we conduct a large-scale replication of rare PD variants. Methods We assessed a total of 27,590 PD cases, 6,701 PD proxies, and 3,106,080 controls from three data sets: 23andMe, Inc., UK Biobank, and AMP-PD. Based on well-known PD genes, 834 variants of interest were selected from the ClinVar annotated 23andMe dataset. We performed a meta-analysis using summary statistics of all three studies. Results The meta-analysis resulted in 11 significant variants after Bonferroni correction, including variants in GBA1 and LRRK2. At least 9 previously reported pathogenic or risk variants for PD did not pass Bonferroni correction in this analysis. Conclusions Here, we provide the largest rare variant meta-analysis to date, providing thorough information of variants confirmed, newly identified, or rebutted for their association with PD.
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9
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Croucher DC, Devasia AJ, Abelman DD, Mahdipour-Shirayeh A, Li Z, Erdmann N, Tiedemann R, Pugh TJ, Trudel S. Single-cell profiling of multiple myeloma reveals molecular response to FGFR3 inhibitor despite clinical progression. Cold Spring Harb Mol Case Stud 2023; 9:a006249. [PMID: 36639200 PMCID: PMC10240837 DOI: 10.1101/mcs.a006249] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/29/2022] [Indexed: 01/15/2023] Open
Abstract
Genomic characterization of cancer has enabled identification of numerous molecular targets, which has led to significant advances in personalized medicine. However, with few exceptions, precision medicine approaches in the plasma cell malignancy multiple myeloma (MM) have had limited success, likely owing to the subclonal nature of molecular targets in this disease. Targeted therapies against FGFR3 have been under development for the past decade in the hopes of targeting aberrant FGFR3 activity in MM. FGFR3 activation results from the recurrent transforming event of t(4;14) found in ∼15% of MM patients, as well as secondary FGFR3 mutations in this subgroup. To evaluate the effectiveness of targeting FGFR3 in MM, we undertook a phase 2 clinical trial evaluating the small-molecule FGFR1-4 inhibitor, erdafitinib, in relapsed/refractory myeloma patients with or without FGFR3 mutations (NCT02952573). Herein, we report on a single t(4;14) patient enrolled on this study who was identified to have a subclonal FGFR3 stop-loss deletion. Although this individual eventually progressed on study and succumbed to their disease, the intended molecular response was revealed through an extensive molecular characterization of the patient's tumor at baseline and on treatment using single-cell genomics. We identified elimination of the FGFR3-mutant subclone after treatment and expansion of a preexisting clone with loss of Chromosome 17p. Altogether, our study highlights the utility of single-cell genomics in targeted trials as they can reveal molecular mechanisms that underlie sensitivity and resistance. This in turn can guide more personalized and targeted therapeutic approaches, including those that involve FGFR3-targeting therapies.
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Affiliation(s)
- Danielle C Croucher
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Anup Joseph Devasia
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
| | - Dor D Abelman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Ali Mahdipour-Shirayeh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
| | - Zhihua Li
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
| | - Natalie Erdmann
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
| | - Rodger Tiedemann
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Trevor J Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada;
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada
| | - Suzanne Trudel
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada;
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
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10
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Rozowsky J, Gao J, Borsari B, Yang YT, Galeev T, Gürsoy G, Epstein CB, Xiong K, Xu J, Li T, Liu J, Yu K, Berthel A, Chen Z, Navarro F, Sun MS, Wright J, Chang J, Cameron CJF, Shoresh N, Gaskell E, Drenkow J, Adrian J, Aganezov S, Aguet F, Balderrama-Gutierrez G, Banskota S, Corona GB, Chee S, Chhetri SB, Cortez Martins GC, Danyko C, Davis CA, Farid D, Farrell NP, Gabdank I, Gofin Y, Gorkin DU, Gu M, Hecht V, Hitz BC, Issner R, Jiang Y, Kirsche M, Kong X, Lam BR, Li S, Li B, Li X, Lin KZ, Luo R, Mackiewicz M, Meng R, Moore JE, Mudge J, Nelson N, Nusbaum C, Popov I, Pratt HE, Qiu Y, Ramakrishnan S, Raymond J, Salichos L, Scavelli A, Schreiber JM, Sedlazeck FJ, See LH, Sherman RM, Shi X, Shi M, Sloan CA, Strattan JS, Tan Z, Tanaka FY, Vlasova A, Wang J, Werner J, Williams B, Xu M, Yan C, Yu L, Zaleski C, Zhang J, Ardlie K, Cherry JM, Mendenhall EM, Noble WS, Weng Z, Levine ME, Dobin A, Wold B, Mortazavi A, Ren B, Gillis J, Myers RM, Snyder MP, Choudhary J, Milosavljevic A, Schatz MC, Bernstein BE, Guigó R, Gingeras TR, Gerstein M. The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell 2023; 186:1493-1511.e40. [PMID: 37001506 PMCID: PMC10074325 DOI: 10.1016/j.cell.2023.02.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 10/16/2022] [Accepted: 02/10/2023] [Indexed: 04/03/2023]
Abstract
Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
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Affiliation(s)
- Joel Rozowsky
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jiahao Gao
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Beatrice Borsari
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Gamze Gürsoy
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Kun Xiong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jinrui Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Tianxiao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jason Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Keyang Yu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ana Berthel
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Zhanlin Chen
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - Fabio Navarro
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Maxwell S Sun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Justin Chang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Christopher J F Cameron
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Noam Shoresh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jorg Drenkow
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jessika Adrian
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sergey Aganezov
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | | | - Sora Chee
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Surya B Chhetri
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Gabriel Conte Cortez Martins
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Cassidy Danyko
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Carrie A Davis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Daniel Farid
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Idan Gabdank
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Yoel Gofin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - David U Gorkin
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Mengting Gu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Vivian Hecht
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin C Hitz
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Robbyn Issner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Melanie Kirsche
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xiangmeng Kong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bonita R Lam
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bian Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Xiqi Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Khine Zin Lin
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, CHN
| | - Mark Mackiewicz
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jonathan Mudge
- European Bioinformatics Institute, Cambridge, Cambridgeshire, GB
| | | | - Chad Nusbaum
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ioann Popov
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Henry E Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Yunjiang Qiu
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Srividya Ramakrishnan
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joe Raymond
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Biological and Chemical Sciences, New York Institute of Technology, Old Westbury, NY, USA
| | - Alexandra Scavelli
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jacob M Schreiber
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Fritz J Sedlazeck
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Lei Hoon See
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Rachel M Sherman
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Shi
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Minyi Shi
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Cricket Alicia Sloan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - J Seth Strattan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Zhen Tan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Forrest Y Tanaka
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Anna Vlasova
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Comparative Genomics Group, Life Science Programme, Barcelona Supercomputing Centre, Barcelona, Spain; Institute of Research in Biomedicine, Barcelona, Spain
| | - Jun Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jonathan Werner
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Brian Williams
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Chengfei Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Lu Yu
- Institute of Cancer Research, London, UK
| | - Christopher Zaleski
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, Irvine, CA, USA
| | | | - J Michael Cherry
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Morgan E Levine
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Alexander Dobin
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Barbara Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Jesse Gillis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | | | - Michael C Schatz
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Bradley E Bernstein
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Roderic Guigó
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
| | - Thomas R Gingeras
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Mark Gerstein
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA.
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11
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Krysiak K, Danos A, Saliba J, McMichael J, Coffman A, Kiwala S, Barnell E, Sheta L, Grisdale C, Kujan L, Pema S, Lever J, Ridd S, Spies N, Andric V, Chiorean A, Rieke D, Clark K, Reisle C, Venigalla A, Evans M, Jani P, Takahashi H, Suda A, Horak P, Ritter D, Zhou X, Ainscough B, Delong S, Kesserwan C, Lamping M, Shen H, Marr A, Hoang M, Singhal K, Khanfar M, Li B, Lin WH, Terraf P, Corson L, Salama Y, Campbell K, Farncombe K, Ji J, Zhao X, Xu X, Kanagal-Shamanna R, King I, Cotto K, Skidmore Z, Walker J, Zhang J, Milosavljevic A, Patel R, Giles R, Kim R, Schriml L, Mardis E, Jones SJM, Raca G, Rao S, Madhavan S, Wagner A, Griffith M, Griffith O. CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase. Nucleic Acids Res 2023; 51:D1230-D1241. [PMID: 36373660 PMCID: PMC9825608 DOI: 10.1093/nar/gkac979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/15/2022] Open
Abstract
CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.
