The Interaction Between Prpf8 and Dzip1 and Its Role in Mitral Valve Prolapse
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Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Identification of Sequence Variants
Wang et al. BMC Medical Genomics (2019) 12:28 https://doi.org/10.1186/s12920-019-0475-x RESEARCH ARTICLE Open Access Identification of sequence variants associated with severe microtia-astresia by targeted sequencing Pu Wang1, Yibei Wang1, Xinmiao Fan1, Yaping Liu2, Yue Fan1, Tao Liu3, Chongjian Chen3, Shuyang Zhang4*† and Xiaowei Chen1*† Abstract Background: Microtia-atresia is characterized by abnormalities of the auricle (microtia) and aplasia or hypoplasia of the external auditory canal, often associated with middle ear abnormalities. To date, no causal genetic mutations or genes have been identified in microtia-atresia patients. Methods: We designed a panel of 131 genes associated with external/middle or inner ear deformity. Targeted genomic capturing combined with next-generation sequencing (NGS) was utilized to screen for mutations in 40 severe microtia-atresia patients. Mutations detected by NGS were filtered and validated. And then mutations were divided into three categories—rare or novel variants, low-frequency variants and common variants—based on their frequency in the public database. The rare or novel mutations were prioritized by pathogenicity analysis. For the low-frequency variants and common variants, we used association studies to explore risk factors of severe microtia-atresia. Results: Sixty-five rare heterozygous mutations of 42 genes were identified in 27 (67.5%) severe microtia-atresia patients. Association studies to determine genes that were potentially pathogenic found that PLEC, USH2A, FREM2, DCHS1, GLI3, POMT1 and GBA genes were significantly associated with severe microtia-atresia. Of these, DCHS1 was strongly suggested to cause severe microtia-atresia as it was identified by both low-frequency and common variants association studies. -
The Genetics of Bipolar Disorder
Molecular Psychiatry (2008) 13, 742–771 & 2008 Nature Publishing Group All rights reserved 1359-4184/08 $30.00 www.nature.com/mp FEATURE REVIEW The genetics of bipolar disorder: genome ‘hot regions,’ genes, new potential candidates and future directions A Serretti and L Mandelli Institute of Psychiatry, University of Bologna, Bologna, Italy Bipolar disorder (BP) is a complex disorder caused by a number of liability genes interacting with the environment. In recent years, a large number of linkage and association studies have been conducted producing an extremely large number of findings often not replicated or partially replicated. Further, results from linkage and association studies are not always easily comparable. Unfortunately, at present a comprehensive coverage of available evidence is still lacking. In the present paper, we summarized results obtained from both linkage and association studies in BP. Further, we indicated new potential interesting genes, located in genome ‘hot regions’ for BP and being expressed in the brain. We reviewed published studies on the subject till December 2007. We precisely localized regions where positive linkage has been found, by the NCBI Map viewer (http://www.ncbi.nlm.nih.gov/mapview/); further, we identified genes located in interesting areas and expressed in the brain, by the Entrez gene, Unigene databases (http://www.ncbi.nlm.nih.gov/entrez/) and Human Protein Reference Database (http://www.hprd.org); these genes could be of interest in future investigations. The review of association studies gave interesting results, as a number of genes seem to be definitively involved in BP, such as SLC6A4, TPH2, DRD4, SLC6A3, DAOA, DTNBP1, NRG1, DISC1 and BDNF. -
Genome-Wide Screen for Differentially Methylated Long Noncoding Rnas
