H00010067-M01 規格 : [ 100 Ug ] List All

Total Page:16

File Type:pdf, Size:1020Kb

H00010067-M01 規格 : [ 100 Ug ] List All SCAMP3 monoclonal antibody (M01), clone 1F6 Catalog # : H00010067-M01 規格 : [ 100 ug ] List All Specification Application Image Product Mouse monoclonal antibody raised against a partial recombinant Western Blot (Recombinant protein) Description: SCAMP3. Sandwich ELISA (Recombinant Immunogen: SCAMP3 (NP_005689, 70 a.a. ~ 169 a.a) partial recombinant protein protein) with GST tag. MW of the GST tag alone is 26 KDa. Sequence: QPSRKLSPTEPKNYGSYSTQASAAAATAELLKKQEELNRKAEELDRRER ELQHAALGGTATRQNNWPPLPSFCPVQPCFFQDISMEIPQEFQKTVSTM YY Host: Mouse enlarge Reactivity: Human ELISA Isotype: IgG1 Kappa Quality Control Antibody Reactive Against Recombinant Protein. Testing: Western Blot detection against Immunogen (36.74 KDa) . Storage Buffer: In 1x PBS, pH 7.4 Storage Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. Instruction: MSDS: Download Datasheet: Download Applications Western Blot (Recombinant protein) Protocol Download Sandwich ELISA (Recombinant protein) Page 1 of 2 2016/5/22 Detection limit for recombinant GST tagged SCAMP3 is approximately 0.3ng/ml as a capture antibody. Protocol Download ELISA Gene Information Entrez GeneID: 10067 GeneBank NM_005698 Accession#: Protein NP_005689 Accession#: Gene Name: SCAMP3 Gene Alias: C1orf3 Gene secretory carrier membrane protein 3 Description: Omim ID: 606913 Gene Ontology: Hyperlink Gene Summary: This gene product belongs to the SCAMP family of proteins which are secretory carrier membrane proteins. They function as carriers to the cell surface in post-golgi recycling pathways. Different family members are highly related products of distinct genes, and are usually expressed together. These findings suggest that the SCAMPs may function at the same site during vesicular transport rather than in separate pathways. Two transcript variants encoding different isoforms have been found for this gene. [provided by RefSeq Other OTTHUMP00000034066,propin 1 Designations: 服務條款 | 隱私權政策 | 著作及商標 | 網站地圖 ©2016 亞諾法生技股份有限公司 Abnova Corporation. 版權所有. Page 2 of 2 2016/5/22.
Recommended publications
  • Supplementary Materials
    Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine
    [Show full text]
  • Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways
    biomolecules Article Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways Aayushi Srivastava 1,2,3,4 , Abhishek Kumar 1,5,6 , Sara Giangiobbe 1, Elena Bonora 7, Kari Hemminki 1, Asta Försti 1,2,3 and Obul Reddy Bandapalli 1,2,3,* 1 Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany; [email protected] (A.S.); [email protected] (A.K.); [email protected] (S.G.); [email protected] (K.H.); [email protected] (A.F.) 2 Hopp Children’s Cancer Center (KiTZ), D-69120 Heidelberg, Germany 3 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), D-69120 Heidelberg, Germany 4 Medical Faculty, Heidelberg University, D-69120 Heidelberg, Germany 5 Institute of Bioinformatics, International Technology Park, Bangalore 560066, India 6 Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India 7 S.Orsola-Malphigi Hospital, Unit of Medical Genetics, 40138 Bologna, Italy; [email protected] * Correspondence: [email protected]; Tel.: +49-6221-42-1709 Received: 29 August 2019; Accepted: 10 October 2019; Published: 13 October 2019 Abstract: Evidence of familial inheritance in non-medullary thyroid cancer (NMTC) has accumulated over the last few decades. However, known variants account for a very small percentage of the genetic burden. Here, we focused on the identification of common pathways and networks enriched in NMTC families to better understand its pathogenesis with the final aim of identifying one novel high/moderate-penetrance germline predisposition variant segregating with the disease in each studied family.
