Structural Variation in the 3D Genome
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Prevalence and Incidence of Rare Diseases: Bibliographic Data
Number 1 | January 2019 Prevalence and incidence of rare diseases: Bibliographic data Prevalence, incidence or number of published cases listed by diseases (in alphabetical order) www.orpha.net www.orphadata.org If a range of national data is available, the average is Methodology calculated to estimate the worldwide or European prevalence or incidence. When a range of data sources is available, the most Orphanet carries out a systematic survey of literature in recent data source that meets a certain number of quality order to estimate the prevalence and incidence of rare criteria is favoured (registries, meta-analyses, diseases. This study aims to collect new data regarding population-based studies, large cohorts studies). point prevalence, birth prevalence and incidence, and to update already published data according to new For congenital diseases, the prevalence is estimated, so scientific studies or other available data. that: Prevalence = birth prevalence x (patient life This data is presented in the following reports published expectancy/general population life expectancy). biannually: When only incidence data is documented, the prevalence is estimated when possible, so that : • Prevalence, incidence or number of published cases listed by diseases (in alphabetical order); Prevalence = incidence x disease mean duration. • Diseases listed by decreasing prevalence, incidence When neither prevalence nor incidence data is available, or number of published cases; which is the case for very rare diseases, the number of cases or families documented in the medical literature is Data collection provided. A number of different sources are used : Limitations of the study • Registries (RARECARE, EUROCAT, etc) ; The prevalence and incidence data presented in this report are only estimations and cannot be considered to • National/international health institutes and agencies be absolutely correct. -
Orphanet Report Series Rare Diseases Collection
Marche des Maladies Rares – Alliance Maladies Rares Orphanet Report Series Rare Diseases collection DecemberOctober 2013 2009 List of rare diseases and synonyms Listed in alphabetical order www.orpha.net 20102206 Rare diseases listed in alphabetical order ORPHA ORPHA ORPHA Disease name Disease name Disease name Number Number Number 289157 1-alpha-hydroxylase deficiency 309127 3-hydroxyacyl-CoA dehydrogenase 228384 5q14.3 microdeletion syndrome deficiency 293948 1p21.3 microdeletion syndrome 314655 5q31.3 microdeletion syndrome 939 3-hydroxyisobutyric aciduria 1606 1p36 deletion syndrome 228415 5q35 microduplication syndrome 2616 3M syndrome 250989 1q21.1 microdeletion syndrome 96125 6p subtelomeric deletion syndrome 2616 3-M syndrome 250994 1q21.1 microduplication syndrome 251046 6p22 microdeletion syndrome 293843 3MC syndrome 250999 1q41q42 microdeletion syndrome 96125 6p25 microdeletion syndrome 6 3-methylcrotonylglycinuria 250999 1q41-q42 microdeletion syndrome 99135 6-phosphogluconate dehydrogenase 67046 3-methylglutaconic aciduria type 1 deficiency 238769 1q44 microdeletion syndrome 111 3-methylglutaconic aciduria type 2 13 6-pyruvoyl-tetrahydropterin synthase 976 2,8 dihydroxyadenine urolithiasis deficiency 67047 3-methylglutaconic aciduria type 3 869 2A syndrome 75857 6q terminal deletion 67048 3-methylglutaconic aciduria type 4 79154 2-aminoadipic 2-oxoadipic aciduria 171829 6q16 deletion syndrome 66634 3-methylglutaconic aciduria type 5 19 2-hydroxyglutaric acidemia 251056 6q25 microdeletion syndrome 352328 3-methylglutaconic -
Copyrighted Material
