Genomic Sequence Analysis of a Potential QTL Region for Fat Trait on Pig Chromosome 6I
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Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Microgenomics of Ameloblastoma
RESEARCH REPORTS Biological P. DeVilliers1, C. Suggs2, D. Simmons2, V. Murrah3, and J.T. Wright2* Microgenomics of 1Department of Pathology, University of Alabama at Ameloblastoma Birmingham; 2Department of Pediatric Dentistry, CB #7450, and 3Department of Oral Pathology, University of North Carolina at Chapel Hill, NC 27599, USA; *corresponding author, [email protected] J Dent Res 90(4):463-469, 2011 ABSTRACT INTRODUCTION Gene expression profiles of human ameloblastoma microdissected cells were characterized with the meloblastoma is an aggressive tumor of odontogenic epithelial origin, purpose of identifying genes and their protein Awith devastating morbidity if left untreated, due to its unlimited growth products that could be targeted as diagnostic and potential. It is characterized by a high rate of recurrence (up to 70%, depend- prognostic markers as well as for potential thera- ing on the treatment modality) and potential to undergo malignant transfor- peutic interventions. Five formalin-fixed, decalci- mation and to metastasize (up to 2% of cases). Malignant ameloblastoma is fied, paraffin-embedded samples of ameloblastoma defined as a histologically benign-appearing ameloblastoma with metastasis were subjected to laser capture microdissection, (Goldenberg et al., 2004; Cardoso et al., 2009). Surgical resection is the treat- linear mRNA amplification, and hybridization to ment of choice, which can cause further morbidity to the craniofacial com- oligonucleotide human 41,000 RNA arrays and plex, with loss of function and esthetics (Olasoji and Enwere, 2003). compared with universal human reference RNA, Gene expression profiling of tumor cell populations has advanced our to determine the gene expression signature. understanding of the pathogenesis of human tumors (Naderi et al., 2004). -
(12) Patent Application Publication (10) Pub. No.: US 2006/0088532 A1 Alitalo Et Al
US 20060O88532A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2006/0088532 A1 Alitalo et al. (43) Pub. Date: Apr. 27, 2006 (54) LYMPHATIC AND BLOOD ENDOTHELIAL Related U.S. Application Data CELL GENES (60) Provisional application No. 60/363,019, filed on Mar. (76) Inventors: Kari Alitalo, Helsinki (FI); Taija 7, 2002. Makinen, Helsinki (FI); Tatiana Petrova, Helsinki (FI); Pipsa Publication Classification Saharinen, Helsinki (FI); Juha Saharinen, Helsinki (FI) (51) Int. Cl. A6IR 48/00 (2006.01) Correspondence Address: A 6LX 39/395 (2006.01) MARSHALL, GERSTEIN & BORUN LLP A6II 38/18 (2006.01) 233 S. WACKER DRIVE, SUITE 6300 (52) U.S. Cl. .............................. 424/145.1: 514/2: 514/44 SEARS TOWER (57) ABSTRACT CHICAGO, IL 60606 (US) The invention provides polynucleotides and genes that are (21) Appl. No.: 10/505,928 differentially expressed in lymphatic versus blood vascular endothelial cells. These genes are useful for treating diseases (22) PCT Filed: Mar. 7, 2003 involving lymphatic vessels, such as lymphedema, various inflammatory diseases, and cancer metastasis via the lym (86). PCT No.: PCT/USO3FO6900 phatic system. Patent Application Publication Apr. 27, 2006 Sheet 1 of 2 US 2006/0088532 A1 integrin O9 integrin O1 KIAAO711 KAAO644 ApoD Fig. 1 Patent Application Publication Apr. 27, 2006 Sheet 2 of 2 US 2006/0088532 A1 CN g uueleo-gº US 2006/0O88532 A1 Apr. 27, 2006 LYMPHATIC AND BLOOD ENDOTHELLAL CELL lymphatic vessels, such as lymphangiomas or lymphang GENES iectasis. Witte, et al., Regulation of Angiogenesis (eds. Goldber, I. D. & Rosen, E. M.) 65-112 (Birkauser, Basel, BACKGROUND OF THE INVENTION Switzerland, 1997). -
A New Efficient Method to Detect Genetic Interactions for Lung Cancer
Luyapan et al. BMC Med Genomics (2020) 13:162 https://doi.org/10.1186/s12920-020-00807-9 TECHNICAL ADVANCE Open Access A new efcient method to detect genetic interactions for lung cancer GWAS Jennifer Luyapan1,2, Xuemei Ji2, Siting Li1,2, Xiangjun Xiao3, Dakai Zhu2,3, Eric J. Duell4, David C. Christiani5,6, Matthew B. Schabath7, Susanne M. Arnold8, Shanbeh Zienolddiny9, Hans Brunnström10, Olle Melander11, Mark D. Thornquist12, Todd A. MacKenzie1,2, Christopher I. Amos1,2,3* and Jiang Gui1,2* Abstract Background: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome- wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efcient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and imple- mented the Quantitative Multifactor Dimensionality Reduction method to identify signifcant interactions associated with age of disease-onset. Methods: To demonstrate efcacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identifed interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset.= We tested the best model in an independent data set from the OncoArray-TRICL data. -
