Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits. Issue 9 (13 September 0207)

• Record Type: Journal Article
• Authors: Chen, Xu; Kuja‐Halkola, Ralf; Chang, Zheng; Karlsson, Robert; Hägg, Sara; Svensson, Per; Pedersen, Nancy L.; Magnusson, Patrik K. E.
• Is Part Of: Journal of the American Heart Association. Volume 7:Issue 9(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 0207-09-13
• Subjects: biomarker -- cardiac metabolism -- environment -- genes -- heritability; Heart -- Diseases -- Periodicals; Cardiovascular system -- Diseases -- Periodicals; Cerebrovascular disease -- Periodicals; Cardiology -- Periodicals; 616.1
• DOI: 10.1161/JAHA.117.007806
• Languages: English
• ISSNs: 2047-9980

Abstract

Abstract : 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.