OPEN Oncogene (2017) 36, 6446–6461 www.nature.com/onc ORIGINAL ARTICLE Genome-wide screen for differentially methylated long noncoding RNAs identifies Esrp2 and lncRNA Esrp2-as regulated by enhancer DNA methylation with prognostic relevance for human breast cancer K Heilmann, R Toth, C Bossmann, K Klimo, C Plass and C Gerhauser The majority of long noncoding RNAs (lncRNAs) is still poorly characterized with respect to function, interactions with protein- coding genes, and mechanisms that regulate their expression. As for protein-coding RNAs, epigenetic deregulation of lncRNA expression by alterations in DNA methylation might contribute to carcinogenesis. To provide genome-wide information on lncRNAs aberrantly methylated in breast cancer we profiled tumors of the C3(1) SV40TAg mouse model by MCIp-seq (Methylated CpG Immunoprecipitation followed by sequencing). This approach detected 69 lncRNAs differentially methylated between tumor tissue and normal mammary glands, with 26 located in antisense orientation of a protein-coding gene. One of the hypomethylated lncRNAs, 1810019D21Rik (now called Esrp2-antisense (as)) was identified in proximity to the epithelial splicing regulatory protein 2 (Esrp2) that is significantly elevated in C3(1) tumors. ESRPs were shown previously to have a dual role in carcinogenesis. Both gain and loss have been associated with poor prognosis in human cancers, but the mechanisms regulating expression are not known. In- depth analyses indicate that coordinate overexpression of Esrp2 and Esrp2-as inversely correlates with DNA methylation. Luciferase reporter gene assays support co-expression of Esrp2 and the major short Esrp2-as variant from a bidirectional promoter, and transcriptional regulation by methylation of a proximal enhancer. -
A Systematic Genome-Wide Association Analysis for Inflammatory Bowel Diseases (IBD)
A systematic genome-wide association analysis for inflammatory bowel diseases (IBD) Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Christian-Albrechts-Universität zu Kiel vorgelegt von Dipl.-Biol. ANDRE FRANKE Kiel, im September 2006 Referent: Prof. Dr. Dr. h.c. Thomas C.G. Bosch Koreferent: Prof. Dr. Stefan Schreiber Tag der mündlichen Prüfung: Zum Druck genehmigt: der Dekan “After great pain a formal feeling comes.” (Emily Dickinson) To my wife and family ii Table of contents Abbreviations, units, symbols, and acronyms . vi List of figures . xiii List of tables . .xv 1 Introduction . .1 1.1 Inflammatory bowel diseases, a complex disorder . 1 1.1.1 Pathogenesis and pathophysiology. .2 1.2 Genetics basis of inflammatory bowel diseases . 6 1.2.1 Genetic evidence from family and twin studies . .6 1.2.2 Single nucleotide polymorphisms (SNPs) . .7 1.2.3 Linkage studies . .8 1.2.4 Association studies . 10 1.2.5 Known susceptibility genes . 12 1.2.5.1 CARD15. .12 1.2.5.2 CARD4. .15 1.2.5.3 TNF-α . .15 1.2.5.4 5q31 haplotype . .16 1.2.5.5 DLG5 . .17 1.2.5.6 TNFSF15 . .18 1.2.5.7 HLA/MHC on chromosome 6 . .19 1.2.5.8 Other proposed IBD susceptibility genes . .20 1.2.6 Animal models. 21 1.3 Aims of this study . 23 2 Methods . .24 2.1 Laboratory information management system (LIMS) . 24 2.2 Recruitment. 25 2.3 Sample preparation. 27 2.3.1 DNA extraction from blood. 27 2.3.2 Plate design . -
Mutations in DCHS1 Cause Mitral Valve Prolapse Ronen Durst, Kimberly Sauls, David S
Mutations in DCHS1 cause mitral valve prolapse Ronen Durst, Kimberly Sauls, David S. Peal, Annemarieke Devlaming, Katelynn Toomer, Maire Leyne, Monica Salani, Michael E. Talkowski, Harrison Brand, Maëlle Perrocheau, et al. To cite this version: Ronen Durst, Kimberly Sauls, David S. Peal, Annemarieke Devlaming, Katelynn Toomer, et al.. Mutations in DCHS1 cause mitral valve prolapse. Nature, Nature Publishing Group, 2015, Equipe 3, 525 (7567), pp.109–113. 10.1038/nature14670. hal-01830584 HAL Id: hal-01830584 https://hal.archives-ouvertes.fr/hal-01830584 Submitted on 12 Jul 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. HHS Public Access Author manuscript Author Manuscript Author ManuscriptNature. Author ManuscriptAuthor manuscript; Author Manuscript available in PMC 2016 March 03. Published in final edited form as: Nature. 2015 September 3; 525(7567): 109–113. doi:10.1038/nature14670. Mutations in DCHS1 Cause Mitral Valve Prolapse Ronen Durst1,2,*, Kimberly Sauls5,*, David S Peal3,*, Annemarieke deVlaming5, Katelynn Toomer5, Maire Leyne1, Monica Salani1, Michael E. Talkowski1, Harrison Brand1, Maëlle Perrocheau9, Charles Simpson1, Christopher Jett1, Matthew R. Stone1, Florie Charles1, Colby Chiang1, Stacey N. Lynch3, Nabila Bouatia-Naji9,10, Francesca N. Delling7, Lisa A. -
