Novel Loci for Metabolic Networks and Multi-Tissue Expression Studies Reveal Genes for Atherosclerosis
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{"title"=>"Novel Loci for Metabolic Networks and Multi-Tissue Expression Studies Reveal Genes for Atherosclerosis", "type"=>"journal", "authors"=>[{"first_name"=>"Michael", "last_name"=>"Inouye", "scopus_author_id"=>"22953271700"}, {"first_name"=>"Samuli", "last_name"=>"Ripatti", "scopus_author_id"=>"6603009457"}, {"first_name"=>"Johannes", "last_name"=>"Kettunen", "scopus_author_id"=>"57194913013"}, {"first_name"=>"Leo Pekka", "last_name"=>"Lyytikäinen", "scopus_author_id"=>"57200872624"}, {"first_name"=>"Niku", "last_name"=>"Oksala", "scopus_author_id"=>"57195386077"}, {"first_name"=>"Pirkka Pekka", "last_name"=>"Laurila", "scopus_author_id"=>"55385631900"}, {"first_name"=>"Antti J.", "last_name"=>"Kangas", "scopus_author_id"=>"24597745500"}, {"first_name"=>"Pasi", "last_name"=>"Soininen", "scopus_author_id"=>"6603924570"}, {"first_name"=>"Markku J.", "last_name"=>"Savolainen", "scopus_author_id"=>"35453963500"}, {"first_name"=>"Jorma", "last_name"=>"Viikari", "scopus_author_id"=>"35446128700"}, {"first_name"=>"Mika", "last_name"=>"Kähönen", "scopus_author_id"=>"7006708451"}, {"first_name"=>"Markus", "last_name"=>"Perola", "scopus_author_id"=>"7003719630"}, {"first_name"=>"Veikko", "last_name"=>"Salomaa", "scopus_author_id"=>"7004714461"}, {"first_name"=>"Olli", "last_name"=>"Raitakari", "scopus_author_id"=>"7004828916"}, {"first_name"=>"Terho", "last_name"=>"Lehtimäki", "scopus_author_id"=>"7006539182"}, {"first_name"=>"Marja Riitta", "last_name"=>"Taskinen", "scopus_author_id"=>"57193256111"}, {"first_name"=>"Marjo Riitta", "last_name"=>"Järvelin", "scopus_author_id"=>"18535933700"}, {"first_name"=>"Mika", "last_name"=>"Ala-Korpela", "scopus_author_id"=>"7003418142"}, {"first_name"=>"Aarno", "last_name"=>"Palotie", "scopus_author_id"=>"7005614368"}, {"first_name"=>"Paul I.W.", "last_name"=>"de Bakker", "scopus_author_id"=>"6701510692"}], "year"=>2012, "source"=>"PLoS Genetics", "identifiers"=>{"scopus"=>"2-s2.0-84866156922", "sgr"=>"84866156922", "issn"=>"15537390", "doi"=>"10.1371/journal.pgen.1002907", "pmid"=>"22916037", "isbn"=>"1553-7404 (Electronic)\\n1553-7390 (Linking)", "pui"=>"365631202"}, "id"=>"2d25f6ee-bb58-35c9-88c5-863167465fe7", "abstract"=>"Association testing of multiple correlated phenotypes offers better power than univariate analysis of single traits. We analyzed 6,600 individuals from two population-based cohorts with both genome-wide SNP data and serum metabolomic profiles. From the observed correlation structure of 130 metabolites measured by nuclear magnetic resonance, we identified 11 metabolic networks and performed a multivariate genome-wide association analysis. We identified 34 genomic loci at genome-wide significance, of which 7 are novel. In comparison to univariate tests, multivariate association analysis identified nearly twice as many significant associations in total. Multi-tissue gene expression studies identified variants in our top loci, SERPINA1 and AQP9, as eQTLs and showed that SERPINA1 and AQP9 expression in human blood was associated with metabolites from their corresponding metabolic networks. Finally, liver expression of AQP9 was associated with atherosclerotic lesion area in mice, and in human arterial tissue both SERPINA1 and AQP9 were shown to be upregulated (6.3-fold and 4.6-fold, respectively) in atherosclerotic plaques. Our study illustrates the power of multi-phenotype GWAS and highlights candidate genes for atherosclerosis.", "link"=>"http://www.mendeley.com/research/novel-loci-metabolic-networks-multitissue-expression-studies-reveal-genes-atherosclerosis", "reader_count"=>98, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>9, "Librarian"=>1, "Student > Doctoral Student"=>3, "Researcher"=>30, "Student > Ph. D. 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Student"=>25, "Student > Postgraduate"=>4, "Student > Master"=>6, "Other"=>3, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>10}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>13, "Agricultural and Biological Sciences"=>49, "Medicine and Dentistry"=>19, "Neuroscience"=>2, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Business, Management and Accounting"=>1, "Psychology"=>2, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>19}, "Neuroscience"=>{"Neuroscience"=>2}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>49}, "Computer Science"=>{"Computer Science"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>6}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}}, "reader_count_by_country"=>{"United States"=>3, "Qatar"=>1, "United Kingdom"=>1, "Mexico"=>1, "France"=>1, "Spain"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/591290"], "description"=>"<p>A Pearson correlation matrix of serum metabolites across both YFS and NFBC66 cohorts was hierarchically clustered and the resulting heatmap and dendrogram are presented here with red indicating high positive correlation, blue high negative correlation, and white no correlation. Clusters of tightly correlated metabolites, metabolic networks, are labeled 1–11.