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The widespread comorbidity among psychiatric disorders demonstrated in epidemiological studies1–5 is mirrored by non-zero, positive genetic correlations from large-scale genetic studies6–10. To identify shared biological processes underpinning this observed phenotypic and genetic covariance and enhance molecular characterization of general psychiatric disorder liability11–13, we used several strategies aimed at uncovering pleiotropic, that is, cross-trait-associated, single-nucleotide polymorphisms (SNPs), genes and biological pathways. We conducted cross-trait meta-analysis on 12 psychiatric disorders to identify pleiotropic SNPs. The meta-analytic signal was driven by schizophrenia, hampering interpretation and joint biological characterization of the cross-trait meta-analytic signal. Subsequent pairwise comparisons of psychiatric disorders identified substantial pleiotropic overlap, but mainly among pairs of psychiatric disorders, and mainly at less stringent P-value thresholds. Only annotations related to evolutionarily conserved genomic regions were significant for multiple (9 out of 12) psychiatric disorders. Overall, identification of shared biological mechanisms remains challenging due to variation in power and genetic architecture between psychiatric disorders.

A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high-density lipoprotein cholesterol, systolic blood pressure, triglycerides, and waist circumference was used; which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components.

Genetic correlation (rg) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, rg is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the rg is confined to particular genomic regions or in opposing directions at different loci. Current tools for local rg analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local rg analysis that, in addition to testing the standard bivariate local rgs between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local rgs across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.

Studies using induced pluripotent stem cells (iPSCs) are gaining momentum in brain disorder modelling, but optimal study designs are poorly defined. Here, we compare commonly used designs and statistical analysis for different research aims. Furthermore, we generated immunocytochemical, electrophysiological, and proteomic data from iPSC-derived neurons of five healthy subjects, analysed data variation and conducted power simulations. These analyses show that published case-control iPSC studies are generally underpowered. Designs using isogenic iPSC lines typically have higher power than case-control designs, but generalization of conclusions is limited. We show that, for the realistic settings used in this study, a multiple isogenic pair design increases absolute power up to 60% or requires up to 5-fold fewer lines. A free web tool is presented to explore the power of different study designs, using any (pilot) data.

Studies using induced pluripotent stem cells (iPSCs) are gaining momentum in brain disorder modelling, but optimal study designs are poorly defined. Here, we compare commonly used designs and statistical analysis for different research aims. Furthermore, we generated immunocytochemical, electrophysiological, and proteomic data from iPSC-derived neurons of five healthy subjects, analysed data variation and conducted power simulations. These analyses show that published case–control iPSC studies are generally underpowered. Designs using isogenic iPSC lines typically have higher power than case–control designs, but generalization of conclusions is limited. We show that, for the realistic settings used in this study, a multiple isogenic pair design increases absolute power up to 60% or requires up to 5-fold fewer lines. A free web tool is presented to explore the power of different study designs, using any (pilot) data.

Genetic correlation (rg) analysis is commonly used to identify traits that may have a shared genetic basis. Traditionally, rg is studied on a global scale, considering only the average of the shared signal across the genome; though this approach may fail to detect scenarios where the rg is confined to particular genomic regions, or show opposing directions at different loci. Tools dedicated to local rg analysis have started to emerge, but are currently restricted to analysis of two phenotypes. For this reason, we have developed LAVA, an integrated framework for local rg analysis which, in addition to testing the standard bivariate local rg’s between two traits, can evaluate the local heritability for all traits of interest, and analyse conditional genetic relations between several traits using partial correlation or multiple regression. Applied to 20 behavioural and health phenotypes, we show considerable heterogeneity in the bivariate local rg’s across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations between traits.

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics, such as the extent of pleiotropy across the genome and variation in genetic architecture across traits, are still unanswered. The current availability of hundreds of GWASs provides a unique opportunity to address these questions. We systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWASs on 558 traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait-associated loci cover more than half of the genome, and 90% of these overlap with loci from multiple traits. We find that potential causal variants are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. Our results provide insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource ( https://atlas.ctglab.nl ).

