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AbstractThis article describes Dr Nathan Gillespie's PhD training and supervision under Professor Nick Martin and their ongoing collaborations. Drs Gillespie and Martin have collaborated on numerous biometrical genetic analyses applied to cross-sectional and longitudinal twin data, combined molecular and phenotypic modeling, as well as genomewide meta-analyses of psychoactive substance use and misuse. Dr Gillespie remains an active collaborator with Professor Martin, including ongoing data collection, analysis and publications related to the Brisbane Longitudinal Twin Study.

AbstractApproaches such as DeFries-Fulker extremes regression (LaBuda et al., 1986) are commonly used in genetically informative studies to assess whether familial resemblance varies as a function of the scores of pairs of twins. While useful for detecting such effects, formal modeling of differences in variance components as a function of pairs' trait scores is rarely attempted. We therefore present a finite mixture model which specifies that the population consists of latent groups which may differ in (i) their means, and (ii) the relative impact of genetic and environmental factors on within-group variation and covariation. This model may be considered as a special case of a factor mixture model, which combines the features of a latent class model with those of a latent trait model. Various models for the class membership of twin pairs may be employed, including additive genetic, common environment, specific environment or major locus (QTL) factors. Simulation results based on variance components derived from Turkheimer and colleagues (2003), illustrate the impact of factors such as the difference in group means and variance components on the feasibility of correctly estimating the parameters of the mixture model. Model-fitting analyses estimated group heritability as .49, which is significantly greater than heritability for the rest of the population in early childhood. These results suggest that factor mixture modeling is sufficiently robust for detecting heterogeneous populations even when group mean differences are modest.

AbstractThe International Cannabis Consortium (ICC) was founded in 2013 by Jacqueline Vink, Nathan Gillespie, Karin Verweij and Eske Derks. The largest contribution to the first meta-analysis was made by Prof. Nick Martin. The ICC has published two primary publications, in Translational Psychiatry and Nature Neuroscience, and many secondary publications. The study's principal investigators will always be grateful for Nick's contribution to science as they would not have been able to do any of this work without the contributions of Nick and others who collected samples. Nick has made unique contributions to the careers of many junior researchers by supporting their development and growth into senior positions.

AbstractPrevious research has shown that self-reports of the amount of social support are heritable. Using the Kessler perceived social support (KPSS) measure, we explored sex differences in the genetic and environmental contributions to individual differences. We did this separately for subscales that captured the perceived support from different members of the network (spouse, twin, children, parents, relatives, friends and confidant). Our sample comprised 7059 male, female and opposite-sex twin pairs aged 18−95 years from the Australian Twin Registry. We found tentative support for different genetic mechanisms in males and females for support from friends and the average KPSS score of all subscales, but otherwise, there are no sex differences. For each subscale alone, the additive genetic (A) and unique environment (E) effects were significant. By contrast, the covariation among the subscales was explained — in roughly equal parts — by A, E and the common environment, with effects of different support constellations plausibly accounting for the latter. A single genetic and common environment factor accounted for between half and three-quarters of the variance across the subscales in both males and females, suggesting little heterogeneity in the genetic and environmental etiology of the different support sources.

Our aim was to test the direction of causation between self-report parental monitoring (PM) and the liability to illicit drug initiation (DI) as indicated by cannabis, cocaine, and stimulants. We fitted a multiple indicator model to test causal and non-causal models based on a large, genetically informative cross-sectional sample of male twins. The sample comprised 1,778 males aged 24–62 years from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders. Data came from self-report measures of lifetime cannabis, stimulants, and cocaine initiation, and retrospective assessment of PM between ages 8–17 years. Multivariate modeling showed that familial aggregation in PM and DI were both explained by a combination of additive genetic and shared environmental effects. Moreover, the significant association between PM and DI was best explained by a correlated liability model versus causal models. PM has typically been assumed to be an environmental, causal risk factor for drug use and has been shown to be among the more salient environmental risk factors for illicit DI. Our data were not consistent with this causal hypothesis. Instead, a correlated liability model in which PM and risk of DI share common genetic and environmental risks provided a better fit to the data.

AbstractMany studies of human behavior and psychological constructs rely on subjects' willingness to disclose information about themselves. This is problematic for phenotypes that require the disclosure of sensitive information, such as sexual behavior or illicit drug use, which are likely to be underreported. We describe a method for evaluating how sensitive variance component estimates are to underreporting. The method involves estimating, by maximum likelihood, the original population proportions of the response classes, and adjusting them for a set of hypothesized underreporting parameters. If the true values of the underreporting parameters were known, the researcher could estimate the variance components based on these values. Usually, underreporting levels are not known with certainty. However, it is possible to assume a specific value for the underreporting rate, obtain response pattern proportions adjusted for this rate, and then to conduct the analyses on these revised estimates. By repeating the procedure across the range of plausible underreporting values, the researcher can assess how sensitive the variance component estimates are to variation in underreporting. We apply this method to a sample of male-male twin pairs who reported on themselves and their co-twins for illicit drug abuse and dependence (DAD). We show how underreporting influences estimates of additive genetic, common environment, and specific environment variance components (A, C, and E) obtained for DAD in a classical twin design.

