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Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits

BACKGROUND: The variation and covariation for many cardiometabolic traits have been decomposed into genetic and environmental fractions, by using twin or single‐nucleotide polymorphism (SNP) models. However, differences in population, age, sex, and other factors hamper the comparison between twin‐ a...

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Autores principales: Chen, Xu, Kuja‐Halkola, Ralf, Chang, Zheng, Karlsson, Robert, Hägg, Sara, Svensson, Per, Pedersen, Nancy L., Magnusson, Patrik K. E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015288/
https://www.ncbi.nlm.nih.gov/pubmed/29669715
http://dx.doi.org/10.1161/JAHA.117.007806
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author Chen, Xu
Kuja‐Halkola, Ralf
Chang, Zheng
Karlsson, Robert
Hägg, Sara
Svensson, Per
Pedersen, Nancy L.
Magnusson, Patrik K. E.
author_facet Chen, Xu
Kuja‐Halkola, Ralf
Chang, Zheng
Karlsson, Robert
Hägg, Sara
Svensson, Per
Pedersen, Nancy L.
Magnusson, Patrik K. E.
author_sort Chen, Xu
collection PubMed
description BACKGROUND: The variation and covariation for many cardiometabolic traits have been decomposed into genetic and environmental fractions, by using twin or single‐nucleotide polymorphism (SNP) models. However, differences in population, age, sex, and other factors hamper the comparison between twin‐ and SNP‐based estimates. METHODS AND RESULTS: Twenty‐four cardiometabolic traits and 700,000 genotyped SNPs were available in the study base of 10 682 twins from TwinGene cohort. For the 27 highly correlated pairs (absolute phenotypic correlation coefficient ≥0.40), twin‐based bivariate structural equation models were performed in 3870 complete twin pairs, and SNP‐based bivariate genomic relatedness matrix restricted maximum likelihood methods were performed in 5779 unrelated individuals. In twin models, the model including additive genetic variance and unique/nonshared environmental variance was the best‐fitted model for 7 pairs (5 of them were between blood pressure traits); the model including additive genetic variance, common/shared environmental variance, and unique/nonshared environmental variance components was best fitted for 4 pairs, but estimates of shared environment were close to zero; and the model including additive genetic variance, dominant genetic variance, and unique/nonshared environmental variance was best fitted for 16 pairs, in which significant dominant genetic effects were identified for 13 pairs (including all 9 obesity‐related pairs). However, SNP models did not identify significant estimates of dominant genetic effects for any pairs. In the paired t test, twin‐ and SNP‐based estimates of additive genetic correlation were not significantly different (both were 0.67 on average), whereas the nonshared environmental correlations from these 2 models differed slightly from each other (on average, twin‐based estimate=0.64 and SNP‐based estimate=0.68). CONCLUSIONS: Beside additive genetic effects and nonshared environment, nonadditive genetic effects (dominance) also contribute to the covariation between certain cardiometabolic traits (especially for obesity‐related pairs); contributions from the shared environment seem to be weak for their covariation in TwinGene samples.
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spelling pubmed-60152882018-07-05 Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits Chen, Xu Kuja‐Halkola, Ralf Chang, Zheng Karlsson, Robert Hägg, Sara Svensson, Per Pedersen, Nancy L. Magnusson, Patrik K. E. J Am Heart Assoc Original Research BACKGROUND: The variation and covariation for many cardiometabolic traits have been decomposed into genetic and environmental fractions, by using twin or single‐nucleotide polymorphism (SNP) models. However, differences in population, age, sex, and other factors hamper the comparison between twin‐ and SNP‐based estimates. METHODS AND RESULTS: Twenty‐four cardiometabolic traits and 700,000 genotyped SNPs were available in the study base of 10 682 twins from TwinGene cohort. For the 27 highly correlated pairs (absolute phenotypic correlation coefficient ≥0.40), twin‐based bivariate structural equation models were performed in 3870 complete twin pairs, and SNP‐based bivariate genomic relatedness matrix restricted maximum likelihood methods were performed in 5779 unrelated individuals. In twin models, the model including additive genetic variance and unique/nonshared environmental variance was the best‐fitted model for 7 pairs (5 of them were between blood pressure traits); the model including additive genetic variance, common/shared environmental variance, and unique/nonshared environmental variance components was best fitted for 4 pairs, but estimates of shared environment were close to zero; and the model including additive genetic variance, dominant genetic variance, and unique/nonshared environmental variance was best fitted for 16 pairs, in which significant dominant genetic effects were identified for 13 pairs (including all 9 obesity‐related pairs). However, SNP models did not identify significant estimates of dominant genetic effects for any pairs. In the paired t test, twin‐ and SNP‐based estimates of additive genetic correlation were not significantly different (both were 0.67 on average), whereas the nonshared environmental correlations from these 2 models differed slightly from each other (on average, twin‐based estimate=0.64 and SNP‐based estimate=0.68). CONCLUSIONS: Beside additive genetic effects and nonshared environment, nonadditive genetic effects (dominance) also contribute to the covariation between certain cardiometabolic traits (especially for obesity‐related pairs); contributions from the shared environment seem to be weak for their covariation in TwinGene samples. John Wiley and Sons Inc. 2018-04-18 /pmc/articles/PMC6015288/ /pubmed/29669715 http://dx.doi.org/10.1161/JAHA.117.007806 Text en © 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Research
Chen, Xu
Kuja‐Halkola, Ralf
Chang, Zheng
Karlsson, Robert
Hägg, Sara
Svensson, Per
Pedersen, Nancy L.
Magnusson, Patrik K. E.
Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits
title Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits
title_full Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits
title_fullStr Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits
title_full_unstemmed Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits
title_short Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits
title_sort genetic and environmental contributions to the covariation between cardiometabolic traits
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015288/
https://www.ncbi.nlm.nih.gov/pubmed/29669715
http://dx.doi.org/10.1161/JAHA.117.007806
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