Objective: Vanishing white matter (VWM) is a fatal, stress-sensitive leukodystrophy that mainly affects children and is currently without treatment. VWM is caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B) that is crucial for initiation of mRNA translation and its regulation during the integrated stress response (ISR). Mutations reduce eIF2B activity. VWM pathomechanisms remain unclear. In contrast with the housekeeping function of eIF2B, astrocytes are selectively affected in VWM. One study objective was to test our hypothesis that in the brain translation of specific mRNAs is altered by eIF2B mutations, impacting primarily astrocytes. The second objective was to investigate whether modulation of eIF2B activity could ameliorate this altered translation and improve the disease. Methods: Mice with biallelic missense mutations in eIF2B that recapitulate human VWM were used to screen for mRNAs with altered translation in brain using polysomal profiling. Findings were verified in brain tissue from VWM patients using qPCR and immunohistochemistry. The compound ISRIB (for “ISR inhibitor”) was administered to VWM mice to increase eIF2B activity. Its effect on translation, neuropathology, and clinical signs was assessed. Results: In brains of VWM compared to wild-type mice we observed the most prominent changes in translation concerning ISR mRNAs; their expression levels correlated with disease severity. We substantiated these findings in VWM patients’ brains. ISRIB normalized expression of mRNA markers, ameliorated brain white matter pathology and improved motor skills in VWM mice. Interpretation: The present findings show that ISR deregulation is central in VWM pathomechanisms and a viable target for therapy.

Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7 , but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

The vast majority of genome-wide association (GWA) studies analyze a single trait while large-scale multivariate data sets are available. As complex traits are highly polygenic, and pleiotropy seems ubiquitous, it is essential to determine when multivariate association tests (MATs) outperform univariate approaches in terms of power. We discuss the statistical background of 19 MATs and give an overview of their statistical properties. We address the Type I error rates of these MATs and demonstrate which factors can cause bias. Finally, we examine, compare, and discuss the power of these MATs, varying the number of traits, the correlational pattern between the traits, the number of affected traits, and the sign of the genetic effects. Our results demonstrate under which circumstances specific MATs perform most optimal. Through sharing of flexible simulation scripts, we facilitate a standard framework for comparing Type I error rate and power of new MATs to that of existing ones.

Objectives: Emotional stimuli differently affect working memory (WM) performance. As sleep deprivation has a known impact on both emotion and WM our aim was to investigate how one night without sle ...

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http://atlas.ctglab.nl).

Vanishing white matter (VWM) is a fatal, stress-sensitive leukodystrophy that mainly affects children and is currently without treatment. VWM is caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B) that is crucial for initiation of mRNA translation and its regulation under stress conditions. Mice with bi-allelic missense mutations in eIF2B recapitulate human VWM. VWM pathomechanisms are unclear. Using polysomal profiling to screen for mRNAs with altered translation we observed most prominent changes in expression of integrated stress response (ISR) mRNAs in brains of mutant compared to wild-type mice; expression levels correlated with disease severity. We substantiated these findings in VWM patients’ brains. ISRIB, an ISR inhibitor, normalized expression of mRNA markers, ameliorated white matter pathology and improved motor skills in VWM mice, thus showing that the ISR is central in VWM pathomechanisms and a viable target for therapy.One Sentence SummaryISRIB ameliorates ISR deregulation and clinical signs in VWM mice

The Trait-based test that uses the Extended Simes procedure (TATES) was developed as a method for conducting multivariate GWAS for correlated phenotypes whose underlying genetic architecture is complex. In this paper, we provide a brief methodological critique of the TATES method using simulated examples and a mathematical proof. Our simulated examples using correlated phenotypes show that more TATES p-values fall outside of the confidence interval relative to expectation, and thus the method may result in systematic inflation when used with correlated phenotypes. In a mathematical proof we further demonstrate that the distribution of TATES p-values deviates from expectation in a manner indicative of inflation. Our findings indicate the need for caution when using TATES for multivariate GWAS of correlated phenotypes.

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P=3×10-8), medium spiny neurons (P=4×10-8) and serotonergic neurons (P=1×10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P=4.4×10-9), behavioural response to cocaine processes (P=1.84×10-7), and axon part (P=5.26×10-8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

In the version of this article originally published, the captions of Fig. 1e,f were incorrectly swapped. The error has been corrected in the HTML and PDF versions of the article.