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. -
In This Table Protein Name, Uniprot Code, Gene Name P-Value
Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2- -
Cytogenetic and Molecular Characterization of the Macro- And
University of Ulm Department of Human Genetics Prof. Dr. med. Walther Vogel Cytogenetic and Molecular Characterization of the Macro- and Micro-inversions, which Distinguish the Human and the Chimpanzee Karyotypes - from Speciation to Polymorphism Thesis Applying for the Degree of Doctor of Human Biology (Dr. hum. biol.) Faculty of Medicine University of Ulm Presented by Justyna Monika Szamalek from Wrze śnia in Poland 2006 Amtierender Dekan: Prof. Dr. Klaus-Michael Debatin 1. Berichterstatter: Prof. Dr. med. Horst Hameister 2. Berichterstatter: Prof. Dr. med. Konstanze Döhner Tag der Promotion: 28.07.2006 Content Content 1. Introduction ...................................................................................................................7 1.1. Primate phylogeny........................................................................................................7 1.2. Africa as the place of human origin and the living area of the present-day chimpanzee populations .................................................................9 1.3. Cytogenetic and molecular differences between human and chimpanzee genomes.............................................................................................10 1.4. Cytogenetic and molecular differences between common chimpanzee and bonobo genomes................................................................................17 1.5. Theory of speciation .....................................................................................................18 1.6. Theory of selection -
A Yeast Bifc-Seq Method for Genome-Wide Interactome Mapping
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.16.154146; this version posted June 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 A yeast BiFC-seq method for genome-wide interactome mapping 2 3 Limin Shang1,a, Yuehui Zhang1,b, Yuchen Liu1,c, Chaozhi Jin1,d, Yanzhi Yuan1,e, 4 Chunyan Tian1,f, Ming Ni2,g, Xiaochen Bo2,h, Li Zhang3,i, Dong Li1,j, Fuchu He1,*,k & 5 Jian Wang1,*,l 6 1State Key Laboratory of Proteomics, Beijing Proteome Research Center, National 7 Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, 8 China; 2Department of Biotechnology, Beijing Institute of Radiation Medicine, 9 Beijing 100850, China; 3Department of Rehabilitation Medicine, Nan Lou; 10 Department of Key Laboratory of Wound Repair and Regeneration of PLA, College 11 of Life Sciences, Chinese PLA General Hospital, Beijing 100853, China; 12 Correspondence should be addressed to F.H. ([email protected]) and J.W. 13 ([email protected]). 14 * Corresponding authors. 15 E-mail:[email protected] (Fuchu He),[email protected] (Jian Wang) 16 a ORCID: 0000-0002-6371-1956. 17 b ORCID: 0000-0001-5257-1671 18 c ORCID: 0000-0003-4691-4951 19 d ORCID: 0000-0002-1477-0255 20 e ORCID: 0000-0002-6576-8112 21 f ORCID: 0000-0003-1589-293X 22 g ORCID: 0000-0001-9465-2787 23 h ORCID: 0000-0003-3490-5812 24 i ORCID: 0000-0002-3477-8860 25 j ORCID: 0000-0002-8680-0468 26 k ORCID: 0000-0002-8571-2351 27 l ORCID: 0000-0002-8116-7691 28 Total word counts:4398 (from “Introduction” to “Conclusions”) 29 Total Keywords: 5 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.16.154146; this version posted June 17, 2020. -
Sputum Proteomics and Airway Cell Transcripts of Current and Ex-Smokers with Severe Asthma in U-BIOPRED
