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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).)

Music engagement is a powerful, influential experience that often begins early in life. Music engagement is moderately heritable in adults (~ 41-69%), but fewer studies have examined genetic influences on childhood music engagement, including their association with language and executive functions. Here we explored genetic and environmental influences on music listening and instrument playing (including singing) in the baseline assessment of the Adolescent Brain Cognitive Development study. Parents reported on their 9-10-year-old children's music experiences (N = 11,876 children; N = 1543 from twin pairs). Both music measures were explained primarily by shared environmental influences. Instrument exposure (but not frequency of instrument engagement) was associated with language skills (r = .27) and executive functions (r = .15-0.17), and these associations with instrument engagement were stronger than those for music listening, visual art, or soccer engagement. These findings highlight the role of shared environmental influences between early music experiences, language, and executive function, during a formative time in development., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2022. The Author(s).)

Nicotine dependence and smoking quantity are both robustly associated with the CHRNA5-A3-B4 gene cluster in the 15q25 region, and SNP rs16969968 in particular. The purpose of this paper is to use structural equation modeling techniques (SEM) to disentangle the complex pattern of relationships between rs16969968, nicotine quantity (as measured by the number of cigarettes an individual smokes per day; CPD) and nicotine dependence (as measured by the Fagerström Test for Nicotine Dependence; FTND). CPD is an indicator, but also a potential cause, of FTND, complicating the interpretation of associations between these constructs and requires a more detailed investigation than standard GWAS or general linear regression models can provide. FTND items and genotypes were collected in four samples, with a combined sample size of 5,373 respondents. A mega-analysis was conducted using a multiple group SEM approach to test competing hypotheses regarding the relationships between the SNP rs16969968, FTND and CPD. In the best fitting model, the FTND items loaded onto two correlated factors. The first, labeled "maintenance," assesses the motivation to maintain constant levels of nicotine through out the day. The second was labeled "urgency" as its items concern the urgency to restore nicotine levels after abstinence. We focus our attention on the "maintenance" factor, of which CPD was an indicator. The best fitting model included a negative feedback loop between the Maintenance factor and CPD. Accordingly, the motivation to maintain higher levels of nicotine increased the quantity of nicotine consumed, which subsequently decreases the maintenance motivation. The fact that the Maintenance-CPD feedback model fits the data best implies that there are at least two biological pathways that lead from rs16969968 to smoking behaviors. The model is consistent with a supply and demand system, which allows individuals to achieve a homeostatic equilibrium for their nicotine concentration.

Since 1987, a group of behavior geneticists have been teaching an annual methodology workshop on how to use state-of-the-art methods to analyze genetically informative data. In the early years, the focus was on analyzing twin and family data, using information of their known genetic relatedness to infer the role of genetic and environmental factors on phenotypic variation. With the rapid evolution of genotyping and sequencing technology and availability of measured genetic data, new methods to detect genetic variants associated with human traits were developed and became the focus of workshop teaching in alternate years. Over the years, many of the methodological advances in the field of statistical genetics have been direct outgrowths of the workshop, as evidence by the software and methodological publications authored by workshop faculty. We provide data and demographics of workshop attendees and evaluate the impact of the methodology workshops on scientific output in the field by evaluating the number of papers applying specific statistical genetic methodologies authored by individuals who have attended workshops.

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.