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Affiliation(s)
- Kilannin Krysiak
- To whom correspondence should be addressed. Tel: +1 314 273 4218;
| | | | | | - Joshua F McMichael
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Adam C Coffman
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Erica K Barnell
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Lana Sheta
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | | | - Lynzey Kujan
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Shahil Pema
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Jake Lever
- School of Computer Science, University of Glasgow, Glasgow, UK
| | - Sarah Ridd
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Nicholas C Spies
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Veronica Andric
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Andreea Chiorean
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Damian T Rieke
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kaitlin A Clark
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Caralyn Reisle
- Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada
- Bioinformatics Graduate Program, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Ajay C Venigalla
- Department of Medicine, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | | | - Payal Jani
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Hideaki Takahashi
- Department of Experimental Therapeutics/Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Avila Suda
- Department of Medicine, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Peter Horak
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Deborah I Ritter
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Cancer Center, Texas Children's Hospital, Houston, TX, USA
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Benjamin J Ainscough
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Sean Delong
- Lassonde School of Engineering, York University, Toronto, Ontario, Canada
| | - Chimene Kesserwan
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA and Genetics Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Mario Lamping
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Haolin Shen
- Department of Medicine, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Alex R Marr
- Department of Pathology and Immunology, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - My H Hoang
- Department of Medicine, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Kartik Singhal
- Department of Medicine, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Mariam Khanfar
- Department of Medicine, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Brian V Li
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | | | - Panieh Terraf
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Laura B Corson
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA
| | - Yasser Salama
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Katie M Campbell
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Kirsten M Farncombe
- Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Jianling Ji
- Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | - Xiaonan Zhao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xinjie Xu
- Division of Hematopathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Rashmi Kanagal-Shamanna
- Department of Hematopathology and Molecular Diagnostics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ian King
- Division of Clinical Laboratory Genetics, Laboratory Medicine Program, University Health Network (UHN), Toronto, ON, Canada
| | - Kelsy C Cotto
- Department of Medicine, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Zachary L Skidmore
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Jason R Walker
- McDonnell Genome Institute, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Ronak Y Patel
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Rachel H Giles
- International Kidney Cancer Coalition, Duivendrecht-Amsterdam, the Netherlands
| | - Raymond H Kim
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Sinai Health System, Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Ontario Institute for Cancer Research, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lynn M Schriml
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elaine R Mardis
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Departments of Pediatrics and Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Steven J M Jones
- Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada
| | - Gordana Raca
- Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | - Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, WA DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, WA DC, USA
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Departments of Pediatrics and Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Malachi Griffith
- Correspondence may also be addressed to Malachi Griffith. Tel: +1 314 286 1274;
| | - Obi L Griffith
- Correspondence may also be addressed to Obi L. Griffith. Tel: +1 314 747 9248;
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12
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Fan S, Zhao T, Sun L. The global prevalence and ethnic heterogeneity of iron-refractory iron deficiency anaemia. Orphanet J Rare Dis 2023; 18:2. [PMID: 36604716 PMCID: PMC9814447 DOI: 10.1186/s13023-022-02612-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Iron-refractory iron deficiency anaemia (IRIDA) is an autosomal recessive iron deficiency anaemia caused by mutations in the TMPRSS6 gene. Iron deficiency anaemia is common, whereas IRIDA is rare. The prevalence of IRIDA is unclear. This study aimed to estimate the carrier frequency and genetic prevalence of IRIDA using Genome Aggregation Database (gnomAD) data. METHODS The pathogenicity of TMPRSS6 variants was interpreted according to the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) standards and guidelines. The minor allele frequency (MAF) of TMPRSS6 gene disease-causing variants in 141,456 unique individuals was examined to estimate the global prevalence of IRIDA in seven ethnicities: African/African American (afr), American Admixed/Latino (amr), Ashkenazi Jewish (asj), East Asian (eas), Finnish (fin), Non-Finnish European (nfe) and South Asian (sas). The global and population-specific carrier frequencies and genetic prevalence of IRIDA were calculated using the Hardy-Weinberg equation. RESULTS In total, 86 pathogenic/likely pathogenic variants (PV/LPV) were identified according to ACMG/AMP guideline. The global carrier frequency and genetic prevalence of IRIDA were 2.02 per thousand and 1.02 per million, respectively. CONCLUSIONS The prevalence of IRIDA is greater than previous estimates.
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Affiliation(s)
- Shanghua Fan
- grid.412632.00000 0004 1758 2270Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, 430060 China
| | - Ting Zhao
- grid.414011.10000 0004 1808 090XDepartment of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, 450003 China
| | - Liu Sun
- Department of Information Technology, School of Mathematics and Information Technology, Yuxi Normal University, Yuxi, 653100, China.
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13
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Goar W, Babb L, Chamala S, Cline M, Freimuth RR, Hart RK, Kuzma K, Lee J, Nelson T, Prlić A, Riehle K, Smith A, Stahl K, Yates AD, Rehm HL, Wagner AH. Development and application of a computable genotype model in the GA4GH Variation Representation Specification. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:383-394. [PMID: 36540993 PMCID: PMC9782714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
As the diversity of genomic variation data increases with our growing understanding of the role of variation in health and disease, it is critical to develop standards for precise inter-system exchange of these data for research and clinical applications. The Global Alliance for Genomics and Health (GA4GH) Variation Representation Specification (VRS) meets this need through a technical terminology and information model for disambiguating and concisely representing variation concepts. Here we discuss the recent Genotype model in VRS, which may be used to represent the allelic composition of a genetic locus. We demonstrate the use of the Genotype model and the constituent Haplotype model for the precise and interoperable representation of pharmacogenomic diplotypes, HGVS variants, and VCF records using VRS and discuss how this can be leveraged to enable interoperable exchange and search operations between assayed variation and genomic knowledgebases.
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Affiliation(s)
- Wesley Goar
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
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14
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Tudini E, Andrews J, Lawrence DM, King-Smith SL, Baker N, Baxter L, Beilby J, Bennetts B, Beshay V, Black M, Boughtwood TF, Brion K, Cheong PL, Christie M, Christodoulou J, Chong B, Cox K, Davis MR, Dejong L, Dinger ME, Doig KD, Douglas E, Dubowsky A, Ellul M, Fellowes A, Fisk K, Fortuno C, Friend K, Gallagher RL, Gao S, Hackett E, Hadler J, Hipwell M, Ho G, Hollway G, Hooper AJ, Kassahn KS, Krishnaraj R, Lau C, Le H, San Leong H, Lundie B, Lunke S, Marty A, McPhillips M, Nguyen LT, Nones K, Palmer K, Pearson JV, Quinn MC, Rawlings LH, Sadedin S, Sanchez L, Schreiber AW, Sigalas E, Simsek A, Soubrier J, Stark Z, Thompson BA, U J, Vakulin CG, Wells AV, Wise CA, Woods R, Ziolkowski A, Brion MJ, Scott HS, Thorne NP, Spurdle AB. Shariant platform: Enabling evidence sharing across Australian clinical genetic-testing laboratories to support variant interpretation. Am J Hum Genet 2022; 109:1960-1973. [PMID: 36332611 PMCID: PMC9674965 DOI: 10.1016/j.ajhg.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Sharing genomic variant interpretations across laboratories promotes consistency in variant assertions. A landscape analysis of Australian clinical genetic-testing laboratories in 2017 identified that, despite the national-accreditation-body recommendations encouraging laboratories to submit genotypic data to clinical databases, fewer than 300 variants had been shared to the ClinVar public database. Consultations with Australian laboratories identified resource constraints limiting routine application of manual processes, consent issues, and differences in interpretation systems as barriers to sharing. This information was used to define key needs and solutions required to enable national sharing of variant interpretations. The Shariant platform, using both the GRCh37 and GRCh38 genome builds, was developed to enable ongoing sharing of variant interpretations and associated evidence between Australian clinical genetic-testing laboratories. Where possible, two-way automated sharing was implemented so that disruption to laboratory workflows would be minimized. Terms of use were developed through consultation and currently restrict access to Australian clinical genetic-testing laboratories. Shariant was designed to store and compare structured evidence, to promote and record resolution of inter-laboratory classification discrepancies, and to streamline the submission of variant assertions to ClinVar. As of December 2021, more than 14,000 largely prospectively curated variant records from 11 participating laboratories have been shared. Discrepant classifications have been identified for 11% (28/260) of variants submitted by more than one laboratory. We have demonstrated that co-design with clinical laboratories is vital to developing and implementing a national variant-interpretation sharing effort. This approach has improved inter-laboratory concordance and enabled opportunities to standardize interpretation practices.
