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Wahl S, Vogt S, Stückler F, Krumsiek J, Bartel J, Kacprowski T, Schramm K, Carstensen M, Rathmann W, Roden M, Jourdan C, Kangas AJ, Soininen P, Ala-Korpela M, Nöthlings U, Boeing H, Theis FJ, Meisinger C, Waldenberger M, Suhre K, Homuth G, Gieger C, Kastenmüller G, Illig T, Linseisen J, Peters A, Prokisch H, Herder C, Thorand B, Grallert H. Multi-omic signature of body weight change: results from a population-based cohort study. BMC Med 2015; 13:48. [PMID: 25857605 PMCID: PMC4367822 DOI: 10.1186/s12916-015-0282-y] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 01/20/2015] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study. METHODS We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits. RESULTS Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules. CONCLUSIONS Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.
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Pfeiffer L, Wahl S, Pilling LC, Reischl E, Sandling JK, Kunze S, Holdt LM, Kretschmer A, Schramm K, Adamski J, Klopp N, Illig T, Hedman ÅK, Roden M, Hernandez DG, Singleton AB, Thasler WE, Grallert H, Gieger C, Herder C, Teupser D, Meisinger C, Spector TD, Kronenberg F, Prokisch H, Melzer D, Peters A, Deloukas P, Ferrucci L, Waldenberger M. DNA methylation of lipid-related genes affects blood lipid levels. ACTA ACUST UNITED AC 2015; 8:334-42. [PMID: 25583993 DOI: 10.1161/circgenetics.114.000804] [Citation(s) in RCA: 132] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Accepted: 12/16/2014] [Indexed: 01/03/2023]
Abstract
BACKGROUND Epigenetic mechanisms might be involved in the regulation of interindividual lipid level variability and thus may contribute to the cardiovascular risk profile. The aim of this study was to investigate the association between genome-wide DNA methylation and blood lipid levels high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol. Observed DNA methylation changes were also further analyzed to examine their relationship with previous hospitalized myocardial infarction. METHODS AND RESULTS Genome-wide DNA methylation patterns were determined in whole blood samples of 1776 subjects of the Cooperative Health Research in the Region of Augsburg F4 cohort using the Infinium HumanMethylation450 BeadChip (Illumina). Ten novel lipid-related CpG sites annotated to various genes including ABCG1, MIR33B/SREBF1, and TNIP1 were identified. CpG cg06500161, located in ABCG1, was associated in opposite directions with both high-density lipoprotein cholesterol (β coefficient=-0.049; P=8.26E-17) and triglyceride levels (β=0.070; P=1.21E-27). Eight associations were confirmed by replication in the Cooperative Health Research in the Region of Augsburg F3 study (n=499) and in the Invecchiare in Chianti, Aging in the Chianti Area study (n=472). Associations between triglyceride levels and SREBF1 and ABCG1 were also found in adipose tissue of the Multiple Tissue Human Expression Resource cohort (n=634). Expression analysis revealed an association between ABCG1 methylation and lipid levels that might be partly mediated by ABCG1 expression. DNA methylation of ABCG1 might also play a role in previous hospitalized myocardial infarction (odds ratio, 1.15; 95% confidence interval=1.06-1.25). CONCLUSIONS Epigenetic modifications of the newly identified loci might regulate disturbed blood lipid levels and thus contribute to the development of complex lipid-related diseases.
