Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality
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{"title"=>"Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality", "type"=>"journal", "authors"=>[{"first_name"=>"Johannes", "last_name"=>"Raffler", "scopus_author_id"=>"47161548000"}, {"first_name"=>"Nele", "last_name"=>"Friedrich", "scopus_author_id"=>"8664503700"}, {"first_name"=>"Matthias", "last_name"=>"Arnold", "scopus_author_id"=>"56350916100"}, {"first_name"=>"Tim", "last_name"=>"Kacprowski", "scopus_author_id"=>"55339946500"}, {"first_name"=>"Rico", "last_name"=>"Rueedi", "scopus_author_id"=>"35769634900"}, {"first_name"=>"Elisabeth", "last_name"=>"Altmaier", "scopus_author_id"=>"36553850700"}, {"first_name"=>"Sven", "last_name"=>"Bergmann", "scopus_author_id"=>"7101828386"}, {"first_name"=>"Kathrin", "last_name"=>"Budde", "scopus_author_id"=>"55447913800"}, {"first_name"=>"Christian", "last_name"=>"Gieger", "scopus_author_id"=>"55138342700"}, {"first_name"=>"Georg", "last_name"=>"Homuth", "scopus_author_id"=>"56357082900"}, {"first_name"=>"Maik", "last_name"=>"Pietzner", "scopus_author_id"=>"56512412800"}, {"first_name"=>"Werner", "last_name"=>"Römisch-Margl", "scopus_author_id"=>"57195572879"}, {"first_name"=>"Konstantin", "last_name"=>"Strauch", "scopus_author_id"=>"57021167300"}, {"first_name"=>"Henry", "last_name"=>"Völzke", "scopus_author_id"=>"56662814100"}, {"first_name"=>"Melanie", "last_name"=>"Waldenberger", "scopus_author_id"=>"57197777520"}, {"first_name"=>"Henri", "last_name"=>"Wallaschofski", "scopus_author_id"=>"6701404955"}, {"first_name"=>"Matthias", "last_name"=>"Nauck", "scopus_author_id"=>"34875588200"}, {"first_name"=>"Uwe", "last_name"=>"Völker", "scopus_author_id"=>"7004594671"}, {"first_name"=>"Gabi", "last_name"=>"Kastenmüller", "scopus_author_id"=>"6507055294"}, {"first_name"=>"Karsten", "last_name"=>"Suhre", "scopus_author_id"=>"56188100500"}], "year"=>2015, "source"=>"PLoS Genetics", "identifiers"=>{"doi"=>"10.1371/journal.pgen.1005487", "sgr"=>"84943579561", "isbn"=>"1553-7404 (Electronic)\\r1553-7390 (Linking)", "pmid"=>"26352407", "issn"=>"15537404", "scopus"=>"2-s2.0-84943579561", "pui"=>"606367578"}, "id"=>"4ddd6fab-eb55-3306-a3c6-387b411f3569", "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.", "link"=>"http://www.mendeley.com/research/genomewide-association-study-targeted-nontargeted-nmr-metabolomics-identifies-15-novel-loci-urinary", "reader_count"=>61, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>30, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>30, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>6, "Unspecified"=>4, "Biochemistry, Genetics and Molecular Biology"=>17, "Mathematics"=>1, "Agricultural and Biological Sciences"=>16, "Medicine and Dentistry"=>9, "Neuroscience"=>2, "Chemistry"=>2, "Psychology"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>6}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Neuroscience"=>{"Neuroscience"=>2}, "Chemistry"=>{"Chemistry"=>2}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>16}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>17}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Sweden"=>1, "Luxembourg"=>1, "Switzerland"=>2, "Germany"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2255711"], "description"=>"<p><sup>a</sup> SNP with the strongest association to targeted metabolic traits (“lead SNP”)</p><p><sup>b</sup> SNP with the strongest association to non-targeted metabolic traits</p><p><sup>c</sup> manually added to the list of most plausible candidate genes derived by evidence-based selection</p><p><sup>d</sup> additional candidates match to other non-targeted traits that also associate with the lead SNP</p><p><sup>e</sup> results from GWAS (<i>P</i> < 5.0×10<sup>−8</sup>)</p><p><sup>f</sup> mutations determined in clinical studies</p><p>For each locus, we selected all variants that displayed genome-wide significant association signals to metabolic traits in the SHIP-0 cohort. We added their proxy variants in LD (r<sup>2</sup> ≥ 0.8; based on 