Part 1 General Dermatology GENERAL DERMATOLOGY COPYRIGHTED MATERIAL Handbook of Dermatology: A Practical Manual, Second Edition. Margaret W. Mann and Daniel L. Popkin. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd. 0004285348.INDD 1 7/31/2019 6:12:02 PM 0004285348.INDD 2 7/31/2019 6:12:02 PM COMMON WORK-UPS, SIGNS, AND MANAGEMENT Dermatologic Differential Algorithm Courtesy of Dr. Neel Patel 1. Is it a rash or growth? AND MANAGEMENT 2. If it is a rash, is it mainly epidermal, dermal, subcutaneous, or a combination? 3. If the rash is epidermal or a combination, try to define the SIGNS, COMMON WORK-UPS, characteristics of the rash. Is it mainly papulosquamous? Papulopustular? Blistering? After defining the characteristics, then think about causes of that type of rash: CITES MVA PITA: Congenital, Infections, Tumor, Endocrinologic, Solar related, Metabolic, Vascular, Allergic, Psychiatric, Latrogenic, Trauma, Autoimmune. When generating the differential, take the history and location of the rash into account. 4. If the rash is dermal or subcutaneous, then think of cells and substances that infiltrate and associated diseases (histiocytes, lymphocytes, mast cells, neutrophils, metastatic tumors, mucin, amyloid, immunoglobulin, etc.). 5. If the lesion is a growth, is it benign or malignant in appearance? Think of cells in the skin and their associated diseases (keratinocytes, fibroblasts, neurons, adipocytes, melanocytes, histiocytes, pericytes, endothelial cells, smooth muscle cells, follicular cells, sebocytes, eccrine -
Blueprint Genetics Comprehensive Growth Disorders / Skeletal
Comprehensive Growth Disorders / Skeletal Dysplasias and Disorders Panel Test code: MA4301 Is a 374 gene panel that includes assessment of non-coding variants. This panel covers the majority of the genes listed in the Nosology 2015 (PMID: 26394607) and all genes in our Malformation category that cause growth retardation, short stature or skeletal dysplasia and is therefore a powerful diagnostic tool. It is ideal for patients suspected to have a syndromic or an isolated growth disorder or a skeletal dysplasia. About Comprehensive Growth Disorders / Skeletal Dysplasias and Disorders This panel covers a broad spectrum of diseases associated with growth retardation, short stature or skeletal dysplasia. Many of these conditions have overlapping features which can make clinical diagnosis a challenge. Genetic diagnostics is therefore the most efficient way to subtype the diseases and enable individualized treatment and management decisions. Moreover, detection of causative mutations establishes the mode of inheritance in the family which is essential for informed genetic counseling. For additional information regarding the conditions tested on this panel, please refer to the National Organization for Rare Disorders and / or GeneReviews. Availability 4 weeks Gene Set Description Genes in the Comprehensive Growth Disorders / Skeletal Dysplasias and Disorders Panel and their clinical significance Gene Associated phenotypes Inheritance ClinVar HGMD ACAN# Spondyloepimetaphyseal dysplasia, aggrecan type, AD/AR 20 56 Spondyloepiphyseal dysplasia, Kimberley -
Mixed Ancestry Analysis of Whole-Genome Sequencing Reveals
medRxiv preprint doi: https://doi.org/10.1101/2021.08.09.21261801; this version posted August 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Mixed ancestry analysis of whole-genome sequencing reveals common, rare, and structural variants associated with posterior urethral valves Melanie MY Chan,1 Omid Sadeghi-Alavijeh,1 Horia C Stanescu,1 Catalin D Voinescu,1 Glenda M Beaman,2,3 Marcin Zaniew,4 Stefanie Weber,5 Alina C Hilger,6,7 William G Newman,2,3 Adrian S Woolf,8,9 John O Connolly,1,10 Dan Wood,10 Alexander Stuckey,11 Athanasios Kousathanas,11 Genomics England Research Consortium,11 Robert Kleta,1,12 Detlef Bockenhauer,1,12 Adam P Levine,1,13 and Daniel P Gale1* 1Department of Renal Medicine, University College London, London, NW3 2PF, UK. 2Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK. 3Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, M13 9PL, UK. 4Department of Pediatrics, University of Zielona Góra, 56-417 Zielona Góra, Poland. 5Department of Pediatric Nephrology, University of Marburg, 35037 Marburg, Germany. 6Children’s Hospital, University of Bonn, 53113 Bonn, Germany. 7Institute of Human Genetics, University of Bonn, 53127 Bonn, Germany. 1 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. -
Comparison of Structural and Short Variants Detectedby Linked-Read