Background: Mast cells are involved in a number of diseases, including inflammatory diseases such as asthma. Tryptase is a known marker of mast cell burden and activity. However, little is known about the genetic influence on serum tryptase variation. Also, only few and conflicting data exist on serum tryptase in asthma. Objective: To estimate the overall contribution of genetic and environmental factors to the variation in serum tryptase and to examine the correlation between serum tryptase and asthma, rhinitis, markers of allergy, airway inflammation, and airway hyperresponsiveness (AHR) in a sample of Danish twins. Methods: A total of 575 twins underwent a skin prick test and had lung function, AHR to methacholine, exhaled nitric oxide and serum tryptase measured. Multiple regression and variance components models (using the statistical package SOLAR) were computed. Results: Serum tryptase values were available in 569 subjects. Intraclass correlations of serum tryptase in monozygotic and dizygotic twin pairs were 0.84 and 0.42 (P

Although it is well established that experience seeking behavior (ES) is positively related to cognitive functioning, the mechanisms underlying this association are not clearly understood. In a large sample of adult twins and siblings (N = 864, age range 23–75), we studied the causes of covariation between ES and general cognitive ability and we studied whether ES moderates the genetic and environmental causes of variation in general cognitive ability. Results demonstrate a phenotypic correlation of .17 (p

Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors. To investigate the effects of assortative mating on the estimates of the variance components of intelligence, we employed an extended twin-family design. Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence. Electronic supplementary material The online version of this article (doi:10.1007/s10519-011-9507-9) contains supplementary material, which is available to authorized users.

Introduction: The tendency to develop asthma runs in families, but whether the severity of asthma symptoms is inherited is not known. Objectives: The aim of this study was to examine whether genetic factors influence the variation in the severity of asthma. Methods: Of a sample of 21 133 adult twins from the Danish Twin Registry, a total of 575 subjects (256 intact pairs and 63 single twins) who themselves and/or their co-twins reported a history of asthma at a questionnaire survey were clinically examined. The severity of asthma symptoms was graded according to a clinical interview, and markers of airway impairment and allergy were measured. Results: After adjusting for confounders, genetic factors explained 24% (10%–37%), P = 0.0004, of the variation in overall asthma symptom severity, whereas non-shared environment accounted for the remaining 76% of the variation. A significant genetic component was also found for the severity of specific asthma symptoms; wheezing 12% (3%–22%), P = 0.007 and shortness of breath 17% (7%–27%), P = 0.0006, but not for chest tightness and cough. Asthma symptom severity correlated weakly with rhinitis severity as well as with objective markers of lung function, airway inflammation, airway responsiveness and allergic sensitization. Conclusion: The individual variation in asthma symptom severity is to some degree influenced by genetic factors, but environmental factors explain the main part of the variation. The genetic architectures underlying the severity of asthma symptoms and objectively measured asthma-related traits, respectively, seem to differ. Please cite this paper as: Thomsen SF, van der Sluis S, Kyvik KO and Backer V. A study of asthma severity in adult twins. Clin Respir J 2011; DOI:10.1111/j.1752-699X.2011.00273.x.

Heritability of forced expiratory volume in one second (FEV(1)), forced vital capacity (FVC), and peak expiratory flow (PEF) has not been previously addressed in large twin studies. We evaluated the genetic contribution to individual differences observed in FEV(1), FVC, and PEF using data from the largest population-based twin study on spirometry. Specially trained lay interviewers with previous experience in spirometric measurements tested 4,314 Danish twins (individuals), 46-68 years of age, in their homes using a hand-held spirometer, and their flow-volume curves were evaluated. Modern variance component sex-limitation models were applied to evaluate possible genetic differences between the sexes for FEV(1), FVC, and PEF. Estimates were adjusted for age, height, and smoking. For FEV(1), additive genetic effects of 61% (95% CI 56-65) were observed. For FVC, the additive genetic contribution was 26% (3-49%) and the dominant genetic contribution was 29% (4-54%). For PEF, our models showed an additive genetic contribution of 43% (31-52%) for men, but genetic influences were not significant in women. We found no significant differences between dizygotic same-sex twins and dizygotic opposite-sex twins for FEV(1), FVC, and PEF, suggesting absence of qualitative genetic differences between the sexes. Sex-difference heritability for PEF suggested possible quantitative genetic differences between the sexes for this index. Genetic effects contributed significantly to individual differences observed in FEV(1), FVC, and PEF. Qualitative sex differences were absent for all spirometric measures, while quantitative sex differences were observed only for PEF, with heritability being substantial in men but negligible in women.