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Herder C, Nuotio ML, Shah S, Blankenberg S, Brunner EJ, Carstensen M, Gieger C, Grallert H, Jula A, Kähönen M, Kettunen J, Kivimäki M, Koenig W, Kristiansson K, Langenberg C, Lehtimäki T, Luotola K, Marzi C, Müller C, Peters A, Prokisch H, Raitakari O, Rathmann W, Roden M, Salmi M, Schramm K, Swerdlow D, Tabak AG, Thorand B, Wareham N, Wild PS, Zeller T, Hingorani AD, Witte DR, Kumari M, Perola M, Salomaa V. Genetic determinants of circulating interleukin-1 receptor antagonist levels and their association with glycemic traits. Diabetes 2014; 63:4343-59. [PMID: 24969107 PMCID: PMC4237993 DOI: 10.2337/db14-0731] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The proinflammatory cytokine interleukin (IL)-1β is implicated in the development of insulin resistance and β-cell dysfunction, whereas higher circulating levels of IL-1 receptor antagonist (IL-1RA), an endogenous inhibitor of IL-1β, has been suggested to improve glycemia and β-cell function in patients with type 2 diabetes. To elucidate the protective role of IL-1RA, this study aimed to identify genetic determinants of circulating IL-1RA concentration and to investigate their associations with immunological and metabolic variables related to cardiometabolic risk. In the analysis of seven discovery and four replication cohort studies, two single nucleotide polymorphisms (SNPs) were independently associated with circulating IL-1RA concentration (rs4251961 at the IL1RN locus [n = 13,955, P = 2.76 × 10(-21)] and rs6759676, closest gene locus IL1F10 [n = 13,994, P = 1.73 × 10(-17)]). The proportion of the variance in IL-1RA explained by both SNPs combined was 2.0%. IL-1RA-raising alleles of both SNPs were associated with lower circulating C-reactive protein concentration. The IL-1RA-raising allele of rs6759676 was also associated with lower fasting insulin levels and lower HOMA insulin resistance. In conclusion, we show that circulating IL-1RA levels are predicted by two independent SNPs at the IL1RN and IL1F10 loci and that genetically raised IL-1RA may be protective against the development of insulin resistance.
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Affiliation(s)
- Christian Herder
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany
| | - Marja-Liisa Nuotio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland
| | - Sonia Shah
- Centre of Neurogenetics and Statistical Genomics, Queensland Brain Institute, University of Queensland, St. Lucia, QLD, Australia
| | - Stefan Blankenberg
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany German Center for Cardiovascular Research (DZHK e.V.), partner site Hamburg, Lübeck, Kiel, Germany
| | - Eric J Brunner
- Department of Epidemiology and Public Health, University College London, London, U.K
| | - Maren Carstensen
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Harald Grallert
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany German Center for Diabetes Research (DZD e.V.), partner site, Munich, Germany
| | - Antti Jula
- National Institute for Health and Welfare, Turku, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - Johannes Kettunen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland Computational Medicine, Institute of Health Sciences, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, U.K
| | - Wolfgang Koenig
- Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Ulm, Germany
| | - Kati Kristiansson
- Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland
| | - Claudia Langenberg
- Department of Epidemiology and Public Health, University College London, London, U.K. MRC Epidemiology Unit, Cambridge University, Cambridge, U.K
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Kari Luotola
- Department of Obstetrics and Gynecology, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Carola Marzi
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany German Center for Diabetes Research (DZD e.V.), partner site, Munich, Germany
| | - Christian Müller
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany German Center for Cardiovascular Research (DZHK e.V.), partner site Hamburg, Lübeck, Kiel, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany German Center for Diabetes Research (DZD e.V.), partner site, Munich, Germany German Center for Cardiovascular Research (DZHK e.V.), partner site Munich, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Olli Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Wolfgang Rathmann
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Roden
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Marko Salmi
- National Institute for Health and Welfare, Turku, Finland MediCity Research Laboratory, University of Turku, Turku, Finland Department of Medical Microbiology and Immunology, University of Turku, Turku, Finland
| | - Katharina Schramm
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Daniel Swerdlow
- Institute of Cardiovascular Sciences, University College London, London, U.K
| | - Adam G Tabak
- Department of Epidemiology and Public Health, University College London, London, U.K. 1st Department of Medicine, Semmelweis University Faculty of Medicine, Budapest, Hungary
| | - Barbara Thorand
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Nick Wareham
- MRC Epidemiology Unit, Cambridge University, Cambridge, U.K
| | - Philipp S Wild
- Department of Medicine 2, University Medical Center Mainz, Mainz, Germany Center for Thrombosis and Hemostasis, University Medical Center Mainz, Mainz, Germany German Center for Cardiovascular Research (DZHK e.V.), partner site Rhine-Main, Mainz, Germany
| | - Tanja Zeller
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany German Center for Cardiovascular Research (DZHK e.V.), partner site Hamburg, Lübeck, Kiel, Germany
| | - Aroon D Hingorani
- Institute of Cardiovascular Sciences, University College London, London, U.K
| | - Daniel R Witte
- Centre de Recherche Public de la Santé, Strassen, Luxembourg
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, U.K
| | - Markus Perola
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Veikko Salomaa
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
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Abstract
The last years have witnessed tremendous technical advances in the field of transcriptomics that enable the simultaneous assessment of nearly all transcripts expressed in a tissue at a given time. These advances harbor the potential to gain a better understanding of the complex biological systems and for the identification and development of novel biomarkers. This article will review the current knowledge of transcriptomics biomarkers in the cardiovascular field and will provide an overview about the promises and challenges of the transcriptomics approach for biomarker identification.
