Computational Analysis of Data from a Genome-Wide Screening Identifies New PARP1 Functional Interactors As Potential Therapeutic Targets
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Cytogenomic SNP Microarray - Fetal ARUP Test Code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells
Patient Report |FINAL Client: Example Client ABC123 Patient: Patient, Example 123 Test Drive Salt Lake City, UT 84108 DOB 2/13/1987 UNITED STATES Gender: Female Patient Identifiers: 01234567890ABCD, 012345 Physician: Doctor, Example Visit Number (FIN): 01234567890ABCD Collection Date: 00/00/0000 00:00 Cytogenomic SNP Microarray - Fetal ARUP test code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells Single fetal genotype present; no maternal cells present. Fetal and maternal samples were tested using STR markers to rule out maternal cell contamination. This result has been reviewed and approved by Maternal Specimen Yes Cytogenomic SNP Microarray - Fetal Abnormal * (Ref Interval: Normal) Test Performed: Cytogenomic SNP Microarray- Fetal (ARRAY FE) Specimen Type: Direct (uncultured) villi Indication for Testing: Patient with 46,XX,t(4;13)(p16.3;q12) (Quest: EN935475D) ----------------------------------------------------------------- ----- RESULT SUMMARY Abnormal Microarray Result (Male) Unbalanced Translocation Involving Chromosomes 4 and 13 Classification: Pathogenic 4p Terminal Deletion (Wolf-Hirschhorn syndrome) Copy number change: 4p16.3p16.2 loss Size: 5.1 Mb 13q Proximal Region Deletion Copy number change: 13q11q12.12 loss Size: 6.1 Mb ----------------------------------------------------------------- ----- RESULT DESCRIPTION This analysis showed a terminal deletion (1 copy present) involving chromosome 4 within 4p16.3p16.2 and a proximal interstitial deletion (1 copy present) involving chromosome 13 within 13q11q12.12. This -
The Rat Genome Database at 20: a Multi-Species Knowledgebase and Analysis Platform Jennifer R
Published online 12 November 2019 Nucleic Acids Research, 2020, Vol. 48, Database issue D731–D742 doi: 10.1093/nar/gkz1041 The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform Jennifer R. Smith 1,*, G. Thomas Hayman1, Shur-Jen Wang1, Stanley J.F. Laulederkind 1, Matthew J. Hoffman1,2, Mary L. Kaldunski 1, Monika Tutaj1, Jyothi Thota1,Harika S. Nalabolu1, Santoshi L.R. Ellanki1, Marek A. Tutaj1, Jeffrey L. De Pons1, Anne E. Kwitek2, Melinda R. Dwinell2 and Mary E. Shimoyama1 1Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA and 2Genomic Sciences and Precision Medicine Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA Received September 15, 2019; Revised October 21, 2019; Editorial Decision October 22, 2019; Accepted October 24, 2019 ABSTRACT In contrast to the current wealth of data and tools, the ‘bare bones’ first iteration (Figure 1,Table1) focused on just a few Formed in late 1999, the Rat Genome Database (RGD, data types––most notably a handful of known genes, plus https://rgd.mcw.edu) will be 20 in 2020, the Year of the EST and SSLP markers––localized only on genetic and RH Rat. Because the laboratory rat, Rattus norvegicus, maps, and offered a minimal number of tools for searching has been used as a model for complex human dis- and analyzing that data. eases such as cardiovascular disease, diabetes, can- Even before the first public release of the rat reference cer, neurological disorders and arthritis, among oth- genome in 2002 (1), RGD was focused on mapping disease- ers, for >150 years, RGD has always been disease- related regions and on comparative genomics between rat, focused and committed to providing data and tools human and mouse to support the use of rat as a model for for researchers doing comparative genomics and human disease. -
Replace This with the Actual Title Using All Caps