    [Show full text]
  • 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.
    [Show full text]
  • A Trafficome-Wide Rnai Screen Reveals Deployment of Early and Late Secretory Host Proteins and the Entire Late Endo-/Lysosomal V
    bioRxiv preprint doi: https://doi.org/10.1101/848549; this version posted November 19, 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 4.0 International license. 1 A trafficome-wide RNAi screen reveals deployment of early and late 2 secretory host proteins and the entire late endo-/lysosomal vesicle fusion 3 machinery by intracellular Salmonella 4 5 Alexander Kehl1,4, Vera Göser1, Tatjana Reuter1, Viktoria Liss1, Maximilian Franke1, 6 Christopher John1, Christian P. Richter2, Jörg Deiwick1 and Michael Hensel1, 7 8 1Division of Microbiology, University of Osnabrück, Osnabrück, Germany; 2Division of Biophysics, University 9 of Osnabrück, Osnabrück, Germany, 3CellNanOs – Center for Cellular Nanoanalytics, Fachbereich 10 Biologie/Chemie, Universität Osnabrück, Osnabrück, Germany; 4current address: Institute for Hygiene, 11 University of Münster, Münster, Germany 12 13 Running title: Host factors for SIF formation 14 Keywords: siRNA knockdown, live cell imaging, Salmonella-containing vacuole, Salmonella- 15 induced filaments 16 17 Address for correspondence: 18 Alexander Kehl 19 Institute for Hygiene 20 University of Münster 21 Robert-Koch-Str. 4148149 Münster, Germany 22 Tel.: +49(0)251/83-55233 23 E-mail: [email protected] 24 25 or bioRxiv preprint doi: https://doi.org/10.1101/848549; this version posted November 19, 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 4.0 International license.
    [Show full text]
  • Chromatin Conformation Links Distal Target Genes to CKD Loci
    BASIC RESEARCH www.jasn.org Chromatin Conformation Links Distal Target Genes to CKD Loci Maarten M. Brandt,1 Claartje A. Meddens,2,3 Laura Louzao-Martinez,4 Noortje A.M. van den Dungen,5,6 Nico R. Lansu,2,3,6 Edward E.S. Nieuwenhuis,2 Dirk J. Duncker,1 Marianne C. Verhaar,4 Jaap A. Joles,4 Michal Mokry,2,3,6 and Caroline Cheng1,4 1Experimental Cardiology, Department of Cardiology, Thoraxcenter Erasmus University Medical Center, Rotterdam, The Netherlands; and 2Department of Pediatrics, Wilhelmina Children’s Hospital, 3Regenerative Medicine Center Utrecht, Department of Pediatrics, 4Department of Nephrology and Hypertension, Division of Internal Medicine and Dermatology, 5Department of Cardiology, Division Heart and Lungs, and 6Epigenomics Facility, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands ABSTRACT Genome-wide association studies (GWASs) have identified many genetic risk factors for CKD. However, linking common variants to genes that are causal for CKD etiology remains challenging. By adapting self-transcribing active regulatory region sequencing, we evaluated the effect of genetic variation on DNA regulatory elements (DREs). Variants in linkage with the CKD-associated single-nucleotide polymorphism rs11959928 were shown to affect DRE function, illustrating that genes regulated by DREs colocalizing with CKD-associated variation can be dysregulated and therefore, considered as CKD candidate genes. To identify target genes of these DREs, we used circular chro- mosome conformation capture (4C) sequencing on glomerular endothelial cells and renal tubular epithelial cells. Our 4C analyses revealed interactions of CKD-associated susceptibility regions with the transcriptional start sites of 304 target genes. Overlap with multiple databases confirmed that many of these target genes are involved in kidney homeostasis.