cancers Article Comparison of Structural and Short Variants Detected by Linked-Read and Whole-Exome Sequencing in Multiple Myeloma Ashwini Kumar 1,2 , Sadiksha Adhikari 1,2, Matti Kankainen 2,3,4,5 and Caroline A. Heckman 1,2,* 1 Institute for Molecular Medicine Finland-FIMM, HiLIFE-Helsinki Institute of Life Science, iCAN Digital Cancer Medicine Flagship, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland; ashwini.kumar@helsinki.fi (A.K.); sadiksha.adhikari@helsinki.fi (S.A.) 2 iCAN Digital Precision Cancer Medicine, University of Helsinki, 00014 Helsinki, Finland; matti.kankainen@helsinki.fi 3 Medical and Clinical Genetics, University of Helsinki, Helsinki University Hospital, 00029 Helsinki, Finland 4 Translational Immunology Research Program and Department of Clinical Chemistry, University of Helsinki, 00290 Helsinki, Finland 5 Hematology Research Unit Helsinki, Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland * Correspondence: caroline.heckman@helsinki.fi; Tel.: +358-29-412-5769 Simple Summary: The wide variety of next-generation sequencing technologies requires thorough evaluation and understanding of their advantages and shortcomings of these different approaches prior to their implementation in a precision medicine setting. Here, we compared the performance of two DNA sequencing methods, whole-exome and linked-read exome sequencing, to detect large Citation: Kumar, A.; Adhikari, S.; structural variants (SVs) and short variants in eight multiple myeloma (MM) patient cases. For three Kankainen, M.; Heckman, C.A. patient cases, matched tumor-normal samples were sequenced with both methods to compare somatic Comparison of Structural and Short SVs and short variants. The methods’ clinical relevance was also evaluated, and their sensitivity Variants Detected by Linked-Read and specificity to detect MM-specific cytogenetic alterations and other short variants were measured. -
How Important Are Structural Variants for Speciation?
G C A T T A C G G C A T genes Review How Important Are Structural Variants for Speciation? Linyi Zhang 1,*, Radka Reifová 2, Zuzana Halenková 2 and Zachariah Gompert 1 1 Department of Biology, Utah State University, Logan, UT 84322, USA; [email protected] 2 Department of Zoology, Faculty of Science, Charles University, 12800 Prague, Czech Republic; [email protected] (R.R.); [email protected] (Z.H.) * Correspondence: [email protected] Abstract: Understanding the genetic basis of reproductive isolation is a central issue in the study of speciation. Structural variants (SVs); that is, structural changes in DNA, including inversions, translocations, insertions, deletions, and duplications, are common in a broad range of organisms and have been hypothesized to play a central role in speciation. Recent advances in molecular and statistical methods have identified structural variants, especially inversions, underlying ecologically important traits; thus, suggesting these mutations contribute to adaptation. However, the contribu- tion of structural variants to reproductive isolation between species—and the underlying mechanism by which structural variants most often contribute to speciation—remain unclear. Here, we review (i) different mechanisms by which structural variants can generate or maintain reproductive isolation; (ii) patterns expected with these different mechanisms; and (iii) relevant empirical examples of each. We also summarize the available sequencing and bioinformatic methods to detect structural variants. Lastly, we suggest empirical approaches and new research directions to help obtain a more complete assessment of the role of structural variants in speciation. Citation: Zhang, L.; Reifová, R.; Keywords: reproductive isolation; hybridization; suppressed recombination Halenková, Z.; Gompert, Z. -
Structural Variation: the Genome’S Hidden Architecture Monya Baker
