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‐ 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.

[1]  N. Wray,et al.  Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. , 2018, American journal of human genetics.

[2]  Jian Yang,et al.  Concepts, estimation and interpretation of SNP-based heritability , 2017, Nature Genetics.

[3]  A. Metspalu,et al.  Hidden heritability due to heterogeneity across seven populations , 2017, Nature Human Behaviour.

[4]  D. Meyre,et al.  Assessing the Heritability of Complex Traits in Humans: Methodological Challenges and Opportunities , 2017, Current genomics.

[5]  P. Visscher,et al.  10 Years of GWAS Discovery: Biology, Function, and Translation. , 2017, American journal of human genetics.

[6]  Doug Speed,et al.  Re-evaluation of SNP heritability in complex human traits , 2016, Nature Genetics.

[7]  Tian Ge,et al.  Phenome-wide heritability analysis of the UK Biobank , 2016, bioRxiv.

[8]  Timothy R. Brick,et al.  OpenMx 2.0: Extended Structural Equation and Statistical Modeling , 2015, Psychometrika.

[9]  Tom R. Gaunt,et al.  LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis , 2016, bioRxiv.

[10]  S. Hägg,et al.  Dominant Genetic Variation and Missing Heritability for Human Complex Traits: Insights from Twin versus Genome-wide Common SNP Models. , 2015, American journal of human genetics.

[11]  W. G. Hill,et al.  Dominance genetic variation contributes little to the missing heritability for human complex traits. , 2015, American journal of human genetics.

[12]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[13]  Tanya M. Teslovich,et al.  Discovery and refinement of loci associated with lipid levels , 2013, Nature Genetics.

[14]  C. Haley,et al.  The heritability of human disease: estimation, uses and abuses , 2013, Nature Reviews Genetics.

[15]  M. Johannesson,et al.  The Swedish Twin Registry: Establishment of a Biobank and Other Recent Developments , 2012, Twin Research and Human Genetics.

[16]  Sang Hong Lee,et al.  Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood , 2012, Bioinform..

[17]  H. M. Draisma,et al.  The continuing value of twin studies in the omics era , 2012, Nature Reviews Genetics.

[18]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[19]  P. Visscher,et al.  Common SNPs explain a large proportion of the heritability for human height , 2010, Nature Genetics.

[20]  M. Neale,et al.  Genetic and environmental influences on factors associated with cardiovascular disease and the metabolic syndrome , 2009, Journal of Lipid Research.

[21]  P. Magnusson,et al.  Genetic Dominance Influences Blood Biomarker Levels in a Sample of 12,000 Swedish Elderly Twins , 2009, Twin Research and Human Genetics.

[22]  B. Maher Personal genomes: The case of the missing heritability , 2008, Nature.

[23]  W. G. Hill,et al.  Heritability in the genomics era — concepts and misconceptions , 2008, Nature Reviews Genetics.

[24]  G. Willemsen,et al.  Non-additive and Additive Genetic Effects on Extraversion in 3314 Dutch Adolescent Twins and Their Parents , 2008, Behavior genetics.

[25]  N. Martin,et al.  Widespread Evidence for Non-Additive Genetic Variation in Cloninger’s and Eysenck’s Personality Dimensions using a Twin Plus Sibling Design , 2005, Behavior genetics.

[26]  John L. Hopper Methodology for genetic studies of twins and families. Michael C. Neale and Lon R. Cardon, Kluwer, Dordrecht, the Netherlands, 1992. no. of pages: XXV + 496. price: £99.00/$ 169.00. ISBN 0‐7923‐1874‐9 , 1994 .

[27]  G. Mcclearn,et al.  Genetic and environmental influences on serum lipid levels in twins. , 1993, The New England journal of medicine.

[28]  L. Cardon,et al.  Methodology for Genetic Studies of Twins and Families , 1992 .

[29]  Environment and Blood Pressure , 1958, British medical journal.

[30]  Danielle Posthuma,et al.  Fifty Years of Twin Studies : A Meta-Analysis of the Heritability of Human Traits , 2015 .

[31]  J. Deleeuw,et al.  Introduction to Akaike (1973) Information Theory and an Extension of the Maximum Likelihood Principle , 1992 .