Pathological gambling and substance use disorders are highly prevalent and comorbid among veteran populations. These disorders also share genetic influences, although the underlying constructs and magnitude of their influence remain unclear. This project utilized the Hierarchical Taxonomy of Psychopathology (HiTOP) as a framework for modeling the underlying dimensions of psychopathology as latent factors and modeled genetic and environmental influences on substance use disorders and pathological gambling. Study 1 examined the structure of psychopathology for 15 common mental disorders in a sample of Vietnam-era veteran twins from the Harvard Drug Study (nMZ = 3,748 and nDZ = 2,996) to determine the appropriate location for pathological gambling within the HiTOP framework. The best fitting model included internalizing and externalizing spectra and an illicit substance use subfactor. Pathological gambling (loading = .30) loaded onto the externalizing spectrum with legal substance use, conduct disorder, antisocial personality disorder, and a subfactor that subsumed all six illicit substance use disorders. The best fitting model in Study 1 did not support the existence of a ‘p’ factor underlying all psychopathology. In Study 2, genetic and environmental components were modeled for the 15 disorders and 3 latent factors modeled in Study 1. Additive genetics explained from 10% (generalized anxiety disorder, panic disorder) to 49% (nicotine use) of the variance in specific disorders and from 24% (internalizing) to 46% (externalizing) of the variance of latent factors. Only cocaine use and conduct disorder demonstrated significant variance attributable to shared environment, the entirety of which occurred at the disorder-specific level. Only 9% of the genetic variance associated with alcohol use was shared across disorders, whereas 100% of genetic variance in cocaine and hallucinogen use was shared with latent factors. In total, 12% of the variance in risk for pathological gambling was associated with additive genetics, and 13% of that variance was shared via the externalizing spectrum. Findings highlight shared risk among illicit substance use disorders and among other disorders on the externalizing spectrum. These findings suggest externalizing and illicit substance use as transdiagnostic targets for treatments aimed at individuals with comorbid substance use disorders, pathological gambling, and other externalizing disorders.

<p>Longitudinal twin data provide important information for exploring sources of variation in human traits. In statistical models for twin data, unobserved genetic and environmental factors influencing the trait are represented by latent variables. In this way, trait variation can be decomposed into genetic and environmental components. With repeated measurements on twins, latent variables can be used to describe individual trajectories, and the genetic and environmental variance components are assessed as functions of age. This thesis contributes to statistical methodology for analysing longitudinal twin data by (i) exploring the use of random change point models for modelling variance as a function of age, (ii) assessing how nonresponse in twin studies may affect estimates of genetic and environmental influences, and (iii) providing a method for hypothesis testing of genetic and environmental variance components. The random change point model, in contrast to linear and quadratic random effects models, is shown to be very flexible in capturing variability as a function of age. Approximate maximum likelihood inference through first-order linearization of the random change point model is contrasted with Bayesian inference based on Markov chain Monte Carlo simulation. In a set of simulations based on a twin model for informative nonresponse, it is demonstrated how the effect of nonresponse on estimates of genetic and environmental variance components depends on the underlying nonresponse mechanism. This thesis also reveals that the standard procedure for testing variance components is inadequate, since the null hypothesis places the variance components on the boundary of the parameter space. The asymptotic distribution of the likelihood ratio statistic for testing variance components in classical twin models is derived, resulting in a mixture of chi-square distributions. Statistical methodology is illustrated with applications to empirical data on cognitive function from a longitudinal twin study of aging. </p>

Longitudinal twin data provide important information for exploring sources of variation in human traits. In statistical models for twin data, unobserved genetic and environmental factors influencing the trait are represented by latent variables. In this way, trait variation can be decomposed into genetic and environmental components. With repeated measurements on twins, latent variables can be used to describe individual trajectories, and the genetic and environmental variance components are assessed as functions of age. This thesis contributes to statistical methodology for analysing longitudinal twin data by (i) exploring the use of random change point models for modelling variance as a function of age, (ii) assessing how nonresponse in twin studies may affect estimates of genetic and environmental influences, and (iii) providing a method for hypothesis testing of genetic and environmental variance components. The random change point model, in contrast to linear and quadratic random effects models, is shown to be very flexible in capturing variability as a function of age. Approximate maximum likelihood inference through first-order linearization of the random change point model is contrasted with Bayesian inference based on Markov chain Monte Carlo simulation. In a set of simulations based on a twin model for informative nonresponse, it is demonstrated how the effect of nonresponse on estimates of genetic and environmental variance components depends on the underlying nonresponse mechanism. This thesis also reveals that the standard procedure for testing variance components is inadequate, since the null hypothesis places the variance components on the boundary of the parameter space. The asymptotic distribution of the likelihood ratio statistic for testing variance components in classical twin models is derived, resulting in a mixture of chi-square distributions. Statistical methodology is illustrated with applications to empirical data on cognitive function from a longitudinal twin study of aging.