TECHNOLOGY FEATURE Uncovering variants 134 Calling for more algorithms 135 Box 1: Getting a bigger picture 136 Structural variation: the genome’s hidden architecture Monya Baker Next-generation sequencing is uncovering more variants than ever before, but it also faces limitations. The Austrian monk Gregor Mendel may tend to take dip- number of nucleotides, structural varia- have founded the science of genetics, but his loidy as the default. tion accounts for more differences between ideas now limit genomic studies, according to Even microarrays human genomes than the more extensively Jim Lupski, a molecular geneticist at Baylor designed to assess studied single-nucleotide differences. A 2010 College of Medicine in Texas. “Mendelian copy number gen- study estimated that such “non-SNP varia- thinking is to genetics as Newtonian think- erally assume that tion” totaled about 50 megabases per human ing is to physics. We saw a whole new world most individuals genome4. when Einstein came along.” carry exactly two Conditions including autism, schizophre- According to Mendelian principles, indi- copies of any partic- nia and Crohn’s disease have all been associ- viduals inherit exactly two copies of each ular region, which ated with structural variation. And uncov- gene—one from each parent. Genes on sex Next-generation can throw off some ering structural variation will be essential chromosomes have long been recognized as sequencing is revealing calculations. for understanding heterogeneity within exceptions, but genetic deletions and duplica- new variation, but With the excep- tumors, says Jan Korbel, who studies struc- tions also break the rules, and in ways that are it won’t be able to tions of very large tural variation at the European Molecular find everything, much harder to track. -
Neurogenomics Coursework (PDF)
Course Curriculum for the Interdisciplinary Graduate Certificate in Neurogenomics This list reflects the currently approved course curriculum for the Interdisciplinary Graduate Certificate in Neurogenomics. Students must complete a total of at least 16 units of coursework including at least 4 units from each of three (3) fields; Neurology, Genetics/Genomics, and Bioinformatics/Computational/Data Analysis. Note: Due to overlapping subject matter, the same course may be listed within multiple fields and may be offered through different departments under a different course number. As students may enter this certificate program via different graduate programs, all such overlapping courses are listed here for completeness. Neurology-Related Courses Neuroscience Graduate Courses M201. Cell, Developmental, and Molecular Neurobiology. (6) Lecture, six hours. Fundamental topics concerning cellular, developmental, and molecular neurobiology, including intracellular signaling, cell-cell communication, neurogenesis and migration, synapse formation and elimination, programmed neuronal death, and neurotropic factors. Letter grading. M203. Anatomy of Central Nervous System. (4) (Same as Bioengineering M263.) Lecture, 75 minutes; discussion/laboratory, two hours. Prior to first laboratory meeting, students must complete Bloodborne Pathogens training course through UCLA Environment, Health and Safety. Study of anatomical locations of and relationships between ascending and descending sensory and motor systems from spinal cord to cerebral cortex. Covers -
A Structural Variation Reference for Medical and Population Genetics
Article A structural variation reference for medical and population genetics https://doi.org/10.1038/s41586-020-2287-8 Ryan L. Collins1,2,3,158, Harrison Brand1,2,4,158, Konrad J. Karczewski1,5, Xuefang Zhao1,2,4, Jessica Alföldi1,5, Laurent C. Francioli1,5,6, Amit V. Khera1,2, Chelsea Lowther1,2,4, Received: 2 March 2019 Laura D. Gauthier1,7, Harold Wang1,2, Nicholas A. Watts1,5, Matthew Solomonson1,5, Accepted: 31 March 2020 Anne O’Donnell-Luria1,5, Alexander Baumann7, Ruchi Munshi7, Mark Walker1,7, Christopher W. Whelan7, Yongqing Huang7, Ted Brookings7, Ted Sharpe7, Matthew R. Stone1,2, Published online: 27 May 2020 Elise Valkanas1,2,3, Jack Fu1,2,4, Grace Tiao1,5, Kristen M. Laricchia1,5, Valentin Ruano-Rubio7, Open access Christine Stevens1, Namrata Gupta1, Caroline Cusick1, Lauren Margolin1, Genome Aggregation Database Production Team*, Genome Aggregation Database Consortium*, Check