Harmonizing behavioral outcomes across studies, raters, and countries: application to the genetic analysis of aggression in the ACTION Consortium.

BACKGROUND: Aggression in children has genetic and environmental causes. Studies of aggression can pool existing datasets to include more complex models of social effects. Such analyses require large datasets with harmonized outcome measures. Here, we made use of a reference panel for phenotype data to harmonize multiple aggression measures in school-aged children to jointly analyze data from five large twin cohorts. METHODS: Individual level aggression data on 86,559 children (42,468 twin pairs) were available in five European twin cohorts measured by different instruments. A phenotypic reference panel was collected which enabled a model-based phenotype harmonization approach. A bi-factor integration model in the integrative data analysis framework was developed to model aggression across studies while adjusting for rater, age, and sex. Finally, harmonized aggression scores were analyzed to estimate contributions of genes, environment, and social interaction to aggression. The large sample size allowed adequate power to test for sibling interaction effects, with unique dynamics permitted for opposite-sex twins. RESULTS: The best-fitting model found a high level of overall heritability of aggression (~60%). Different heritability rates of aggression across sex were marginally significant, with heritability estimates in boys of ~64% and ~58% in girls. Sibling interaction effects were only significant in the opposite-sex twin pairs: the interaction effect of males on their female co-twin differed from the effect of females on their male co-twin. An aggressive female had a positive effect on male co-twin aggression, whereas more aggression in males had a negative influence on a female co-twin. CONCLUSIONS: Opposite-sex twins displayed unique social dynamics of aggressive behaviors in a joint analysis of a large, multinational dataset. The integrative data analysis framework, applied in combination with a reference panel, has the potential to elucidate broad, generalizable results in the investigation of common psychological traits in children.

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.

Joint developmental trajectories of internalizing and externalizing disorders between childhood and adolescence.

This study sought to identify trajectories of DSM-IV based internalizing (INT) and externalizing (EXT) problem scores across childhood and adolescence and to provide insight into the comorbidity by modeling the co-occurrence of INT and EXT trajectories. INT and EXT were measured repeatedly between age 7 and age 15 years in over 7,000 children and analyzed using growth mixture models. Five trajectories were identified for both INT and EXT, including very low, low, decreasing, and increasing trajectories. In addition, an adolescent onset trajectory was identified for INT and a stable high trajectory was identified for EXT. Multinomial regression showed that similar EXT and INT trajectories were associated. However, the adolescent onset INT trajectory was independent of high EXT trajectories, and persisting EXT was mainly associated with decreasing INT. Sex and early life environmental risk factors predicted EXT and, to a lesser extent, INT trajectories. The association between trajectories indicates the need to consider comorbidity when a child presents with INT or EXT disorders, particularly when symptoms start early. This is less necessary when INT symptoms start at adolescence. Future studies should investigate the etiology of co-occurring INT and EXT and the specific treatment needs of these severely affected children.

Genetic analyses benefit from using less heterogeneous phenotypes: an illustration with the hospital anxiety and depression scale (HADS).

Phenotypic heterogeneity of depression has been cited as one of the causes of the limited success to detect genetic variants in genome-wide studies. The 7-item Hospital Anxiety and Depression Scale (HADS-D) was developed to detect depression in individuals with physical health problems. An initial psychometric analysis showed that a short version ("HADS-4") is less heterogeneous and hence more reliable than the full scale, and correlates equally strong with a DSM-oriented depression scale. We compared the HADS-D and the HADS-4 to assess the benefits of using less heterogeneous phenotype measures in genetic analyses. We compared HADS-D and HADS-4 in three separate analyses: (1) twin- and family-based heritability estimation, (2) SNP-based heritability estimation using the software GCTA, and (3) a genome-wide association study (GWAS). The twin study resulted in heritability estimates between 18% and 25%, with additive genetic variance being the largest component. There was also evidence for assortative mating and a dominance component of genetic variance, with HADS-4 having slightly lower estimates of assortment. Importantly, when estimating heritability from SNPs, the HADS-D did not show a significant genetic variance component, while for the HADS-4, a statistically significant amount of heritability was estimated. Moreover, the HADS-4 had substantially more SNPs with small P-values in the GWAS analysis than did the HADS-D. Our results underline the benefits of using more homogeneous phenotypes in psychiatric genetic analyses. Homogeneity can be increased by focusing on core symptoms of disorders, thus reducing the noise in aggregate phenotypes caused by substantially different symptom profiles.