UNDERSTANDING THE GENETICS UNDERLYING MASTITIS USING A MULTI-PRONGED APPROACH A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Asha Marie Miles December 2019 © 2019 Asha Marie Miles UNDERSTANDING THE GENETICS UNDERLYING MASTITIS USING A MULTI-PRONGED APPROACH Asha Marie Miles, Ph. D. Cornell University 2019 This dissertation addresses deficiencies in the existing genetic characterization of mastitis due to granddaughter study designs and selection strategies based primarily on lactation average somatic cell score (SCS). Composite milk samples were collected across 6 sampling periods representing key lactation stages: 0-1 day in milk (DIM), 3- 5 DIM, 10-14 DIM, 50-60 DIM, 90-110 DIM, and 210-230 DIM. Cows were scored for front and rear teat length, width, end shape, and placement, fore udder attachment, udder cleft, udder depth, rear udder height, and rear udder width. Independent multivariable logistic regression models were used to generate odds ratios for elevated SCC (≥ 200,000 cells/ml) and farm-diagnosed clinical mastitis. Within our study cohort, loose fore udder attachment, flat teat ends, low rear udder height, and wide rear teats were associated with increased odds of mastitis. Principal component analysis was performed on these traits to create a single new phenotype describing mastitis susceptibility based on these high-risk phenotypes. Cows (N = 471) were genotyped on the Illumina BovineHD 777K SNP chip and considering all 14 traits of interest, a total of 56 genome-wide associations (GWA) were performed and 28 significantly associated quantitative trait loci (QTL) were identified. -
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. -
Noninvasive Sleep Monitoring in Large-Scale Screening of Knock-Out Mice
bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Noninvasive sleep monitoring in large-scale screening of knock-out mice reveals novel sleep-related genes Shreyas S. Joshi1*, Mansi Sethi1*, Martin Striz1, Neil Cole2, James M. Denegre2, Jennifer Ryan2, Michael E. Lhamon3, Anuj Agarwal3, Steve Murray2, Robert E. Braun2, David W. Fardo4, Vivek Kumar2, Kevin D. Donohue3,5, Sridhar Sunderam6, Elissa J. Chesler2, Karen L. Svenson2, Bruce F. O'Hara1,3 1Dept. of Biology, University of Kentucky, Lexington, KY 40506, USA, 2The Jackson Laboratory, Bar Harbor, ME 04609, USA, 3Signal solutions, LLC, Lexington, KY 40503, USA, 4Dept. of Biostatistics, University of Kentucky, Lexington, KY 40536, USA, 5Dept. of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA. 6Dept. of Biomedical Engineering, University of Kentucky, Lexington, KY 40506, USA. *These authors contributed equally Address for correspondence and proofs: Shreyas S. Joshi, Ph.D. Dept. of Biology University of Kentucky 675 Rose Street 101 Morgan Building Lexington, KY 40506 U.S.A. Phone: (859) 257-2805 FAX: (859) 257-1717 Email: [email protected] Running title: Sleep changes in knockout mice bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Genome-Wide Association Study for Circulating Fibroblast Growth Factor
www.nature.com/scientificreports OPEN Genome‑wide association study for circulating fbroblast growth factor 21 and 23 Gwo‑Tsann Chuang1,2,14, Pi‑Hua Liu3,4,14, Tsui‑Wei Chyan5, Chen‑Hao Huang5, Yu‑Yao Huang4,6, Chia‑Hung Lin4,7, Jou‑Wei Lin8, Chih‑Neng Hsu8, Ru‑Yi Tsai8, Meng‑Lun Hsieh9, Hsiao‑Lin Lee9, Wei‑shun Yang2,9, Cassianne Robinson‑Cohen10, Chia‑Ni Hsiung11, Chen‑Yang Shen12,13 & Yi‑Cheng Chang2,9,12* Fibroblast growth factors (FGFs) 21 and 23 are recently identifed hormones regulating metabolism of glucose, lipid, phosphate and vitamin D. Here we conducted a genome‑wide association study (GWAS) for circulating FGF21 and FGF23 concentrations to identify their genetic determinants. We enrolled 5,000 participants from Taiwan Biobank for this GWAS. After excluding participants with diabetes mellitus and quality control, association of single nucleotide polymorphisms (SNPs) with log‑transformed FGF21 and FGF23 serum concentrations adjusted for age, sex and principal components of ancestry were analyzed. A second model additionally adjusted for body mass index (BMI) and a third model additionally adjusted for BMI and estimated glomerular fltration rate (eGFR) were used. A total of 4,201 participants underwent GWAS analysis. rs67327215, located within RGS6 (a gene involved in fatty acid synthesis), and two other SNPs (rs12565114 and rs9520257, located between PHC2-ZSCAN20 and ARGLU1-FAM155A respectively) showed suggestive associations with serum FGF21 level (P = 6.66 × 10–7, 6.00 × 10–7 and 6.11 × 10–7 respectively). The SNPs rs17111495 and rs17843626 were signifcantly associated with FGF23 level, with the former near PCSK9 gene and the latter near HLA-DQA1 gene (P = 1.04 × 10–10 and 1.80 × 10–8 respectively). -
Controls Homeostatic Splicing of ARGLU1 Mrna Stephan P