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Affiliation(s)
- Emma Tudini
- Australian Genomics, Melbourne, VIC 3052, Australia,Population Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - James Andrews
- Australian Genomics, Melbourne, VIC 3052, Australia,Australian Cancer Research Foundation Cancer Genomics Facility, Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA 5000, Australia
| | - David M. Lawrence
- Australian Cancer Research Foundation Cancer Genomics Facility, Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA 5000, Australia
| | - Sarah L. King-Smith
- Australian Genomics, Melbourne, VIC 3052, Australia,Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Naomi Baker
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia,University of Melbourne, Melbourne, VIC 3052, Australia
| | | | - John Beilby
- PathWest Laboratory Medicine Western Australia, Perth, WA 6009, Australia,School of Biomedical Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Bruce Bennetts
- Sydney Genome Diagnostics, Western Sydney Genetics Program, The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia,Disciplines of Child and Adolescent Health and Genomic Medicine, University of Sydney, Sydney, NSW 2145, Australia
| | - Victoria Beshay
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC 3052, Australia
| | - Michael Black
- Department of Diagnostic Genomics, PathWest Laboratory Medicine Western Australia, Perth, WA 6009, Australia
| | - Tiffany F. Boughtwood
- Australian Genomics, Melbourne, VIC 3052, Australia,Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
| | | | - Pak Leng Cheong
- Department of Medical Genomics, Royal Prince Alfred Hospital, NSW Health Pathology, Sydney, NSW 2050, Australia,University of Sydney, Sydney, NSW 2006, Australia
| | - Michael Christie
- Department of Pathology, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia
| | - John Christodoulou
- Australian Genomics, Melbourne, VIC 3052, Australia,Disciplines of Child and Adolescent Health and Genomic Medicine, University of Sydney, Sydney, NSW 2145, Australia,Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia,Department of Paediatrics, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Belinda Chong
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
| | - Kathy Cox
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Mark R. Davis
- Department of Diagnostic Genomics, PathWest Laboratory Medicine Western Australia, Perth, WA 6009, Australia,Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Lucas Dejong
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Marcel E. Dinger
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Kenneth D. Doig
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC 3052, Australia,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Evelyn Douglas
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Andrew Dubowsky
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Melissa Ellul
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Andrew Fellowes
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC 3052, Australia
| | - Katrina Fisk
- Sydney Genome Diagnostics, Western Sydney Genetics Program, The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia
| | - Cristina Fortuno
- Population Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Kathryn Friend
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | | | - Song Gao
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Emma Hackett
- Sydney Genome Diagnostics, Western Sydney Genetics Program, The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia
| | - Johanna Hadler
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Michael Hipwell
- Division of Molecular Medicine, NSW Health Pathology North, Newcastle, NSW 2305, Australia
| | - Gladys Ho
- Sydney Genome Diagnostics, Western Sydney Genetics Program, The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia,Disciplines of Child and Adolescent Health and Genomic Medicine, University of Sydney, Sydney, NSW 2145, Australia
| | - Georgina Hollway
- Garvan Institute of Medical Research, Sydney, NSW 2010, Australia,Cancer Research, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Amanda J. Hooper
- Department of Clinical Biochemistry, PathWest Laboratory Medicine Western Australia, Fiona Stanley Hospital Network, Perth, WA 6150, Australia,School of Medicine, The University of Western Australia, Perth, WA 6009, Australia
| | - Karin S. Kassahn
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia,Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia
| | - Rahul Krishnaraj
- Sydney Genome Diagnostics, Western Sydney Genetics Program, The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia
| | - Chiyan Lau
- Pathology Queensland, Brisbane, QLD 4006, Australia,The University of Queensland, Brisbane, QLD 4072, Australia
| | - Huong Le
- Department of Medical Genomics, Royal Prince Alfred Hospital, NSW Health Pathology, Sydney, NSW 2050, Australia
| | - Huei San Leong
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC 3052, Australia
| | - Ben Lundie
- Pathology Queensland, Brisbane, QLD 4006, Australia
| | - Sebastian Lunke
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia,University of Melbourne, Melbourne, VIC 3052, Australia
| | - Anthony Marty
- Melbourne Genomics Health Alliance, Melbourne, VIC 3052, Australia
| | - Mary McPhillips
- Division of Molecular Medicine, NSW Health Pathology North, Newcastle, NSW 2305, Australia
| | - Lan T. Nguyen
- Department of Clinical Biochemistry, PathWest Laboratory Medicine Western Australia, Fiona Stanley Hospital Network, Perth, WA 6150, Australia
| | - Katia Nones
- Cancer Research, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Kristen Palmer
- Genomics Statewide Services, New South Wales Health Pathology, Newcastle, NSW 2300, Australia
| | - John V. Pearson
- Genome Informatics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Michael C.J. Quinn
- Australian Genomics, Melbourne, VIC 3052, Australia,Genetic Health Queensland, Royal Brisbane and Women’s Hospital, Brisbane, QLD 4006, Australia
| | - Lesley H. Rawlings
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Simon Sadedin
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia,University of Melbourne, Melbourne, VIC 3052, Australia,Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
| | - Louisa Sanchez
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Andreas W. Schreiber
- Australian Cancer Research Foundation Cancer Genomics Facility, Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA 5000, Australia,School of Biological Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Emanouil Sigalas
- Department of Pathology, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia
| | - Aygul Simsek
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Julien Soubrier
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia,School of Biological Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Zornitza Stark
- Australian Genomics, Melbourne, VIC 3052, Australia,Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia,University of Melbourne, Melbourne, VIC 3052, Australia
| | - Bryony A. Thompson
- Department of Pathology, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia
| | - James U
- Melbourne Genomics Health Alliance, Melbourne, VIC 3052, Australia
| | | | - Amanda V. Wells
- Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia
| | - Cheryl A. Wise
- Department of Diagnostic Genomics, PathWest Laboratory Medicine Western Australia, Perth, WA 6009, Australia
| | - Rick Woods
- Pathology Queensland, Brisbane, QLD 4006, Australia
| | - Andrew Ziolkowski
- Division of Molecular Medicine, NSW Health Pathology North, Newcastle, NSW 2305, Australia
| | - Marie-Jo Brion
- Australian Genomics, Melbourne, VIC 3052, Australia,Population Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Hamish S. Scott
- Australian Genomics, Melbourne, VIC 3052, Australia,Australian Cancer Research Foundation Cancer Genomics Facility, Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA 5000, Australia,Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia,Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia
| | - Natalie P. Thorne
- Australian Genomics, Melbourne, VIC 3052, Australia,University of Melbourne, Melbourne, VIC 3052, Australia,Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia,Melbourne Genomics Health Alliance, Melbourne, VIC 3052, Australia,Walter and Eliza Hall Institute, Melbourne, VIC 3052, Australia
| | - Amanda B. Spurdle
- Australian Genomics, Melbourne, VIC 3052, Australia,Population Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia,Corresponding author
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15
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Wei CH, Allot A, Riehle K, Milosavljevic A, Lu Z. tmVar 3.0: an improved variant concept recognition and normalization tool. Bioinformatics 2022; 38:4449-4451. [PMID: 35904569 PMCID: PMC9477515 DOI: 10.1093/bioinformatics/btac537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/07/2022] [Accepted: 07/27/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large scale. Despite multiple attempts in the past, existing tools are still of limited recognition scope and precision. RESULT We propose tmVar 3.0: an improved variant recognition and normalization system. Compared to its predecessors, tmVar 3.0 recognizes a wider spectrum of variant-related entities (e.g. allele and copy number variants), and groups together different variant mentions belonging to the same genomic sequence position in an article for improved accuracy. Moreover, tmVar 3.0 provides advanced variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry. tmVar 3.0 exhibits state-of-the-art performance with over 90% in F-measure for variant recognition and normalization, when evaluated on three independent benchmarking datasets. tmVar 3.0 as well as annotations for the entire PubMed and PMC datasets are freely available for download. AVAILABILITY AND IMPLEMENTATION https://github.com/ncbi/tmVar3.