for updates Kent D. Taylor8, Henry J. Lin8, Stephen S. Rich9, Wendy S. Post10, Yii-Der Ida Chen8, Jerome I. Rotter8, Chad Nusbaum1,155, Anthony Philippakis7, Eric Lander1,11,12, Stacey Gabriel1, Benjamin M. Neale1,2,5,13, Sekar Kathiresan1,2,6,14, Mark J. Daly1,2,5,13, Eric Banks7, Daniel G. MacArthur1,2,5,6,156,157 & Michael E. Talkowski1,2,4,13 ✉ Structural variants (SVs) rearrange large segments of DNA1 and can have profound consequences in evolution and human disease2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD)4 have become integral in the interpretation of single-nucleotide variants (SNVs)5. -
Revealing the Impact of Structural Variants in Multiple Myeloma
RESEARCH ARTICLE Revealing the Impact of Structural Variants in Multiple Myeloma Even H. Rustad1, Venkata D. Yellapantula1, Dominik Glodzik2, Kylee H. Maclachlan1, Benjamin Diamond1, Eileen M. Boyle3, Cody Ashby4, Patrick Blaney3, Gunes Gundem2, Malin Hultcrantz1, Daniel Leongamornlert5, Nicos Angelopoulos5,6, Luca Agnelli7, Daniel Auclair8, Yanming Zhang9, Ahmet Dogan10, Niccolò Bolli11,12, Elli Papaemmanuil2, Kenneth C. Anderson13, Philippe Moreau14, Hervé Avet-Loiseau15, Nikhil C. Munshi13,16, Jonathan J. Keats17, Peter J. Campbell5, Gareth J. Morgan3, Ola Landgren1, and Francesco Maura1 Downloaded from https://bloodcancerdiscov.aacrjournals.org by guest on September 24, 2021. Copyright 2020 American Copyright 2020 by AssociationAmerican for Association Cancer Research. for Cancer Research. ABSTRACT The landscape of structural variants (SV) in multiple myeloma remains poorly understood. Here, we performed comprehensive analysis of SVs in a large cohort of 752 patients with multiple myeloma by low-coverage long-insert whole-genome sequencing. We identified 68 SV hotspots involving 17 new candidate driver genes, including the therapeutic targets BCMA (TNFRSF17), SLAM7, and MCL1. Catastrophic complex rearrangements termed chromothripsis were present in 24% of patients and independently associated with poor clinical outcomes. Templated insertions were the second most frequent complex event (19%), mostly involved in super-enhancer hijacking and activation of oncogenes such as CCND1 and MYC. Importantly, in 31% of patients, two or more seemingly independent putative driver events were caused by a single structural event, dem- onstrating that the complex genomic landscape of multiple myeloma can be acquired through few key events during tumor evolutionary history. Overall, this study reveals the critical role of SVs in multiple myeloma pathogenesis. -
Mapping and Characterization of Structural Variation in 17,795 Human Genomes
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nature. Manuscript Author Author manuscript; Manuscript Author available in PMC 2020 November 27. Published in final edited form as: Nature. 2020 July ; 583(7814): 83–89. doi:10.1038/s41586-020-2371-0. Mapping and characterization of structural variation in 17,795 human genomes Haley J. Abel1,2,*, David E. Larson1,2,*, Allison A. Regier1,14, Colby Chiang1, Indraniel Das1, Krishna L. Kanchi1, Ryan M. Layer3,4, Benjamin M. Neale5,6,7, William J. Salerno8, Catherine Reeves9, Steven Buyske10, NHGRI Centers for Common Disease Genomics‡, Tara C. Matise11, Donna M. Muzny8, Michael C. Zody9, Eric S. Lander5,12,13, Susan K. Dutcher1,2, Nathan O. Stitziel1,2,14, Ira M. Hall1,2,14,† 1McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA 2Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA 3BioFrontiers Institute, University of Colorado, Boulder, CO, USA 4Department of Computer Science, University of Colorado, Boulder, CO, USA 5Broad Institute of MIT and Harvard, Cambridge, MA, USA 6Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA 7Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA 8Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA 9New York Genome Center, New York, NY, USA 10Department of Statistics, Rutgers University, Piscataway, NJ, USA 11Department of Genetics, Rutgers University, Piscataway, NJ, USA Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms †to whom correspondence should be addressed.