Genetic and social influences on starting to smoke: a study of Dutch adolescent twins and their parents

In a study of 1600 Dutch adolescent twin pairs we found that 59% of the inter‐individual variation in smoking behaviour could be attributed to shared environmental influences and 31% to genetic factors. The magnitude of the genetic and environmental effects did not differ between boys and girls. However, environmental effects shared by male twins and environmental effects shared by female twins were imperfectly correlated in twins from opposite‐sex pairs, indicating that different environmental factors influence smoking in adolescent boys and girls. In the parents of these twins, the correlation between husband and wife for‘currently smoking’(r = 0.43) was larger than for‘ever smoked’(r = 0.18). There was no evidence that smoking of parents (at present or in the past) encouraged smoking in their offspring. Resemblance between parents and offspring was significant but rather low and could be accounted for completely by their genetic relatedness. Moreover, the association between‘currently smoking’in the parents and smoking behaviour in their children was not larger than the association between‘ever smoking’in parents and smoking in their children. Copyright © 1994, Wiley Blackwell. All rights reserve

Application of nonlinear factor analysis to genotype-environment interaction

The intention of this paper is to show how the methods of nonlinear factor analysis as developed by McDonald (Br. J. Math. Stat. Psychol. 20:205-215, 1967) can be used to study genotype-environment interaction. The method is applied to the interaction of genotype and within-family en-vironmental influences. Simulated twin data are used to illustrate how this type of interaction may be detected and estimated. It is shown that estimates of genetic influences are not affected by G x E interaction. KEY WORDS: genotype-environment interaction; nonlinear factor analysis; twin data

Using LISREL to analyze genetic and environmental covariance structure

Describes a method in which the linear structural relationships (LISREL) computer program is used for the genetic analysis of covariance structure. The method is illustrated with simulated and published twin data, including an analysis of twin data by N. G. Martin et al (1981) on psychomotor performance during alcohol intoxication

Power calculations using exact data simulation: A useful tool for genetic study designs.

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes

Reconsidering the Heritability of Intelligence in Adulthood: Taking Assortative Mating and Cultural Transmission into Account

Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors. To investigate the effects of assortative mating on the estimates of the variance components of intelligence, we employed an extended twin-family design. Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence

Resemblances of Parents and Twins in Sport Participation and Heart Rate

A model to analyze resemblances of twins and parents using LISREL is outlined and applied to sports participation and heart-rate data. Sports participation and heart rate were measured in 44 monozygotic and 46 dizygotic adolescent twin pairs and in their parents. Genetic factors influence variation in both sports behavior and heart rate, while there is no evidence for transmission from parental environment to offspring environment. For sports participation the data support a model in which there is a high positive correlation between environments of spouses and between environments of female twins. This correlation is absent for male twins and negative for opposite sex twins. For heart rate, a positive correlation between environmental influences was observed for all twins; there is no evidence for assortative mating. The proposed model can also handle data sets where parents and twins have been measured on more than one variable. This is illustrated by an application to the observed association of sports participation and heart rate

Gene-Environment Interaction in Adults’ IQ Scores: Measures of Past and Present Environment

Gene-environment interaction was studied in a sample of young (mean age 26 years, N = 385) and older (mean age 49 years, N = 370) adult males and females. Full scale IQ scores (FSIQ) were analyzed using biometric models in which additive genetic (A), common environmental (C), and unique environmental (E) effects were allowed to depend on environmental measures. Moderators under study were parental and partner educational level, as well as urbanization level and mean real estate price of the participants’ residential area. Mean effects were observed for parental education, partner education and urbanization level. On average, FSIQ scores were roughly 5 points higher in participants with highly educated parents, compared to participants whose parents were less well educated. In older participants, IQ scores were about 2 points higher when their partners were highly educated. In younger males, higher urbanization levels were associated with slightly higher FSIQ scores. Our analyses also showed increased common environmental variation in older males whose parents were more highly educated, and increased unique environmental effects in older males living in more affluent areas. Contrary to studies in children, however, the variance attributable to additive genetic effects was stable across all levels of the moderators under study. Most results were replicated for VIQ and PIQ

Mx Scripts Library: Structural Equation Modeling Scripts for Twin and Family Data

Structural equation modeling (SEM) provides a flexible tool to carry out genetic analyses of family and twin data. The basic model which decomposes the variance between and within families for a particular trait into genetic and non-genetic components can be generalized to multivariate and/ or longitudinal data, incorporate sex differences in parameter estimates, and model the effects of measured environment, candidate genes or DNA marker data. We introduce a web-based library ( http://www.psy.vu.nl/mxbib ) of scripts for uni- and multivariate genetic epidemiological analyses, as well as for linkage and genetic association tests. The scripts are written to be used with the freely available software package Mx and provide a flexible and uniform approach to the analysis of data from relatives. © 2005 Springer Science+Business Media, Inc

Simultaneous genetic analysis of means and covariance structure: Pearson-Lawley selection rules

The object of this paper is to indicate that the Pearson-Lawley selection rules form a plausible general theory for the simultaneous genetic analysis of means and covariance structure. Models are presented based on phenotypic selection and latent selection. Previously presented quantitative genetic models to decompose means and covariance structure simultaneously are reconsidered as instances of latent selection. The selection rules are very useful in the context of behavior genetic modeling because they lead to testable models and a conceptual framework for explaining variation between and within groups by the same genetic and environmental factors. © 1994 Plenum Publishing Corporation

A Multivariate Genetic Analysis of Sensation Seeking

The genetic architecture of sensation seeking was analyzed in 1591 adolescent twin pairs. Individual differences in sensation seeking were best explained by a simple additive genetic model. Between 48 and 63 % of the total variance in sensation seeking subscales was attributable to genetic factors. There were no sex differences in the magnitude of the genetic and environmental effects. The different dimensions of sensation seeking were moderately correlated. The strongest correlations were between the subscales Thrill and Adventure Seeking and Experience Seeking (r = 0.4) and between Boredom Susceptibility and Disinhibition (r = 0.4 in males, r = 0.5 in females). A triangular decomposition showed that the correlations between the sensation seeking subscales were induced mainly by correlated genetic factors and, to a smaller extent, by correlated unique enviromnental factors. The genetic and environmental correlation structures differed between males and females. For females, higher genetic correlations for Experience Seeking with Boredom Susceptibility and Disinhibition and higher correlations among the unique environmental factors were found. There was no evidence that sex-specific genes influenced sensation seeking behavior in males and females. KEY WORDS: Sensation seeking; adolescent twins; multivariate genetic analysis