    [Show full text]
  • (Cnvs) in Hepatocellular Carcinoma; in Silico Analysis
    The pattern of gene copy number variations (CNVs) in hepatocellular carcinoma; in silico analysis Hossein Ansari Islamic Azad University Arman Shahrisa Tarbiat Modares University Faculty of Biological Sciences Maryam Tahmaseby ( [email protected] ) Ahvaz Jondishapour University of Medical Sciences Zahra Mohammadi Ahvaz Jondishapour University of Medical Sciences Vinicio Carloni Florence University Javad Mohammadi Asl Ahvaz Jondishapour University of Medical Sciences Research Keywords: Hepatocellular carcinoma, copy number alteration, RNA dysregulation, 8p, 1q Posted Date: April 6th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-20514/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/12 Abstract Cancer-associated death is the second leading cause of death worldwide. Study of the involved molecular networks of cancers can identify the potential target for early diagnosis, ecient therapies, and predictive prognosis of patients with cancer. Copy number variations are one type of DNA mutations which has been connected with human cancers. The CNVs can be used to nd the regions of the genome involved in cancer phenotypes. This study is aimed to perform genome-wide chromosomal CNVs in HCC samples to nd hotspot regions of disease using in silico analysis. The obtained data showed that gain of chromosome 1q and loss of 8p were frequently observed in target cancerous tissues. All the gains and losses were associated with tumor grade and metastasis. However, the amplication of YY1AP1 (Yin Yang-1 Associated Protein 1) and deletion of CHMP7 (Charged Multivesicular Body Protein 7) were observed in most of patients. These expression levels of YY1AP1 and CHMP7 were also upregulated and downregulated in cancerous samples respectively.
    [Show full text]
  • Identification of Hub Genes Associated with Hepatocellular Carcinoma Prognosis by Bioinformatics Analysis
    Journal of Cancer Therapy, 2021, 12, 186-207 https://www.scirp.org/journal/jct ISSN Online: 2151-1942 ISSN Print: 2151-1934 Identification of Hub Genes Associated with Hepatocellular Carcinoma Prognosis by Bioinformatics Analysis Xi Zhang*, Xiaojun Luo*, Wenbin Liu, Ai Shen# Department of Hepatobiliary and Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, China How to cite this paper: Zhang, X., Luo, Abstract X.J., Liu, W.B. and Shen, A. (2021) Identi- fication of Hub Genes Associated with He- Objective: This study aimed to identify hub genes that are associated with patocellular Carcinoma Prognosis by Bio- hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. Me- informatics Analysis. Journal of Cancer The- thods: Data were collected from the Gene Expression Omnibus (GEO) and rapy, 12, 186-207. https://doi.org/10.4236/jct.2021.124019 The Cancer Genome Atlas (TCGA) liver HCC datasets. The robust rank ag- gregation algorithm was used in integrating the data on differentially expressed Received: March 23, 2021 genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for Accepted: April 26, 2021 gene ontology and pathway enrichment analysis. R software version 3.5.1, Cy- Published: April 29, 2021 toscape, and Kaplan-Meier plotter were used to identify hub genes. Results: Copyright © 2021 by author(s) and Six GEO datasets and the TCGA liver HCC dataset were included in this Scientific Research Publishing Inc. analysis. A total of 151 upregulated and 245 downregulated DEGs were iden- This work is licensed under the Creative tified. The upregulated DEGs most significantly enriched in the functional Commons Attribution International License (CC BY 4.0).