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Affiliation(s)
- Marten Antoon Siemelink
- />Laboratory of Experimental Cardiology, University Medical Center Utrecht, Heidelberglaanes 100 Room G02.523, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - Tanja Zeller
- />Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Martinistr. 52, 20246 Hamburg, Germany
- />German Center for Cardiovascular Research (DZHK), Hamburg/Lübeck/Kiel Partner Site, Hamburg, Germany
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55
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Schulte EC, Schramm K, Schurmann C, Lichtner P, Herder C, Roden M, Gieger C, Peters A, Trenkwalder C, Högl B, Frauscher B, Berger K, Fietze I, Gross N, Stiasny-Kolster K, Oertel W, Bachmann CG, Paulus W, Zimprich A, Völzke H, Schminke U, Nauck M, Illig T, Meitinger T, Müller-Myhsok B, Prokisch H, Winkelmann J. Blood cis-eQTL analysis fails to identify novel association signals among sub-threshold candidates from genome-wide association studies in restless legs syndrome. PLoS One 2014; 9:e98092. [PMID: 24875634 PMCID: PMC4038519 DOI: 10.1371/journal.pone.0098092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2013] [Accepted: 04/28/2014] [Indexed: 11/19/2022] Open
Abstract
Restless legs syndrome (RLS) is a common neurologic disorder characterized by nightly dysesthesias affecting the legs primarily during periods of rest and relieved by movement. RLS is a complex genetic disease and susceptibility factors in six genomic regions have been identified by means of genome-wide association studies (GWAS). For some complex genetic traits, expression quantitative trait loci (eQTLs) are enriched among trait-associated single nucleotide polymorphisms (SNPs). With the aim of identifying new genetic susceptibility factors for RLS, we assessed the 332 best-associated SNPs from the genome-wide phase of the to date largest RLS GWAS for cis-eQTL effects in peripheral blood from individuals of European descent. In 740 individuals belonging to the KORA general population cohort, 52 cis-eQTLs with pnominal<10−3 were identified, while in 976 individuals belonging to the SHIP-TREND general population study 53 cis-eQTLs with pnominal<10−3 were present. 23 of these cis-eQTLs overlapped between the two cohorts. Subsequently, the twelve of the 23 cis-eQTL SNPs, which were not located at an already published RLS-associated locus, were tested for association in 2449 RLS cases and 1462 controls. The top SNP, located in the DET1 gene, was nominally significant (p<0.05) but did not withstand correction for multiple testing (p = 0.42). Although a similar approach has been used successfully with regard to other complex diseases, we were unable to identify new genetic susceptibility factor for RLS by adding this novel level of functional assessment to RLS GWAS data.
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Affiliation(s)
- Eva C. Schulte
- Neurologische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Institut für Humangenetik, Helmholtz Zentrum München, Munich, Germany
| | - Katharina Schramm
- Institut für Humangenetik, Helmholtz Zentrum München, Munich, Germany
- Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Claudia Schurmann
- Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt Universität Greifswald, Greifswald, Germany
| | - Peter Lichtner
- Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), partner Düsseldorf, Düsseldorf, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), partner Düsseldorf, Düsseldorf, Germany
- University Clinics of Endocrinology and Diabetology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Christian Gieger
- Institute for Genetic Epidemiology, Helmholtz Zentrum München, Munich, Germany
| | - Annette Peters
- Institute for Epidemiology II, Helmholtz Zentrum München, Munich, Germany
| | - Claudia Trenkwalder
- Paracelsus Elena Klinik, Kassel, Germany
- Department of Neurosurgery, University Medical Center, Georg August Universität Göttingen, Göttingen, Germany
| | - Birgit Högl
- Neurologische Klinik, Medizinische Universität Innsbruck, Innsbruck, Austria
| | - Birgit Frauscher
- Neurologische Klinik, Medizinische Universität Innsbruck, Innsbruck, Austria
| | - Klaus Berger
- Institut für Epidemiologie und Sozialmedizin, Westfälische Wilhelms Universität Münster, Münster, Germany
| | - Ingo Fietze
- Zentrum für Schlafmedizin, Charite Universitätsmedizin, Berlin, Germany
| | - Nadine Gross
- Neurologische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Karin Stiasny-Kolster
- Neurologische Klinik, Philips Universität Marburg, Marburg, Germany
- Somnomar Institut für Medizinische Forschung und Schlafmedizin, Marburg, Germany
| | - Wolfgang Oertel
- Neurologische Klinik, Philips Universität Marburg, Marburg, Germany
| | | | - Walter Paulus
- Department of Clinical Neurophysiology, University Medical Center, Georg August Universität Göttingen, Göttingen, Germany
| | | | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Ulf Schminke
- Institute of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Munich, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Thomas Meitinger
- Institut für Humangenetik, Helmholtz Zentrum München, Munich, Germany
- Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Bertram Müller-Myhsok
- Max-Planck Institute for Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Holger Prokisch
- Institut für Humangenetik, Helmholtz Zentrum München, Munich, Germany
- Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Juliane Winkelmann
- Neurologische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Institut für Humangenetik, Helmholtz Zentrum München, Munich, Germany
- Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- * E-mail:
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56
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Schramm K, Marzi C, Schurmann C, Carstensen M, Reinmaa E, Biffar R, Eckstein G, Gieger C, Grabe HJ, Homuth G, Kastenmüller G, Mägi R, Metspalu A, Mihailov E, Peters A, Petersmann A, Roden M, Strauch K, Suhre K, Teumer A, Völker U, Völzke H, Wang-Sattler R, Waldenberger M, Meitinger T, Illig T, Herder C, Grallert H, Prokisch H. Mapping the genetic architecture of gene regulation in whole blood. PLoS One 2014; 9:e93844. [PMID: 24740359 PMCID: PMC3989189 DOI: 10.1371/journal.pone.0093844] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Accepted: 03/07/2014] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND We aimed to assess whether whole blood expression quantitative trait loci (eQTLs) with effects in cis and trans are robust and can be used to identify regulatory pathways affecting disease susceptibility. MATERIALS AND METHODS We performed whole-genome eQTL analyses in 890 participants of the KORA F4 study and in two independent replication samples (SHIP-TREND, N = 976 and EGCUT, N = 842) using linear regression models and Bonferroni correction. RESULTS In the KORA F4 study, 4,116 cis-eQTLs (defined as SNP-probe pairs where the SNP is located within a 500 kb window around the transcription unit) and 94 trans-eQTLs reached genome-wide significance and overall 91% (92% of cis-, 84% of trans-eQTLs) were confirmed in at least one of the two replication studies. Different study designs including distinct laboratory reagents (PAXgene™ vs. Tempus™ tubes) did not affect reproducibility (separate overall replication overlap: 78% and 82%). Immune response pathways were enriched in cis- and trans-eQTLs and significant cis-eQTLs were partly coexistent in other tissues (cross-tissue similarity 40-70%). Furthermore, four chromosomal regions displayed simultaneous impact on multiple gene expression levels in trans, and 746 eQTL-SNPs have been previously reported to have clinical relevance. We demonstrated cross-associations between eQTL-SNPs, gene expression levels in trans, and clinical phenotypes as well as a link between eQTLs and human metabolic traits via modification of gene regulation in cis. CONCLUSIONS Our data suggest that whole blood is a robust tissue for eQTL analysis and may be used both for biomarker studies and to enhance our understanding of molecular mechanisms underlying gene-disease associations.