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Affiliation(s)
- Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Alexis Allot
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
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16
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Zhang D, Wu J, Yuan Y, Li X, Gao X, Han M, Gao S, Huang S, Dai P. A novel missense variant in CEACAM16 gene causes autosomal dominant nonsyndromic hearing loss. Ann Hum Genet 2022; 86:207-217. [PMID: 35292975 PMCID: PMC9314904 DOI: 10.1111/ahg.12463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/13/2022] [Accepted: 02/16/2022] [Indexed: 11/27/2022]
Abstract
AbstractAimAutosomal dominant non‐syndromic hearing loss is a common sensorineural disorder with extremely high genetic heterogeneity. CEA antigen‐related cell adhesion molecule 16(CEACAM16)is a secreted glycoprotein encoded by the CEACAM16 gene. Mutations in CEACAM16 lead to autosomal dominant non‐syndromic hearing loss in humans, due defects in the tectorial membrane of the inner ear. Here we reported a novel missense variant in CEACAM16 gene causes autosomal dominant non‐syndromic hearing loss.Material and methodsA four‐generation Chinese family affected by late‐onset and progressive hearing loss was enrolled in this study. The proband was analyzed by targeted next‐generation sequencing and bioinformatic analysis. And in vitro experiments were performed in overexpressed transfected HEK293T cells to investigate the pathogenesis of the mutant protein.ResultsWe identified a novel missense variant in the CEACAM16 gene c.763A>G; (p.Arg255Gly) as causing autosomal dominant non‐syndromic hearing loss in the Chinese family. Using Western blot analysis, ELISA, and immunofluorescence we found increased expression level of the secreted mutant CEACAM16 protein, both intracellularly and extracellularly, compared with wild type CEACAM16 protein.ConclusionOur study showed that the p.Arg255Gly variant leads to increased secretion of mutant CEACAM16 protein, with potential deleterious effect to the function of the protein. Our findings expand the mutation spectrum of CEACAM16, and further the understanding CEACAM16 function and implications in disease.
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Affiliation(s)
- Dejun Zhang
- College of Otolaryngology Head and Neck SurgeryChinese PLA General HospitalBeijingChina
- Department of Otolaryngology Head and Neck SurgeryThe Second Hospital of Jilin UniversityChangchunChina
- State Key Lab of Hearing Science, Ministry of EducationNational Clinical Research Center for Otolaryngologic DiseasesBeijingChina
- Beijing Key Lab of Hearing Impairment for Prevention and TreatmentBeijingChina
| | - Jie Wu
- College of Otolaryngology Head and Neck SurgeryChinese PLA General HospitalBeijingChina
- State Key Lab of Hearing Science, Ministry of EducationNational Clinical Research Center for Otolaryngologic DiseasesBeijingChina
- Beijing Key Lab of Hearing Impairment for Prevention and TreatmentBeijingChina
| | - Yongyi Yuan
- College of Otolaryngology Head and Neck SurgeryChinese PLA General HospitalBeijingChina
- State Key Lab of Hearing Science, Ministry of EducationNational Clinical Research Center for Otolaryngologic DiseasesBeijingChina
- Beijing Key Lab of Hearing Impairment for Prevention and TreatmentBeijingChina
| | - Xiaohong Li
- Department of Otolaryngology, Head and Neck Surgery, National Children's Medical Center/Beijing Children's HospitalCapital Medical UniversityBeijingPR China
| | - Xue Gao
- Department of OtolaryngologyPLA Rocket Force Characteristic Medical CenterBeijingChina
| | - Mingyu Han
- College of Otolaryngology Head and Neck SurgeryChinese PLA General HospitalBeijingChina
- State Key Lab of Hearing Science, Ministry of EducationNational Clinical Research Center for Otolaryngologic DiseasesBeijingChina
- Beijing Key Lab of Hearing Impairment for Prevention and TreatmentBeijingChina
| | - Song Gao
- Department of OtolaryngologySouth‐East Hospital Affiliated to Xiamen UniversityZhangzhouChina
| | - Shasha Huang
- College of Otolaryngology Head and Neck SurgeryChinese PLA General HospitalBeijingChina
- State Key Lab of Hearing Science, Ministry of EducationNational Clinical Research Center for Otolaryngologic DiseasesBeijingChina
- Beijing Key Lab of Hearing Impairment for Prevention and TreatmentBeijingChina
| | - Pu Dai
- College of Otolaryngology Head and Neck SurgeryChinese PLA General HospitalBeijingChina
- State Key Lab of Hearing Science, Ministry of EducationNational Clinical Research Center for Otolaryngologic DiseasesBeijingChina
- Beijing Key Lab of Hearing Impairment for Prevention and TreatmentBeijingChina
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17
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Horak P, Griffith M, Danos AM, Pitel BA, Madhavan S, Liu X, Chow C, Williams H, Carmody L, Barrow-Laing L, Rieke D, Kreutzfeldt S, Stenzinger A, Tamborero D, Benary M, Rajagopal PS, Ida CM, Lesmana H, Satgunaseelan L, Merker JD, Tolstorukov MY, Campregher PV, Warner JL, Rao S, Natesan M, Shen H, Venstrom J, Roy S, Tao K, Kanagal-Shamanna R, Xu X, Ritter DI, Pagel K, Krysiak K, Dubuc A, Akkari YM, Li XS, Lee J, King I, Raca G, Wagner AH, Li MM, Plon SE, Kulkarni S, Griffith OL, Chakravarty D, Sonkin D. Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC). Genet Med 2022; 24:986-998. [PMID: 35101336 PMCID: PMC9081216 DOI: 10.1016/j.gim.2022.01.001] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/22/2021] [Accepted: 01/03/2022] [Indexed: 01/21/2023] Open
Abstract
PURPOSE Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. METHODS Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. CONCLUSION The comprehensive SOP is now available for classification of oncogenicity of somatic variants.