    [Show full text]
  • Upstream Open Reading Frames Cause Widespread Reduction of Protein Expression and Are Polymorphic Among Humans
    Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans Sarah E. Calvoa,b,c,d,1, David J. Pagliarinia,b,c,1, and Vamsi K. Moothaa,b,c,2 aBroad Institute of MIT and Harvard, Cambridge, MA 02142; bCenter for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114; cDepartment of Systems Biology, Harvard Medical School, Boston, MA 02115; and dDivision of Health Sciences and Technology, Harvard–MIT, Cambridge, MA 02139 Edited by Jonathan Weissman, University of California, San Francisco, CA, and accepted by the Editorial Board March 18, 2009 (received for review October 29, 2008) Upstream ORFs (uORFs) are mRNA elements defined by a start codon in the 5؅ UTR that is out-of-frame with the main coding sequence. A cap 5’ UTR main coding sequence 3’ UTR polyA Although uORFs are present in approximately half of human and AUG AUG AUG mouse transcripts, no study has investigated their global impact on AAAAAA protein expression. Here, we report that uORFs correlate with signif- uORF uORF icantly reduced protein expression of the downstream ORF, based on analysis of 11,649 matched mRNA and protein measurements from 4 B # Transcripts with: Human Mouse published mammalian studies. Using reporter constructs to test 25 annotated 5' UTR 23775 18663 selected uORFs, we estimate that uORFs typically reduce protein ≥1 uORF 11670 8253 expression by 30–80%, with a modest impact on mRNA levels. We ≥2 uORFs 6268 4197 additionally identify polymorphisms that alter uORF presence in 509 ≥1 uORF fully upstream 9879 6935 human genes. Finally, we report that 5 uORF-altering mutations, ≥1 uORF overlapping CDS 4275 2872 GENETICS detected within genes previously linked to human diseases, dramat- Median Length (nt): ically silence expression of the downstream protein.
    [Show full text]
  • Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2010 Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Renuka Nayak University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computational Biology Commons, and the Genomics Commons Recommended Citation Nayak, Renuka, "Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress" (2010). Publicly Accessible Penn Dissertations. 1559. https://repository.upenn.edu/edissertations/1559 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1559 For more information, please contact [email protected]. Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Abstract Genes interact in networks to orchestrate cellular processes. Here, we used coexpression networks based on natural variation in gene expression to study the functions and interactions of human genes. We asked how these networks change in response to stress. First, we studied human coexpression networks at baseline. We constructed networks by identifying correlations in expression levels of 8.9 million gene pairs in immortalized B cells from 295 individuals comprising three independent samples. The resulting networks allowed us to infer interactions between biological processes. We used the network to predict the functions of poorly-characterized human genes, and provided some experimental support. Examining genes implicated in disease, we found that IFIH1, a diabetes susceptibility gene, interacts with YES1, which affects glucose transport. Genes predisposing to the same diseases are clustered non-randomly in the network, suggesting that the network may be used to identify candidate genes that influence disease susceptibility.
    [Show full text]
  • Patterns of Sequence Conservation in Presynaptic Neural Genes
    University of Pennsylvania ScholarlyCommons Departmental Papers (CIS) Department of Computer & Information Science November 2006 Patterns of Sequence Conservation in Presynaptic Neural Genes Dexter Hadley University of Pennsylvania Tara Murphy University of Pennsylvania Otto Valladares University of Pennsylvania Sridhar Hannenhalli University of Pennsylvania Lyle H. Ungar University of Pennsylvania, [email protected] See next page for additional authors Follow this and additional works at: https://repository.upenn.edu/cis_papers Recommended Citation Dexter Hadley, Tara Murphy, Otto Valladares, Sridhar Hannenhalli, Lyle H. Ungar, Junhyong Kim, and Maja Bucan, "Patterns of Sequence Conservation in Presynaptic Neural Genes", . November 2006. Reprinted from Genome Biology, Volume 7, Issue 11, November 2006, pages R105.1-R105.19. Publisher URL: http://genomebiology.com/2006/7/11/R105 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_papers/282 For more information, please contact [email protected]. Patterns of Sequence Conservation in Presynaptic Neural Genes Abstract Background: The neuronal synapse is a fundamental functional unit in the central nervous system of animals. Because synaptic function is evolutionarily conserved, we reasoned that functional sequences of genes and related genomic elements known to play important roles in neurotransmitter release would also be conserved. Results: Evolutionary rate analysis revealed that presynaptic proteins evolve slowly, although some members of large gene families exhibit accelerated evolutionary rates relative to other family members. Comparative sequence analysis of 46 megabases spanning 150 presynaptic genes identified more than 26,000 elements that are highly conserved in eight vertebrate species, as well as a small subset of sequences (6%) that are shared among unrelated presynaptic genes.