Improving the accuracy of phenotyping through the use of advanced psychometric tools will increase the power to find significant associations with genetic variants and expand the range of possible hypotheses that can be tested on a genome-wide scale. Multivariate methods, such as structural equation modeling (SEM), are valuable in the phenotypic analysis of psychiatric and substance use phenotypes, but these methods have not been integrated into standard genome-wide association analyses because fitting a SEM at each single nucleotide polymorphism (SNP) along the genome was hitherto considered to be too computationally demanding. By developing a method that can efficiently fit SEMs, it is possible to expand the set of models that can be tested. This is particularly necessary in psychiatric and behavioral genetics, where the statistical methods are often handicapped by phenotypes with large components of stochastic variance. Due to the enormous amount of data that genome-wide scans produce, the statistical methods used to analyze the data are relatively elementary and do not directly correspond with the rich theoretical development, and lack the potential to test more complex hypotheses about the measurement of, and interaction between, comorbid traits. In this paper, we present a method to test the association of a SNP with multiple phenotypes or a latent construct on a genome-wide basis using a diagonally weighted least squares (DWLS) estimator for four common SEMs: a one-factor model, a one-factor residuals model, a two-factor model, and a latent growth model. We demonstrate that the DWLS parameters and p-values strongly correspond with the more traditional full information maximum likelihood parameters and p-values. We also present the timing of simulations and power analyses and a comparison with and existing multivariate GWAS software package.

Using Swedish nationwide registry data, the authors investigated the correlation of genetic and environmental risk factors in the etiology of drug abuse as ascertained from medical and criminal registries by modeling twin and sibling data. Medical drug abuse was defined using public inpatient and outpatient records, while criminal drug abuse was ascertained through legal records. Twin, full and half sibling pairs were obtained from the national twin and genealogical registers. Information about sibling pair residence within the same household was obtained from Statistics Sweden. Standard bivariate genetic structural equation modeling was applied to the population-based data on drug abuse ascertained through medical and crime registries, using OpenMx. Analyses of all possible pairs of twins (MZ: N = 4482; DZ: N = 9838 pairs), full- (N = 1,278,086) and half-siblings (paternal: N = 7767; maternal N = 70,553) who grew up together suggested that factors explaining familial resemblance for drug abuse as defined through medical or criminal registries were mostly the same. Results showed substantial heritability and moderate contributions of shared environmental factors to drug abuse; both were higher in males versus females, and higher for drug abuse ascertained through criminal than medical records. Because of the low prevalence of both assessments of drug abuse, having access to population data was crucial to obtain stable estimates. Using objective registry data, the authors found that drug abuse-whether ascertained through medical versus criminal records-was highly heritable. Furthermore, shared environmental factors contributed significantly to the liability of drug abuse. Genetic and shared environmental risk factors for these two forms of drug abuse were highly correlated.

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.

Chronic cigarette use has been consistently associated with differences in the neuroanatomy of smokers relative to nonsmokers in case-control studies. However, the etiology underlying the relationships between brain structure and cigarette use is unclear. A community-based sample of male twin pairs ages 51-59 (110 monozygotic pairs, 92 dizygotic pairs) was used to determine the extent to which there are common genetic and environmental influences between brain structure and average lifetime cigarette use. Brain structure was measured by high-resolution structural magnetic resonance imaging, from which subcortical volume and cortical volume, thickness and surface area were derived. Bivariate genetic models were fitted between these measures and average lifetime cigarette use measured as cigarette pack-years. Widespread, negative phenotypic correlations were detected between cigarette pack-years and several cortical as well as subcortical structures. Shared genetic and unique environmental factors contributed to the phenotypic correlations shared between cigarette pack-years and subcortical volume as well as cortical volume and surface area. Brain structures involved in many of the correlations were previously reported to play a role in specific aspects of networks of smoking-related behaviors. These results provide evidence for conducting future research on the etiology of smoking-related behaviors using measures of brain morphology.

We begin this special issue by providing a glimpse into the career of Dr. Lindon J. Eaves, from the perspectives of a student, postdoc, instructor, assistant to associate and full professor over the last 20 odd years. We focus primarily on Lindon's contributions to methodological issues and research designs to address them, in particular those related to models for extended twin-family designs, for the development of adolescent behavior, for genotype-environment covariation and interaction, and their application to the Virginia 30,000 and the Virginia Twin Study of Adolescent Behavioral Development. We then introduce the collection of papers in this special festschrift issue of Behavior Genetics, celebrating Dr. Eaves achievements over the last 40 years.

