DOCSLIB.ORG

Submission Tylee Et Al Pyschimmunegeneticcorrelations

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Submission Tylee Et Al Pyschimmunegeneticcorrelations Edinburgh Research Explorer Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data Citation for published version: 23 and Me Research Team 2018, 'Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data', American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, vol. 177, no. 7, pp. 641-657. https://doi.org/10.1002/ajmg.b.32652 Digital Object Identifier (DOI): 10.1002/ajmg.b.32652 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: American Journal of Medical Genetics Part B: Neuropsychiatric Genetics General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 11. Oct. 2021 American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data. Journal: American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Manuscript ForID NPG-18-0024 Peer Review Wiley - Manuscript type: Research Article Date Submitted by the Author: 20-Feb-2018 Complete List of Authors: Tylee, Daniel; SUNY Upstate Medical University, Deprtment of Psychiatry and Behavioral Sciences Sun, Jiayin; SUNY Upstate Medical University, Deprtment of Psychiatry and Behavioral Sciences Hess, Jonathan; SUNY Upstate Medical University, Neuroscience & Physiology Tahir, Muhammad; SUNY Upstate Medical University, Neuroscience & Physiology Sharma, Esha; SUNY Upstate Medical University, Neuroscience & Physiology Malik, Rainer; Klinikum der Universität München, Ludwig-Maximilians- University Worrall, Bradford; University of Virginia School of Medicine Levine, Andrew; University of California Los Angeles, Neurology Martinson, Jeremy; University of Pittsburgh Nejentsev, Sergey; University of Cambridge Speed, Doug; University College London Fischer, Annegret; Kiel University Mick, Erick; University of Massachusetts Medical School Walker, Brian; University of Arkansas for Medical Sciences Crawford, Andrew; University of Bristol Grant, Struan; University of Pennsylvania Perelman School of Medicine Polychronakos, Constantin; The McGill University Health Center, Departments of Pediatrics and Human Genetics Bradfield, Jonathan; Children's Hospital of Philadelphia, Sleiman, Patrick; University of Pennsylvania Perelman School of Medicine Hakonarson, Hakon; Children's Hospital of Philadelphia, Ellinghaus, Eva; Kiel University Elder, James; University of Michigan Tsoi, Lam; University of Michigan Trembath, Richard; King's College London Barker, Jonathan; King's College London Franke, Andre; University of Kiel Dehghan, Abbas; Erasmus University Medical Center Faraone, Stephen; SUNY Upstate Medical University, Psychaitry Glatt, Stephen; Neuropsychiatric Genetics, SUNY Keywords: genome-wide association, genetic correlation, pleiotropy John Wiley & Sons, Inc. Page 1 of 414 American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 John Wiley & Sons, Inc. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Page 2 of 414 1 2 3 4 5 6 7 Reviewer Comments to Author: 8 9 Reviewer: 1 10 11 Reviewer Comment 1: “..the datasets used to obtain several of the key findings (ulcerative colitis and Crohn's disease, 12 from Liu et al) is a trans-ethnic, multi-platform expansion of the dataset from the IBD consortium. Correctly, the authors 13 (based on the sample numbers) appear to have removed the Immunochip and non-European samples, to avoid bias in the 14 correlations. However, the remaining GWAS samples form a subset of the samples in a previous publication, Jostins L et 15 al, 2012, and which have been previously analyzed in Bulik-Sullivan B et al, 2015 (and shown to not correlate with the 16 same bipolar and schizophrenia datasets which are reported as significantly correlated here). To their credit, the authors 17 discuss the issue (in relation to the different filtering scheme used in this paper), but nevertheless using a less-stringent 18 filtering approach, to analyze a subset of a previously non-correlating dataset, to arrive at significant correlations, raises 19 serious questions regarding the reliabilityFor of those Peer results. At Review a minimum, it would be required to see either a similarly 20 significant result using the larger dataset from Jostins L et al 2012, and preferably also a demonstration that the Liu et al 21 2015 dataset produces this significant correlation without extra filtering.” 