In Silico Analysis of Gene Expression Data from Bald Frontal and Haired Occipital Scalp to Identify Candidate Genes in Male Androgenetic Alopecia
Archives of Dermatological Research (2019) 311:815–824 https://doi.org/10.1007/s00403-019-01973-2 ORIGINAL PAPER In silico analysis of gene expression data from bald frontal and haired occipital scalp to identify candidate genes in male androgenetic alopecia A. Premanand1 · B. Reena Rajkumari1 Received: 5 September 2018 / Revised: 6 July 2019 / Accepted: 30 August 2019 / Published online: 11 September 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Androgenetic alopecia (AGA) is a progressive dermatological disorder of frontal and vertex scalp hair loss leading to bald- ness in men. This study aimed to identify candidate genes involved in AGA through an in silico search strategy. The gene expression profle GS36169, which contains microarray gene expression data from bald frontal and haired occipital scalps of fve men with AGA, was downloaded from the Gene Expression Omnibus (GEO) database. The diferential gene expression analysis for all fve subjects was carried out separately by PUMA package in R and identifed 32 diferentially expressed genes (DEGs) common to all fve subjects. Gene ontology (GO) biological process and pathway- enrichment analyses of the DEGs were conducted separately for the up-regulated and down-regulated genes. ReactomeFIViz app was utilized to construct the protein functional interaction network for the DEGs. Through GO biological process and pathway analysis on the clusters of the Reactome FI network, we found that the down-regulated DEGs participate in Wnt signaling, TGF-beta signaling, -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
Detecting Differential Copy Number Variation Between Groups of Samples
Downloaded from genome.cshlp.org on October 7, 2021 - Published by Cold Spring Harbor Laboratory Press Method Detecting differential copy number variation between groups of samples Craig B. Lowe,1,2,5 Nicelio Sanchez-Luege,1,5 Timothy R. Howes,1 Shannon D. Brady,1 Rhea R. Daugherty,1,3 Felicity C. Jones,1,3,6 Michael A. Bell,4 and David M. Kingsley1,2 1Department of Developmental Biology, Stanford University School of Medicine, Stanford, California 94305, USA; 2Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA; 3Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; 4Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York 11794, USA We present a method to detect copy number variants (CNVs) that are differentially present between two groups of sequenced samples. We use a finite-state transducer where the emitted read depth is conditioned on the mappability and GC-content of all reads that occur at a given base position. In this model, the read depth within a region is a mixture of binomials, which in simulations matches the read depth more closely than the often-used negative binomial distribution. The method analyzes all samples simultaneously, preserving uncertainty as to the breakpoints and magnitude of CNVs present in an individual when it identifies CNVs differentially present between the two groups. We apply this method to identify CNVs that are recurrently associated with postglacial adaptation of marine threespine stickleback (Gasterosteus aculeatus) to freshwater. We identify 6664 regions of the stickleback genome, totaling 1.7 Mbp, which show consistent copy number differences between marine and freshwater populations. -
Peripheral Nerve Single-Cell Analysis Identifies Mesenchymal Ligands That Promote Axonal Growth