This study sought to determine the relationship between body mass index (BMI) fluctuation and cardiovascular disease phenotypes, diabetes, and depression and the role of genetic and environmental factors in individual differences in BMI fluctuation using the extended twin-family model (ETFM). This study included 14,763 twins and their relatives. Health and Lifestyle Questionnaires were obtained from 28,492 individuals from the Virginia 30,000 dataset including twins, parents, siblings, spouses, and children of twins. Self-report cardiovascular disease, diabetes, and depression data were available. From self-reported height and weight, BMI fluctuation was calculated as the difference between highest and lowest BMI after age 18, for individuals 18-80 years. Logistic regression analyses were used to determine the relationship between BMI fluctuation and disease status. The ETFM was used to estimate the significance and contribution of genetic and environmental factors, cultural transmission, and assortative mating components to BMI fluctuation, while controlling for age. We tested sex differences in additive and dominant genetic effects, parental, non-parental, twin, and unique environmental effects. BMI fluctuation was highly associated with disease status, independent of BMI. Genetic effects accounted for ~34 % of variance in BMI fluctuation in males and ~43 % of variance in females. The majority of the variance was accounted for by environmental factors, about a third of which were shared among twins. Assortative mating, and cultural transmission accounted for only a small proportion of variance in this phenotype. Since there are substantial health risks associated with BMI fluctuation and environmental components of BMI fluctuation account for over 60 % of variance in males and over 50 % of variance in females, environmental risk factors may be appropriate targets to reduce BMI fluctuation.

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.

To determine the relationship between the genetic and environmental risk factors for common internalizing psychopathology (IP) and mental well-being (MWB), we examined detailed measures of emotional, social and psychological well-being, and a history of major depression (MD), generalized anxiety disorder (GAD) and panic attacks in the last year, in 1,386 twins from same-sex pairs from the MIDUS national USA sample assessed in 1995 and then again in 2005. Statistical analyses were performed with the Mx program. In the 1995 data, the best fit model contained one substantially heritable common factor for MD, GAD and panic attacks, and one strongly heritable common factor for the three well-being measures. Genetic and environmental risk factors for IP accounted for, respectively, 50 and 5%, of the genetic and environmental influences on MWB. We then constructed, using 1995 and 2005 data, two common factors that reflected temporally stable influences on (i) MD and GAD, and (ii) on emotional and psychological well-being. Genetic and environmental risk factors for the stable liability to IP accounted for 41 and 29% of the stable genetic and environmental influences, respectively, on MWB. This study suggests that genetic risk factors for IP make up 41-50% of the genetic influences on MWB. The overlap of environmental risk factors is more modest. Although low levels of IP on average reflect a high genetic propensity for MWB, other independent genetic influences play an important role in producing good mental health.

Twin and family studies have provided overwhelming evidence for the genetic basis of individual differences in tobacco initiation (TI), regular smoking (RS) and nicotine dependence (ND). However, only a few genes have been reliably associated with ND. We used a finite mixture distribution model to examine the significance and effect size of the association of previously identified and replicated specific variants in the CHRNA5 and CHRNA3 receptor genes with ND, against the background of genetic and environmental risk factors for ND. We hypothesize that additional phenotypic information in relatives who have not been genotyped can be used to increase the power of detecting the genetic variant. The nicotine measures were assessed by personal interview in female, male and opposite sex twin pairs (N = 4,153) from the population-based Virginia Twin Registry. Three SNPs in the CHRNA5 and CHRNA3 receptor genes, previously shown to be significantly associated with ND in this sample, were replicated in the augmented analyses; they accounted for less than one percent of the genetic variance in liability to ND, which is estimated to be over 50% of the phenotypic variance. The significance of these effects was increased by adding twins with phenotype but without genotype data, but gains are limited and variable. The SNPs associated with ND did not show a significant association with either TI or RS and appear to be specific to the addictive stage of ND, characterized by current smoking and smoking a large amount of cigarettes per day. Furthermore, these SNPs did not appear to be associated with the remaining items comprising the FTND scale. This study confirmed a significant contribution of the CHRNA receptor on different forms of tobacco dependence. However, the genetic variant only accounted for little of the total genetic variance for liability to ND. Including phenotypic data on ungenotyped relatives can improve the statistical power to detect the effects of genetic variants when they contribute to individual differences in the phenotype.