22 23 Author Response 1: The reviewer raises an excellent point; our findings appear to be discrepant with other 24 reports, and that deserves a more thorough investigation. We’ve made major revisions to address this concern. 25 We obtained multiple versions of each data set and processed them in various ways to explore the effects on LD- 26 27 score regression findings. Specifically, we examined the following data sets: 28 29 Bipolar Sklar et al., 2011, N=16k, 1KGv3_MAF>0.05 30 Bipolar Sklar et al., 2011, N=16k, INFO > 0.9 31 Bipolar Hou et al., 2016, N= 40k, 1KGv3_MAF>0.05 32 Bipolar Hou et al., 2016, N= 40k, All SNPS (No INFO available) 33 PGC Schizophrenia European = 75k, 1KGv3_MAF>0.05 34 PGC Schizophrenia European = 75k, INFO > 0.9 35 Crohn’s Disease, Frank, N=24K, 1KGv3_MAF>0.05 36 Crohn’s Disease, Frank, N=24K, 1KGv3_MAF>0.05 37 Crohn’s Disease, Liu, N=21K, 1KGv3_MAF>0.05 38 Crohn’s Disease, Liu, N=21K, INFO > 0.9 39 [Jostins et al., does not provide full summary data, so is not analyzed in our study] 40 Ulcerative Colitis, Andersen, N=21K, 1KGv3_MAF>0.05 41 Ulcerative Colitis, Andersen, N=21K, All SNPS (No INFO available) 42 Ulcerative Colitis, Liu, N=27K, 1KGv3_MAF>0.05 43 Ulcerative Colitis, Liu, N=27K, All SNPS (No INFO available) 44 45 We compared the correlations among these different versions of the data. The findings are depicted in 46 47 Supplementary Figures 3 and 4 [embedded below]. We describe these analyses and the pattern of results in the 48 Discussion section of the paper [excerpted below]. We demonstrate that different patterns of pre-filtering alter the 49 results, such that correlations with schizophrenia and bipolar (for Sklar et al.,) generally become less significant 50 when relatively rare variants (below MAF 5%) are included in the analyses. There is evidence from another 51 genetic correlation study, using a similar method, that differences in magnitude and significance (and in rare 52 cases, even the directionality) of the correlation can change when the analyses are stratified by MAF 53 (https://www.ncbi.nlm.nih.gov/pubmed/29220677). For our study, we chose to exclude relatively rare variation. 54 We discuss this as a limitation as follows: 55 56 57 58 Stephen J. Glatt, Ph.D. 750 East Adams Street 59 Syracuse, NY 13210, U.S.A. Director, Psychiatric Genetic Epidemiology & Neurobiology LaboratoryJohn (PsychGENe Wiley & Sons, Lab) Inc. E-mail: [email protected] 60 Associate Professor, Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology Facsimile: (315) 464-7744 Associate Director, Medical Genetics Research Center Telephone: (315) 464-7742 Page 3 of 414 American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 John Wiley & Sons, Inc. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Page 4 of 414 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 Response 1 Continued [Excerpt from Discussion]: “The LDSC approach featured here attempts to quantitate 30 similarities and differences in association signals across the entire genome. Some of our phenotype pairs have 31 been examined previously using genome-wide assessment methods, yielding apparently contradictory 32 findings.24,25,71 For example, a previous study implementing a REML-based approach did not find significant 33 SNP-based co-heritabilities between CD and the major psychiatric phenotypes.105 Additionally, the first study 34 implementing the LDSC method found no significant correlation (rg = 0.08 ± 0.08, uncorrected p = 0.33) between 35 BD and UC;22 this study used a smaller data set for BD (Sklar et al., 2011; N = 16,731) and a different version of 36 the UC data set (reported as Jostins et al.,2012; N = 27,432). A similar non-correlation is also reported in LD- 37 Hub (http://ldsc.broadinstitute.org/), using what appears to be the same data sets, although referencing a related 38 article (Liu et al., 2015; N = 27,432). The analyses portrayed in our main text utilized a larger BD data set (Hou 39 et al., N = 40,225), the same data set for UC (Liu et al., 2015; N = 27,432), and uniform criteria for SNP retention 40 based on inclusion in the HapMap3 panel and MAF > 5 % within the 1000 Genomes Project Phase 3 European 41 samples.
