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.

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.

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.

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.

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.

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).

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.