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Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9–10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background. [ABSTRACT FROM AUTHOR]

Objective: To 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. Methods: Behavior 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. Results: We 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. Conclusions: We 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. [ABSTRACT FROM AUTHOR]

Twin data can provide valuable insight into the relationship between the stages of phenomena such as disease or substance abuse. Initiation of substance use may be caused by factors that are the same as, partially shared with, or completely independent of those that cause progression from use to abuse. Comparison of rates of progression among the cotwins of twins who do vs. do not initiate provides indirect information about the relationship between initiation and progression. Existing models for this relationship have been difficult to extend because they are usually expressed in terms of explicit integrals. In this paper, the problem is overcome by regarding the analysis of twin data on initiation and progression as a special case of missing data, in which individuals who do not initiate are regarded as having missing data on progression measures. Using the general framework for the analysis of ordinal data with missing values available in Mx makes extensions that include other variables much easier. The effects of continuous covariates such as age on initiation and progression becomes simple. Also facilitated are the examination of initiation and progression in two or more substances, and transition models with two or more steps. The methods are illustrated with data on the effects of cohort on liability to cannabis use and abuse, bivariate analysis of tobacco use and dependence and cannabis use and abuse, and the relationships between initiation of smoking, regular smoking and nicotine dependence. Other suitable applications include the relationship between symptoms and diagnosis, such as fears and the progression to phobia. [ABSTRACT FROM AUTHOR]

Regular cigarette smoking and cannabis consumption are strongly positively related to each other, yet few studies explore their underlying variation and covariation. We evaluated the genetic and environmental decomposition of variance and covariance of these two traits in twin data from three countries with different social norms and legislation. Data from the Netherlands Twin Register, FinnTwin12/16, and the Minnesota Center for Twin Family Research (total N = 21,617) were analyzed in bivariate threshold models of lifetime regular smoking initiation (RSI) and lifetime cannabis initiation (CI). We ran unstratified models and models stratified by sex and country. Prevalence of RSI was lowest in the Netherlands and prevalence of CI was highest in Minnesota. In the unstratified model, genetic (A) and common environmental factors (C) contributed substantially to the liabilities of RSI (A = 0.47, C = 0.34) and CI (A = 0.28, C = 0.51). The two liabilities were significantly phenotypically (rP = 0.56), genetically (rA = 0.74), and environmentally correlated in the unstratified model (rC = 0.47and rE = 0.48, representing correlations between common and unique environmental factors). The magnitude of phenotypic correlation between liabilities varied by country but not sex (Minnesota rP ~ 0.70, Netherlands rP ~ 0.59, Finland rP ~ 0.45). Comparisons of decomposed correlations could not be reliably tested in the stratified models. The prevalence and association of RSI and CI vary by sex and country. These two behaviors are correlated because there is genetic and environmental overlap between their underlying latent liabilities. There is heterogeneity in the genetic architecture of these traits across country., (© 2024. The Author(s).)

In epidemiological and twin populations, prior interview studies have identified an externalizing spectrum of disorders. Could this be detected utilizing objective registry data? In 20,603 twin pairs from the Swedish Twin Registry, we obtained information from national medical, criminal and pharmacy records on drug abuse (DA), criminal behavior (CB) and alcohol use disorders (AUD). Multivariate twin modeling was performed with the OpenMx package. A common pathway model with quantitative but not qualitative sex effects fit best with twin resemblance for the latent liability to externalizing syndromes due to both genetic and shared environmental factors. Heritability of the liability was higher in females (76 vs. 62%) while shared environmental influences were considerably stronger in males (23 vs. 3%). In both sexes, this latent liability was most strongly indexed by DA and least by CB. All three syndromes had specific genetic influences (especially CB and AUD in males, and CB in females) and specific shared environmental effects (especially DA and CB in males, and AUD in females). For DA, CB and AUD in men, and DA and AUD in women, at least 75% of the genetic risk arose through the common factor. The best fit model assumed that genetic and environmental influences on these externalizing syndromes in males and females were the same. We identified, in registry data, a highly heritable externalizing spectrum. DA, CB and AUD share substantial genetic and modest to moderate shared environmental influences. The nature of the externalizing spectrum differed meaningfully between the sexes.

The current study examined the association between substance use in the household during childhood, parental attitudes towards substance use and lifetime substance use in males. Subjects included 1081 monozygotic and 707 dizygotic twins from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders. Retrospective reports of substance use and features of the family environment (adult household substance use and parental attitudes towards substance use) were obtained using a life history interview. A trivariate Cholesky decomposition was conducted using the program Mx to decompose common shared environmental variance. Findings suggest that family environmental factors accounted for a large proportion of the shared environmental effects for illicit drug use. Results illustrate an important way of extending behavior genetic research to reveal specific etiological environmental mechanisms.

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.