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Ladner JT, Grubaugh ND, Pybus OG, Andersen KG. Precision epidemiology for infectious disease control. Nat Med 2019; 25:206-211. [PMID: 30728537 PMCID: PMC7095960 DOI: 10.1038/s41591-019-0345-2] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 01/03/2019] [Indexed: 12/18/2022]
Abstract
Advances in genomics and computing are transforming the capacity for the characterization of biological systems, and researchers are now poised for a precision-focused transformation in the way they prepare for, and respond to, infectious diseases. This includes the use of genome-based approaches to inform molecular diagnosis and individual-level treatment regimens. In addition, advances in the speed and granularity of pathogen genome generation have improved the capability to track and understand pathogen transmission, leading to potential improvements in the design and implementation of population-level public health interventions. In this Perspective, we outline several trends that are driving the development of precision epidemiology of infectious disease and their implications for scientists' ability to respond to outbreaks.
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Affiliation(s)
- Jason T Ladner
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | | | - Kristian G Andersen
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA.
- Scripps Research Translational Institute, La Jolla, CA, USA.
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2
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Sekine M, Makino T. Inference of Causative Genes for Alzheimer's Disease Due to Dosage Imbalance. Mol Biol Evol 2017; 34:2396-2407. [PMID: 28666362 DOI: 10.1093/molbev/msx183] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Copy number variations (CNVs) have recently drawn attention as an important genetic factor for diseases, especially common neuropsychiatric disorders including Alzheimer's disease (AD). Because most of the pathogenic CNV regions overlap with multiple genes, it has been challenging to identify the true disease-causing genes amongst them. Notably, a recent study reported that CNV regions containing ohnologs, which are dosage-sensitive genes, are likely to be deleterious. Utilizing the unique feature of ohnologs could be useful for identifying causative genes with pathogenic CNVs, however its effectiveness is still unclear. Although it has been reported that AD is strongly affected by CNVs, most of AD-causing genes with pathogenic CNVs have not been identified yet. Here, we show that dosage-sensitive ohnologs within CNV regions reported in patients with AD are related to the nervous system and are highly expressed in the brain, similar to other known susceptible genes for AD. We found that CNV regions in patients with AD contained dosage-sensitive genes, which are ohnologs not overlapping with control CNV regions, frequently. Furthermore, these dosage-sensitive genes in pathogenic CNV regions had a strong enrichment in the nervous system for mouse knockout phenotype and high expression in the brain similar to the known susceptible genes for AD. Our results demonstrated that selecting dosage-sensitive ohnologs out of multiple genes with pathogenic CNVs is effective in identifying the causative genes for AD. This methodology can be applied to other diseases caused by dosage imbalance and might help to establish the medical diagnosis by analysis of CNVs.
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Affiliation(s)
- Mizuka Sekine
- Department of Biology, Faculty of Science, Tohoku University, Sendai, Japan
| | - Takashi Makino
- Department of Ecology and Evolutionary Biology, Graduate School of Life Sciences, Tohoku University, Sendai, Japan
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3
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Epistasis in Neuropsychiatric Disorders. Trends Genet 2017; 33:256-265. [PMID: 28268034 DOI: 10.1016/j.tig.2017.01.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 01/25/2017] [Accepted: 01/27/2017] [Indexed: 12/12/2022]
Abstract
The contribution of epistasis to human disease remains unclear. However, several studies have now identified epistatic interactions between common variants that increase the risk of a neuropsychiatric disorder, while there is growing evidence that genetic interactions contribute to the pathogenicity of rare, multigenic copy-number variants (CNVs) that have been observed in patients. This review discusses the current evidence for epistatic events and genetic interactions in neuropsychiatric disorders, how paradigm shifts in the phenotypic classification of patients would empower the search for epistatic effects, and how network and cellular models might be employed to further elucidate relevant epistatic interactions.