    [Show full text]
  • Mouse Scamp3 Conditional Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Scamp3 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Scamp3 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Scamp3 gene (NCBI Reference Sequence: NM_011886 ; Ensembl: ENSMUSG00000028049 ) is located on Mouse chromosome 3. 9 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 9 (Transcript: ENSMUST00000029684). Exon 3~4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Scamp3 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-214B5 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 3 starts from about 13.85% of the coding region. The knockout of Exon 3~4 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 1027 bp, and the size of intron 4 for 3'-loxP site insertion: 814 bp. The size of effective cKO region: ~846 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 13 1 2 3 4 5 6 7 9 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Clk2 Homology arm Exon of mouse Scamp3 cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • Genome-Wide Association Studies Identify Genetic Loci Associated With
    Page 1 of 86 Diabetes Genome-wide Association Studies Identify Genetic Loci Associated with Albuminuria in Diabetes Alexander Teumer1,2*, Adrienne Tin3*, Rossella Sorice4*, Mathias Gorski5,6*, Nan Cher Yeo7*, Audrey Y. Chu8,9, Man Li3, Yong Li10, Vladan Mijatovic11, Yi-An Ko12, Daniel Taliun13, Alessandro Luciani14, Ming-Huei Chen15,16, Qiong Yang16, Meredith C. Foster17, Matthias Olden5,18, Linda T. Hiraki19, Bamidele O. Tayo20, Christian Fuchsberger13, Aida Karina Dieffenbach21,22, Alan R. Shuldiner23, Albert V. Smith24,25, Allison M. Zappa26, Antonio Lupo27, Barbara Kollerits28, Belen Ponte29, Bénédicte Stengel30,31, Bernhard K. Krämer32, Bernhard Paulweber33, Braxton D. Mitchell23, Caroline Hayward34, Catherine Helmer35, Christa Meisinger36, Christian Gieger37, Christian M. Shaffer38, Christian Müller39,40, Claudia Langenberg41, Daniel Ackermann42, David Siscovick43, DCCT/EDIC44, Eric Boerwinkle45, Florian Kronenberg28, Georg B. Ehret46, Georg Homuth47, Gerard Waeber48, Gerjan Navis49, Giovanni Gambaro50, Giovanni Malerba11, Gudny Eiriksdottir24, Guo Li43, H. Erich Wichmann51-53, Harald Grallert36,54,55, Henri Wallaschofski56, Henry Völzke1,2, Herrmann Brenner57, Holly Kramer20, I. Mateo Leach58, Igor Rudan59, J.L. Hillege60, Jacques S. Beckmann61,62, Jean Charles Lambert63, Jian'an Luan41, Jing Hua Zhao41, John Chalmers64, Josef Coresh3,65, Joshua C. Denny66, Katja Butterbach57, Lenore J. Launer67, Luigi Ferrucci68, Lyudmyla Kedenko33, Margot Haun28, Marie Metzger30,31, Mark Woodward3,64,69, Matthew J. Hoffman7, Matthias Nauck2,56, Melanie Waldenberger36, Menno Pruijm70, Murielle Bochud71, Myriam Rheinberger72, N. Verweij58, Nicholas J. Wareham41, Nicole Endlich73, Nicole Soranzo74,75, Ozren Polasek76, P. van der Harst60, Peter Paul Pramstaller13, Peter Vollenweider48, Philipp S. Wild77-79, R.T. Gansevoort60, Rainer Rettig80, Reiner Biffar81, Robert J. Carroll66, Ronit Katz82, Ruth J.F. Loos41,83, Shih-Jen Hwang9, Stefan Coassin28, Sven Bergmann84, Sylvia E.
    [Show full text]