Load more

Recommended publications
  • “A Very Stinking Herbe” Is Cilantro Love/Hate a Genetic Trait? So Fresh Or So Clean? Methods Results and Discussion Referenc
    A Genetic Variant Near Olfactory Receptor Genes Associates With Cilantro Preference S. Wu, J.Y. Tung, A.B. Chowdry, A.K. Kiefer, J.L. Mountain, N. Eriksson 23andMe, Inc., Mountain View, CA “A very stinking herbe”! Many people love the taste of fresh cilantro (Coriandrum sativum, also called coriander), while Oh, coriander.! others despise it with every fiber of their being. The debate has raged for centuries. Pliny extolled If "pure evil# had a taste?! its “refreshing” properties, a medieval herbalist claimed that the leaves had a “venomous quality,” It would taste like you.! by SilverSpoon at and, more recently, beloved culinary icon Julia Child opined that cilantro had “kind of a dead taste” IHateCilantro.com! and that she would “pick it out if [she] saw it and throw it on the floor.”! So fresh or so clean?! Is cilantro love/hate a genetic trait?! Cilantro"s dual nature may lie in its key aroma components, which consist of It is not known why cilantro is so differentially perceived. Cilantro various aldehydes. The unsaturated aldehydes (mostly decanal and preference varies with ancestry and may be influenced by dodecanal) are described as fruity, green, and pungent; the (E)-2-alkenals exposure. Genetic factors are also thought to play a role but to as soapy, fatty, pungent, and “like cilantro.” Of the many descriptors that date no studies have found genetic variants influencing cilantro “haters” use, soapy is the most common.! taste preference. ! Methods! Results and discussion! We conducted the first-ever genome-wide association study of cilantro preference. Participants were drawn from the more than 180,000 genotyped customers of 23andMe, Inc., a consumer genetics company.! Phenotype data collection! Participants reported their age and answered Figure 1.
    [Show full text]
  • Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
    bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. 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 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity.
    [Show full text]
  • The Title of the Article
    Mechanism-Anchored Profiling Derived from Epigenetic Networks Predicts Outcome in Acute Lymphoblastic Leukemia Xinan Yang, PhD1, Yong Huang, MD1, James L Chen, MD1, Jianming Xie, MSc2, Xiao Sun, PhD2, Yves A Lussier, MD1,3,4§ 1Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637 USA 2State Key Laboratory of Bioelectronics, Southeast University, 210096 Nanjing, P.R.China 3The University of Chicago Cancer Research Center, and The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL 60637 USA 4The Institute for Genomics and Systems Biology, and the Computational Institute, The University of Chicago, Chicago, IL 60637 USA §Corresponding author Email addresses: XY: [email protected] YH: [email protected] JC: [email protected] JX: [email protected] XS: [email protected] YL: [email protected] - 1 - Abstract Background Current outcome predictors based on “molecular profiling” rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate “mechanism-anchored profiling”. We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.
    [Show full text]
  • Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
    Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together.
    [Show full text]
  • Psychostimulant-Regulated Plasticity in Interneurons of the Nucleus Accumbens
    Psychostimulant-Regulated Plasticity in Interneurons of the Nucleus Accumbens by David A. Gallegos Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Anne E. West, Supervisor ___________________________ Jorg Grandl ___________________________ Debra Silver ___________________________ Gregory Crawford ___________________________ Hiro Matsunami Psychostimulant-Regulated Epigenetic Plasticity in Interneurons of the Nucleus Accumbens submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2019 ABSTRACT Psychostimulant-Regulated Epigenetic Plasticity in Interneurons of the Nucleus Accumbens by David A. Gallegos Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Anne E. West, Supervisor ___________________________ Jorg Grandl ___________________________ Debra Silver ___________________________ Gregory Crawford ___________________________ Hiro Matsunami An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2019 Copyright by David Andres Gallegos 2019 Abstract Exposure to psychostimulant drugs of abuse exerts lasting influences on brain function via the regulation of immediate and persistent gene transcription. These changes in gene transcription drive the development of addictive-like
    [Show full text]
  • Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers
    Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers. by Kelly Kennaley A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Molecular and Cellular Pathology) in the University of Michigan 2019 Doctoral Committee: Associate Professor Zaneta Nikolovska-Coleska, Co-Chair Adjunct Associate Professor Scott A. Tomlins, Co-Chair Associate Professor Eric R. Fearon Associate Professor Alexey I. Nesvizhskii Kelly R. Kennaley [email protected] ORCID iD: 0000-0003-2439-9020 © Kelly R. Kennaley 2019 Acknowledgements I have immeasurable gratitude for the unwavering support and guidance I received throughout my dissertation. First and foremost, I would like to thank my thesis advisor and mentor Dr. Scott Tomlins for entrusting me with a challenging, interesting, and impactful project. He taught me how to drive a project forward through set-backs, ask the important questions, and always consider the impact of my work. I’m truly appreciative for his commitment to ensuring that I would get the most from my graduate education. I am also grateful to the many members of the Tomlins lab that made it the supportive, collaborative, and educational environment that it was. I would like to give special thanks to those I’ve worked closely with on this project, particularly Dr. Moloy Goswami for his mentorship, Lei Lucy Wang, Dr. Sumin Han, and undergraduate students Bhavneet Singh, Travis Weiss, and Myles Barlow. I am also grateful for the support of my thesis committee, Dr. Eric Fearon, Dr. Alexey Nesvizhskii, and my co-mentor Dr. Zaneta Nikolovska-Coleska, who have offered guidance and critical evaluation since project inception.