Research Article: New Research Development Peripheral Nerve Single-Cell Analysis Identifies Mesenchymal Ligands that Promote Axonal Growth Jeremy S. Toma,1 Konstantina Karamboulas,1,ª Matthew J. Carr,1,2,ª Adelaida Kolaj,1,3 Scott A. Yuzwa,1 Neemat Mahmud,1,3 Mekayla A. Storer,1 David R. Kaplan,1,2,4 and Freda D. Miller1,2,3,4 https://doi.org/10.1523/ENEURO.0066-20.2020 1Program in Neurosciences and Mental Health, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada, 2Institute of Medical Sciences University of Toronto, Toronto, Ontario M5G 1A8, Canada, 3Department of Physiology, University of Toronto, Toronto, Ontario M5G 1A8, Canada, and 4Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada Abstract Peripheral nerves provide a supportive growth environment for developing and regenerating axons and are es- sential for maintenance and repair of many non-neural tissues. This capacity has largely been ascribed to paracrine factors secreted by nerve-resident Schwann cells. Here, we used single-cell transcriptional profiling to identify ligands made by different injured rodent nerve cell types and have combined this with cell-surface mass spectrometry to computationally model potential paracrine interactions with peripheral neurons. These analyses show that peripheral nerves make many ligands predicted to act on peripheral and CNS neurons, in- cluding known and previously uncharacterized ligands. While Schwann cells are an important ligand source within injured nerves, more than half of the predicted ligands are made by nerve-resident mesenchymal cells, including the endoneurial cells most closely associated with peripheral axons. At least three of these mesen- chymal ligands, ANGPT1, CCL11, and VEGFC, promote growth when locally applied on sympathetic axons. -
Program Book
The Genetics Society of America Conferences 15th International Xenopus Conference August 24-28, 2014 • Pacific Grove, CA PROGRAM GUIDE LEGEND Information/Guest Check-In Disabled Parking E EV Charging Station V Beverage Vending Machine N S Ice Machine Julia Morgan Historic Building W Roadway Pedestrian Pathway Outdoor Group Activity Area Program and Abstracts Meeting Organizers Carole LaBonne, Northwestern University John Wallingford, University of Texas at Austin Organizing Committee: Julie Baker, Stanford Univ Chris Field, Harvard Medical School Carmen Domingo, San Francisco State Univ Anna Philpott, Univ of Cambridge 9650 Rockville Pike, Bethesda, Maryland 20814-3998 Telephone: (301) 634-7300 • Fax: (301) 634-7079 E-mail: [email protected] • Web site: genetics-gsa.org Thank You to the Following Companies for their Generous Support Platinum Sponsor Gold Sponsors Additional Support Provided by: Carl Zeiss Microscopy, LLC Monterey Convention & Gene Tools, LLC Visitors Bureau Leica Microsystems Xenopus Express 2 Table of Contents General Information ........................................................................................................................... 5 Schedule of Events ............................................................................................................................. 6 Exhibitors ........................................................................................................................................... 8 Opening Session and Plenary/Platform Sessions ............................................................................ -
Using an Atlas of Gene Regulation Across 44 Human Tissues to Inform Complex Disease- and Trait-Associated Variation
Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Gamazon, Eric R. "Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation." Nature Genetics 50, 7 (July 2018): 956–967 © 2018 The Author(s) As Published http://dx.doi.org/10.1038/S41588-018-0154-4 Publisher Springer Nature Version Author's final manuscript Citable link https://hdl.handle.net/1721.1/121227 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ Europe PMC Funders Group Author Manuscript Nat Genet. Author manuscript; available in PMC 2018 December 28. Published in final edited form as: Nat Genet. 2018 July ; 50(7): 956–967. doi:10.1038/s41588-018-0154-4. Europe PMC Funders Author Manuscripts Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation Eric R. Gamazon1,2,§,*, Ayellet V. Segrè3,4,§,*, Martijn van de Bunt5,6,§, Xiaoquan Wen7, Hualin S. Xi8, Farhad Hormozdiari9,10, Halit Ongen11,12,13, Anuar Konkashbaev1, Eske M. Derks14, François Aguet3, Jie Quan8, GTEx Consortium15, Dan L. Nicolae16,17,18, Eleazar Eskin9, Manolis Kellis3,19, Gad Getz3,20, Mark I. McCarthy5,6, Emmanouil T. Dermitzakis11,12,13, Nancy J. Cox1, and Kristin G. Ardlie3 1Division of Genetic Medicine, Department of Medicine,