MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS
Publication Date
May 02, 2012
Journal
PLOS ONE
Authors
Paul F. O’reilly, Clive J. Hoggart, Yotsawat Pomyen, Federico C. F. Calboli, et al
Volume
7
Issue
5
Pages
e34861
DOI
http://doi.org/10.1371/journal.pone.0034861
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0034861
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/22567092
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3342314
Europe PMC
http://europepmc.org/abstract/MED/22567092
Web of Science
000305341500010
Scopus
84860473457
Mendeley
http://www.mendeley.com/research/multiphen-joint-model-multiple-phenotypes-increase-discovery-gwas
Events
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Mendeley | Further Information

{"title"=>"MultiPhen: Joint model of multiple phenotypes can increase discovery in GWAS", "type"=>"journal", "authors"=>[{"first_name"=>"Paul F.", "last_name"=>"O'Reilly", "scopus_author_id"=>"24504286800"}, {"first_name"=>"Clive J.", "last_name"=>"Hoggart", "scopus_author_id"=>"6602233276"}, {"first_name"=>"Yotsawat", "last_name"=>"Pomyen", "scopus_author_id"=>"55203609900"}, {"first_name"=>"Federico C F", "last_name"=>"Calboli", "scopus_author_id"=>"6507818904"}, {"first_name"=>"Paul", "last_name"=>"Elliott", "scopus_author_id"=>"26643338400"}, {"first_name"=>"Marjo Riitta", "last_name"=>"Jarvelin", "scopus_author_id"=>"18535933700"}, {"first_name"=>"Lachlan J M", "last_name"=>"Coin", "scopus_author_id"=>"55889865800"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84860473457", "pui"=>"364721397", "doi"=>"10.1371/journal.pone.0034861", "isbn"=>"2007201550", "sgr"=>"84860473457", "pmid"=>"22567092"}, "id"=>"ec89f31b-faf4-3fcc-a821-09e93859ee3a", "abstract"=>"The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes.", "link"=>"http://www.mendeley.com/research/multiphen-joint-model-multiple-phenotypes-increase-discovery-gwas", "reader_count"=>186, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>9, "Researcher"=>60, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>51, "Student > Postgraduate"=>7, "Student > Master"=>16, "Other"=>8, "Student > Bachelor"=>6, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>9, "Researcher"=>60, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>51, "Student > Postgraduate"=>7, "Student > Master"=>16, "Other"=>8, "Student > Bachelor"=>6, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>11}, "reader_count_by_subject_area"=>{"Unspecified"=>15, "Agricultural and Biological Sciences"=>85, "Computer Science"=>13, "Economics, Econometrics and Finance"=>1, "Engineering"=>6, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>22, "Nursing and Health Professions"=>1, "Mathematics"=>10, "Medicine and Dentistry"=>18, "Neuroscience"=>1, "Psychology"=>7, "Social Sciences"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>18}, "Social Sciences"=>{"Social Sciences"=>5}, "Psychology"=>{"Psychology"=>7}, "Mathematics"=>{"Mathematics"=>10}, "Unspecified"=>{"Unspecified"=>15}, "Environmental Science"=>{"Environmental Science"=>2}, "Engineering"=>{"Engineering"=>6}, "Neuroscience"=>{"Neuroscience"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>85}, "Computer Science"=>{"Computer Science"=>13}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>22}}, "reader_count_by_country"=>{"Canada"=>1, "Austria"=>1, "United States"=>5, "Norway"=>1, "Finland"=>1, "Brazil"=>1, "United Kingdom"=>6, "Italy"=>1}, "group_count"=>5}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/643975"], "description"=>"*<p>indicates that the SNP did not have a univariate genome-wide significant <i>P</i> value. Each row indicates the linear combination of phenotypes (given by the corresponding regression coefficients) which is most associated with the given SNP under the MultiPhen regression, after removing the most associated phenotype. The regression coefficients have been scaled so that the CHOL coefficient is always equal to one. The last row contains the expected coefficients according to the Friedewald Formula (Equation 1).</p>", "links"=>[], "tags"=>["linear", "combinations", "phenotypes", "genome-wide"], "article_id"=>314464, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Paul F. O’Reilly", "Clive J. Hoggart", "Yotsawat Pomyen", "Federico C. F. Calboli", "Paul Elliott", "Marjo-Riitta Jarvelin", "Lachlan J. M. Coin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034861.t003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Most_associated_linear_combinations_of_phenotypes_at_genome_wide_significant_SNPs_/314464", "title"=>"Most associated linear combinations of phenotypes at genome-wide significant SNPs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-02 01:14:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/643761"], "description"=>"<p>Each bar shows the number of SNPs reaching genome-wide significance for a given phenotype-combination analysis (specified by the first letters of each trait, such that CHL refers to an analysis on the CHOL, HDL and LDL), with the SNPs discovered by both the univariate approach and MultiPhen shown by the white segment of the bar, the SNPs discovered by the univariate approach only shown by the grey segment, and the SNPs discovered by MultiPhen only illustrated by the black segment. The bars labelled ALL2 and ALL3 combine results across analyses on all combinations of two and three lipid traits, respectively, while ALL combines the results across the analyses of all 2, 3 and 4 combinations of the traits. A complete breakdown of these results is presented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s018\" target=\"_blank\">Tables S5</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s019\" target=\"_blank\">S6</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s020\" target=\"_blank\">S7</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s021\" target=\"_blank\">S8</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s022\" target=\"_blank\">S9</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s023\" target=\"_blank\">S10</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s024\" target=\"_blank\">S11</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s025\" target=\"_blank\">S12</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s026\" target=\"_blank\">S13</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s027\" target=\"_blank\">S14</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s028\" target=\"_blank\">S15</a>.