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Affiliation(s)
- Peter Horak
- National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Malachi Griffith
- Washington University School of Medicine in St. Louis, St. Louis, MO
| | - Arpad M Danos
- Washington University School of Medicine in St. Louis, St. Louis, MO
| | | | | | - Xuelu Liu
- Dana-Farber Cancer Institute, Boston, MA
| | - Cynthia Chow
- BC Cancer Agency, Vancouver, British Columbia, Canada
| | | | - Leigh Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
| | | | - Damian Rieke
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon Kreutzfeldt
- National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | | | - Padma Sheila Rajagopal
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | | | - Harry Lesmana
- Genomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH
| | | | - Jason D Merker
- UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | | | | | - Shruti Rao
- Georgetown University Medical Center, Washington, DC
| | - Maya Natesan
- Washington University School of Medicine in St. Louis, St. Louis, MO
| | - Haolin Shen
- Washington University School of Medicine in St. Louis, St. Louis, MO
| | | | - Somak Roy
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Kayoko Tao
- National Cancer Center Hospital, Tokyo, Japan
| | | | | | | | - Kym Pagel
- Johns Hopkins University, Baltimore, MD
| | - Kilannin Krysiak
- Washington University School of Medicine in St. Louis, St. Louis, MO
| | - Adrian Dubuc
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | - Jennifer Lee
- Frederick National Laboratory for Cancer Research, National Cancer Institute, Rockville, MD
| | - Ian King
- University Health Network, Toronto, Ontario, Canada
| | - Gordana Raca
- University of Southern California, Los Angeles, CA
| | - Alex H Wagner
- Nationwide Children's Hospital, Columbus, OH; The Ohio State University College of Medicine, Columbus, OH
| | - Marylin M Li
- Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | - Obi L Griffith
- Washington University School of Medicine in St. Louis, St. Louis, MO
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18
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Krysiak K, Danos AM, Kiwala S, McMichael JF, Coffman AC, Barnell EK, Sheta L, Saliba J, Grisdale CJ, Kujan L, Pema S, Lever J, Spies NC, Chiorean A, Rieke DT, Clark KA, Jani P, Takahashi H, Horak P, Ritter DI, Zhou X, Ainscough BJ, Delong S, Lamping M, Marr AR, Li BV, Lin WH, Terraf P, Salama Y, Campbell KM, Farncombe KM, Ji J, Zhao X, Xu X, Kanagal-Shamanna R, Cotto KC, Skidmore ZL, Walker JR, Zhang J, Milosavljevic A, Patel RY, Giles RH, Kim RH, Schriml LM, Mardis ER, Jones SJM, Raca G, Rao S, Madhavan S, Wagner AH, Griffith OL, Griffith M. A community approach to the cancer-variant-interpretation bottleneck. NATURE CANCER 2022; 3:522-525. [PMID: 35624339 PMCID: PMC9872366 DOI: 10.1038/s43018-022-00379-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
As guidelines, therapies, and literature on cancer variants expand, the lack of consensus variant interpretations impedes clinical applications. CIViC is a public domain, crowd-sourced, and adaptable knowledgebase of evidence for the Clinical Interpretation of Variants in Cancer, designed to reduce barriers to knowledge sharing and alleviate the variant interpretation bottleneck.
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Affiliation(s)
- Kilannin Krysiak
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
| | - Arpad M Danos
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua F McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Adam C Coffman
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Erica K Barnell
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Lana Sheta
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason Saliba
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Cameron J Grisdale
- Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada
| | - Lynzey Kujan
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Shahil Pema
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jake Lever
- School of Computer Science, University of Glasgow, Glasgow, UK
| | - Nicholas C Spies
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Andreea Chiorean
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Damian T Rieke
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kaitlin A Clark
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Payal Jani
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Hideaki Takahashi
- Department of Experimental Therapeutics/Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Peter Horak
- Department of Translational Medical Oncology, National Center for Tumor Diseases Heidelberg and German Cancer Research Center, Heidelberg, Germany
| | - Deborah I Ritter
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Cancer Center, Texas Children's Hospital, Houston, Texas, USA
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Benjamin J Ainscough
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Sean Delong
- Lassonde School of Engineering, York University, Toronto, Ontario, Canada
| | - Mario Lamping
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Alex R Marr
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian V Li
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Panieh Terraf
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yasser Salama
- Department of Medicine, Division of Medical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Katie M Campbell
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Kirsten M Farncombe
- Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Jianling Ji
- Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | - Xiaonan Zhao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xinjie Xu
- Division of Hematopathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Rashmi Kanagal-Shamanna
- Department of Hematopathology and Molecular Diagnostics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kelsy C Cotto
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Zachary L Skidmore
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason R Walker
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Ronak Y Patel
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Rachel H Giles
- International Kidney Cancer Coalition, Amsterdam, the Netherlands
| | - Raymond H Kim
- Fred A. Litwin Family Centre in Genetic Medicine, Familial Cancer Clinic, Princess Margaret Cancer Centre, University Health Network, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lynn M Schriml
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elaine R Mardis
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Steven J M Jones
- Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada
| | - Gordana Raca
- Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | - Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Obi L Griffith
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
| | - Malachi Griffith
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
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19
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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] [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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20
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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] [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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21
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Ware SM. Pediatric cardiomyopathy and the PCM Genes study: A summary with insights on genetic testing, variant interpretation, race and ethnicity. PROGRESS IN PEDIATRIC CARDIOLOGY 2021. [DOI: 10.1016/j.ppedcard.2021.101468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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22
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Wagner AH, Babb L, Alterovitz G, Baudis M, Brush M, Cameron DL, Cline M, Griffith M, Griffith OL, Hunt SE, Kreda D, Lee JM, Li S, Lopez J, Moyer E, Nelson T, Patel RY, Riehle K, Robinson PN, Rynearson S, Schuilenburg H, Tsukanov K, Walsh B, Konopko M, Rehm HL, Yates AD, Freimuth RR, Hart RK. The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification. CELL GENOMICS 2021; 1. [PMID: 35311178 PMCID: PMC8929418 DOI: 10.1016/j.xgen.2021.100027] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Maximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced "verse"), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH).
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Affiliation(s)
- Alex H. Wagner
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43215, USA
- Corresponding author
| | - Lawrence Babb
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Corresponding author
| | - Gil Alterovitz
- Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Michael Baudis
- University of Zurich and Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Matthew Brush
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Daniel L. Cameron
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia
| | - Melissa Cline
- UC Santa Cruz Genomics Institute, Santa Cruz, CA 95060, USA
| | - Malachi Griffith
- Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Obi L. Griffith
- Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Sarah E. Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - David Kreda
- Department of Biomedical Informatics, Harvard Medical School, Boston MA 02115, USA
| | - Jennifer M. Lee
- Essex Management LLC and National Cancer Institute, Rockville, MD 20850, USA
| | - Stephanie Li
- The Global Alliance for Genomics and Health, Toronto, ON, Canada
| | | | - Eric Moyer
- National Center for Biotechnology Information, National Library of Medicine National Institutes of Health, Bethesda, MD 20894, USA
| | | | | | - Kevin Riehle
- Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Shawn Rynearson
- Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA
| | - Helen Schuilenburg
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Kirill Tsukanov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Brian Walsh
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Melissa Konopko
- The Global Alliance for Genomics and Health, Toronto, ON, Canada
| | - Heidi L. Rehm
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Cambridge, MA 02142, USA
| | - Andrew D. Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Robert R. Freimuth
- Center for Individualized Medicine, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA
| | - Reece K. Hart
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- MyOme, Inc., Menlo Park, CA 94070, USA
- Corresponding author
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23
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Murugan M, Babb LJ, Overby Taylor C, Rasmussen LV, Freimuth RR, Venner E, Yan F, Yi V, Granite SJ, Zouk H, Aronson SJ, Power K, Fedotov A, Crosslin DR, Fasel D, Jarvik GP, Hakonarson H, Bangash H, Kullo IJ, Connolly JJ, Nestor JG, Caraballo PJ, Wei W, Wiley K, Rehm HL, Gibbs RA. Genomic considerations for FHIR®; eMERGE implementation lessons. J Biomed Inform 2021; 118:103795. [PMID: 33930535 PMCID: PMC8583906 DOI: 10.1016/j.jbi.2021.103795] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/06/2021] [Accepted: 04/25/2021] [Indexed: 01/17/2023]
Abstract
Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.
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Affiliation(s)
- Mullai Murugan
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
| | - Lawrence J Babb
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Casey Overby Taylor
- Departments of Medicine and Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Robert R Freimuth
- Department of Digital Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Fei Yan
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Victoria Yi
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Stephen J Granite
- Departments of Medicine and Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hana Zouk
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Samuel J Aronson
- Partners Personalized Medicine, Partners HealthCare, Cambridge, MA, USA
| | | | - Alex Fedotov
- Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA
| | - David R Crosslin
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - David Fasel
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA, USA
| | - Hana Bangash
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - John J Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA, USA
| | - Jordan G Nestor
- Department of Medicine, Division of Nephrology, Columbia University, New York, NY, USA
| | - Pedro J Caraballo
- Department of Medicine and Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - WeiQi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ken Wiley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Heidi L Rehm
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
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24
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Palmisano A, Vural S, Zhao Y, Sonkin D. MutSpliceDB: A database of splice sites variants with RNA-seq based evidence on effects on splicing. Hum Mutat 2021; 42:342-345. [PMID: 33600011 PMCID: PMC9867871 DOI: 10.1002/humu.24185] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/02/2020] [Accepted: 02/13/2021] [Indexed: 01/26/2023]
Abstract
Splice site variants may lead to transcript alterations, causing exons inclusion, exclusion, truncation, or intron retention. Interpreting the consequences of a specific splice site variant is not straightforward, especially if the variant is located outside of the canonical splice sites. We developed MutSpliceDB: https://brb.nci.nih.gov/splicing, a public resource to facilitate the interpretation of splice sites variants effects on splicing based on manually reviewed RNA-seq BAM files from samples with splice site variants.