A powerful phenotype for gene-finding studies derived from trajectory analyses of symptoms of anxiety and depression between age seven and 18.

To investigate the utility of longitudinal data in genetic analyses of symptoms of anxiety and depression, we assessed individual differences between age 7 and 18 using growth mixture models, and investigated the genetic and non-genetic factors contributing to the trajectories. Mothers of 7,706 girl and 7,418 boy twins from the Netherlands Twin Register rated the anxious depression scale (SxAnxDep) of the Child Behavior Check List (CBCL) at age 7, 10, and 12 years. Two thousand seven hundred and six girl and 1,856 boy twins completed the Youth Self Report (YSR) at age 14, 16, and 18. While individual trajectories varied considerably, these differences were largely idiosyncratic and could not be grouped into separate latent classes with class-specific average growth curves. The intercept, which reflects the individuals' baseline level of SxAnxDep across time, explained 55-58% of the total phenotypic variance. The slope factor, which captures a common average trend over time, did not explain variance in the phenotype. This finding also underlines the high level of idiosyncrasy of trajectories that lack a common longitudinal structure. The analyses of twin data showed that the random intercept factor of SxAnxDep during childhood and during adolescence is considerably more heritable than the observations at any single age, namely between 60% and 84%. One explanation is that different factors contribute to the level of symptoms of anxiety and depression at any given time point, including temporary events and emotions. When considering baseline stability, these temporary influences average out, with the result of a more reliable and more heritable phenotype. © 2015 Wiley Periodicals, Inc.

Childhood ODD and ADHD Behavior: The Effect of Classroom Sharing, Gender, Teacher Gender and Their Interactions.

One criterion for a diagnostic and statistical manual of mental disorders (DSM-IV) diagnosis of attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) is that symptoms are present in at least two settings, and often teacher ratings are taken into account. The short Conners' Teacher Rating Scales-Revised (CTRS-R) is a widely used standardized instrument measuring ODD and ADHD behavior in a school setting. In the current study CTRS-R data were available for 7, 9 and 12-year-old twins from the Netherlands Twin Register. Measurement invariance (MI) across student gender and teacher gender was established for three of the four scales (Oppositional Behavior, Hyperactivity and ADHD Index) of the CTRS-R. The fourth scale (ATT) showed an unacceptable model fit even without constraints on the data and revision of this scale is recommended. Gene-environment (GxE) interaction models revealed that heritability was larger for children sharing a classroom. There were some gender differences in the heritability of ODD and ADHD behavior and there was a moderating effect of teacher's gender at some of the ages. Taken together, this indicates that there was evidence for GxE interaction for classroom sharing, gender of the student and gender of the teacher.

The predictive value of smoking expectancy and the heritability of its accuracy.

INTRODUCTION: Among smokers, former smokers, and never-smokers, this study aimed to (a) determine the predictive value of smoking expectancy on future smoking status, and (b) test the relative contribution of genes and environment to a person's ability to accurately predict future smoking status. For smokers, smoking expectancy reflects the intention to continue smoking; for former smokers, it reflects the intention to take up smoking again; and for never-smokers, it reflects the intention to initiate smoking. METHODS: A longitudinal design was employed in which participants of the Netherlands Twin Register completed 2 consecutive surveys 2 years apart between 1993 and 2011 (3,591 adolescents aged 14-18 years), or between 1993 and 2004 (11,568 adults, aged 18+ years). Smoking expectancy was measured by asking, "Do you think you'll smoke in a year's time?", with answer categories ranging from "certainly not" to "absolutely yes" on a 5-point scale. To determine the predictive value of smoking expectancy, analyses were performed in smokers, former smokers, and never-smokers separately. Data of 2,987 adolescents and 4,911 adult twins were analyzed to estimate heritability. A dichotomous variable reflected the ability to predict future smoking status (correct/incorrect). RESULTS: Smoking expectancy significantly predicted future smoking status among former smokers and never-smokers. The ability to accurately predict future smoking status was explained by additive genetic factors for 59% of adolescents and 27% of adults, with the remainder being explained by unique environmental factors. CONCLUSIONS: A single question on smoking expectancy helps predict future smoking status. Variation in how well subjects predict their future smoking behavior is influenced by genetic factors, especially during adolescence.

Epi2Loc: an R package to investigate two-locus epistatic models.

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data.

