Structural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.

Aim: In this study, we introduce the first twin study in Turkey, focusing on smoking behavior, and laying the foundation to register all twins born in Turkey for research purposes. Using Turkish twins will contribute to our understanding of health problems in the context of cultural differences. Materials and methods: We assessed 309 twin pairs (339 males and 279 females) aged between 15 and 45 years living in the Kırıkkale and Ankara regions of Turkey, and administered a health and lifestyle interview that included questions about smoking status and smoking history. We analyzed the data using descriptive statistics, t-tests, chi-square tests, and bivariate and multivariate clustered logistic regression. In addition, we fit bivariate Structural Equation Models (SEM) to determine contributions of latent genetic and environmental factors to smoking outcomes in this sample. Results: One hundred seventy-eight participants (28.8%) were identified as smokers, smoking every day for a month or longer, of whom 79.2% were males and 20.8% were females. Mean values for number of cigarettes per day and the Fagerstrom Test of Nicotine Dependence (FTND; Fagerstrom, 1978) score were higher in males than in females, and age of onset was earlier in males. There was a significant positive correlation between the FTND score and number of cigarettes smoked per day, and a significant negative correlation between both variables and age at onset of smoking. Our study showed that gender, presence of a smoking twin in the family, age, alcohol use, marital status, daily sports activities, and feeling moody all played a significant role in smoking behavior among twins. The twin analysis suggested that 79.5% of the liability to FTND was influenced by genetic factors and 20.5% by unique environment, while familial resemblance for smoking initiation was best explained by common environmental factors. Conclusions: Marked differences in the prevalence of smoking behavior in men versus women were observed for the Turkish population. Genetic analyses showed that common environmental factors primarily contributed to smoking initiation, while genetic factors explained a greater proportion of variance in liability to nicotine dependence. Our study shows higher heritability estimate of the FTND scores and higher shared environmental influence on smoking initiation for both males and females than reported in previous studies.

Despite some evidence that genotype-environment interaction (G×E) effects may be involved in the variation observed in behavioral and biological traits, few attempts have been made to detect and quantify this component of genetic variation in humans. We propose that one way to achieve this goal is to challenge several genotypes in a similar manner, submitting both members of several MZ twin pairs to an ethically acceptable experimental treatment capable of inducing an adaptative response. In this situation, the G×E effect can be assessed with a two-way analysis of variance for repeated measures on one factor, the treatment effect. In this design, twins are considered nested within the pair, whereas the treatment effect is considered a fixed variable. The intrapair resemblance in the response to the treatment is quantified with an intraclass correlation coefficient computed with between-sibhips and within-sibhips means of squares. To illustrate this approach, changes induced by long-term endurance training were studied in 10 MZ twin pairs. Significant intrapair resemblance in the response of maximal oxygen uptake was observed, with about 7 to 8 times more variance between pairs than within pairs. This design with MZ twins may be helpful in the study of human variation for multifactorial phenotypes.

A statistical model and analysis for genetic and environmental effects in twin-family data are presented. The model is used to derive expressions for phenotypic correlations of 22 essential pair relationships in twin-family units. The analysis proceeds in two steps. First, differential effects of sex, generation, and sex-zygosity of twin-family units and correlations due to cluster sampling are eliminated from correlation data. Then, estimates and tests of model parameters are calculated from the adjusted data. The theory and methods were developed for a Swedish twin-family study of many behaviors possibly related to the smoking habit. There, it is important to screen for behaviors that clearly are under genetic control and to assess relative influences of various biological and social environments on the development of all behaviors. Height data from the Swedish study are used to illustrate concepts and methods presented in this paper.

A formal model is presented for the analysis of longitudinal twin data, based on the underlying analysis-of-variance model for repeated measures. The model is developed in terms of the expected values for the variance components representing twin concordance, and the derivation is provided for computing within-pair (intraclass) correlations, and for estimating the percent of variance explained by each component. The procedures are illustrated with physical growth data extending from birth to six years, and concordance estimates are obtained for average size and for the pattern of spurts and lags in growth. A test of significance is also described for comparing monozygotic twins with dizygotic twins. The procedures are particularly useful for assessing chronogenetic influences on development, especially whether the episodes of acceleration and lag occur in parallel for genetically matched twins. The model may be employed with psychological data also.