1000 genomes project data [<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref050\" target=\"_blank\">50</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref051\" target=\"_blank\">51</a>]). These variant sets were used for the selection of candidate genes and the comparison with association results from other studies. <b>Candidate genes:</b> selection of genes based on variant evidence (genes hit or close-by, eQTL, potentially regulatory effects, or missense variants) (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.s007\" target=\"_blank\">S3 Table</a>). Genes with the highest evidence counts are listed. Genes with the most plausible biochemical relation to the associated trait are highlighted in bold typeface. <b>Associated traits</b>: Targeted/non-targeted metabolomics: traits that display genome-wide significant association signals in SHIP-0. The arrows indicate whether the trait increases (↗) or decreases (↘) per copy of the effect allele. For non-targeted traits, the most plausible metabolite candidates according to metabomatching are given. <b>Other mGWAS in urine/blood:</b> metabolic traits that were previously found to be associated with a locus variant. The arrows indicate the directionality of the effect for the reported effect allele (where available). <b>Clinical phenotypes:</b> overlap with variants found to be associated with clinical traits. <b>Comment</b>: Gene expression rates were taken from the Illumina Body Map 2.0 (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.s008\" target=\"_blank\">S4 Table</a>). Protein localizations were taken from the Human Protein Atlas (version 12) [<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref052\" target=\"_blank\">52</a>]. For genes linked to clinical traits, we provide OMIM or OrphaNet accession numbers if available. <b>Functional match</b>: Indicates which associations exhibit a sound biological link between gene function and the biochemical nature of the associated metabolite(s).</p><p>Twenty-two identified and replicated loci and their overlap with associations to metabolic traits and clinical phenotypes.</p>", "links"=>[], "tags"=>["cps", "ACSM", "novel insights", "associations target", "SLC 6A DMGDH", "13a", "nmr", "gim", "22 loci", "Ischemic stroke", "ETNPPL", "novel hypotheses", "SLC 36A GLDC", "tkt", "HIBCH", "urine samples", "mGWAS", "trait", "kidney disease", "AGXT", "XYLB", "6 loci", "metabolite association", "variant", "blood homeostasis", "SLC 5A locus", "KORA F 4 cohort", "pnmt"], "article_id"=>1537677, "categories"=>["Uncategorised"], "users"=>["Johannes Raffler", "Nele Friedrich", "Matthias Arnold", "Tim Kacprowski", "Rico Rueedi", "Elisabeth Altmaier", "Sven Bergmann", "Kathrin Budde", "Christian Gieger", "Georg Homuth", "Maik Pietzner", "Werner Römisch-Margl", "Konstantin Strauch", "Henry Völzke", "Melanie Waldenberger", "Henri Wallaschofski", "Matthias Nauck", "Uwe Völker", "Gabi Kastenmüller", "Karsten Suhre"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005487.t003", "stats"=>{"downloads"=>9, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Twenty_two_identified_and_replicated_loci_and_their_overlap_with_associations_to_metabolic_traits_and_clinical_phenotypes_/1537677", "title"=>"Twenty-two identified and replicated loci and their overlap with associations to metabolic traits and clinical phenotypes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-09 02:55:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2255710"], "description"=>"<p><sup>a</sup> alternative candidates for metabomatching exist (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.s001\" target=\"_blank\">S1 Fig</a>)</p><p><sup>b</sup> additional candidates match to other non-targeted traits that also associate with SNP (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.s001\" target=\"_blank\">S1 Fig</a>)</p><p><b>Chr/Position</b>: Chromosomal location of the lead SNP according to the human reference genome (GRCh37). <b>EA/EAF</b>: Effect allele and frequency. <b>Trait or pairwise ratio</b>: Tested