Effects of censoring on parameter estimates and power in genetic modeling.

Genetic and environmental influences on variance in phenotypic traits may be estimated with normal theory Maximum Likelihood (ML). However, when the assumption of multivariate normality is not met, this method may result in biased parameter estimates and incorrect likelihood ratio tests. We simulated multivariate normal distributed twin data under the assumption of three different genetic models. Genetic model fitting was performed in six data sets: multivariate normal data, discrete uncensored data, censored data, square root transformed censored data, normal scores of censored data, and categorical data. Estimates were obtained with normal theory ML (data sets 1-5) and with categorical data analysis (data set 6). Statistical power was examined by fitting reduced models to the data. When fitting an ACE model to censored data, an unbiased estimate of the additive genetic effect was obtained. However, the common environmental effect was underestimated and the unique environmental effect was overestimated. Transformations did not remove this bias. When fitting an ADE model, the additive genetic effect was underestimated while the dominant and unique environmental effects were overestimated. In all models, the correct parameter estimates were recovered with categorical data analysis. However, with categorical data analysis, the statistical power decreased. The analysis of L-shaped distributed data with normal theory ML results in biased parameter estimates. Unbiased parameter estimates are obtained with categorical data analysis, but the power decreases

Assessment and genetic aetiology of attention problems, hyperactivity, and related disorders

Boomsma, D.I. [Promotor]Dolan, C.V. [Copromotor]Hudziak, J.J. [Copromotor

The genetic and environmental contributions to attention deficit hyperactivity disorder as measured by the Conners' Rating Scales-revised

Objective: The majority of published reports on twin studies of attention deficit hyperactivity disorder (ADHD) have indicated robust additive genetic influences and unique environmental influences. These studies typically used DSM ADHD symptoms collected by telephone or interviews with mothers. The purpose of this study was to test the genetic architecture of ADHD by using the ADHD index from Conners' Rating Scales - Revised. Method: From the Conners' scale forms, data for the ADHD index were collected from the mothers of 1,595 7-year-old twin pairs from the Netherlands Twin Registry. Rates of ADHD diagnoses were computed by using Conners' gender- and age-specific cutoff points. Contributions from additive, dominant, unique environmental, interaction, and gender effects were computed by using gender-genetic models. Results: The prevalence of ADHD across the sample of 7-year-old twin pairs was about 4% according to the mothers' reports, consistent with other reported rates of ADHD. However, using the gender norms provided with the ADHD index, the authors found slightly higher rates of ADHD in girls than previously reported. Genetic analyses yielded a model that includes genetic dominance (48%), additive genetic factors (30%), and unique environmental factors (22%). Conclusions: The ADHD index from Conners' Rating Scales - Revised identified an appropriate percentage of children across this epidemiologic twin sample as being at risk for ADHD. The results of the genetic analyses are consistent with prior reports that ADHD is predominantly influenced by genetic factors that are both dominant and additive

Individual Differences in Aggression: Genetic Analyses by age, gender, and informant in 3-, 7-, and 10-year-old Dutch Twins

Aggression in humans is associated with substantial morbidity and mortality. In this study we report on the aggressive behavior syndrome (AGG) in young children as defined by the Child Behavior Checklist (CBCL) and the Teacher Report Form (TRF). We assessed aggression in a large sample of Dutch twins at ages 3, 7, and 10 years. The purpose of this study was three-fold. First, we determined the number of children who are "clinically deviant" on the AGG scale. Second, we assessed the genetic and environmental contributions to AGG for the maternal, paternal, and teacher ratings at each age, for boys and girls. Third, we explored issues of rater bias by analyzing parental and teacher data simultaneously. CBCL data were available from mothers on 6436 three-year-old, 5451 seven-year-old, and 2972 ten-year-old twin pairs and CBCL data from fathers on 4207 three-year-old, 4269 seven-year-old, and 2295 ten-year-old twin pairs. Teacher report data from the TRF were collected for 1036 seven-year-old and 903 ten-year-old twin pairs from the Netherlands Twin Registry. Structural equation modeling was employed to obtain genetic and environmental estimates at each age. Analyses were conducted separately by age and informant, as well as simultaneously, for all informants. Differences in raw scores across gender were found, with boys being rated as more aggressive than girls by all informants. Mothers reported more symptoms than fathers, who reported more symptoms than teachers. Evidence for moderate to high genetic influence (51%-72%) was seen for AGG by all three informants at all ages with only small sex differences in heritability estimates. Best fitting models for AGG by parent reports also included a small contribution of common environment. The largest sex differences in heritabilities were seen at age 10. Contributions of common (13%-27%) and unique (16%-31%) environment were small to moderate. There was some evidence of genetic dominance by teacher report for 10-year-old girls