</p>", "links"=>[], "tags"=>["metabolic"], "article_id"=>261760, "categories"=>["Genetics"], "users"=>["Michael Inouye", "Samuli Ripatti", "Johannes Kettunen", "Leo-Pekka Lyytikäinen", "Niku Oksala", "Pirkka-Pekka Laurila", "Antti J. Kangas", "Pasi Soininen", "Markku J. Savolainen", "Jorma Viikari", "Mika Kähönen", "Markus Perola", "Veikko Salomaa", "Olli Raitakari", "Terho Lehtimäki", "Marja-Riitta Taskinen", "Marjo-Riitta Jarvelin", "Mika Ala-Korpela", "Aarno Palotie", "Paul I. W. de Bakker"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002907.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Serum_metabolic_networks_/261760", "title"=>"Serum metabolic networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-16 00:29:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/591505"], "description"=>"<p>(a) Boxplots show SNPs associated with metabolic networks are also <i>cis</i> eQTLs for <i>SERPINA1</i> (human blood and liver) and <i>AQP9</i> (human liver). Boxplots consist of median log2-normalised expression for each genotype with first and third quartiles designated by box edges. Whiskers extend to +/−1.5 times interquartile range. (b) Human blood expression of <i>SERPINA1</i> and <i>AQP9</i> was associated with metabolites derived from the same metabolic networks as their corresponding genetic variants. Edge widths are proportional to the strength of association (<i>P</i> value). (c) Liver expression of <i>AQP9</i> (but not <i>SERPINA1</i>) in mice on a hyperlipidemic <i>APOE</i><sup>−/−</sup> background showed significant positive association with aortic lesion area. (d) Boxplots for log2-normalised expression of <i>SERPINA1</i> and <i>AQP9</i> in healthy human arterial tissue versus that for atherosclerotic plaques.</p>", "links"=>[], "tags"=>["atherosclerosis"], "article_id"=>261981, "categories"=>["Genetics"], "users"=>["Michael Inouye", "Samuli Ripatti", "Johannes Kettunen", "Leo-Pekka Lyytikäinen", "Niku Oksala", "Pirkka-Pekka Laurila", "Antti J. Kangas", "Pasi Soininen", "Markku J. Savolainen", "Jorma Viikari", "Mika Kähönen", "Markus Perola", "Veikko Salomaa", "Olli Raitakari", "Terho Lehtimäki", "Marja-Riitta Taskinen", "Marjo-Riitta Jarvelin", "Mika Ala-Korpela", "Aarno Palotie", "Paul I. W. de Bakker"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002907.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Connecting_genetic_variation_gene_expression_metabolites_and_atherosclerosis_for_SERPINA1_and_AQP9_/261981", "title"=>"Connecting genetic variation, gene expression, metabolites, and atherosclerosis for <i>SERPINA1</i> and <i>AQP9</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-16 00:33:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/591170"], "description"=>"<p>Overview of the study design.</p>", "links"=>[], "tags"=>["genetics and genomics"], "article_id"=>261639, "categories"=>["Genetics"], "users"=>["Michael Inouye", "Samuli Ripatti", "Johannes Kettunen", "Leo-Pekka Lyytikäinen", "Niku Oksala", "Pirkka-Pekka Laurila", "Antti J. Kangas", "Pasi Soininen", "Markku J. Savolainen", "Jorma Viikari", "Mika Kähönen", "Markus Perola", "Veikko Salomaa", "Olli Raitakari", "Terho Lehtimäki", "Marja-Riitta Taskinen", "Marjo-Riitta Jarvelin", "Mika Ala-Korpela", "Aarno Palotie", "Paul I. W. de Bakker"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002907.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_the_study_design_/261639", "title"=>"Overview of the study design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-16 00:27:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/591419"], "description"=>"<p>A Venn diagram showing the number of associations between all genomic loci and metabolic networks stratified by joint multivariate and univariate analysis (for univariate, at least one metabolite from a network need be associated).