Sputum proteomics and airway cell transcripts of current and ex-smokers with severe asthma in U-BIOPRED: an exploratory analysis Kentaro Takahashi 1,2, Stelios Pavlidis3, Francois Ng Kee Kwong 1, Uruj Hoda 1, Christos Rossios1, Kai Sun3, Matthew Loza4, Fred Baribaud4, Pascal Chanez5, Steve J Fowler6, Ildiko Horvath7, Paolo Montuschi8, Florian Singer9, Jacek Musial10, Barbro Dahlen11, Sven-Eric Dahlen11, N. Krug12, Thomas Sandstrom13, Dominic E. Shaw14, Rene Lutter 15, Per Bakke16, Louise J. Fleming1, Peter H. Howarth17, Massimo Caruso18, Ana R Sousa19, Julie Corfield20, Charles Auffray21, Bertrand De Meulder21, Diane Lefaudeux21, Ratko Djukanovic17, Peter J Sterk16, Yike Guo3, Ian M. Adcock1,3, Kian Fan Chung1,3 on behalf of the U-BIOPRED study group# 1National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Research Centre for Allergy and Clinical Immunology, Asahi General Hospital, Japan; 3Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 4Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 5 Assistance Publique des Hôpitaux de Marseille - Clinique des bronches, allergies et sommeil, Aix Marseille Université, Marseille, France 6 Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester and University Hospital of South Manchester, Manchester Academic Health Sciences Centre, Manchester, United Kingdom -
Supplementary Table 1: Genes Located on Chromosome 18P11-18Q23, an Area Significantly Linked to TMPRSS2-ERG Fusion
Supplementary Table 1: Genes located on Chromosome 18p11-18q23, an area significantly linked to TMPRSS2-ERG fusion Symbol Cytoband Description LOC260334 18p11 HSA18p11 beta-tubulin 4Q pseudogene IL9RP4 18p11.3 interleukin 9 receptor pseudogene 4 LOC100132166 18p11.32 hypothetical LOC100132166 similar to Rho-associated protein kinase 1 (Rho- associated, coiled-coil-containing protein kinase 1) (p160 LOC727758 18p11.32 ROCK-1) (p160ROCK) (NY-REN-35 antigen) ubiquitin specific peptidase 14 (tRNA-guanine USP14 18p11.32 transglycosylase) THOC1 18p11.32 THO complex 1 COLEC12 18pter-p11.3 collectin sub-family member 12 CETN1 18p11.32 centrin, EF-hand protein, 1 CLUL1 18p11.32 clusterin-like 1 (retinal) C18orf56 18p11.32 chromosome 18 open reading frame 56 TYMS 18p11.32 thymidylate synthetase ENOSF1 18p11.32 enolase superfamily member 1 YES1 18p11.31-p11.21 v-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 LOC645053 18p11.32 similar to BolA-like protein 2 isoform a similar to 26S proteasome non-ATPase regulatory LOC441806 18p11.32 subunit 8 (26S proteasome regulatory subunit S14) (p31) ADCYAP1 18p11 adenylate cyclase activating polypeptide 1 (pituitary) LOC100130247 18p11.32 similar to cytochrome c oxidase subunit VIc LOC100129774 18p11.32 hypothetical LOC100129774 LOC100128360 18p11.32 hypothetical LOC100128360 METTL4 18p11.32 methyltransferase like 4 LOC100128926 18p11.32 hypothetical LOC100128926 NDC80 homolog, kinetochore complex component (S. NDC80 18p11.32 cerevisiae) LOC100130608 18p11.32 hypothetical LOC100130608 structural maintenance -
Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Olecm Ular Determinants Critical to the Development of Glioblastoma Brian W
Florida International University FIU Digital Commons Robert Stempel College of Public Health & Social Environmental Health Sciences Work 5-30-2013 Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines olecM ular Determinants Critical to the Development of Glioblastoma Brian W. Kunkle Department of Environmental Health Sciences, Florida International University Changwon Yoo Department of Biostatistics, Florida International University, [email protected] Deodutta Roy Department of Environmental Health Sciences, Florida International University, [email protected] Follow this and additional works at: https://digitalcommons.fiu.edu/eoh_fac Part of the Medicine and Health Sciences Commons Recommended Citation Kunkle BW, Yoo C, Roy D (2013) Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma. PLoS ONE 8(5): e64140. https://doi.org/10.1371/ journal.pone.0064140 This work is brought to you for free and open access by the Robert Stempel College of Public Health & Social Work at FIU Digital Commons. It has been accepted for inclusion in Environmental Health Sciences by an authorized administrator of FIU Digital Commons. For more information, please contact [email protected]. Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma Brian W. Kunkle1, Changwon Yoo2, Deodutta Roy1* 1 Department of Environmental and Occupational Health, Florida International University, Miami, Florida, United States of America, 2 Department of Biostatistics, Florida International University, Miami, Florida, United States of America Abstract In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma.