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Search Results: 1 - 10 of 14764 matches for " Christian Gieger "
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On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies
Ann-Kristin Petersen, Jan Krumsiek, Brigitte W?gele, Fabian J Theis, H.-Erich Wichmann, Christian Gieger, Karsten Suhre
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-120
Abstract: Here we explore the statistical properties of the p-gain through simulation of its density and by sampling of experimental data. We derive critical values of the p-gain for different levels of correlation between metabolite pairs and show that B/(2*α) is a conservative critical value for the p-gain, where α is the level of significance and B the number of tested metabolite pairs.We show that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and provide a conservative significance cut-off for the p-gain for use in future association studies with metabolic traits.
Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality
Johannes Raffler?,Nele Friedrich?,Matthias Arnold?,Tim Kacprowski?,Rico Rueedi?,Elisabeth Altmaier?,Sven Bergmann?,Kathrin Budde?,Christian Gieger,Georg Homuth
PLOS Genetics , 2015, DOI: 10.1371/journal.pgen.1005487
Abstract: Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR analysis of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases.
The Human Blood Metabolome-Transcriptome Interface
J?rg Bartel?,Jan Krumsiek?,Katharina Schramm?,Jerzy Adamski?,Christian Gieger,Christian Herder?,Maren Carstensen?,Annette Peters?,Wolfgang Rathmann?,Michael Roden
PLOS Genetics , 2015, DOI: 10.1371/journal.pgen.1005274
Abstract: Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease.
Database Optimizing Services
Database Systems Journal , 2010,
Abstract: Almost every organization has at its centre a database. The database provides support for conducting different activities, whether it is production, sales and marketing or internal operations. Every day, a database is accessed for help in strategic decisions. The satisfaction therefore of such needs is entailed with a high quality security and availability. Those needs can be realised using a DBMS (Database Management System) which is, in fact, software for a database. Technically speaking, it is software which uses a standard method of cataloguing, recovery, and running different data queries. DBMS manages the input data, organizes it, and provides ways of modifying or extracting the data by its users or other programs. Managing the database is an operation that requires periodical updates, optimizing and monitoring.
CONAN: copy number variation analysis software for genome-wide association studies
Lukas Forer, Sebastian Sch?nherr, Hansi Weissensteiner, Florian Haider, Thomas Kluckner, Christian Gieger, Heinz-Erich Wichmann, Günther Specht, Florian Kronenberg, Anita Kloss-Brandst?tter
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-318
Abstract: CONAN is a freely available client-server software solution which provides an intuitive graphical user interface for categorizing, analyzing and associating CNVs with phenotypes. Moreover, CONAN assists the evaluation process by visualizing detected associations via Manhattan plots in order to enable a rapid identification of genome-wide significant CNV regions. Various file formats including the information on CNVs in population samples are supported as input data.CONAN facilitates the performance of GWAS based on CNVs and the visual analysis of calculated results. CONAN provides a rapid, valid and straightforward software solution to identify genetic variation underlying the 'missing' heritability for complex traits that remains unexplained by recent GWAS. The freely available software can be downloaded at http://genepi-conan.i-med.ac.at webcite.Genome-wide association studies (GWAS) have identified associations between various phenotypes and common sequence polymorphisms, which might play a role for disease development (for a comprehensive overview see [1]). For most common diseases, these discoveries collectively explain only a modest fraction (1-15%) of heritable variation of the phenotype [2]. Recently, genome re-sequencing studies demonstrated that most bases that vary among human genomes reside in copy number variations (CNVs) [3]. CNVs are genomic segments which are duplicated or deleted among different individuals, ranging from kilobases to several megabases in length [4]. Although at least 20% of the genome was found to be copy number variable, this class of variation is, nonetheless, poorly integrated into human genetic studies. However, part of the heritability void left by GWAS could be accounted for common CNVs. Indeed, several CNVs were recently described to be associated with complex traits: a 20-kb deletion upstream of the IRGM gene with Crohn's disease [5], a 45-kb deletion upstream of NEGR1 with body mass index [6], a 32-kb deletion with psoriasi
Tobacco Smoking Leads to Extensive Genome-Wide Changes in DNA Methylation
Sonja Zeilinger, Brigitte Kühnel, Norman Klopp, Hansj?rg Baurecht, Anja Kleinschmidt, Christian Gieger, Stephan Weidinger, Eva Lattka, Jerzy Adamski, Annette Peters, Konstantin Strauch, Melanie Waldenberger, Thomas Illig
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0063812
Abstract: Environmental factors such as tobacco smoking may have long-lasting effects on DNA methylation patterns, which might lead to changes in gene expression and in a broader context to the development or progression of various diseases. We conducted an epigenome-wide association study (EWAs) comparing current, former and never smokers from 1793 participants of the population-based KORA F4 panel, with replication in 479 participants from the KORA F3 panel, carried out by the 450K BeadChip with genomic DNA obtained from whole blood. We observed wide-spread differences in the degree of site-specific methylation (with p-values ranging from 9.31E-08 to 2.54E-182) as a function of tobacco smoking in each of the 22 autosomes, with the percent of variance explained by smoking ranging from 1.31 to 41.02. Depending on cessation time and pack-years, methylation levels in former smokers were found to be close to the ones seen in never smokers. In addition, methylation-specific protein binding patterns were observed for cg05575921 within AHRR, which had the highest level of detectable changes in DNA methylation associated with tobacco smoking (–24.40% methylation; p = 2.54E-182), suggesting a regulatory role for gene expression. The results of our study confirm the broad effect of tobacco smoking on the human organism, but also show that quitting tobacco smoking presumably allows regaining the DNA methylation state of never smokers.
Gene Set of Nuclear-Encoded Mitochondrial Regulators Is Enriched for Common Inherited Variation in Obesity
Nadja Knoll, Ivonne Jarick, Anna-Lena Volckmar, Martin Klingenspor, Thomas Illig, Harald Grallert, Christian Gieger, Heinz-Erich Wichmann, Annette Peters, Johannes Hebebrand, André Scherag, Anke Hinney
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0055884
Abstract: There are hints of an altered mitochondrial function in obesity. Nuclear-encoded genes are relevant for mitochondrial function (3 gene sets of known relevant pathways: (1) 16 nuclear regulators of mitochondrial genes, (2) 91 genes for oxidative phosphorylation and (3) 966 nuclear-encoded mitochondrial genes). Gene set enrichment analysis (GSEA) showed no association with type 2 diabetes mellitus in these gene sets. Here we performed a GSEA for the same gene sets for obesity. Genome wide association study (GWAS) data from a case-control approach on 453 extremely obese children and adolescents and 435 lean adult controls were used for GSEA. For independent confirmation, we analyzed 705 obesity GWAS trios (extremely obese child and both biological parents) and a population-based GWAS sample (KORA F4, n = 1,743). A meta-analysis was performed on all three samples. In each sample, the distribution of significance levels between the respective gene set and those of all genes was compared using the leading-edge-fraction-comparison test (cut-offs between the 50th and 95th percentile of the set of all gene-wise corrected p-values) as implemented in the MAGENTA software. In the case-control sample, significant enrichment of associations with obesity was observed above the 50th percentile for the set of the 16 nuclear regulators of mitochondrial genes (pGSEA,50 = 0.0103). This finding was not confirmed in the trios (pGSEA,50 = 0.5991), but in KORA (pGSEA,50 = 0.0398). The meta-analysis again indicated a trend for enrichment (pMAGENTA,50 = 0.1052, pMAGENTA,75 = 0.0251). The GSEA revealed that weak association signals for obesity might be enriched in the gene set of 16 nuclear regulators of mitochondrial genes.
A Systematic Evaluation of Short Tandem Repeats in Lipid Candidate Genes: Riding on the SNP-Wave
Claudia Lamina, Margot Haun, Stefan Coassin, Anita Kloss-Brandst?tter, Christian Gieger, Annette Peters, Harald Grallert, Konstantin Strauch, Thomas Meitinger, Lyudmyla Kedenko, Bernhard Paulweber, Florian Kronenberg
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0102113
Abstract: Structural genetic variants as short tandem repeats (STRs) are not targeted in SNP-based association studies and thus, their possible association signals are missed. We systematically searched for STRs in gene regions known to contribute to total cholesterol, HDL cholesterol, LDL cholesterol and triglyceride levels in two independent studies (KORA F4, n = 2553 and SAPHIR, n = 1648), resulting in 16 STRs that were finally evaluated. In a combined dataset of both studies, the sum of STR alleles was regressed on each phenotype, adjusted for age and sex. The association analyses were repeated for SNPs in a 200 kb region surrounding the respective STRs in the KORA F4 Study. Three STRs were significantly associated with total cholesterol (within LDLR, the APOA1/C3/A4/A5/BUD13 gene region and ABCG5/8), five with HDL cholesterol (3 within CETP, one in LPL and one inAPOA1/C3/A4/A5/BUD13), three with LDL cholesterol (LDLR, ABCG5/8 and CETP) and two with triglycerides (APOA1/C3/A4/A5/BUD13 and LPL). None of the investigated STRs, however, showed a significant association after adjusting for the lead or adjacent SNPs within that gene region. The evaluated STRs were found to be well tagged by the lead SNP within the respective gene regions. Therefore, the STRs reflect the association signals based on surrounding SNPs. In conclusion, none of the STRs contributed additionally to the SNP-based association signals identified in GWAS on lipid traits.
Identification and MS assisted interpretation of genetically influenced NMR signals in human plasma
Johannes Raffler, Werner Romisch-Margl, Ann-Kristin Petersen, Philipp Pagel, Florian Blochl, Christian Hengstenberg, Thomas Illig, Christa Meisinger, Klaus Stark, H-Erich Wichmann, Jerzy Adamski, Christian Gieger, Gabi Kastenmuller, Karsten Suhre
Genome Medicine , 2013, DOI: 10.1186/gm417
Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information
Jan Krumsiek,Karsten Suhre,Anne M. Evans,Matthew W. Mitchell,Robert P. Mohney,Michael V. Milburn,Brigitte W?gele,Werner R?misch-Margl,Thomas Illig,Jerzy Adamski,Christian Gieger,Fabian J. Theis,Gabi Kastenmüller
PLOS Genetics , 2012, DOI: 10.1371/journal.pgen.1003005
Abstract: Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.
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