metabolic trait (chemical shift). In case of ratios, the trait that drives the association (i.e., shows the stronger association signal) is named first. <b>N</b>: Number of samples where both genotype and phenotype data were available for the tested SNP/metabolic trait pair. <b>beta’</b>: beta’ is defined as 10<sup>beta</sup>-1 where beta depicts the relative effect size representing the slope of the regression line in the linear model when using log<sub>10</sub>-scaled metabolic traits and the occurrence of the SNP’s minor allele (coded as 0,1, and 2). Thus, beta’ describes the relative difference per minor allele copy for non-scaled metabolic traits in comparison to the estimated mean of the metabolic trait in the major homozygote test subjects. <b><i>P</i>-gain</b>: Defined as min(<i>P</i>(M<sub>1</sub>)/<i>P</i>(M<sub>1</sub>/M<sub>2</sub>), <i>P</i>(M<sub>2</sub>)/<i>P</i>(M<sub>1</sub>/M<sub>2</sub>)), where M<sub>1</sub> and M<sub>2</sub> represent the two traits of which the ratio M<sub>1</sub>/M<sub>2</sub> is built. <b>Metabomatching</b>: Annotation of the non-targeted metabolic trait at the given chemical shift as suggested by metabomatching (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.s001\" target=\"_blank\">S1 Fig</a>). <b>Replicated</b>: SNP/metabolic trait pair was replicated in KORA F4 (<i>P <</i> 1.32×10<sup>−3</sup>) (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.s006\" target=\"_blank\">S2 Table</a><b>)</b>. <b>Targeted</b>: SNP or proxy in LD is also associated with a targeted metabolic trait (<i>P <</i> 3.25×10<sup>−12</sup>) (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.t001\" target=\"_blank\">Table 1</a>).</p><p>Twenty genetic loci as discovered in the SHIP-0 data set and their most significant associations to non-targeted metabolic traits.</p>", "links"=>[], "tags"=>["cps", "ACSM", "novel insights", "associations target", "SLC 6A DMGDH", "13a", "nmr", "gim", "22 loci", "Ischemic stroke", "ETNPPL", "novel hypotheses", "SLC 36A GLDC", "tkt", "HIBCH", "urine samples", "mGWAS", "trait", "kidney disease", "AGXT", "XYLB", "6 loci", "metabolite association", "variant", "blood homeostasis", "SLC 5A locus", "KORA F 4 cohort", "pnmt"], "article_id"=>1537676, "categories"=>["Uncategorised"], "users"=>["Johannes Raffler", "Nele Friedrich", "Matthias Arnold", "Tim Kacprowski", "Rico Rueedi", "Elisabeth Altmaier", "Sven Bergmann", "Kathrin Budde", "Christian Gieger", "Georg Homuth", "Maik Pietzner", "Werner Römisch-Margl", "Konstantin Strauch", "Henry Völzke", "Melanie Waldenberger", "Henri Wallaschofski", "Matthias Nauck", "Uwe Völker", "Gabi Kastenmüller", "Karsten Suhre"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005487.t002", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Twenty_genetic_loci_as_discovered_in_the_SHIP_0_data_set_and_their_most_significant_associations_to_non_targeted_metabolic_traits_/1537676", "title"=>"Twenty genetic loci as discovered in the SHIP-0 data set and their most significant associations to non-targeted metabolic traits.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-09 02:55:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2255707"], "description"=>"<p>SNPs are plotted according to chromosomal location and the-log<sub>10</sub> transformed <i>P</i>-value of the strongest association with targeted traits (top) and non-targeted traits (bottom). In case of associations with ratios, only associations with <i>P</i>-gain exceeding 15,180 (targeted metabolic traits) or 138,610 (non-targeted traits) were considered. Associations of genome-wide significance (<i>P</i> < 3.25×10<sup>−12</sup>) are plotted in red. Triangles indicate associations with <i>P</i> < 1.0×10<sup>−100</sup>. Significant associations within a physical distance of 1 Mb were assigned to a locus labeled after the most likely causative gene (as determined using an evidence-based approach for the identification of candidate genes; see <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#sec014\" target=\"_blank\">Methods</a>).