</p>", "links"=>[], "tags"=>["GWAS", "multiphen", "tested", "combinations", "lipids", "nfbc1966"], "article_id"=>314243, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Paul F. O’Reilly", "Clive J. Hoggart", "Yotsawat Pomyen", "Federico C. F. Calboli", "Paul Elliott", "Marjo-Riitta Jarvelin", "Lachlan J. M. Coin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034861.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_wide_significant_results_from_standard_GWAS_approach_and_MultiPhen_tested_on_combinations_of_the_lipids_using_NFBC1966_data_/314243", "title"=>"Genome-wide significant results from standard GWAS approach and MultiPhen tested on combinations of the lipids using NFBC1966 data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-02 01:10:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/643590"], "description"=>"<p>Power results based on simulations described in the text for MultiPhen (red lines) and the standard single-phenotype approach (black lines). Left panel: causal variant explains 0.5% of phenotypic variance of both phenotypes. Middle panel: causal variant explains 0.5% on the phenotypic variance of the first phenotype and 0.1% of the variance in the second phenotype. Right panel: causal variant explains 0.5% of phenotypic variance of the first phenotype and 0% of the second phenotype.</p>", "links"=>[], "tags"=>["multiphen", "scenarios"], "article_id"=>314074, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Paul F. O’Reilly", "Clive J. Hoggart", "Yotsawat Pomyen", "Federico C. F. Calboli", "Paul Elliott", "Marjo-Riitta Jarvelin", "Lachlan J. M. Coin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034861.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_power_of_MultiPhen_in_different_scenarios_of_effect_and_correlation_between_phenotypes_/314074", "title"=>"The power of MultiPhen in different scenarios of effect and correlation between phenotypes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-02 01:07:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/643936"], "description"=>"<p>¶ Nyholt-Šidák corrected for 4 comparisons. § Nyholt-Šidák corrected for 3 comparisons. Results compare univariate and MultiPhen <i>P</i> values, presented on the -log10 scale for ease of comparison, for all SNPs with genome-wide significant <i>P</i> values (>7.301 on the -log10 scale) from either approach. Genome-wide significant results shown in bold. The difference in terms of orders of magnitude of the MultiPhen <i>P</i> value on all phenotypes is relative to the most associated univariate phenotype; and the order of magnitude difference for MultiPhen where the most associated phenotype is excluded is relative to the univariate result also excluding the most associated phenotype.</p>", "links"=>[], "tags"=>["GWAS", "multiphen", "approaches", "genome-wide"], "article_id"=>314421, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Paul F. O’Reilly", "Clive J. Hoggart", "Yotsawat Pomyen", "Federico C. F. Calboli", "Paul Elliott", "Marjo-Riitta Jarvelin", "Lachlan J. M. Coin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034861.t002", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_under_standard_GWAS_and_MultiPhen_approaches_for_genome_wide_significant_SNPs_/314421", "title"=>"Results under standard GWAS and MultiPhen approaches for genome-wide significant SNPs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-02 01:13:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/643660"], "description"=>"<p>The left panel shows the correlation structure between total cholesterol (CHOL) and low-density lipoprotein (LDL) in 5655 individuals from the Northern Finland Birth Cohort 1966. Each circle depicts the value of CHOL (X-axis) and LDL (Y-axis) in mmol/L for each individual. The right panel shows the correlation structure between low-density lipoprotein (LDL) and high-density lipoprotein (HDL), in mmol/L, in the same individuals. The arrows in each plot show the direction of effect of a variant affecting only CHOL or only HDL, such that the genotypes of individuals underlying each plotted point are more likely to contain risk alleles for the labelled lipid moving through the points in the direction of the arrow. The diagonal arrows are based on the Friedewald Formula (Friedewald.72). The arrows indicate that effects of variants can be in very different directions in the 2-dimensional spaces shown; the aim of modelling and testing linear combinations of phenotypes is to capture effects in any direction.</p>", "links"=>[], "tags"=>["pairs"], "article_id"=>314147, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Paul F. O’Reilly", "Clive J. Hoggart", "Yotsawat Pomyen", "Federico C. F. Calboli", "Paul Elliott", "Marjo-Riitta Jarvelin", "Lachlan J. M. Coin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034861.