Background: Genes that contribute to the risk of developing Chronic Obstructive Pulmonary Disease (COPD) have been identified, but an attempt to accurately quantify the total genetic contribution to COPD has to our knowledge never been conducted. Methods: Hospital discharge diagnoses data on COPD were analysed in 22,422 Danish twin pairs, 20-71 years of age. The analyses were replicated in a population of 27,668 Swedish twin pairs, 45-108 years of age. A Cox-regression model was applied to the discordant time from the age at first hospital admission for COPD in the co-twin of an affected twin. Latent factor models were used to estimate genetic and environmental effects. Results: The probandwise concordance rate for COPD was higher in monozygotic (MZ) than in dizygotic (DZ) twins, 0.19 vs. 0.07 (p = 0.08) in the Danish population, and 0.20 vs. 0.08 (p = 0.006) in the Swedish population. After adjusting for sex, smoking and age at first hospital admission the risk of developing COPD in the co-twin of an affected twin was higher in MZ than in DZ twins, with hazards ratio 4.3 (95% confidence interval 1.2-15.8, p = 0.03) in Danish twins and 3.4 (1.5-7.7, p = 0.004) in Swedish twins. According to the most parsimonious model, additive genetic factors explained 63% (46-77%) of the individual COPD-susceptibility in the Danish population and 61% (48-72%) in the Swedish population. Conclusion: The susceptibility to develop severe COPD, as defined by hospitalizations, is strongly influenced by genetic factors. Approximately 60% of the individual susceptibility can be explained by genetic factors. © 2010 Elsevier Ltd. All rights reserved.

Achievement motivation is considered a prerequisite for success in academic as well as non-academic settings. We studied sex differences in academic and general achievement motivation in an adult sample of 338 men and 497 women (ages 18–70 years). Multi-group covariance and means structure analysis (MG-CMSA) for ordered categorical data was used to establish the location of possible sex differences, i.e., on the level of the latent factors or on the level of the observed items (i.e., sex-related item bias). Five of the 28 achievement motivation items showed severe bias with respect to sex, exemplifying the usefulness of MG-CMSA in locating the source of sex differences. The Academic Achievement Motivation scale consisted of two latent factors: Dedication and Persistence. Sex differences were observed for the factor Dedication only, with women showing more dedication towards their academic work than men. The General Achievement Motivation scale consisted of five latent factors: Pressure, Accomplishment, Work Approach, Future Orientation, and Competition. Sex differences were significant for the factor Future Orientation, with women contemplating less about the future than men, and a trend towards significance (p = .06) was observed for the factor Competition, with women being less actuated by competitive motives than men. These results suggest that sex-related item bias merits attention in achievement motivation research, but that men and women still differ in aspects of achievement motivation when biased items are eliminated from the analyses.

This article concerns the power of various data analytic strategies to detect the effect of a single genetic variant (GV) in multivariate data. We simulated exactly fitting monozygotic and dizygotic phenotypic data according to single and two common factor models, and simplex models. We calculated the power to detect the GV in twin 1 data in an ANOVA of phenotypic sum scores, in a MANOVA, and in exploratory factor analysis (EFA), in which the common factors are regressed on the genetic variant. We also report power in the full twin model, and power of the single phenotype ANOVA. The results indicate that (1) if the GV affects all phenotypes, the sum score ANOVA and the EFA are most powerful, while the MANOVA is less powerful. Increasing phenotypic correlations further decreases the power of the MANOVA; and (2) if the GV affects only a subset of the phenotypes, the EFA or the MANOVA are most powerful, while sum score ANOVA is less powerful. In this case, an increase in phenotypic correlations may enhance the power of MANOVA and EFA. If the effect of the GV is modeled directly on the phenotypes in the EFA, the power of the EFA is approximately equal to the power of the MANOVA.

Prevalence and severity of childhood behavioral problems differ between boys and girls, and in psychiatry, testing for gender differences is common practice. Population-based studies show that many psychopathology scales are (partially) Measurement Invariance (MI) with respect to gender, i.e. are unbiased. It is, however, unclear whether these studies generalize towards clinical samples. In a psychiatric outpatient sample, we tested whether the Child Behavior Checklist 6-18 (CBCL) is unbiased with respect to gender. We compared mean scores across gender of all syndrome scales of the CBCL in 3271 patients (63.3% boys) aged 6-18. Second, we tested for MI on both the syndrome scale and the item-level using a stepwise modeling procedure. Six of the eight CBCL syndrome scales included one or more gender-biased items (12.6% of all items), resulting in slight over- or under-estimation of the absolute gender difference in mean scores. Two scales, Somatic Complaints and Rule-breaking Behavior, contained no biased items. The CBCL is a valid instrument to measure gender differences in problem behavior in children and adolescents from a clinical sample; while various gender-biased items were identified, the resulting bias was generally clinically irrelevant, and sufficient items per subscale remained after exclusion of biased items. Copyright © 2016 John Wiley & Sons, Ltd.