</p>", "links"=>[], "tags"=>["cps", "ACSM", "novel insights", "associations target", "SLC 6A DMGDH", "13a", "nmr", "gim", "22 loci", "Ischemic stroke", "ETNPPL", "novel hypotheses", "SLC 36A GLDC", "tkt", "HIBCH", "urine samples", "mGWAS", "trait", "kidney disease", "AGXT", "XYLB", "6 loci", "metabolite association", "variant", "blood homeostasis", "SLC 5A locus", "KORA F 4 cohort", "pnmt"], "article_id"=>1537673, "categories"=>["Uncategorised"], "users"=>["Johannes Raffler", "Nele Friedrich", "Matthias Arnold", "Tim Kacprowski", "Rico Rueedi", "Elisabeth Altmaier", "Sven Bergmann", "Kathrin Budde", "Christian Gieger", "Georg Homuth", "Maik Pietzner", "Werner Römisch-Margl", "Konstantin Strauch", "Henry Völzke", "Melanie Waldenberger", "Henri Wallaschofski", "Matthias Nauck", "Uwe Völker", "Gabi Kastenmüller", "Karsten Suhre"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005487.g002", "stats"=>{"downloads"=>3, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Manhattan_plot_of_genetic_associations_to_targeted_and_non_targeted_traits_/1537673", "title"=>"Manhattan plot of genetic associations to targeted and non-targeted traits.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-09 02:55:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2255706"], "description"=>"<p>(a) Genotyping and metabotyping of 3,861 SHIP-0 study participants. One-dimensional <sup>1</sup>H NMR spectra of the urine samples were recorded to derive targeted and non-targeted metabolic traits. (b) Two-staged mGWAS. First stage: genome-wide association tests using genotyped SNPs and 15,379 targeted and non-targeted traits. Second stage: fine mapping of regions with potentially significant associations using imputed SNPs. (c) Replication and interpretation. Genome-wide significantly associated SNPs were assigned to one of 23 distinct genetic loci. The loci and the significantly associated non-targeted traits were annotated using algorithmic approaches. 22 of the 23 loci could be replicated using genotype and metabotype data from 1,691 KORA F4 participants.</p>", "links"=>[], "tags"=>["cps", "ACSM", "novel insights", "associations target", "SLC 6A DMGDH", "13a", "nmr", "gim", "22 loci", "Ischemic stroke", "ETNPPL", "novel hypotheses", "SLC 36A GLDC", "tkt", "HIBCH", "urine samples", "mGWAS", "trait", "kidney disease", "AGXT", "XYLB", "6 loci", "metabolite association", "variant", "blood homeostasis", "SLC 5A locus", "KORA F 4 cohort", "pnmt"], "article_id"=>1537672, "categories"=>["Uncategorised"], "users"=>["Johannes Raffler", "Nele Friedrich", "Matthias Arnold", "Tim Kacprowski", "Rico Rueedi", "Elisabeth Altmaier", "Sven Bergmann", "Kathrin Budde", "Christian Gieger", "Georg Homuth", "Maik Pietzner", "Werner Römisch-Margl", "Konstantin Strauch", "Henry Völzke", "Melanie Waldenberger", "Henri Wallaschofski", "Matthias Nauck", "Uwe Völker", "Gabi Kastenmüller", "Karsten Suhre"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005487.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Study_design_/1537672", "title"=>"Study design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-09 02:55:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2255713", "https://ndownloader.figshare.com/files/2255714", "https://ndownloader.figshare.com/files/2255715", "https://ndownloader.figshare.com/files/2255716", "https://ndownloader.figshare.com/files/2255717", "https://ndownloader.figshare.com/files/2255718", "https://ndownloader.figshare.com/files/2255719", "https://ndownloader.figshare.com/files/2255720"], "description"=>"<div><p>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 <sup>1</sup>H 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 (<i>HIBCH</i>, <i>CPS1</i>, <i>AGXT</i>, <i>XYLB</i>, <i>TKT</i>, <i>ETNPPL</i>, <i>SLC6A19</i>, <i>DMGDH</i>, <i>SLC36A2</i>, <i>GLDC</i>, <i>SLC6A13</i>, <i>ACSM3</i>, <i>SLC5A11</i>, <i>PNMT</i>, <i>SLC13A3</i>). 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 <i>SLC5A11</i> locus, we found increased levels of <i>myo</i>-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of <i>myo</i>-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 (<i>CPS1</i>, <i>SLC6A13</i>), pulmonary hypertension (<i>CPS1</i>), and ischemic stroke (<i>XYLB</i>). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases.</p></div>", "links"=>[], "tags"=>["cps", "ACSM", "novel insights", "associations target", "SLC 6A DMGDH", "13a", "nmr", "gim", "22 loci", "Ischemic stroke", "ETNPPL", "novel hypotheses", "SLC 36A GLDC", "tkt", "HIBCH", "urine samples", "mGWAS", "trait", "kidney disease", "AGXT", "XYLB", "6 loci", "metabolite association", "variant", "blood homeostasis", "SLC 5A locus", "KORA F 4 cohort", "pnmt"], "article_id"=>1537679, "categories"=>["Uncategorised"], "users"=>["Johannes Raffler", "Nele Friedrich", "Matthias Arnold", "Tim Kacprowski", "Rico Rueedi", "Elisabeth Altmaier", "Sven Bergmann", "Kathrin Budde", "Christian Gieger", "Georg Homuth", "Maik Pietzner", "Werner Römisch-Margl", "Konstantin Strauch", "Henry Völzke", "Melanie Waldenberger", "Henri Wallaschofski", "Matthias Nauck", "Uwe Völker", "Gabi Kastenmüller", "Karsten Suhre"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1005487.s001", "https://dx.doi.org/10.1371/journal.pgen.1005487.s002", "https://dx.doi.org/10.1371/journal.pgen.1005487.s003", "https://dx.doi.org/10.1371/journal.pgen.1005487.s004", "https://dx.doi.org/10.1371/journal.pgen.1005487.s005", "https://dx.doi.org/10.1371/journal.pgen.1005487.s006", "https://dx.doi.org/10.1371/journal.pgen.1005487.s007", "https://dx.doi.org/10.1371/journal.pgen.1005487.s008"], "stats"=>{"downloads"=>14, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_Wide_Association_Study_with_Targeted_and_Non_targeted_NMR_Metabolomics_Identifies_15_Novel_Loci_of_Urinary_Human_Metabolic_Individuality_/1537679", "title"=>"Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-09 02:55:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2255709"], "description"=>"<p><b>Chr/Position</b>: Chromosomal location of the SNP according to the human reference genome (GRCh37). <b>EA/EAF</b>: Effect allele and frequency. <b>Trait or pairwise ratio</b>: Tested metabolic trait. In case of ratios, the trait that shows the stronger association signal is in the numerator. <b>N</b>: Number of samples for which both genotype and phenotype data were available for the tested SNP/metabolic trait pair. <b>beta’</b>: beta’ is defined as 10<sup>beta</sup>-1 where beta depicts the relative effect size representing the slope of the regression line in the linear model when using log<sub>10</sub>-scaled metabolic traits and the occurrence of the SNP’s minor allele (coded as 0,1, and 2). Thus, beta’ describes the relative difference per minor allele copy for non-scaled metabolic traits in comparison to the estimated mean of the metabolic trait in the major homozygote test subjects. <b><i>P</i>-gain</b>: Defined as min(<i>P</i>(M<sub>1</sub>)/<i>P</i>(M<sub>1</sub>/M<sub>2</sub>), <i>P</i>(M<sub>2</sub>)/<i>P</i>(M<sub>1</sub>/M<sub>2</sub>)), where M<sub>1</sub> and M<sub>2</sub> represent the two traits of which the ratio M<sub>1</sub>/M<sub>2</sub> is built. <b>Replicated</b>: SNP/metabolic trait pair was replicated in KORA F4 (<i>P <</i> 1.32×10<sup>−3</sup>) (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.s005\" target=\"_blank\">S1 Table</a>). <b>Non-targeted</b>: SNP or proxy in linkage disequilibrium (LD) is also associated with a non-targeted metabolic trait (<i>P <</i> 3.25×10<sup>−12</sup>) (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.t002\" target=\"_blank\">Table 2</a>).</p><p>Fifteen genetic loci as discovered in the SHIP-0 data set and their most significant associations to targeted metabolic traits.