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Chatzimichali EA, Brent S, Hutton B, Perrett D, Wright CF, Bevan AP, Hurles ME, Firth HV, Swaminathan GJ. Facilitating collaboration in rare genetic disorders through effective matchmaking in DECIPHER. Hum Mutat 2015. [PMID: 26220709 PMCID: PMC4832335 DOI: 10.1002/humu.22842] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
DECIPHER (https://decipher.sanger.ac.uk) is a web‐based platform for secure deposition, analysis, and sharing of plausibly pathogenic genomic variants from well‐phenotyped patients suffering from genetic disorders. DECIPHER aids clinical interpretation of these rare sequence and copy‐number variants by providing tools for variant analysis and identification of other patients exhibiting similar genotype–phenotype characteristics. DECIPHER also provides mechanisms to encourage collaboration among a global community of clinical centers and researchers, as well as exchange of information between clinicians and researchers within a consortium, to accelerate discovery and diagnosis. DECIPHER has contributed to matchmaking efforts by enabling the global clinical genetics community to identify many previously undiagnosed syndromes and new disease genes, and has facilitated the publication of over 700 peer‐reviewed scientific publications since 2004. At the time of writing, DECIPHER contains anonymized data from ∼250 registered centers on more than 51,500 patients (∼18000 patients with consent for data sharing and ∼25000 anonymized records shared privately). In this paper, we describe salient features of the platform, with special emphasis on the tools and processes that aid interpretation, sharing, and effective matchmaking with other data held in the database and that make DECIPHER an invaluable clinical and research resource.
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Affiliation(s)
- Eleni A Chatzimichali
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Simon Brent
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Benjamin Hutton
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Daniel Perrett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Caroline F Wright
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Andrew P Bevan
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Helen V Firth
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom.,Cambridge University Department of Medical Genetics, Addenbrooke's Hospital, Cambridge, CB2 2QQ, United Kingdom
| | - Ganesh J Swaminathan
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
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5
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The clustering of functionally related genes contributes to CNV-mediated disease. Genome Res 2015; 25:802-13. [PMID: 25887030 PMCID: PMC4448677 DOI: 10.1101/gr.184325.114] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 04/13/2015] [Indexed: 12/20/2022]
Abstract
Clusters of functionally related genes can be disrupted by a single copy number variant (CNV). We demonstrate that the simultaneous disruption of multiple functionally related genes is a frequent and significant characteristic of de novo CNVs in patients with developmental disorders (P = 1 × 10−3). Using three different functional networks, we identified unexpectedly large numbers of functionally related genes within de novo CNVs from two large independent cohorts of individuals with developmental disorders. The presence of multiple functionally related genes was a significant predictor of a CNV's pathogenicity when compared to CNVs from apparently healthy individuals and a better predictor than the presence of known disease or haploinsufficient genes for larger CNVs. The functionally related genes found in the de novo CNVs belonged to 70% of all clusters of functionally related genes found across the genome. De novo CNVs were more likely to affect functional clusters and affect them to a greater extent than benign CNVs (P = 6 × 10−4). Furthermore, such clusters of functionally related genes are phenotypically informative: Different patients possessing CNVs that affect the same cluster of functionally related genes exhibit more similar phenotypes than expected (P < 0.05). The spanning of multiple functionally similar genes by single CNVs contributes substantially to how these variants exert their pathogenic effects.
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6
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Ibn-Salem J, Köhler S, Love MI, Chung HR, Huang N, Hurles ME, Haendel M, Washington NL, Smedley D, Mungall CJ, Lewis SE, Ott CE, Bauer S, Schofield PN, Mundlos S, Spielmann M, Robinson PN. Deletions of chromosomal regulatory boundaries are associated with congenital disease. Genome Biol 2014; 15:423. [PMID: 25315429 PMCID: PMC4180961 DOI: 10.1186/s13059-014-0423-1] [Citation(s) in RCA: 115] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Accepted: 07/24/2014] [Indexed: 12/21/2022] Open
Abstract
Background Recent data from genome-wide chromosome conformation capture analysis indicate that the human genome is divided into conserved megabase-sized self-interacting regions called topological domains. These topological domains form the regulatory backbone of the genome and are separated by regulatory boundary elements or barriers. Copy-number variations can potentially alter the topological domain architecture by deleting or duplicating the barriers and thereby allowing enhancers from neighboring domains to ectopically activate genes causing misexpression and disease, a mutational mechanism that has recently been termed enhancer adoption. Results We use the Human Phenotype Ontology database to relate the phenotypes of 922 deletion cases recorded in the DECIPHER database to monogenic diseases associated with genes in or adjacent to the deletions. We identify combinations of tissue-specific enhancers and genes adjacent to the deletion and associated with phenotypes in the corresponding tissue, whereby the phenotype matched that observed in the deletion. We compare this computationally with a gene-dosage pathomechanism that attempts to explain the deletion phenotype based on haploinsufficiency of genes located within the deletions. Up to 11.8% of the deletions could be best explained by enhancer adoption or a combination of enhancer adoption and gene-dosage effects. Conclusions Our results suggest that enhancer adoption caused by deletions of regulatory boundaries may contribute to a substantial minority of copy-number variation phenotypes and should thus be taken into account in their medical interpretation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0423-1) contains supplementary material, which is available to authorized users.
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MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J, Abecasis GR, Adams DR, Altman RB, Antonarakis SE, Ashley EA, Barrett JC, Biesecker LG, Conrad DF, Cooper GM, Cox NJ, Daly MJ, Gerstein MB, Goldstein DB, Hirschhorn JN, Leal SM, Pennacchio LA, Stamatoyannopoulos JA, Sunyaev SR, Valle D, Voight BF, Winckler W, Gunter C. Guidelines for investigating causality of sequence variants in human disease. Nature 2014; 508:469-76. [PMID: 24759409 PMCID: PMC4180223 DOI: 10.1038/nature13127] [Citation(s) in RCA: 940] [Impact Index Per Article: 94.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Accepted: 02/05/2014] [Indexed: 11/26/2022]
Abstract
The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.
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Affiliation(s)
- D G MacArthur
- 1] Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA [2] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - T A Manolio
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - D P Dimmock
- Division of Genetics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
| | - H L Rehm
- 1] Laboratory for Molecular Medicine, Partners Healthcare Center for Personalized Genetic Medicine, Cambridge, Massachusetts 02139, USA [2] Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - J Shendure
- Department of Genome Sciences, University of Washington, Seattle, Washington 98115, USA
| | - G R Abecasis
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - D R Adams
- 1] NIH Undiagnosed Diseases Program, National Institutes of Health Office of Rare Diseases Research and National Human Genome Research Institute, Bethesda, Maryland 20892, USA [2] Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - R B Altman
- Departments of Bioengineering & Genetics, Stanford University, Stanford, California 94305, USA
| | - S E Antonarakis
- 1] Department of Genetic Medicine, University of Geneva Medical School, 1211 Geneva, Switzerland [2] iGE3 Institute of Genetics and Genomics of Geneva, 1211 Geneva, Switzerland
| | - E A Ashley
- Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, California 94305, USA
| | - J C Barrett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK
| | - L G Biesecker
- Genetic Disease Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland 20892, USA
| | - D F Conrad
- Departments of Genetics, Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - G M Cooper
- HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, Alabama 35806, USA
| | - N J Cox
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA
| | - M J Daly
- 1] Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA [2] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - M B Gerstein
- 1] Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA [2] Departments of Computer Science, Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA
| | - D B Goldstein
- Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA
| | - J N Hirschhorn
- 1] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA [2] Divisions of Genetics and Endocrinology, Children's Hospital, Boston, Massachusetts 02115, USA
| | - S M Leal
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - L A Pennacchio
- 1] Genomics Division, MS 84-171, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] US Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA
| | - J A Stamatoyannopoulos
- Department of Genome Sciences, University of Washington, 1705 Northeast Pacific Street, Seattle, Washington 98195, USA
| | - S R Sunyaev
- 1] Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA [2] Harvard Medical School, Boston, Massachusetts 02115, USA
| | - D Valle
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - B F Voight
- Department of Pharmacology and Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - W Winckler
- 1] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA [2] Next Generation Diagnostics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA (W.W.); Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia 30329, USA (C.G.)
| | - C Gunter
- 1] HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, Alabama 35806, USA [2] Next Generation Diagnostics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA (W.W.); Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia 30329, USA (C.G.)
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8
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Abstract
The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.
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Affiliation(s)
- Peter N. Robinson
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Brandenburg Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Planck Institute for Molecular Genetics, Berlin, Germany
- Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
- * E-mail: (PNR); (CW)
| | - Caleb Webber
- MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
- * E-mail: (PNR); (CW)
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9
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Abstract
A number of rare copy number variants (CNVs), including both deletions and duplications, have been associated with developmental disorders, including schizophrenia, autism, intellectual disability, and epilepsy. Pathogenicity may derive from dosage sensitivity of one or more genes contained within the CNV locus. To understand pathophysiology, the specific disease-causing gene(s) within each CNV need to be identified. In the present study, we test the hypothesis that ohnologs (genes retained after ancestral whole-genome duplication events, which are frequently dosage sensitive) are overrepresented in pathogenic CNVs. We selected three sets of genes implicated in copy number pathogenicity: (i) genes mapping within rare disease-associated CNVs, (ii) genes within de novo CNVs under negative genetic selection, and (iii) genes identified by clinical array comparative genome hybridization studies as potentially pathogenic. We compared the proportion of ohnologs between these gene sets and control genes, mapping to CNVs not known to be disease associated. We found that ohnologs are significantly overrepresented in genes mapping to pathogenic CNVs, irrespective of how CNVs were identified, with over 90% containing an ohnolog, compared with control CNVs >100 kb, where only about 30% contained an ohnolog. In some CNVs, such as del15p11.2 (CYFIP1) and dup/del16p13.11 (NDE1), the most plausible prior candidate gene was also an ohnolog, as were the genes VIPR2 and NRXN1, each found in short CNVs containing no other genes. Our results support the hypothesis that ohnologs represent critical dosage-sensitive elements of the genome, possibly responsible for some of the deleterious phenotypes observed for pathogenic CNVs and as such are readily identifiable candidate genes for further study.
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10
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Bragin E, Chatzimichali EA, Wright CF, Hurles ME, Firth HV, Bevan AP, Swaminathan GJ. DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation. Nucleic Acids Res 2013; 42:D993-D1000. [PMID: 24150940 PMCID: PMC3965078 DOI: 10.1093/nar/gkt937] [Citation(s) in RCA: 160] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The DECIPHER database (https://decipher.sanger.ac.uk/) is an accessible online repository of genetic variation with associated phenotypes that facilitates the identification and interpretation of pathogenic genetic variation in patients with rare disorders. Contributing to DECIPHER is an international consortium of >200 academic clinical centres of genetic medicine and ≥1600 clinical geneticists and diagnostic laboratory scientists. Information integrated from a variety of bioinformatics resources, coupled with visualization tools, provides a comprehensive set of tools to identify other patients with similar genotype–phenotype characteristics and highlights potentially pathogenic genes. In a significant development, we have extended DECIPHER from a database of just copy-number variants to allow upload, annotation and analysis of sequence variants such as single nucleotide variants (SNVs) and InDels. Other notable developments in DECIPHER include a purpose-built, customizable and interactive genome browser to aid combined visualization and interpretation of sequence and copy-number variation against informative datasets of pathogenic and population variation. We have also introduced several new features to our deposition and analysis interface. This article provides an update to the DECIPHER database, an earlier instance of which has been described elsewhere [Swaminathan et al. (2012) DECIPHER: web-based, community resource for clinical interpretation of rare variants in developmental disorders. Hum. Mol. Genet., 21, R37–R44].
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Affiliation(s)
- Eugene Bragin
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Cambridge University Department of Medical Genetics, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK
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11
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Vulto-van Silfhout AT, van Ravenswaaij CMA, Hehir-Kwa JY, Verwiel ETP, Dirks R, van Vooren S, Schinzel A, de Vries BBA, de Leeuw N. An update on ECARUCA, the European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations. Eur J Med Genet 2013; 56:471-4. [PMID: 23851227 DOI: 10.1016/j.ejmg.2013.06.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2013] [Accepted: 06/25/2013] [Indexed: 10/26/2022]
Abstract
The European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations (ECARUCA, www.ecaruca.net) is an online database initiated in 2003 that collects and provides detailed, curated clinical and molecular information on rare unbalanced chromosome aberrations. ECARUCA now contains over 4800 cases with a total of more than 6600 genetic aberrations and has over 3000 account holders worldwide. Recently, the ECARUCA web site was renewed, including the presentation of interesting case reports in collaboration with the European Journal of Medical Genetics. This article gives an overview of the current status and future plans of the online ECARUCA database.
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12
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Baynam G, Walters M, Claes P, Kung S, LeSouef P, Dawkins H, Gillett D, Goldblatt J. The facial evolution: looking backward and moving forward. Hum Mutat 2012; 34:14-22. [PMID: 23033261 DOI: 10.1002/humu.22219] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2012] [Accepted: 08/30/2012] [Indexed: 01/16/2023]
Abstract
Three-dimensional (3D) facial analysis is ideal for high-resolution, nonionizing, noninvasive objective, high-throughput phenotypic, and phenomic studies. It is a natural complement to (epi)genetic technologies to facilitate advances in the understanding of rare and common diseases. The face is uniquely reflective of the primordial tissues, and there is evidence supporting the application of 3D facial analysis to the investigation of variation and disease including studies showing that the face can reflect systemic health, provides diagnostic clues to disorders, and that facial variation reflects biological pathways. In addition, facial variation has been related to evolutionary factors. The purpose of this review is to look backward to suggest that knowledge of human evolution supports, and may instruct, the application and interpretation of studies of facial morphology for documentation of human variation and investigation of its relationships with health and disease. Furthermore, in the context of advances of deep phenotyping and data integration, to look forward to suggest approaches to scalable implementation of facial analysis, and to suggest avenues for future research and clinical application of this technology.
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Affiliation(s)
- Gareth Baynam
- Genetic Services of Western Australia, Princess Margaret and King Edward Memorial Hospitals, Perth, Australia
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13
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Doelken SC, Köhler S, Mungall CJ, Gkoutos GV, Ruef BJ, Smith C, Smedley D, Bauer S, Klopocki E, Schofield PN, Westerfield M, Robinson PN, Lewis SE. Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish. Dis Model Mech 2012; 6:358-72. [PMID: 23104991 PMCID: PMC3597018 DOI: 10.1242/dmm.010322] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Numerous disease syndromes are associated with regions of copy number variation (CNV) in the human genome and, in most cases, the pathogenicity of the CNV is thought to be related to altered dosage of the genes contained within the affected segment. However, establishing the contribution of individual genes to the overall pathogenicity of CNV syndromes is difficult and often relies on the identification of potential candidates through manual searches of the literature and online resources. We describe here the development of a computational framework to comprehensively search phenotypic information from model organisms and single-gene human hereditary disorders, and thus speed the interpretation of the complex phenotypes of CNV disorders. There are currently more than 5000 human genes about which nothing is known phenotypically but for which detailed phenotypic information for the mouse and/or zebrafish orthologs is available. Here, we present an ontology-based approach to identify similarities between human disease manifestations and the mutational phenotypes in characterized model organism genes; this approach can therefore be used even in cases where there is little or no information about the function of the human genes. We applied this algorithm to detect candidate genes for 27 recurrent CNV disorders and identified 802 gene-phenotype associations, approximately half of which involved genes that were previously reported to be associated with individual phenotypic features and half of which were novel candidates. A total of 431 associations were made solely on the basis of model organism phenotype data. Additionally, we observed a striking, statistically significant tendency for individual disease phenotypes to be associated with multiple genes located within a single CNV region, a phenomenon that we denote as pheno-clustering. Many of the clusters also display statistically significant similarities in protein function or vicinity within the protein-protein interaction network. Our results provide a basis for understanding previously un-interpretable genotype-phenotype correlations in pathogenic CNVs and for mobilizing the large amount of model organism phenotype data to provide insights into human genetic disorders.
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Affiliation(s)
- Sandra C Doelken
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
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14
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Andrews T, Webber C. Characterizing epistatic hotspots of human disease. BMC Proc 2012. [PMCID: PMC3467678 DOI: 10.1186/1753-6561-6-s6-o12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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15
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Swaminathan GJ, Bragin E, Chatzimichali EA, Corpas M, Bevan AP, Wright CF, Carter NP, Hurles ME, Firth HV. DECIPHER: web-based, community resource for clinical interpretation of rare variants in developmental disorders. Hum Mol Genet 2012; 21:R37-44. [PMID: 22962312 DOI: 10.1093/hmg/dds362] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Patients with developmental disorders often harbour sub-microscopic deletions or duplications that lead to a disruption of normal gene expression or perturbation in the copy number of dosage-sensitive genes. Clinical interpretation for such patients in isolation is hindered by the rarity and novelty of such disorders. The DECIPHER project (https://decipher.sanger.ac.uk) was established in 2004 as an accessible online repository of genomic and associated phenotypic data with the primary goal of aiding the clinical interpretation of rare copy-number variants (CNVs). DECIPHER integrates information from a variety of bioinformatics resources and uses visualization tools to identify potential disease genes within a CNV. A two-tier access system permits clinicians and clinical scientists to maintain confidential linked anonymous records of phenotypes and CNVs for their patients that, with informed consent, can subsequently be shared with the wider clinical genetics and research communities. Advances in next-generation sequencing technologies are making it practical and affordable to sequence the whole exome/genome of patients who display features suggestive of a genetic disorder. This approach enables the identification of smaller intragenic mutations including single-nucleotide variants that are not accessible even with high-resolution genomic array analysis. This article briefly summarizes the current status and achievements of the DECIPHER project and looks ahead to the opportunities and challenges of jointly analysing structural and sequence variation in the human genome.
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Affiliation(s)
- Ganesh J Swaminathan
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
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Abstract
In medical contexts, the word "phenotype" is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation.
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Affiliation(s)
- Peter N Robinson
- Institut für Medizinische Genetik und Humangenetik, Charité-Universitätsmedizin Berlin, Berlin, Germany.
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