Published online 28 November 2016 Nucleic Acids Research, 2017, Vol. 45, No. 6 3473–3486 doi: 10.1093/nar/gkw1140 An Ultraconserved Element (UCE) controls homeostatic splicing of ARGLU1 mRNA Stephan P. Pirnie, Ahmad Osman, Yinzhou Zhu and Gordon G. Carmichael* Department of Genetics and Genome Sciences, UCONN Health Center, 400 Farmington Avenue, Farmington, CT 06030, USA Received January 13, 2016; Revised October 25, 2016; Editorial Decision October 28, 2016; Accepted October 31, 2016 ABSTRACT or inhibit the usage of a particular splice site (1,2). The ex- pression of trans-acting factors in a developmental and tis- Arginine and Glutamate-Rich protein 1 (ARGLU1) is sue specific manner results in regulated splicing that isof- a protein whose function is poorly understood, but ten cell specific. Most notably, trans-acting proteins such as may act in both transcription and pre-mRNA splicing. the NOVA (3–6), the RBFOX (7) and SR protein (8–11) We demonstrate that the ARGLU1 gene expresses families, and a number of hnRNP (12,13) proteins compete at least three distinct RNA splice isoforms – a fully to bind nascent RNAs at specific motifs and drive regula- spliced isoform coding for the protein, an isoform tion of alternative splicing in a tissue and developmentally containing a retained intron that is detained in the specific manner. Alternative splicing is an important pro- nucleus, and an isoform containing an alternative cess that is seen in at least 95% of multi-exon genes in the exon that targets the transcript for nonsense medi- human transcriptome (14). Furthermore, alternative splic- ated decay. -
Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement
School of Natural Sciences and Mathematics Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement UT Dallas Author(s): Michael Q. Zhang Rights: CC BY 4.0 (Attribution) ©2017 The Authors Citation: Liu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-z This document is being made freely available by the Eugene McDermott Library of the University of Texas at Dallas with permission of the copyright owner. All rights are reserved under United States copyright law unless specified otherwise. File name: Supplementary Information Description: Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references. CCNE1 CCNE1 CCNE1 CCNE1 36 40 32 34 32 35 30 32 28 30 30 28 28 26 24 25 Normalized Expression Normalized Expression Normalized Expression Normalized Expression 26 G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage CCNE1 CCNE1 CCNE1 CCNE1 40 32 40 40 35 30 38 30 30 28 36 25 26 20 20 34 Normalized Expression Normalized Expression Normalized Expression 24 Normalized Expression G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Supplementary Figure 1 | High stochasticity of single-cell gene expression means, as demonstrated by relative expression levels of gene Ccne1 using the mESC-SMARTer data. For every panel, 20 sample cells were randomly selected for each of the three stages, followed by plotting the mean expression levels at each stage. -
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, -
Chromosome Abnormalities in Neonates with Congenital Heart Defects
CHROMOSOME ABNORMALITIES IN NEONATES WITH CONGENITAL HEART DEFECTS by Kristine Kay Bachman B.A. Biology and Spanish, Bucknell University, 2010 Submitted to the Graduate Faculty of Human Genetics, Genetic Counseling Graduate School of Public Health in partial fulfillment of the requirements for the degree of Master of Science University of Pittsburgh 2012 UNIVERSITY OF PITTSBURGH Graduate School of Public Health This thesis was presented by Kristine Kay Bachman It was defended on April 11, 2012 and approved by Thesis Advisor: Suneeta Madan-Khetarpal, MD Associate Professor of Pediatrics Division of Medical Genetics School of Medicine University of Pittsburgh Committee Member: Robin Grubs, MS, PhD, CGC Assistant Professor of Human Genetics Co-Director, Genetic Counseling Program Department of Human Genetics Graduate School of Public Health University of Pittsburgh Committee Member: Susanne Gollin, PhD Professor of Human Genetics Department of Human Genetics Graduate School of Public Health University of Pittsburgh Committee Member: Stephanie DeWard, MS, CGC Division of Medical Genetics School of Medicine University of Pittsburgh ii Kristine Kay Bachman 2012 iii Thesis Advisor: Suneeta