Young Netherlands Twin Register (Y-NTR): A longitudinal multiple informant study of problem behavior.

The Netherlands Twin Register (NTR) was established around 1987 at the Vrije Universiteit in Amsterdam, the Netherlands. The current article summarizes the longitudinal genetic analyses of maternal and paternal ratings of twins' behavior as a function of the sex of the children for the traits of aggression (AGG), attention problems (AP), anxious/depression (ANX), internalizing behavior (INT) and externalizing behavior (EXT). We found that genetic influences are the most important factor in explaining individual differences in these traits. For most phenotypes, influences of genetic factors fluctuate throughout development, with the exception of AP, for which genetic influences remain of similar magnitude. Changes in genetic influences parallel those in shared environmental influences, while nonshared environmental influences remain relatively constant. Around 10% to 20% of the variance is accounted for by parent-specific shared environment, which includes rater bias. For all phenotypes, stability throughout childhood is accounted for by genetic and shared environmental factors, while nonshared environmental influences are mainly age/measurement specific. About 15% of the phenotypic stability is accounted for by rater-specific shared environmental influences, which include rater bias. In conclusion, between ages 3 and 12 genetic factors are the most important cause of individual differences in emotional and behavioral problem

Unraveling the genetic architecture of major depressive disorder: Merits and pitfalls of the approaches used in genome-wide association studies

To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression

A structural MRI study in monozygotic twins concordant or discordant for attention/hyperactivity problems: Evidence for genetic and environmental heterogeneity in the developing brain.

Several structural brain abnormalities have been reported in patients with Attention Deficit Hyperactivity Disorder (ADHD). However, the etiology of these brain changes is still unclear. To investigate genetic and environmental influences on ADHD related neurobiological changes, we performed Voxel-Based Morphometry on MRI scans from monozygotic (MZ) twins selected from a large longitudinal population database to be highly concordant or highly discordant for ratings on the Child Behavior Checklist Attention Problem scale (CBCL-AP). Children scoring low on the CBCL-AP are at low risk for ADHD, whereas children scoring high on this scale are at high-risk for ADHD. Brain differences between concordant high-risk twin pairs and concordant low-risk twin pairs likely reflect the genetic risk for ADHD; brain differences between the low-risk and high-risk twins from discordant MZ twin pairs reflect the environmental risk for ADHD. A major difference between comparisons of high and low-risk twins from concordant pairs and high/low twins from discordant pairs was found for the prefrontal lobes. The concordant high-risk pairs showed volume loss in orbitofrontal subdivisions. High-risk members from the discordant twin pairs exhibited volume reduction in the right inferior dorsolateral prefontal cortex. In addition, the posterior corpus callosum was compromised in concordant high-risk pairs, only. Our findings indicate that inattention and hyperactivity symptoms are associated with anatomical abnormalities of a distributed action-attentional network. Different brain areas of this network appear to be affected in inattention/hyperactivity caused by genetic (i.e., high concordant MZ pairs) vs. environmental (i.e., high-low discordant MZ pairs) risk factors. These results provide clues that further our understanding of brain alterations in ADHD. © 2007 Elsevier Inc. All rights reserved

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.Peer reviewe