MOTIVATION: There is growing momentum to develop statistical learning (SL) methods as an alternative to conventional genome-wide association studies (GWAS). Methods such as random forests (RF) and gradient boosting machine (GBM) result in variable importance measures that indicate how well each single-nucleotide polymorphism (SNP) predicts the phenotype. For RF, it has been shown that variable importance measures are systematically affected by minor allele frequency (MAF) and linkage disequilibrium (LD). To establish RF and GBM as viable alternatives for analyzing genome-wide data, it is necessary to address this potential bias and show that SL methods do not significantly under-perform conventional GWAS methods. RESULTS: Both LD and MAF have a significant impact on the variable importance measures commonly used in RF and GBM. Dividing SNPs into overlapping subsets with approximate linkage equilibrium and applying SL methods to each subset successfully reduces the impact of LD. A welcome side effect of this approach is a dramatic reduction in parallel computing time, increasing the feasibility of applying SL methods to large datasets. The created subsets also facilitate a potential correction for the effect of MAF using pseudocovariates. Simulations using simulated SNPs embedded in empirical data-assessing varying effect sizes, minor allele frequencies and LD patterns-suggest that the sensitivity to detect effects is often improved by subsetting and does not significantly under-perform the Armitage trend test, even under ideal conditions for the trend test. AVAILABILITY: Code for the LD subsetting algorithm and pseudocovariate correction is available at http://www.nd.edu/~glubke/code.html.

The genetic architecture of liver enzyme levels: GGT, ALT and AST.

High levels of liver enzymes GGT, ALT and AST are predictive of disease and all-cause mortality and can reflect liver injury, fatty liver and/or oxidative stress. Variation in GGT, ALT and AST levels is heritable. Moderation of the heritability of these liver enzymes by age and sex has not often been explored, and it is not clear to what extent non-additive genetic and shared environmental factors may play a role. To examine the genetic architecture of GGT, ALT and AST, plasma levels were assessed in a large sample of twins, their siblings, parents and spouses (N = 8,371; age range 18-90). For GGT and ALT, but not for AST, genetic structural equation modeling showed evidence for quantitative sex differences in the genetic architecture. There was no evidence for qualitative sex differences, i.e. the same genes were expressed in males and females. Both additive and non-additive genetic factors were important for GGT in females (total heritability h(2) 60 %) and AST in both sexes (total h(2) 43 %). The heritability of GGT in males and ALT for both sexes was due to additive effects only (GGT males 30 %; ALT males 40 %, females 22 %). Evidence emerged for shared environmental factors influencing GGT in the male offspring generation (variance explained 28 %). Thus, the same genes influence liver enzyme levels across sex and age, but their relative contribution to the variation in GGT and ALT differs in males and females and for GGT across age. Given adequate sample sizes these results suggest that genome-wide association studies may result in the detection of new susceptibility loci for liver enzyme levels when pooling results over sex and age.

The CHRNA5/A3/B4 gene cluster and tobacco, alcohol, cannabis, inhalants and other substance use initiation: replication and new findings using mixture analyses.

Multiple studies have provided evidence for genetic associations between single nucleotide polymorphisms (SNPs) located on the CHRNA5/A3/B4 gene cluster and various phenotypes related to Nicotine Dependence (Greenbaum et al. 2009). Only a few studies have investigated other substances of abuse. The current study has two aims, (1) to extend previous findings by focusing on associations between the CHRNA5/A3/B4 gene cluster and age of initiation of several different substances, and (2) to investigate heterogeneity in age of initiation across the different substances. All analyses were conducted with a subset of the Add Health study with available genetic data. The first aim was met by modeling onset of tobacco, alcohol, cannabis, inhalants, and other substance use using survival mixture analysis (SMA). Ten SNPs in CHRNA5/A3/B4 were used to predict phenotypic differences in the risk of onset, and differences between users and non-users. The survival models aim at investigating differences in the risk of initiation across the 5-18 age range for each phenotype separately. Significant or marginally significant genetic effects were found for all phenotypes. The genetic effects were mainly related to the risk of initiation and to a lesser extent to discriminating between users and non-users. To address the second goal, the survival analyses were complemented by a latent class analysis that modeled all phenotypes jointly. One of the ten SNPs was found to predict differences between the early and late onset classes. Taken together, our study provides evidence for a general role of the CHRNA5/A3/B4 gene cluster in substance use initiation that is not limited to nicotine and alcohol.

Trajectories of CBCL attention problems in childhood.

