Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits
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{"title"=>"Heritability and genetic correlations explained by common SNPS for metabolic syndrome traits", "type"=>"journal", "authors"=>[{"first_name"=>"Shashaank", "last_name"=>"Vattikuti", "scopus_author_id"=>"6506952059"}, {"first_name"=>"Juen", "last_name"=>"Guo", "scopus_author_id"=>"26535878900"}, {"first_name"=>"Carson C.", "last_name"=>"Chow", "scopus_author_id"=>"7402578443"}], "year"=>2012, "source"=>"PLoS Genetics", "identifiers"=>{"scopus"=>"2-s2.0-84859261092", "sgr"=>"84859261092", "issn"=>"15537390", "isbn"=>"1553-7404 (Electronic)\\r1553-7390 (Linking)", "pmid"=>"22479213", "doi"=>"10.1371/journal.pgen.1002637", "pui"=>"364556436"}, "id"=>"1ec8b0bc-7f30-3453-962a-4e838e87e45e", "abstract"=>"We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h(2)) and heritability explained by the common SNPs (h(g)(2)) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h(2) accounted for by common SNPs to be 58% of h(2) for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations.", "link"=>"http://www.mendeley.com/research/heritability-genetic-correlations-explained-common-snps-metabolic-syndrome-traits", "reader_count"=>153, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>42, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>4, "Other"=>4, "Student > Master"=>16, "Student > Bachelor"=>10, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>6, "Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>42, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>4, "Other"=>4, "Student > Master"=>16, "Student > Bachelor"=>10, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Agricultural and Biological Sciences"=>78, "Arts and Humanities"=>1, "Veterinary Science and Veterinary Medicine"=>1, "Business, Management and Accounting"=>1, "Computer Science"=>9, "Economics, Econometrics and Finance"=>2, "Biochemistry, Genetics and Molecular Biology"=>13, "Mathematics"=>5, "Medicine and Dentistry"=>20, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Psychology"=>5, "Social Sciences"=>4}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>20}, "Social Sciences"=>{"Social Sciences"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>5}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>12}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>78}, "Computer Science"=>{"Computer Science"=>9}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"New Zealand"=>1, "Austria"=>1, "United States"=>7, "United Kingdom"=>3, "France"=>1, "Kenya"=>1, "Portugal"=>1, "India"=>1}, "group_count"=>4}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/339531", "https://ndownloader.figshare.com/files/339612", "https://ndownloader.figshare.com/files/339674", "https://ndownloader.figshare.com/files/339728", "https://ndownloader.figshare.com/files/339774", "https://ndownloader.figshare.com/files/339820", "https://ndownloader.figshare.com/files/339875", "https://ndownloader.figshare.com/files/339922", "https://ndownloader.figshare.com/files/339960"], "description"=>"<div><p>We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (<em>h<sup>2</sup></em>) and heritability explained by the common SNPs (<em>h<sub>g</sub><sup>2</sup></em>) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of <em>h<sup>2</sup></em> accounted for by common SNPs to be 58% of <em>h<sup>2</sup></em> for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations.</p> </div>", "links"=>[], "tags"=>["heritability", "correlations", "explained", "snps", "metabolic", "traits"], "article_id"=>127131, "categories"=>["Genetics"], "users"=>["Shashaank Vattikuti", "Juen Guo", "Carson C. Chow"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1002637.s001", "https://dx.doi.org/10.1371/journal.pgen.1002637.s002", "https://dx.doi.org/10.1371/journal.pgen.1002637.s003", "https://dx.doi.org/10.1371/journal.pgen.1002637.s004", "https://dx.doi.org/10.1371/journal.pgen.1002637.s005", "https://dx.doi.org/10.1371/journal.pgen.1002637.s006", "https://dx.doi.org/10.1371/journal.pgen.1002637.s007", "https://dx.doi.org/10.1371/journal.pgen.1002637.s008", "https://dx.doi.org/10.1371/journal.pgen.1002637.s009"], "stats"=>{"downloads"=>7, "page_views"=>74, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Heritability_and_Genetic_Correlations_Explained_by_Common_SNPs_for_Metabolic_Syndrome_Traits/127131", "title"=>"Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-03-29 01:58:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/660898"], "description"=>"<p>Mean and standard error estimates from univariate models.</p>", "links"=>[], "tags"=>["estimates"], "article_id"=>331396, "categories"=>["Genetics"], "users"=>["Shashaank Vattikuti", "Juen Guo", "Carson C. Chow"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002637.t001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_h_2_and_h_g_2_estimates_ARIC_population_/331396", "title"=>"<i>h<sup>2</sup></i> and <i>h<sub>g</sub><sup>2</sup></i> estimates (ARIC population).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-03-29 00:23:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/660866"], "description"=>"<p>Mean and standard error of the Pearson correlation coefficient for genetic correlations (upper triangle) and residual correlations (lower triangle). An asterisk indicates significance with p<0.05 adjusted for 21 hypotheses using the two-tailed hypothesis test and normal distribution of the Fisher transformed correlation coefficient.</p>", "links"=>[], "tags"=>["residual", "coefficients", "mets", "aric", "individuals", "bivariate", "reml"], "article_id"=>331354, "categories"=>["Genetics"], "users"=>["Shashaank Vattikuti", "Juen Guo", "Carson C. Chow"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002637.t002", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genetic_and_residual_correlation_coefficients_between_MetS_traits_in_the_ARIC_population_among_related_individuals_from_the_bivariate_REML_model_/331354", "title"=>"Genetic and residual correlation coefficients between MetS traits in the ARIC population among related individuals from the bivariate REML model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-03-29 00:22:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/660827"], "description"=>"<p>Mean and standard error of the Pearson correlation coefficient for genetic correlations (upper triangle) and residual correlations (lower triangle). An asterisk indicates significance with p<0.05 adjusted for 21 hypotheses using the two-tailed hypothesis test and normal distribution of the Fisher transformed correlation coefficient.</p>", "links"=>[], "tags"=>["residual", "correlations", "mets", "aric", "unrelated", "individuals", "bivariate", "reml"], "article_id"=>331306, "categories"=>["Genetics"], "users"=>["Shashaank Vattikuti", "Juen Guo", "Carson C. Chow"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1002637.t003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genetic_and_residual_correlations_between_MetS_traits_in_the_ARIC_population_among_unrelated_individuals_from_the_bivariate_REML_model_/331306", "title"=>"Genetic and residual correlations between MetS traits in the ARIC population among unrelated individuals from the bivariate REML model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-03-29 00:21:46"}

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Relative Metric

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