AbstractWhile snus has been the focus of increasing public health interest, twin studies have examined neither sources of individual variation for its use nor the sources of resemblance between snus and cigarette use. Twins from the Norwegian Institute of Public Health Panel were assessed by self-report questionnaire for the initiation of regular use and maximal quantity used for snus and cigarettes. Twin modeling was performed using OpenMx on data from 2767 twins including 856 complete pairs. Fitting univariate twin models produced similar results for cigarette initiation and quantity with estimates of additive genetic, shared environmental and unique environmental effects of approximately 77%, 0% and 23%, respectively. Estimates of snus initiation and quantity were, respectively, approximately 53%, 26% and 21%. Joint analyses suggested that the genetic, shared environmental and unique environmental correlations between cigarette and snus initiation and quantity were +.82, 0 and +.42, respectively. However, these results could not be statistically distinguished from a model which postulated that resemblance between cigarette initiation and quantity resulted from genetic and unique environmental correlations of +.47 and +.43. Compared with cigarette initiation and quantity of use in Norwegian twins, the role of genes was less prominent and shared environment more prominent for initiation and quantity of use of snus. Joint analyses of both tobacco phenotypes suggested, but did not confirm definitively, that genetic risk factors for cigarette and snus use were similar but not identical, while shared environmental factors existed that were specific to snus use.

Excessive internet use has been linked to psychopathology. Therefore, understanding the genetic and environmental risks underpinning internet use and their relation to psychopathology is important. This study aims to explore the genetic and environmental etiology of internet use measures and their associations with internalizing disorders and substance use disorders. The sample included 2,059 monozygotic (MZ) and dizygotic (DZ) young adult twins from the Brisbane Longitudinal Twin Study (BLTS). Younger participants reported more frequent internet use, while women were more likely to use the internet for interpersonal communication. Familial aggregation in 'frequency of internet use' was entirely explained by additive genetic factors accounting for 41% of the variance. Familial aggregation in 'frequency of use after 11 pm', 'using the internet to contact peers', and 'using the internet primarily to access social networking sites' was attributable to varying combinations of additive genetic and shared environmental factors. In terms of psychopathology, there were no significant associations between internet use measures and major depression (MD), but there were positive significant associations between 'frequency of internet use' and 'frequency of use after 11 pm' with social phobia (SP). 'Using the internet to contact peers' was positively associated with alcohol abuse, whereas 'using the internet to contact peers' and 'using the internet primarily to access social networking sites' were negatively associated with cannabis use disorders and nicotine symptoms. Individual differences in internet use can be attributable to varying degrees of genetic and environmental risks. Despite some significant associations of small effect, variation in internet use appears mostly unrelated to psychopathology.

Objective: Despite an increasing recognition that psychiatric disorders can be diagnosed as early as preschool, little is known how early genetic and environmental risk factors contribute to the development of psychiatric disorders during this very early period of development. Method: We assessed infant temperament at age 1, and attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and separation anxiety disorder (SAD) at ages 3 through 5 years in a sample of Hispanic twins. Genetic, shared, and non-shared environmental effects were estimated for each temperamental construct and psychiatric disorder using the statistical program MX. Multivariate genetic models were fitted to determine whether the same or different sets of genes and environments account for the co-occurrence between early temperament and preschool psychiatric disorders. Results: Additive genetic factors accounted for 61% of the variance in ADHD, 21% in ODD, and 28% in SAD. Shared environmental factors accounted for 34% of the variance in ODD and 15% of SAD. The genetic influence on difficult temperament was significantly associated with preschool ADHD, SAD, and ODD. The association between ODD and SAD was due to both genetic and family environmental factors. The temperamental trait of resistance to control was entirely accounted for by the shared family environment. Conclusions: There are different genetic and family environmental pathways between infant temperament and psychiatric diagnoses in this sample of Puerto Rican preschool age children.

Psychological distress (PSYCH), somatic distress (SOMA), affective disorders (AD), and substance use (SU) frequently co-occur. The genetic relationship between PSYCH and SOMA, however, remains understudied. We examined the genetic and environmental influences on these two disorders and their comorbid AD and SU using structural equation modeling. Self-reported PSYCH and SOMA were measured in 1,548 twins using the two subscales of a 12-item questionnaire, the Somatic and Psychological Health Report. Its reliability and psychometric properties were examined. Six ADs, involvement of licit and illicit substance, and two SU disorders were obtained from 1,663–2,132 twins using the World Mental Health Composite International Diagnostic Interview and/or from an online adaption of the same. SU phenotypes (heritability: 49–79%) were found to be more heritable than the affective disorder phenotypes (heritability: 32–42%), SOMA (heritability: 25%), and PSYCH (heritability: 23%). We fit separate non-parametric item response theory models for PSYCH, SOMA, AD, and SU. The IRT scores were used as the refined phenotypes for fitting multivariate genetic models. The best-fitting model showed the similar amount of genetic overlap between PSYCH–AD (genetic correlationrG= 0.49) and SOMA–AD (rG=0.53), as well as between PSYCH–SU (rG= 0.23) and SOMA–SU (rG= 0.25). Unique environmental factors explained 53% to 76% of the variance in each of these four phenotypes, whereas additive genetic factors explained 17% to 46% of the variance. The covariance between the four phenotypes was largely explained by unique environmental factors. Common genetic factor had a significant influence on all the four phenotypes, but they explained a moderate portion of the covariance.

We describe the data being collected from the Brisbane Longitudinal Twin Study in Australia as part of the US National Institute on Drug Abuse (NIDA)-funded project, Pathways to Cannabis Use, Abuse and Dependence. The history, recruitment, assessment, and retention of twin families in this project are described in detail, along with preliminary findings and plans for future research. The goal of this NIDA project is to make a significant contribution to the discovery of quantitative trait loci influencing cannabis use disorders. Although the focus is cannabis use, abuse, and dependence in young adults, measures of comorbid illicit drug use disorders are also being collected. In addition, a variety of internalizing and externalizing disorders are being assessed, funded by support from the Australian National Health and Medical Research Council. Because these same twins have participated in numerous twin studies since 1992, future plans will include linking different phenotypes to investigate relationships between drug use, psychiatric disorders, and psychological phenotypes within cross-sectional and longitudinal or developmental frameworks.

Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on allelesnot transmittedto the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children's own EA PRS significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.