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Affiliation(s)
- Katharina Schramm
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, München, Germany
| | - Carola Marzi
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Claudia Schurmann
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Maren Carstensen
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany
| | - Eva Reinmaa
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Reiner Biffar
- Department of Prosthetic Dentistry, Gerostomatology and Dental Materials, University Medicine Greifswald, Greifswald, Germany
| | - Gertrud Eckstein
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Hans-Jörgen Grabe
- Department of Psychiatry and Psychotherapy, Helios Hospital Stralsund, University Medicine of Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Gabriele Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Evelin Mihailov
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Estonian Biocentre, Tartu, Estonia
| | - Annette Peters
- Institute of Human Genetics, Technical University Munich, München, Germany
| | - Astrid Petersmann
- Institute of Clinical Chemistry and Laboratory Medicine, Greifswald, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany
- Division of Endocrinology and Diabetology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität Munich, Neuherberg, Germany
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Alexander Teumer
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, München, Germany
- Munich Heart Alliance, München, Germany
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), partner site Düsseldorf, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, München, Germany
- * E-mail:
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Herder C, Kowall B, Tabak AG, Rathmann W. The potential of novel biomarkers to improve risk prediction of type 2 diabetes. Diabetologia 2014; 57:16-29. [PMID: 24078135 DOI: 10.1007/s00125-013-3061-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2013] [Accepted: 08/24/2013] [Indexed: 01/05/2023]
Abstract
The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.
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Li J, Yuan J, Cheng KCC, Inglese J, Su XZ. Chemical genomics for studying parasite gene function and interaction. Trends Parasitol 2013; 29:603-11. [PMID: 24215777 DOI: 10.1016/j.pt.2013.10.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 10/08/2013] [Accepted: 10/08/2013] [Indexed: 12/20/2022]
Abstract
With the development of new technologies in genome sequencing, gene expression profiling, genotyping, and high-throughput screening of chemical compound libraries, small molecules are playing increasingly important roles in studying gene expression regulation, gene-gene interaction, and gene function. Here we briefly review and discuss some recent advancements in drug target identification and phenotype characterization using combinations of high-throughput screening of small-molecule libraries and various genome-wide methods such as whole-genome sequencing, genome-wide association studies (GWAS), and genome-wide expression analysis. These approaches can be used to search for new drugs against parasite infections, to identify drug targets or drug resistance genes, and to infer gene function.
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Affiliation(s)
- Jian Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361005, P.R. China
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60
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Tian M, Tian Y, Li Y, Lu H, Li X, Li C, Xue F, Jin N. Microarray multiplex assay for the simultaneous detection and discrimination of influenza a and influenza B viruses. Indian J Microbiol 2013; 54:211-7. [PMID: 25320424 DOI: 10.1007/s12088-013-0432-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2013] [Accepted: 10/13/2013] [Indexed: 10/26/2022] Open
Abstract
In this study, we present a microarray approach for the typing of influenza A and B viruses, and the subtyping of H1 and H3 subtypes. We designed four pairs of specific multiplex RT-PCR primers and eight specific oligonucleotide probes and prepared microarrays to identify the specific subtype of influenza virus. Through amplification and fluorescent marking of the multiplex RT-PCR products on the M gene of influenza A and B viruses and the HA gene of subtypes H1 and H3, the PCR products were hybridized with the microarray, and the results were analyzed using a microarray scanner. The results demonstrate that the chip developed by our research institute can detect influenza A and B viruses specifically and identify the subtypes H1 and H3 at a minimum concentration of 1 × 10(2) copies/μL of viral RNA. We tested 35 clinical samples and our results were identical to other fluorescent methods. The microarray approach developed in this study provides a reliable method for the monitoring and testing of seasonal influenza.