Madan-Khetarpal, MD CHROMOSOME ABNORMALITIES IN NEONATES WITH CONGENITAL HEART DEFECTS Kristine Kay Bachman, M.S. University of Pittsburgh, 2012 Congenital heart disease (CHD) contributes to the rate of birth defects in the population with an incidence of nearly 1% of all live births (1). CHD has significant Public Health importance due to its high incidence, clinical severity, and complexity of medical management. In some cases, efficiently diagnosing the underlying cause of CHD is essential to providing optimal clinical care. The etiology of CHD is hypothesized to be largely multifactorial in nature, but chromosome abnormalities account for 8-13% of all CHD (2, 3). -
Genome-Wide DNA Methylation Map of Human Neutrophils Reveals Widespread Inter-Individual Epigenetic Variation
www.nature.com/scientificreports OPEN Genome-wide DNA methylation map of human neutrophils reveals widespread inter-individual Received: 15 June 2015 Accepted: 29 October 2015 epigenetic variation Published: 27 November 2015 Aniruddha Chatterjee1,2, Peter A. Stockwell3, Euan J. Rodger1, Elizabeth J. Duncan2,4, Matthew F. Parry5, Robert J. Weeks1 & Ian M. Morison1,2 The extent of variation in DNA methylation patterns in healthy individuals is not yet well documented. Identification of inter-individual epigenetic variation is important for understanding phenotypic variation and disease susceptibility. Using neutrophils from a cohort of healthy individuals, we generated base-resolution DNA methylation maps to document inter-individual epigenetic variation. We identified 12851 autosomal inter-individual variably methylated fragments (iVMFs). Gene promoters were the least variable, whereas gene body and upstream regions showed higher variation in DNA methylation. The iVMFs were relatively enriched in repetitive elements compared to non-iVMFs, and were associated with genome regulation and chromatin function elements. Further, variably methylated genes were disproportionately associated with regulation of transcription, responsive function and signal transduction pathways. Transcriptome analysis indicates that iVMF methylation at differentially expressed exons has a positive correlation and local effect on the inclusion of that exon in the mRNA transcript. Methylation of DNA is a mechanism for regulating gene function in all vertebrates. It has a role in gene silencing, tissue differentiation, genomic imprinting, chromosome X inactivation, phenotypic plasticity, and disease susceptibility1,2. Aberrant DNA methylation has been implicated in the pathogenesis of sev- eral human diseases, especially cancer3–5. Variation in DNA methylation patterns in healthy individuals has been hypothesised to alter human phenotypes including susceptibility to common diseases6 and response to drug treatments7. -
WO 2018/067991 Al 12 April 2018 (12.04.2018) W !P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2018/067991 Al 12 April 2018 (12.04.2018) W !P O PCT (51) International Patent Classification: achusetts 021 15 (US). THE BROAD INSTITUTE, A61K 51/10 (2006.01) G01N 33/574 (2006.01) INC. [US/US]; 415 Main Street, Cambridge, Massachu C07K 14/705 (2006.01) A61K 47/68 (2017.01) setts 02142 (US). MASSACHUSETTS INSTITUTE OF G01N 33/53 (2006.01) TECHNOLOGY [US/US]; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (US). (21) International Application Number: PCT/US2017/055625 (72) Inventors; and (71) Applicants: KUCHROO, Vijay K. [IN/US]; 30 Fairhaven (22) International Filing Date: Road, Newton, Massachusetts 02149 (US). ANDERSON, 06 October 2017 (06.10.2017) Ana Carrizosa [US/US]; 110 Cypress Street, Brookline, (25) Filing Language: English Massachusetts 02445 (US). MADI, Asaf [US/US]; c/o The Brigham and Women's Hospital, Inc., 75 Francis (26) Publication Language: English Street, Boston, Massachusetts 021 15 (US). CHIHARA, (30) Priority Data: Norio [US/US]; c/o The Brigham and Women's Hospital, 62/405,835 07 October 2016 (07.10.2016) US Inc., 75 Francis Street, Boston, Massachusetts 021 15 (US). REGEV, Aviv [US/US]; 15a Ellsworth Ave, Cambridge, (71) Applicants: THE BRIGHAM AND WOMEN'S HOSPI¬ Massachusetts 02139 (US). SINGER, Meromit [US/US]; TAL, INC. [US/US]; 75 Francis Street, Boston, Mass c/o The Broad Institute, Inc., 415 Main Street, Cambridge, (54) Title: MODULATION OF NOVEL IMMUNE CHECKPOINT TARGETS CD4 FIG.