</p>", "links"=>[], "tags"=>["genomic", "loci", "metabolic"], "article_id"=>261890, "categories"=>["Genetics"], "users"=>["Michael Inouye", "Samuli Ripatti", "Johannes Kettunen", "Leo-Pekka Lyytikäinen", "Niku Oksala", "Pirkka-Pekka Laurila", "Antti J. Kangas", "Pasi Soininen", "Markku J. Savolainen", "Jorma Viikari", "Mika Kähönen", "Markus Perola", "Veikko Salomaa", "Olli Raitakari", "Terho Lehtimäki", "Marja-Riitta Taskinen", "Marjo-Riitta Jarvelin", "Mika Ala-Korpela", "Aarno Palotie", "Paul I. W. de Bakker"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002907.g003", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_detected_between_genomic_loci_and_metabolic_networks_/261890", "title"=>"Associations detected between genomic loci and metabolic networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-16 00:31:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/310850", "https://ndownloader.figshare.com/files/311476", "https://ndownloader.figshare.com/files/311450", "https://ndownloader.figshare.com/files/311425", "https://ndownloader.figshare.com/files/311220", "https://ndownloader.figshare.com/files/311174", "https://ndownloader.figshare.com/files/311133", "https://ndownloader.figshare.com/files/311106", "https://ndownloader.figshare.com/files/311057", "https://ndownloader.figshare.com/files/310999", "https://ndownloader.figshare.com/files/310925", "https://ndownloader.figshare.com/files/311497"], "description"=>"<div><p>Association testing of multiple correlated phenotypes offers better power than univariate analysis of single traits. We analyzed 6,600 individuals from two population-based cohorts with both genome-wide SNP data and serum metabolomic profiles. From the observed correlation structure of 130 metabolites measured by nuclear magnetic resonance, we identified 11 metabolic networks and performed a multivariate genome-wide association analysis. We identified 34 genomic loci at genome-wide significance, of which 7 are novel. In comparison to univariate tests, multivariate association analysis identified nearly twice as many significant associations in total. Multi-tissue gene expression studies identified variants in our top loci, <em>SERPINA1</em> and <em>AQP9</em>, as eQTLs and showed that <em>SERPINA1</em> and <em>AQP9</em> expression in human blood was associated with metabolites from their corresponding metabolic networks. Finally, liver expression of <em>AQP9</em> was associated with atherosclerotic lesion area in mice, and in human arterial tissue both <em>SERPINA1</em> and <em>AQP9</em> were shown to be upregulated (6.3-fold and 4.6-fold, respectively) in atherosclerotic plaques. Our study illustrates the power of multi-phenotype GWAS and highlights candidate genes for atherosclerosis.</p> </div>", "links"=>[], "tags"=>["loci", "metabolic", "networks", "multi-tissue", "studies", "genes", "atherosclerosis"], "article_id"=>121309, "categories"=>["Genetics"], "users"=>["Michael Inouye", "Samuli Ripatti", "Johannes Kettunen", "Leo-Pekka Lyytikäinen", "Niku Oksala", "Pirkka-Pekka Laurila", "Antti J. Kangas", "Pasi Soininen", "Markku J. Savolainen", "Jorma Viikari", "Mika Kähönen", "Markus Perola", "Veikko Salomaa", "Olli Raitakari", "Terho Lehtimäki", "Marja-Riitta Taskinen", "Marjo-Riitta Jarvelin", "Mika Ala-Korpela", "Aarno Palotie", "Paul I. W. de Bakker"], "doi"=>[nil, nil, nil, nil, nil, nil, nil, nil, nil, nil, nil, nil], "stats"=>{"downloads"=>28, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Novel_Loci_for_Metabolic_Networks_and_Multi_Tissue_Expression_Studies_Reveal_Genes_for_Atherosclerosis/121309", "title"=>"Novel Loci for Metabolic Networks and Multi-Tissue Expression Studies Reveal Genes for Atherosclerosis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-08-16 00:21:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/591596"], "description"=>"*<p>Complete metabolic network loadings, indicating the relative contributions of single metabolites to the overall association, are given in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002907#pgen.1002907.s004\" target=\"_blank\">Figure S4</a>.</p>**<p>Multiple test corrected significance threshold for a metabolic network association was <i>P</i><4.5×10<sup>−9</sup>.</p>#<p>Indicates that the top metabolite was also detected by univariate test.</p>", "links"=>[], "tags"=>["multivariate"], "article_id"=>262070, "categories"=>["Genetics"], "users"=>["Michael Inouye", "Samuli Ripatti", "Johannes Kettunen", "Leo-Pekka Lyytikäinen", "Niku Oksala", "Pirkka-Pekka Laurila", "Antti J. Kangas", "Pasi Soininen", "Markku J. Savolainen", "Jorma Viikari", "Mika Kähönen", "Markus Perola", "Veikko Salomaa", "Olli Raitakari", "Terho Lehtimäki", "Marja-Riitta Taskinen", "Marjo-Riitta Jarvelin", "Mika Ala-Korpela", "Aarno Palotie", "Paul I. W. de Bakker"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002907.t001", "stats"=>{"downloads"=>3, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Loci_detected_using_joint_multivariate_association_analysis_/262070", "title"=>"Loci detected using joint multivariate association analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-16 00:34:30"}

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Relative Metric

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