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Affiliation(s)
- Alida Palmisano
- National Cancer Institute, Division of Cancer Treatment and Diagnosis, Biometric Research Program, Rockville, MD, USA, General Dynamics Information Technology, Falls Church, VA, USA
| | - Suleyman Vural
- National Cancer Institute, Division of Cancer Treatment and Diagnosis, Biometric Research Program, Rockville, MD, USA
| | - Yingdong Zhao
- National Cancer Institute, Division of Cancer Treatment and Diagnosis, Biometric Research Program, Rockville, MD, USA
| | - Dmitriy Sonkin
- National Cancer Institute, Division of Cancer Treatment and Diagnosis, Biometric Research Program, Rockville, MD, USA
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25
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Holmes JB, Moyer E, Phan L, Maglott D, Kattman B. SPDI: data model for variants and applications at NCBI. Bioinformatics 2020; 36:1902-1907. [PMID: 31738401 DOI: 10.1093/bioinformatics/btz856] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 11/04/2019] [Accepted: 11/15/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Normalizing sequence variants on a reference, projecting them across congruent sequences and aggregating their diverse representations are critical to the elucidation of the genetic basis of disease and biological function. Inconsistent representation of variants among variant callers, local databases and tools result in discrepancies that complicate analysis. NCBI's genetic variation resources, dbSNP and ClinVar, require a robust, scalable set of principles to manage asserted sequence variants. RESULTS The SPDI data model defines variants as a sequence of four attributes: sequence, position, deletion and insertion, and can be applied to nucleotide and protein variants. NCBI web services convert representations among HGVS, VCF and SPDI and provide two functions to aggregate variants. One, based on the NCBI Variant Overprecision Correction Algorithm, returns a unique, normalized representation termed the 'Contextual Allele'. The SPDI data model, with its four operations, defines exactly the reference subsequence affected by the variant, even in repeat regions, such as homopolymer and other sequence repeats. The second function projects variants across congruent sequences and depends on an alignment dataset of non-assembly NCBI RefSeq sequences (prefixed NM, NR and NG), as well as inter- and intra-assembly-associated genomic sequences (NCs, NTs and NWs), supporting robust projection of variants across congruent sequences and assembly versions. The variant is projected to all congruent Contextual Alleles. One of these Contextual Alleles, typically the allele based on the latest assembly version, represents the entire set, is designated the unique 'Canonical Allele' and is used directly to aggregate variants across congruent sequences. AVAILABILITY AND IMPLEMENTATION The SPDI services are available for open access at: https://api.ncbi.nlm.nih.gov/variation/v0. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- J Bradley Holmes
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Eric Moyer
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Lon Phan
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Donna Maglott
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Brandi Kattman
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
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Wagner AH, Walsh B, Mayfield G, Tamborero D, Sonkin D, Krysiak K, Deu-Pons J, Duren RP, Gao J, McMurry J, Patterson S, Del Vecchio Fitz C, Pitel BA, Sezerman OU, Ellrott K, Warner JL, Rieke DT, Aittokallio T, Cerami E, Ritter DI, Schriml LM, Freimuth RR, Haendel M, Raca G, Madhavan S, Baudis M, Beckmann JS, Dienstmann R, Chakravarty D, Li XS, Mockus S, Elemento O, Schultz N, Lopez-Bigas N, Lawler M, Goecks J, Griffith M, Griffith OL, Margolin AA. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer. Nat Genet 2020; 52:448-457. [PMID: 32246132 PMCID: PMC7127986 DOI: 10.1038/s41588-020-0603-8] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 02/26/2020] [Indexed: 12/19/2022]
Abstract
Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.
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Affiliation(s)
- Alex H Wagner
- Washington University School of Medicine, St. Louis, MO, USA
| | - Brian Walsh
- Oregon Health and Science University, Portland, OR, USA
| | | | - David Tamborero
- Pompeu Fabra University, Barcelona, Spain
- Karolinska Institute, Solna, Sweden
| | | | | | - Jordi Deu-Pons
- Institute for Research in Biomedicine, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, Barcelona, Spain
| | | | - Jianjiong Gao
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Julie McMurry
- Oregon Health and Science University, Portland, OR, USA
| | - Sara Patterson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | | | | | - Kyle Ellrott
- Oregon Health and Science University, Portland, OR, USA
| | | | | | - Tero Aittokallio
- Institute for Molecular Medicine Finland, Helsinki, Finland
- University of Turku, Turku, Finland
| | | | - Deborah I Ritter
- Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - Lynn M Schriml
- University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Melissa Haendel
- Oregon Health and Science University, Portland, OR, USA
- Linus Pauling Institute at Oregon State University, Corvallis, OR, USA
| | - Gordana Raca
- Children's Hospital Los Angeles, Los Angeles, CA, USA
- Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Subha Madhavan
- Georgetown University Medical Center, Washington, DC, USA
| | | | | | | | | | | | - Susan Mockus
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | | | - Nuria Lopez-Bigas
- Pompeu Fabra University, Barcelona, Spain
- Institute for Research in Biomedicine, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, Barcelona, Spain
| | | | - Jeremy Goecks
- Oregon Health and Science University, Portland, OR, USA
| | | | - Obi L Griffith
- Washington University School of Medicine, St. Louis, MO, USA.
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Nie A, Pineda AL, Wright MW, Wand H, Wulf B, Costa H, Patel R, Bustamante CD, Zou J. LitGen: Genetic Literature Recommendation Guided by Human Explanations. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:67-78. [PMID: 31797587 PMCID: PMC7478937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
As genetic sequencing costs decrease, the lack of clinical interpretation of variants has become the bottleneck in using genetics data. A major rate limiting step in clinical interpretation is the manual curation of evidence in the genetic literature by highly trained biocurators. What makes curation particularly time-consuming is that the curator needs to identify papers that study variant pathogenicity using different types of approaches and evidences-e.g. biochemical assays or case control analysis. In collaboration with the Clinical Genomic Resource (ClinGen)-the flagship NIH program for clinical curation-we propose the first machine learning system, LitGen, that can retrieve papers for a particular variant and filter them by specific evidence types used by curators to assess for pathogenicity. LitGen uses semi-supervised deep learning to predict the type of evi+dence provided by each paper. It is trained on papers annotated by ClinGen curators and systematically evaluated on new test data collected by ClinGen. LitGen further leverages rich human explanations and unlabeled data to gain 7.9%-12.6% relative performance improvement over models learned only on the annotated papers. It is a useful framework to improve clinical variant curation.
