Parsing the Hypomanic Personality: Explicating the Nature of Specific Dimensions Defining Mania Risk.

Considerable research has used the Hypomanic Personality Scale (HPS) to assess traits conferring risk for hypomanic and manic episodes. Although the HPS has been shown to be defined by several distinct sets of content, most research has continued to rely exclusively on HPS total scores, due to (a) little research having examined its structure and (b) the discrepant structural results obtained in the few available studies. Therefore, we examined the structure and relations of the HPS in a large sample of community adults ( N = 737) receiving psychiatric treatment. Our structural results indicated a five-factor structure of Activation, Charisma, Intellectual Confidence, Lability, and Modesty. Subscales modeling these emergent factors showed divergent patterns of relations with personality and other forms of psychopathology. These findings underscore the importance of examining HPS subscale relations in addition to HPS total scores in future research.

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes.

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.

Genetic and environmental contributions to the development of childhood aggression.

Longitudinal data from a large sample of twins participating in the Netherlands Twin Register (n = 42,827, age range 3-16) were analyzed to investigate the genetic and environmental contributions to childhood aggression. Genetic auto-regressive (simplex) models were used to assess whether the same genes are involved or whether new genes come into play as children grow up. The authors compared 2 different simplex models to disentangle potentially changing behavioral expressions from changes in genetic and environmental effects. One model provided estimates of genetic and environmental effects at the level of individual aggression questionnaire items, and the other model assessed the effects at the level of an aggression sum score computed from the individual items. The results from both models provided evidence for largely stable genetic effects throughout childhood. The results also highlighted the differential heritability of the different indicators of aggression measured with the Childhood Behavior Checklist, with destruction of property showing a very high genetic component during early childhood and fighting behaviors being more heritable in early adolescence. (PsycINFO Database Record

Sum Scores in Twin Growth Curve Models: Practicality Versus Bias.

To study behavioral or psychiatric phenotypes, multiple indices of the behavior or disorder are often collected that are thought to best reflect the phenotype. Combining these items into a single score (e.g. a sum score) is a simple and practical approach for modeling such data, but this simplicity can come at a cost in longitudinal studies, where the relevance of individual items often changes as a function of age. Such changes violate the assumptions of longitudinal measurement invariance (MI), and this violation has the potential to obfuscate the interpretation of the results of latent growth models fit to sum scores. The objectives of this study are (1) to investigate the extent to which violations of longitudinal MI lead to bias in parameter estimates of the average growth curve trajectory, and (2) whether absence of MI affects estimates of the heritability of these growth curve parameters. To this end, we analytically derive the bias in the estimated means and variances of the latent growth factors fit to sum scores when the assumption of longitudinal MI is violated. This bias is further quantified via Monte Carlo simulation, and is illustrated in an empirical analysis of aggression in children aged 3-12 years. These analyses show that measurement non-invariance across age can indeed bias growth curve mean and variance estimates, and our quantification of this bias permits researchers to weigh the costs of using a simple sum score in longitudinal studies. Simulation results indicate that the genetic variance decomposition of growth factors is, however, not biased due to measurement non-invariance across age, provided the phenotype is measurement invariant across birth-order and zygosity in twins.

Using a multivariate model to assess the interactive effects of demographics and lifestyle on the hematological profile.

AIM: To assess the extent to which a multivariate approach to modeling interrelated hematological indices provides more informative results than the traditional approach of modeling each index separately. MATERIALS & METHODS: The effects of demographics and lifestyle on ten hematological indices collected from a Dutch population-based sample (n = 3278) were studied, jointly using multivariate distance matrix regression and separately using linear regression. RESULTS: The multivariate approach highlighted the main effects of all predictors and several interactions; the traditional approach highlighted only main effects. CONCLUSION: The multivariate approach provides more power than traditional methods to detect effects on interrelated biomarkers, suggesting that its use in future research may help identify subgroups that benefit from different treatment or prevention measures.

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

Finding structure in data using multivariate tree boosting.

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause 2 or more outcome variables to covary. We provide the R package "mvtboost" to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package "gbm" (Ridgeway, 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. (PsycINFO Database Record