    [Show full text]
  • 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.
    [Show full text]
  • Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
    Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen,
    [Show full text]
  • (MINA) (NM 032778) Human Tagged ORF Clone Product Data
    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC212554L4 MINA53 (MINA) (NM_032778) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: MINA53 (MINA) (NM_032778) Human Tagged ORF Clone Tag: mGFP Symbol: RIOX2 Synonyms: JMJD10; MDIG; MINA; MINA53; NO52; ROX Vector: pLenti-C-mGFP-P2A-Puro (PS100093) E. coli Selection: Chloramphenicol (34 ug/mL) Cell Selection: Puromycin ORF Nucleotide The ORF insert of this clone is exactly the same as(RC212554). Sequence: Restriction Sites: SgfI-MluI Cloning Scheme: ACCN: NM_032778 ORF Size: 1392 bp This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 MINA53 (MINA) (NM_032778) Human Tagged ORF Clone – RC212554L4 OTI Disclaimer: The molecular sequence of this clone aligns with the gene accession number as a point of reference only. However, individual transcript sequences of the same gene can differ through naturally occurring variations (e.g. polymorphisms), each with its own valid existence. This clone is substantially in agreement with the reference, but a complete review of all prevailing variants is recommended prior to use. More info OTI Annotation: This clone was engineered to express the complete ORF with an expression tag. Expression varies depending on the nature of the gene. RefSeq: NM_032778.3, NP_116167.3 RefSeq Size: 2221 bp RefSeq ORF: 1395 bp Locus ID: 84864 UniProt ID: Q8IUF8 Protein Families: Druggable Genome MW: 52.5 kDa Gene Summary: MINA is a c-Myc (MYC; MIM 190080) target gene that may play a role in cell proliferation or regulation of cell growth.
    [Show full text]
  • Inhibiting TG2 Sensitizes Lung Cancer to Radiotherapy Through Interfering
    bioRxiv preprint doi: https://doi.org/10.1101/597112; this version posted April 6, 2019. 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 Title page 2 3 Inhibiting TG2 sensitizes lung cancer to radiotherapy through interfering 4 TOPOIIα-mediated DNA repair 5 6 Xiao Lei#, Zhe Liu#, Kun Cao#, Yuanyuan Chen#, Jianming Cai, Fu Gao*, Yanyong 7 Yang* 8 9 #Authors contributed equally to this work. 10 11 Department of Radiation Medicine, Faculty of Naval Medicine, Second Military 12 Medical University, 800, Xiangyin Road, 200433, Shanghai, P.R. China; 13 14 *Corresponding author: Yanyong Yang, Fu Gao and Jianming Cai. 15 Address: Department of Radiation Medicine, Faculty of Naval Medicine, Second 16 Military Medical University; 800, Xiangyin Road, 200433, Shanghai, P.R. China. Fax: 17 +86-21-81871148. E-mail: [email protected], [email protected], 18 [email protected]; 19 20 Running title: Targeting TG2 sensitizes lung cancer to radiotherapy 21 22 Keywords: TG2, Radiosensitization, TOPOIIα, NSCLC, DNA repair 23 24 Conflicts of interest 25 The authors have no conflicts of interest to disclose. 26 1 bioRxiv preprint doi: https://doi.org/10.1101/597112; this version posted April 6, 2019. 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. 27 Abstract 28 Radiotherapy is an indispensable strategy for lung cancer, however, treatment failure 29 or reoccurrence is often found in patients due to the developing radioresistance.
    [Show full text]
  • CSE642 Final Version
    Eindhoven University of Technology MASTER Dimensionality reduction of gene expression data Arts, S. Award date: 2018 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Eindhoven University of Technology MASTER THESIS Dimensionality Reduction of Gene Expression Data Author: S. (Sako) Arts Daily Supervisor: dr. V. (Vlado) Menkovski Graduation Committee: dr. V. (Vlado) Menkovski dr. D.C. (Decebal) Mocanu dr. N. (Nikolay) Yakovets May 16, 2018 v1.0 Abstract The focus of this thesis is dimensionality reduction of gene expression data. I propose and test a framework that deploys linear prediction algorithms resulting in a reduced set of selected genes relevant to a specified case. Abstract In cancer research there is a large need to automate parts of the process of diagnosis, this is mainly to reduce cost, make it faster and more accurate.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]