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Affiliation(s)
- Allen Nie
- Department of Biomedical Data Science, Stanford University School of Medicine,Department of Computer Science, Stanford University
| | - Arturo L. Pineda
- Department of Biomedical Data Science, Stanford University School of Medicine
| | - Matt W. Wright
- Department of Biomedical Data Science, Stanford University School of Medicine,Department of Pathology, Stanford University School of Medicine
| | - Hannah Wand
- Department of Biomedical Data Science, Stanford University School of Medicine,Department of Pathology, Stanford University School of Medicine,Department of Cardiology, Stanford Healthcare
| | - Bryan Wulf
- Department of Biomedical Data Science, Stanford University School of Medicine
| | - Helio Costa
- Department of Biomedical Data Science, Stanford University School of Medicine
| | - Ronak Patel
- Department of Molecular and Human Genetics, Baylor College of Medicine
| | - Carlos D. Bustamante
- Department of Biomedical Data Science, Stanford University School of Medicine,Chan-Zuckerberg Biohub
| | - James Zou
- Department of Biomedical Data Science, Stanford University School of Medicine,Department of Computer Science, Stanford University,Chan-Zuckerberg Biohub
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28
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A pediatric perspective on genomics and prevention in the twenty-first century. Pediatr Res 2020; 87:338-344. [PMID: 31578042 DOI: 10.1038/s41390-019-0597-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 09/18/2019] [Indexed: 12/19/2022]
Abstract
We present evidence from diverse disciplines and populations to identify the current and emerging role of genomics in prevention from both medical and public health perspectives as well as key challenges and potential untoward consequences of increasing the role of genomics in these endeavors. We begin by comparing screening in healthy populations (newborn screening), with testing in symptomatic populations, which may incidentally identify secondary findings and at-risk relatives. Emerging evidence suggests that variants in genes subject to the reporting of secondary findings are more common than expected in patients who otherwise would not meet the criteria for testing and population testing for variants in these genes may more precisely identify discrete populations to target for various prevention strategies starting in childhood. Conversely, despite its theoretical promise, recent studies attempting to demonstrate benefits of next-generation sequencing for newborn screening have instead demonstrated numerous barriers and pitfalls to this approach. We also examine the special cases of pharmacogenomics and polygenic risk scores as examples of ways genomics can contribute to prevention amongst a broader population than that affected by rare Mendelian disease. We conclude with unresolved questions which will benefit from future investigations of the role of genomics in disease prevention.
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29
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Gelman H, Dines JN, Berg J, Berger AH, Brnich S, Hisama FM, James RG, Rubin AF, Shendure J, Shirts B, Fowler DM, Starita LM. Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation. Genome Med 2019; 11:85. [PMID: 31862013 PMCID: PMC6925490 DOI: 10.1186/s13073-019-0698-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 11/20/2019] [Indexed: 01/31/2023] Open
Abstract
Variants of uncertain significance represent a massive challenge to medical genetics. Multiplexed functional assays, in which the functional effects of thousands of genomic variants are assessed simultaneously, are increasingly generating data that can be used as additional evidence for or against variant pathogenicity. Such assays have the potential to resolve variants of uncertain significance, thereby increasing the clinical utility of genomic testing. Existing standards from the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) and new guidelines from the Clinical Genome Resource (ClinGen) establish the role of functional data in variant interpretation, but do not address the specific challenges or advantages of using functional data derived from multiplexed assays. Here, we build on these existing guidelines to provide recommendations to experimentalists for the production and reporting of multiplexed functional data and to clinicians for the evaluation and use of such data. By following these recommendations, experimentalists can produce transparent, complete, and well-validated datasets that are primed for clinical uptake. Our recommendations to clinicians and diagnostic labs on how to evaluate the quality of multiplexed functional datasets, and how different datasets could be incorporated into the ACMG/AMP variant-interpretation framework, will hopefully clarify whether and how such data should be used. The recommendations that we provide are designed to enhance the quality and utility of multiplexed functional data, and to promote their judicious use.
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Affiliation(s)
- Hannah Gelman
- Department of Genome Sciences, University of Washington School of Medicine, 15th Avenue NE, Seattle, WA, 98195, USA
- Current affiliation: Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, S Columbian Way, Seattle, WA, 98108, USA
| | - Jennifer N Dines
- Department of Genome Sciences, University of Washington School of Medicine, 15th Avenue NE, Seattle, WA, 98195, USA
- Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Current affiliation: Adaptive Biotechnologies, Eastlake Avenue E, Seattle, WA, 98102, USA
| | - Jonathan Berg
- Department of Genetics, University of North Carolina at Chapel Hill,, Mason Farm Road, Chapel Hill, NC, 27514, USA
| | - Alice H Berger
- Human Biology Division, Fred Hutchinson Cancer Research Center, Fairview Avenue, Seattle, WA, 98109, USA
- Brotman Baty Institute for Precision Medicine, NE Pacific Street, Seattle, WA, 98195, USA
| | - Sarah Brnich
- Department of Genetics, University of North Carolina at Chapel Hill,, Mason Farm Road, Chapel Hill, NC, 27514, USA
| | - Fuki M Hisama
- Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Brotman Baty Institute for Precision Medicine, NE Pacific Street, Seattle, WA, 98195, USA
| | - Richard G James
- Brotman Baty Institute for Precision Medicine, NE Pacific Street, Seattle, WA, 98195, USA
- Department of Pediatrics, University of Washington School of Medicine, NE Pacific Street, Seattle, WA, 98195, USA
- Center for Immunity and Immunotherapies, Seattle Children, Research Institute, Ninth Avenue, Seattle, WA, 98145, USA
| | - Alan F Rubin
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Royal Parade, Parkville, VIC, 3052, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC, 3010, Australia
- Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Grattan Street, Melbourne, VIC, 3000, Australia
| | - Jay Shendure
- Department of Genome Sciences, University of Washington School of Medicine, 15th Avenue NE, Seattle, WA, 98195, USA
- Brotman Baty Institute for Precision Medicine, NE Pacific Street, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, Pacific Street, Seattle, WA, 98195, USA
| | - Brian Shirts
- Brotman Baty Institute for Precision Medicine, NE Pacific Street, Seattle, WA, 98195, USA
- Department of Laboratory Medicine, University of Washington School of Medicine, NE Pacific Street, Seattle, WA, 98195, USA
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington School of Medicine, 15th Avenue NE, Seattle, WA, 98195, USA.
- Brotman Baty Institute for Precision Medicine, NE Pacific Street, Seattle, WA, 98195, USA.
- Department of Bioengineering, University of Washington, 15th Avenue NE, Seattle, WA, 98195, USA.
| | - Lea M Starita
- Department of Genome Sciences, University of Washington School of Medicine, 15th Avenue NE, Seattle, WA, 98195, USA.
- Brotman Baty Institute for Precision Medicine, NE Pacific Street, Seattle, WA, 98195, USA.
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30
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Danos AM, Krysiak K, Barnell EK, Coffman AC, McMichael JF, Kiwala S, Spies NC, Sheta LM, Pema SP, Kujan L, Clark KA, Wollam AZ, Rao S, Ritter DI, Sonkin D, Raca G, Lin WH, Grisdale CJ, Kim RH, Wagner AH, Madhavan S, Griffith M, Griffith OL. Standard operating procedure for curation and clinical interpretation of variants in cancer. Genome Med 2019; 11:76. [PMID: 31779674 PMCID: PMC6883603 DOI: 10.1186/s13073-019-0687-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 11/07/2019] [Indexed: 02/04/2023] Open
Abstract
Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretation of Variants in Cancer - www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications and meeting abstracts, and currently holds over 6300 Evidence Items for over 2300 variants derived from over 400 genes. CIViC has seen increased adoption by, and also undertaken collaboration with, a wide range of users and organizations involved in research. To enhance CIViC’s clinical value, regular submission to the ClinVar database and pursuit of other regulatory approvals is necessary. For this reason, a formal peer reviewed curation guideline and discussion of the underlying principles of curation is needed. We present here the CIViC knowledge model, standard operating procedures (SOP) for variant curation, and detailed examples to support community-driven curation of cancer variants.
