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Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.
In contrast to many phenotypes that have been studied using twin designs, substance use shows considerable evidence of environmental influence. Accordingly, specifying the relevant environments and understanding the nature of their effects is an important research priority. Twin studies also have demonstrated that the importance of genetic and environmental influences varies across development for a variety of behavioral outcomes, including substance use. Here, we report analyses exploring moderating effects associated with parenting and peer characteristics on adolescent smoking and drinking, measured at ages 14 and 17. We find significant evidence of moderating effects associated with two dimensions of parenting (parental monitoring and time spent in activities with parents) on adolescent smoking, measured at two time points across development, but no moderating effects on adolescent drinking. Genetic influences on smoking increased, and common environmental effects decreased, as adolescents reported less parental monitoring and spending more time with their parents. Conversely, we find evidence that adolescent drinking is more strongly influenced by peer characteristics. The importance of genetic predispositions was increased among adolescents who reported more friends who used alcohol. These analyses illustrate the importance of incorporating measured aspects of the environment into genetically informative twin models to begin to understand how specific environments are related to various outcomes. Furthermore, they illustrate the importance of using a developmental perspective to understand how specific influences may vary across different ages, and across different phenotypes.
Gene–environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i) be continuous or binary ii) differ between twins within a pair iii) interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v) show scalar (different magnitudes) or qualitative (different genes) interactions vi) be correlated with genetic effects acting upon the trait, to allow for a test of gene–environment interaction in the presence of gene–envi-ronment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene–environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.
Gene–environment interaction (G × E) is likely to be a common and important source of variation for complex behavioral traits. Gene–environment interaction, or genetic control of sensitivity to the environment, can be incorporated into variance components twin and sib-pair analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. An approach described in a companion paper (Purcell, 2002) is applied to sib-pair variance components linkage analysis in two ways: allowing for quantitative trait locus by environment interaction and utilizing information on any residual interactions detected prior to analysis. As well as elucidating environmental pathways, consideration of G × E in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.
Research suggests that exposure to extreme stress in childhood, such as domestic violence, affects children's neurocognitive development, leading to lower intelligence. But studies have been unable to account for genetic influences that might confound the association between domestic violence and lower intelligence. This twin study tested whether domestic violence had environmentally mediated effects on young children's intelligence. Children's IQs were assessed for a population sample of 1116 monozygotic and dizygotic 5-year-old twin pairs in England. Mothers reported their experience of domestic violence in the previous 5 years. Ordinary least squares regression showed that domestic violence was uniquely associated with IQ suppression in a dose–response relationship. Children exposed to high levels of domestic violence had IQs that were, on average, 8 points lower than unexposed children. Structural equation models showed that adult domestic violence accounted for 4% of the variation, on average, in child IQ, independent of latent genetic influences. The findings are consistent with animal experiments and human correlational studies documenting the harmful effects of extreme stress on brain development. Programs that successfully reduce domestic violence should also have beneficial effects on children's cognitive development.
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