Dataset Information

Genetic and Environmental Contributions to Covariation Between DHEA and Testosterone in Adolescent Twins.

ABSTRACT: Although several studies have shown that pubertal tempo and timing are shaped by genetic and environmental factors, few studies consider to what extent endocrine triggers of puberty are shaped by genetic and environmental factors. Doing so moves the field from examining correlated developmentally-sensitive biomarkers toward understanding what drives those associations. Two puberty related hormones, dehydroepiandrosterone and testosterone, were assayed from salivary samples in 118 MZ (62 % female), 111 same sex DZ (46 % female) and 103 opposite-sex DZ twin pairs, aged 12-16 years (M = 13.1, SD = 1.3). Pubertal status was assessed with a composite of mother- and self-reports. We used biometric models to estimate the genetic and environmental influences on the variance and covariance in testosterone and DHEA, with and without controlling for their association with puberty, and to test for sex differences. In males, the variance in testosterone and pubertal status was due to shared and non-shared environmental factors; variation in DHEA was due to genetic and non-shared environmental factors. In females, variance in testosterone was due to genetic and non-shared environmental factors; genetic, shared, and non-shared environmental factors contributed equally to variation in DHEA. In males, the testosterone-DHEA covariance was primarily due to shared environmental factors that overlapped with puberty as well as shared and non-shared environmental covariation specific to testosterone and DHEA. In females, the testosterone-DHEA covariance was due to genetic factors overlapping with pubertal status, and shared and non-shared environmental covariation specific to testosterone and DHEA.

SUBMITTER: Van Hulle CA

PROVIDER: S-EPMC4425578 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Genetic and Environmental Contributions to Covariation Between DHEA and Testosterone in Adolescent Twins.

Van Hulle Carol A CA Moore Mollie N MN Shirtcliff Elizabeth A EA Lemery-Chalfant Kathryn K Goldsmith H Hill HH

Behavior genetics 20150131 3

Although several studies have shown that pubertal tempo and timing are shaped by genetic and environmental factors, few studies consider to what extent endocrine triggers of puberty are shaped by genetic and environmental factors. Doing so moves the field from examining correlated developmentally-sensitive biomarkers toward understanding what drives those associations. Two puberty related hormones, dehydroepiandrosterone and testosterone, were assayed from salivary samples in 118 MZ (62 % female ...[more]

PMID: 25633628

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Project 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.

Project description:ObjectiveTo explore and apply multimodel inference to test the relative contributions of latent genetic, environmental and direct causal factors to the covariation between two variables with data from the classical twin design by estimating model-averaged parameters.MethodsBehavior genetics is concerned with understanding the causes of variation in phenotypes and the causes of covariation between two or more phenotypes. Two variables may correlate as a result of genetic, shared environmental or unique environmental factors contributing to variation in both variables. Two variables may also correlate because one or both directly cause variation in the other. Furthermore, covariation may result from any combination of these sources, leading to 25 different identified structural equation models. OpenMx was used to fit all these models to account for covariation between two variables collected in twins. Multimodel inference and model averaging were used to summarize the key sources of covariation, and estimate the magnitude of these causes of covariance. Extensions of these models to test heterogeneity by sex are discussed.ResultsWe illustrate the application of multimodel inference by fitting a comprehensive set of bivariate models to twin data from the Virginia Twin Study of Psychiatric and Substance Use Disorders. Analyses of body mass index and tobacco consumption data show sufficient power to reject distinct models, and to estimate the contribution of each of the five potential sources of covariation, irrespective of selecting the best fitting model. Discrimination between models on sample size, type of variable (continuous versus binary or ordinal measures) and the effect size of sources of variance and covariance.ConclusionsWe introduce multimodel inference and model averaging approaches to the behavior genetics community, in the context of testing models for the causes of covariation between traits in term of genetic, environmental and causal explanations.

Dataset Information

Genetic and Environmental Contributions to Covariation Between DHEA and Testosterone in Adolescent Twins.

Publications

Genetic and Environmental Contributions to Covariation Between DHEA and Testosterone in Adolescent Twins.

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