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_correlation_structure_between_pairs_of_lipids_/314147", "title"=>"The correlation structure between pairs of lipids.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-02 01:09:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/643893"], "description"=>"<p>This table relates to the simulation study to test the type 1 error rates of MultiPhen, CCA, and the univariate approach, described in the text. The elements of the table show the number of results with <i>P</i><1e<sup>–5</sup> in the scenario described by the corresponding row and column (which give the minor allele frequencies) headers. Since 100000 replicates of SNP-phenotype associations were simulated under the null hypothesis of no association, the expectation for all elements of the table is 1; those with >1 indicating inflation of the type 1 error rate. Simulations with MAF = 30%, 0.5% were performed on a sample size of N = 5000. For the full results see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s001\" target=\"_blank\">Figures S1</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s008\" target=\"_blank\">S8</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s014\" target=\"_blank\">Table S1</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s016\" target=\"_blank\">S3</a>.</p>", "links"=>[], "tags"=>["methods"], "article_id"=>314382, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Paul F. O’Reilly", "Clive J. Hoggart", "Yotsawat Pomyen", "Federico C. F. Calboli", "Paul Elliott", "Marjo-Riitta Jarvelin", "Lachlan J. M. Coin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034861.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Behaviour_of_the_different_methods_under_the_null_/314382", "title"=>"Behaviour of the different methods under the null.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-02 01:13:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/332328", "https://ndownloader.figshare.com/files/332364", "https://ndownloader.figshare.com/files/332399", "https://ndownloader.figshare.com/files/332431", "https://ndownloader.figshare.com/files/332461", "https://ndownloader.figshare.com/files/332490", "https://ndownloader.figshare.com/files/332510", "https://ndownloader.figshare.com/files/332539", "https://ndownloader.figshare.com/files/332578", "https://ndownloader.figshare.com/files/332620", "https://ndownloader.figshare.com/files/332671", "https://ndownloader.figshare.com/files/332723", "https://ndownloader.figshare.com/files/332789", "https://ndownloader.figshare.com/files/332834", "https://ndownloader.figshare.com/files/332881", "https://ndownloader.figshare.com/files/332923", "https://ndownloader.figshare.com/files/332965", "https://ndownloader.figshare.com/files/333003", "https://ndownloader.figshare.com/files/333054", "https://ndownloader.figshare.com/files/333088", "https://ndownloader.figshare.com/files/333126", "https://ndownloader.figshare.com/files/333169", "https://ndownloader.figshare.com/files/333222", "https://ndownloader.figshare.com/files/333267", "https://ndownloader.figshare.com/files/333316", "https://ndownloader.figshare.com/files/333379", "https://ndownloader.figshare.com/files/333423", "https://ndownloader.figshare.com/files/333481"], "description"=>"<div><p>The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes.</p> </div>", "links"=>[], "tags"=>["phenotypes", "GWAS"], "article_id"=>125622, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Paul F. O’Reilly", "Clive J. Hoggart", "Yotsawat Pomyen", "Federico C. F. Calboli", "Paul Elliott", "Marjo-Riitta Jarvelin", "Lachlan J. M. Coin"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0034861.s001", "https://dx.doi.org/10.1371/journal.pone.0034861.s002", "https://dx.doi.org/10.1371/journal.pone.0034861.s003", "https://dx.doi.org/10.1371/journal.pone.0034861.s004", "https://dx.doi.org/10.1371/journal.pone.0034861.s005", "https://dx.doi.org/10.1371/journal.pone.0034861.s006", "https://dx.doi.org/10.1371/journal.pone.0034861.s007", "https://dx.doi.org/10.1371/journal.pone.0034861.s008", "https://dx.doi.org/10.1371/journal.pone.0034861.s009", "https://dx.doi.org/10.1371/journal.pone.0034861.s010", "https://dx.doi.org/10.1371/journal.pone.0034861.s011", "https://dx.doi.org/10.1371/journal.pone.0034861.s012", "https://dx.doi.org/10.1371/journal.pone.0034861.s013", "https://dx.doi.org/10.1371/journal.pone.0034861.s014", "https://dx.doi.org/10.1371/journal.pone.0034861.s015", "https://dx.doi.org/10.1371/journal.pone.0034861.s016", "https://dx.doi.org/10.1371/journal.pone.0034861.s017", "https://dx.doi.org/10.1371/journal.pone.0034861.s018", "https://dx.doi.org/10.1371/journal.pone.0034861.s019", "https://dx.doi.org/10.1371/journal.pone.0034861.s020", "https://dx.doi.org/10.1371/journal.pone.0034861.s021", "https://dx.doi.org/10.1371/journal.pone.0034861.s022", "https://dx.doi.org/10.1371/journal.pone.0034861.s023", "https://dx.doi.org/10.1371/journal.pone.0034861.s024", "https://dx.doi.org/10.1371/journal.pone.0034861.s025", "https://dx.doi.org/10.1371/journal.pone.0034861.s026", "https://dx.doi.org/10.1371/journal.pone.0034861.s027", "https://dx.doi.org/10.1371/journal.pone.0034861.s028"], "stats"=>{"downloads"=>4, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/MultiPhen_Joint_Model_of_Multiple_Phenotypes_Can_Increase_Discovery_in_GWAS/125622", "title"=>"MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-05-02 01:33:42"}

PMC Usage Stats | Further Information

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Relative Metric

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