Background: Considerable evidence from twin and adoption studies indicates that genetic and shared environmental factors play a significant role in the initiation of smoking behavior. Although twin and adoption designs are powerful to detect genetic and environmental influences, they do not provide information on the processes of assortative mating and parent-offspring transmission and their contribution to the variability explained by genetic and/or environmental factors., Methods: We examined the role of genetic and environmental factors for smoking initiation using an extended kinship design. This design allows the simultaneous testing of additive and non-additive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission. A dichotomous lifetime smoking measure was obtained from twins and relatives in the Virginia 30,000 sample., Results: Results demonstrate that both genetic and environmental factors play a significant role in the liability to smoking initiation. Major influences on individual differences appeared to be additive genetic and unique environmental effects, with smaller contributions from assortative mating, shared sibling environment, twin environment, cultural transmission and resulting genotype-environment covariance. The finding of negative cultural transmission without dominance led us to investigate more closely two possible mechanisms for the lower parent-offspring correlations compared to the sibling and DZ twin correlations in subsets of the data: (i) age x gene interaction, and (ii) social homogamy. Neither mechanism provided a significantly better explanation of the data, although age regression was significant., Conclusions: This study showed significant heritability, partly due to assortment, and significant effects of primarily non-parental shared environment on smoking initiation.

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.

Several "genetic" item-response theory (IRT) models are fitted to the responses of 1086 adolescent female twins to the 33 multi-category item Mood and Feeling Questionnaire relating to depressive symptomatology in adolescence. A Markov-chain Monte Carlo (MCMC) algorithm is used within a Bayesian framework for inference using Gibbs sampling, implemented in the program WinBUGS 1.4. The final model incorporated separate genetic and non-shared environmental traits ("A and E") and item-specific genetic effects. Simpler models gave markedly poorer fit to the observations judged by the deviance information criterion (DIC). The common genetic factor showed major loadings on melancholic items, while the environmental factor loaded most highly on items relating to self-deprecation. The MCMC approach provides a convenient and flexible alternative to Maximum Likelihood for estimating the parameters of IRT models for relatively large numbers of items in a genetic context. Additional benefits of the IRT approach are discussed including the estimation of latent trait scores, including genetic factor scores, and their sampling errors.

Purpose: To determine whether the observed phenotypic stability in explosive strength during adolescence, as measured by inter-age correlations in vertical jump (VTJ), is mainly caused by genetic and/or environmental factors., Methods: Subjects are from the Leuven Longitudinal Twin Study (LLTS) (n = 105 pairs, equally divided over five zygosity groups). VTJ data were aligned on age at peak height velocity (APHV) to attenuate the temporal fluctuations in inter-age correlations caused by differences in timing of the adolescent growth spurt. Simplex models were fitted using structural equation modelling., Results: After aligning the data on APHV, the annual inter-age correlations show a clear simplex structure over a 4 year interval. The best fitting models included additive genetic and unique environmental sources of variation. Heritability estimates ranged between 60.8% (CI 37.7%-77.2%) and 87.3% (CI 74.2%-94.0%) for boys and between 76.5% (CI 56.7%-89.0%) and 88.6% (CI 77.8%-94.1%) for girls. Up to 56.4% and 62.8% of the total variation at the last measurement occasion is explained by additive genetic factors that already explained a significant amount of variation at previous measurement occasions in boys and girls respectively. It thus can be concluded that the observed stability of explosive strength during adolescence is mainly caused by a stable genetic influence in boys and girls., Conclusions: Additive genetic factors seem to be the main cause of the observed phenotypic stability in VTJ performance in boys and girls during adolescence.