The first aim of this study was to identify developmental trajectories of Attention Problems in twins followed from age 6 to 12 years. Second, we investigated whether singletons follow similar trajectories. Maternal longitudinal ratings on the Attention Problems (AP) subscale of the Child Behavior Checklist were obtained for a sample of 12,486 twins from the Netherlands Twin Register and for a general population sample of 1,346 singletons. Trajectories were analyzed by growth mixture modeling in twins, and compared with singletons. Teacher ratings on the AP subscale of the Teachers' Report Form were available for 7,179 twins and 1,211 singletons, and were used for cross-sectional mean comparisons at each age. All analyses were conducted for boys and girls separately. We identified three linear trajectories in both boys and girls, i.e., stable low (62-71%), low-increasing (15-18%), and high-decreasing (14-21%). Singletons followed three identical trajectories, with similar class proportions. Teacher ratings yielded no differences in mean levels of Attention Problems between twins and singletons. The development of Attention Problems from age 6 to 12 years can be characterized by stable low, low-increasing, and high-decreasing developmental trajectories. Twins and singletons are comparable with respect to the development of Attention Problems in childhood.

Borderline personality traits and adult attention-deficit hyperactivity disorder symptoms: a genetic analysis of comorbidity.

Previous research has established the comorbidity of adult Attention-Deficit Hyperactivity Disorder (ADHD) with different personality disorders including Borderline Personality Disorder (BPD). The association between adult ADHD and BPD has primarily been investigated at the phenotypic level and not yet at the genetic level. The present study investigates the genetic and environmental contributions to the association between borderline personality traits (BPT) and ADHD symptoms in a sample of 7,233 twins and siblings (aged 18-90 years) registered with the Netherlands Twin Register and the East Flanders Prospective Twin Survey (EFPTS) . Participants completed the Conners' Adult ADHD Rating Scales (CAARS-S:SV) and the Personality Assessment Inventory-Borderline Features Scale (PAI-BOR). A bivariate genetic analysis was performed to determine the extent to which genetic and environmental factors influence variation in BPT and ADHD symptoms and the covariance between them. The heritability of BPT and ADHD symptoms was estimated at 45 and 36%, respectively. The remaining variance in BPT and ADHD symptoms was explained by unique environmental influences. The phenotypic correlation between BPT and ADHD symptoms was estimated at r = 0.59, and could be explained for 49% by genetic factors and 51% by environmental factors. The genetic and environmental correlations between BPT and ADHD symptoms were 0.72 and 0.51, respectively. The shared etiology between BPT and ADHD symptoms is thus a likely cause for the comorbidity of the two disorders.

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

Subtypes versus severity differences in attention-deficit/hyperactivity disorder in the Northern Finnish Birth Cohort.

OBJECTIVE: To investigate whether behaviors of inattention, hyperactivity, and impulsivity among adolescents in Northern Finland reflect qualitatively distinct subtypes of ADHD, variants along a single continuum of severity, or of severity differences within subtypes. METHOD: Latent class models, exploratory factor models, and factor mixture models were applied to questionnaire data of ADHD behaviors obtained from the Northern Finland Birth Cohort (NFBC). Latent class models correspond to qualitatively distinct subtypes, factor analysis corresponds to severity differences, and factor mixture analysis allows for both subtypes and severity differences within subtypes. RESULTS: A comparison of the different models shows that models that distinguish between a low scoring majority class (unaffecteds) and a high scoring minority class (affecteds), and allow for two factors (inattentive, hyperactive-impulsive) with severity differences provide the best fit. CONCLUSIONS: The analysis provides support that a high-scoring minority group (8.8% of males and 6.8% of females) likely reflects an ADHD group in the Northern Finland Birth Cohort, whereas the majority of the population falls into a low-scoring group of unaffecteds. Distinct factors composed of items of inattention and hyperactivity-impulsivity are evident for both sexes with considerable variability in severity within each class.

Fitting latent variable mixture models.

Latent variable mixture models (LVMMs) are models for multivariate observed data from a potentially heterogeneous population. The responses on the observed variables are thought to be driven by one or more latent continuous factors (e.g. severity of a disorder) and/or latent categorical variables (e.g., subtypes of a disorder). Decomposing the observed covariances in the data into the effects of categorical group membership and the effects of continuous trait differences is not trivial, and requires the consideration of a number of different aspects of LVMMs. The first part of this paper provides the theoretical background of LVMMs and emphasizes their exploratory character, outlines the general framework together with assumptions and necessary constraints, highlights the difference between models with and without covariates, and discusses the interrelation between the number of classes and the complexity of the within-class model as well as the relevance of measurement invariance. The second part provides a growth mixture modeling example with simulated data and covers several practical issues when fitting LVMMs.