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Affiliation(s)
- Arpad M Danos
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Kilannin Krysiak
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.,Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Erica K Barnell
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Adam C Coffman
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua F McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicholas C Spies
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Lana M Sheta
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shahil P Pema
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Lynzey Kujan
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Kaitlin A Clark
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Amber Z Wollam
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University, Washington DC, USA
| | - Deborah I Ritter
- Department of Pediatrics, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA
| | - Dmitriy Sonkin
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - Gordana Raca
- Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Wan-Hsin Lin
- Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida, USA
| | - Cameron J Grisdale
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Raymond H Kim
- Fred A. Litwin Family Center in Genetic Medicine, University Health Network, Toronto, ON, Canada
| | - Alex H Wagner
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington DC, USA.,Georgetown Lombardi Comprehensive Cancer Center, Washington DC, USA
| | - Malachi Griffith
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. .,Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
| | - Obi L Griffith
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. .,Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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31
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Bizon C, Cox S, Balhoff J, Kebede Y, Wang P, Morton K, Fecho K, Tropsha A. ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources. J Chem Inf Model 2019; 59:4968-4973. [DOI: 10.1021/acs.jcim.9b00683] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Chris Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27517, United States
| | - Steven Cox
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27517, United States
| | - James Balhoff
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27517, United States
| | - Yaphet Kebede
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27517, United States
| | - Patrick Wang
- CoVar Applied Technologies, Durham, North Carolina 27701, United States
| | - Kenneth Morton
- CoVar Applied Technologies, Durham, North Carolina 27701, United States
| | - Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27517, United States
| | - Alexander Tropsha
- School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
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32
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Bergom C, West CM, Higginson DS, Abazeed ME, Arun B, Bentzen SM, Bernstein JL, Evans JD, Gerber NK, Kerns SL, Keen J, Litton JK, Reiner AS, Riaz N, Rosenstein BS, Sawakuchi GO, Shaitelman SF, Powell SN, Woodward WA. The Implications of Genetic Testing on Radiation Therapy Decisions: A Guide for Radiation Oncologists. Int J Radiat Oncol Biol Phys 2019; 105:698-712. [PMID: 31381960 DOI: 10.1016/j.ijrobp.2019.07.026] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 06/21/2019] [Accepted: 07/08/2019] [Indexed: 02/06/2023]
Abstract
The advent of affordable and rapid next-generation DNA sequencing technology, along with the US Supreme Court ruling invalidating gene patents, has led to a deluge of germline and tumor genetic variant tests that are being rapidly incorporated into clinical cancer decision-making. A major concern for clinicians is whether the presence of germline mutations may increase the risk of radiation toxicity or secondary malignancies. Because scarce clinical data exist to inform decisions at this time, the American Society for Radiation Oncology convened a group of radiation science experts and clinicians to summarize potential issues, review relevant data, and provide guidance for adult patients and their care teams regarding the impact, if any, that genetic testing should have on radiation therapy recommendations. During the American Society for Radiation Oncology workshop, several main points emerged, which are discussed in this manuscript: (1) variants of uncertain significance should be considered nondeleterious until functional genomic data emerge to demonstrate otherwise; (2) possession of germline alterations in a single copy of a gene critical for radiation damage responses does not necessarily equate to increased risk of radiation-induced toxicity; (3) deleterious ataxia-telangiesctasia gene mutations may modestly increase second cancer risk after radiation therapy, and thus follow-up for these patients after indicated radiation therapy should include second cancer screening; (4) conveying to patients the difference between relative and absolute risk is critical to decision-making; and (5) more work is needed to assess the impact of tumor somatic alterations on the probability of response to radiation therapy and the potential for individualization of radiation doses. Data on radiosensitivity related to specific genetic mutations is also briefly discussed.
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Affiliation(s)
- Carmen Bergom
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Catharine M West
- Division of Cancer Sciences, National Institute for Health Research Manchester Biomedical Research Centre, University of Manchester, Christie National Health Service Foundation Trust Hospital, Manchester, UK
| | - Daniel S Higginson
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mohamed E Abazeed
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio; Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, Ohio
| | - Banu Arun
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Soren M Bentzen
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
| | - Jonine L Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jaden D Evans
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota; Department of Radiation Oncology and Precision Genomics, Intermountain Healthcare, Ogden, Utah
| | - Naamit K Gerber
- Department of Radiation Oncology, New York University Langone Health, New York, New York
| | - Sarah L Kerns
- Department of Radiation Oncology, University of Rochester, Rochester, New York
| | - Judy Keen
- Scientific Affairs, American Society for Radiation Oncology, Arlington, Virginia
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Anne S Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Barry S Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gabriel O Sawakuchi
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Simona F Shaitelman
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Simon N Powell
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wendy A Woodward
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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33
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Maroilley T, Tarailo-Graovac M. Uncovering Missing Heritability in Rare Diseases. Genes (Basel) 2019; 10:E275. [PMID: 30987386 PMCID: PMC6523881 DOI: 10.3390/genes10040275] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/29/2019] [Accepted: 04/01/2019] [Indexed: 12/14/2022] Open
Abstract
The problem of 'missing heritability' affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The 'missing heritability' concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases-the majority of RDs are also facing the 'missing heritability' problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources.
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Affiliation(s)
- Tatiana Maroilley
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
| | - Maja Tarailo-Graovac
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
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Pawliczek P, Patel RY, Ashmore LR, Jackson AR, Bizon C, Nelson T, Powell B, Freimuth RR, Strande N, Shah N, Paithankar S, Wright MW, Dwight S, Zhen J, Landrum M, McGarvey P, Babb L, Plon SE, Milosavljevic A. ClinGen Allele Registry links information about genetic variants. Hum Mutat 2018; 39:1690-1701. [PMID: 30311374 PMCID: PMC6519371 DOI: 10.1002/humu.23637] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 08/01/2018] [Accepted: 08/28/2018] [Indexed: 11/18/2022]
Abstract
Effective exchange of information about genetic variants is currently hampered by the lack of readily available globally unique variant identifiers that would enable aggregation of information from different sources. The ClinGen Allele Registry addresses this problem by providing (1) globally unique "canonical" variant identifiers (CAids) on demand, either individually or in large batches; (2) access to variant-identifying information in a searchable Registry; (3) links to allele-related records in many commonly used databases; and (4) services for adding links to information about registered variants in external sources. A core element of the Registry is a canonicalization service, implemented using in-memory sequence alignment-based index, which groups variant identifiers denoting the same nucleotide variant and assigns unique and dereferenceable CAids. More than 650 million distinct variants are currently registered, including those from gnomAD, ExAC, dbSNP, and ClinVar, including a small number of variants registered by Registry users. The Registry is accessible both via a web interface and programmatically via well-documented Hypertext Transfer Protocol (HTTP) Representational State Transfer Application Programming Interface (REST-APIs). For programmatic interoperability, the Registry content is accessible in the JavaScript Object Notation for Linked Data (JSON-LD) format. We present several use cases and demonstrate how the linked information may provide raw material for reasoning about variant's pathogenicity.
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Affiliation(s)
- Piotr Pawliczek
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexas
| | - Ronak Y. Patel
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexas
| | - Lillian R. Ashmore
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexas
| | - Andrew R. Jackson
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexas
| | - Chris Bizon
- Renaissance Computing InstituteUniversity of North CarolinaChapel HillNorth Carolina
| | - Tristan Nelson
- Geisinger's Autism and Developmental MedicineLewisburgPennsylvania
| | - Bradford Powell
- Department of GeneticsUniversity of North CarolinaChapel HillNorth Carolina
| | | | - Natasha Strande
- Department of GeneticsUniversity of North CarolinaChapel HillNorth Carolina
| | - Neethu Shah
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexas
| | - Sameer Paithankar
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexas
| | - Matt W. Wright
- Department of Biomedical Data SciencesStanford University School of MedicinePalo AltoCalifornia
| | - Selina Dwight
- Department of Biomedical Data SciencesStanford University School of MedicinePalo AltoCalifornia
| | - Jimmy Zhen
- Department of Biomedical Data SciencesStanford University School of MedicinePalo AltoCalifornia
| | - Melissa Landrum
- National Center for Biotechnology InformationNational Institutes of HealthBethesdaMaryland
| | - Peter McGarvey
- Innovation Center for Biomedical InformaticsGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Larry Babb
- Sunquest Information Systems CompanyBostonMassachusetts
| | - Sharon E. Plon
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexas
- Department of PediatricsBaylor College of Medicine HoustonTexas
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Rehm HL, Berg JS, Plon SE. ClinGen and ClinVar – Enabling Genomics in Precision Medicine. Hum Mutat 2018. [DOI: 10.1002/humu.23654] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Heidi L. Rehm
- Medical and Population Genetics ProgramThe Broad Institute of MIT and Harvard Cambridge Massachusetts
- Center for Genomic MedicineMassachusetts General Hospital Boston Massachusetts
| | - Jonathan S. Berg
- Department of GeneticsUniversity of North Carolina at Chapel Hill Chapel Hill North Carolina
| | - Sharon E. Plon
- Departments of Molecular and Human Genetics and PediatricsBaylor College of Medicine Houston Texas
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