</p>", "links"=>[], "tags"=>["cps", "ACSM", "novel insights", "associations target", "SLC 6A DMGDH", "13a", "nmr", "gim", "22 loci", "Ischemic stroke", "ETNPPL", "novel hypotheses", "SLC 36A GLDC", "tkt", "HIBCH", "urine samples", "mGWAS", "trait", "kidney disease", "AGXT", "XYLB", "6 loci", "metabolite association", "variant", "blood homeostasis", "SLC 5A locus", "KORA F 4 cohort", "pnmt"], "article_id"=>1537675, "categories"=>["Uncategorised"], "users"=>["Johannes Raffler", "Nele Friedrich", "Matthias Arnold", "Tim Kacprowski", "Rico Rueedi", "Elisabeth Altmaier", "Sven Bergmann", "Kathrin Budde", "Christian Gieger", "Georg Homuth", "Maik Pietzner", "Werner Römisch-Margl", "Konstantin Strauch", "Henry Völzke", "Melanie Waldenberger", "Henri Wallaschofski", "Matthias Nauck", "Uwe Völker", "Gabi Kastenmüller", "Karsten Suhre"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005487.t001", "stats"=>{"downloads"=>10, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fifteen_genetic_loci_as_discovered_in_the_SHIP_0_data_set_and_their_most_significant_associations_to_targeted_metabolic_traits_/1537675", "title"=>"Fifteen genetic loci as discovered in the SHIP-0 data set and their most significant associations to targeted metabolic traits.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-09 02:55:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2255708"], "description"=>"<p>We identified and replicated genome-wide significant associations between metabolic traits and genetic variants in 22 genetic loci (named after the most likely causative gene). Three loci could only be identified using targeted metabolic traits, while 7 loci were exclusively discovered with non-targeted traits. 12 loci were identified using both targeted and non-targeted approaches. Loci with hitherto unknown associations with urinary metabolic traits are highlighted (totaling 15). We identified and replicated significant associations in 7 of the 11 loci that were reported in previous mGWAS in urine [<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref005\" target=\"_blank\">5</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref010\" target=\"_blank\">10</a>–<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref012\" target=\"_blank\">12</a>]. We also discovered significant associations of the <i>ABO</i> locus (marked with an asterisk) with non-targeted traits, but this locus could not be replicated in KORA F4. When compared to previous mGWAS in blood, we find 14 loci that display associations with metabolic traits in both urine and blood [<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref001\" target=\"_blank\">1</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref004\" target=\"_blank\">4</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref006\" target=\"_blank\">6</a>–<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref008\" target=\"_blank\">8</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref014\" target=\"_blank\">14</a>–<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005487#pgen.1005487.ref024\" target=\"_blank\">24</a>].</p>", "links"=>[], "tags"=>["cps", "ACSM", "novel insights", "associations target", "SLC 6A DMGDH", "13a", "nmr", "gim", "22 loci", "Ischemic stroke", "ETNPPL", "novel hypotheses", "SLC 36A GLDC", "tkt", "HIBCH", "urine samples", "mGWAS", "trait", "kidney disease", "AGXT", "XYLB", "6 loci", "metabolite association", "variant", "blood homeostasis", "SLC 5A locus", "KORA F 4 cohort", "pnmt"], "article_id"=>1537674, "categories"=>["Uncategorised"], "users"=>["Johannes Raffler", "Nele Friedrich", "Matthias Arnold", "Tim Kacprowski", "Rico Rueedi", "Elisabeth Altmaier", "Sven Bergmann", "Kathrin Budde", "Christian Gieger", "Georg Homuth", "Maik Pietzner", "Werner Römisch-Margl", "Konstantin Strauch", "Henry Völzke", "Melanie Waldenberger", "Henri Wallaschofski", "Matthias Nauck", "Uwe Völker", "Gabi Kastenmüller", "Karsten Suhre"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005487.g003", "stats"=>{"downloads"=>8, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Loci_with_associated_urinary_metabolic_traits_and_their_overlap_with_previous_mGWAS_in_blood_and_urine_/1537674", "title"=>"Loci with associated urinary metabolic traits and their overlap with previous mGWAS in blood and urine.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-09 02:55:52"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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