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Affiliation(s)
- Mingyao Tian
- College of Animal Science and Veterinary Medicine, Jilin University, Changchun, 130062 People's Republic of China ; Institute of Military Veterinary, Academy of Military Medical Sciences of PLA, Liuying West Road 666, Changchun, 130122 People's Republic of China
| | - Yufei Tian
- College of Animal Science and Veterinary Medicine, Jilin University, Changchun, 130062 People's Republic of China ; Institute of Military Veterinary, Academy of Military Medical Sciences of PLA, Liuying West Road 666, Changchun, 130122 People's Republic of China
| | - Yang Li
- Department of Respiration, The First Hospital of Jilin University, Changchun, 130021 People's Republic of China
| | - Huijun Lu
- Institute of Military Veterinary, Academy of Military Medical Sciences of PLA, Liuying West Road 666, Changchun, 130122 People's Republic of China
| | - Xiao Li
- Institute of Military Veterinary, Academy of Military Medical Sciences of PLA, Liuying West Road 666, Changchun, 130122 People's Republic of China
| | - Chang Li
- Institute of Military Veterinary, Academy of Military Medical Sciences of PLA, Liuying West Road 666, Changchun, 130122 People's Republic of China
| | - Fei Xue
- Institute of Military Veterinary, Academy of Military Medical Sciences of PLA, Liuying West Road 666, Changchun, 130122 People's Republic of China
| | - Ningyi Jin
- Institute of Military Veterinary, Academy of Military Medical Sciences of PLA, Liuying West Road 666, Changchun, 130122 People's Republic of China
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McHale CM, Zhang L, Thomas R, Smith MT. Analysis of the transcriptome in molecular epidemiology studies. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2013; 54:500-517. [PMID: 23907930 PMCID: PMC5142298 DOI: 10.1002/em.21798] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Revised: 06/07/2013] [Accepted: 06/08/2013] [Indexed: 05/29/2023]
Abstract
The human transcriptome is complex, comprising multiple transcript types, mostly in the form of non-coding RNA (ncRNA). The majority of ncRNA is of the long form (lncRNA, ≥ 200 bp), which plays an important role in gene regulation through multiple mechanisms including epigenetics, chromatin modification, control of transcription factor binding, and regulation of alternative splicing. Both mRNA and ncRNA exhibit additional variability in the form of alternative splicing and RNA editing. All aspects of the human transcriptome can potentially be dysregulated by environmental exposures. Next-generation RNA sequencing (RNA-Seq) is the best available methodology to measure this although it has limitations, including experimental bias. The third phase of the MicroArray Quality Control Consortium project (MAQC-III), also called Sequencing Quality Control (SeQC), aims to address these limitations through standardization of experimental and bioinformatic methodologies. A limited number of toxicogenomic studies have been conducted to date using RNA-Seq. This review describes the complexity of the human transcriptome, the application of transcriptomics by RNA-Seq or microarray in molecular epidemiology studies, and limitations of these approaches including the type of cell or tissue analyzed, experimental variation, and confounding. By using good study designs with precise, individual exposure measurements, sufficient power and incorporation of phenotypic anchors, studies in human populations can identify biomarkers of exposure and/or early effect and elucidate mechanisms of action underlying associated diseases, even at low doses. Analysis of datasets at the pathway level can compensate for some of the limitations of RNA-Seq and, as more datasets become available, will increasingly elucidate the exposure-disease continuum.
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Affiliation(s)
- Cliona M McHale
- Division of Environmental Health Sciences, Genes and Environment Laboratory, School of Public Health, University of California, Berkeley, California 94720, USA.
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Genome-Wide Detection of Gene Coexpression Domains Showing Linkage to Regions Enriched with Polymorphic Retrotransposons in Recombinant Inbred Mouse Strains. G3-GENES GENOMES GENETICS 2013; 3:597-605. [PMID: 23550129 PMCID: PMC3618347 DOI: 10.1534/g3.113.005546] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Although gene coexpression domains have been reported in most eukaryotic organisms, data available to date suggest that coexpression rarely concerns more than doublets or triplets of adjacent genes in mammals. Using expression data from hearts of mice from the panel of AxB/BxA recombinant inbred mice, we detected (according to window sizes) 42−53 loci linked to the expression levels of clusters of three or more neighboring genes. These loci thus formed “cis-expression quantitative trait loci (eQTL) clusters” because their position matched that of the genes whose expression was linked to the loci. Compared with matching control regions, genes contained within cis-eQTL clusters showed much greater levels of coexpression. Corresponding regions showed: (1) a greater abundance of polymorphic elements (mostly short interspersed element retrotransposons), and (2) significant enrichment for the motifs of binding sites for various transcription factors, with binding sites for the chromatin-organizing CCCTC-binding factor showing the greatest levels of enrichment in polymorphic short interspersed elements. Similar cis-eQTL clusters also were detected when we used data obtained with several tissues from BxD recombinant inbred mice. In addition to strengthening the evidence for gene expression domains in mammalian genomes, our data suggest a possible mechanism whereby noncoding polymorphisms could affect the coordinate expression of several neighboring genes.
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