The aims of this study were to investigate whether the executive functions, inhibition, shifting, and updating, are distinguishable as latent variables (common factors) in children aged 9 to 12, and to examine the relations between these executive functions and reading, arithmetic, and (non)verbal reasoning. Confirmatory factor analysis was used to decompose variance due to the executive and the non-executive processing demands of the executive tasks. A Shifting factor and an Updating factor, but not an Inhibition factor, were distinguishable after controlling for non-executive variance. Updating was related to reading, arithmetic, and (non)verbal reasoning. Shifting was mainly related to non-verbal reasoning and reading. However, in terms of variance explained, arithmetic and reading were primarily related to the non-executive processing demands of the executive measures. The results are discussed in light of the “task impurity problem”.

The extent to which various measures of ambulatory respiratory sinus arrhythmia (RSA) capture the same information across conditions in different subjects remains unclear. In this study the root mean square of successive differences (RMSSD), peak valley RSA (pvRSA), and high frequency power (HF power) were assessed during ambulatory recording in 84 subjects, of which 64 were retested after about 3 years. We used covariance structure modeling to test the equality of the correlations among three RSA measures over two test days and three conditions (daytime sitting or walking and nighttime sleep) and in groups with low, medium, and high mean heart rate (HR), or low, medium, and high mean respiration rate (RR). Results showed that ambulatory RMSSD, pvRSA, and HF power are highly correlated and that their correlation is stable across time, ambulatory conditions, and a wide range of resting HR and RR values. RMSSD appears to be the most cost-efficient measure of RSA. Copyright © 2007 Society for Psychophysiological Research.

The size and shape of dendrites and axons are strong determinants of neuronal information processing. Our knowledge on neuronal structure and function is primarily based on brains of laboratory animals. Whether it translates to human is not known since quantitative data on "full" human neuronal morphologies are lacking. Here, we obtained human brain tissue during resection surgery and reconstructed basal and apical dendrites and axons of individual neurons across all cortical layers in temporal cortex (Brodmann area 21). Importantly, morphologies did not correlate to etiology, disease severity, or disease duration. Next, we show that human L(ayer) 2 and L3 pyramidal neurons have 3-fold larger dendritic length and increased branch complexity with longer segments compared with temporal cortex neurons from macaque and mouse. Unsupervised cluster analysis classified 88% of human L2 and L3 neurons into human-specific clusters distinct from mouse and macaque neurons. Computational modeling of passive electrical properties to assess the functional impact of large dendrites indicates stronger signal attenuation of electrical inputs compared with mouse. We thus provide a quantitative analysis of "full" human neuron morphologies and present direct evidence that human neurons are not "scaled-up" versions of rodent or macaque neurons, but have unique structural and functional properties.

This article is concerned with the power to detect the presence of genotype by environment interaction (G x E) in the case that both genes and environment feature as latent (i.e., unmeasured) variables. The power of the test proposed by Jinks and Fulker (1970), which is based on regressing the absolute difference between the scores of monozygotic twins on the sums of these scores, is compared to the power of an alternative test, which is based on Marginal Maximum Likelihood (MML). Simulation studies showed that generally the power of the MML-based test was greater than the power of the Jinks and Fulker test in detecting linear and curvilinear G x E interaction, regardless of whether the distribution of the data deviated significantly from normality. However, after a normalizing transformation, the Jinks and Fulker test performed slightly better. Some possible future extensions of the MML-based test are briefly discussed.

Using multi-group covariance and means structure analysis (MG-CMSA), this study investigated whether sex differences were present on the Dutch WAIS-III, and if so, whether these sex differences were attributable to differences in general intelligence (g). The sample consisted of 294 females and 228 males between 18 and 46 years old. Both first and second order common factor models were fitted, the latter including g as second order factor. The results indicated that on the level of the subtests, females outperform males on Digit-Symbol Substitution, and males outperform females on Information and Arithmetic. In addition, the subtests Information proved to be biased in favor of males. With respect to the first order common factors, no sex differences were found with respect to the factor Verbal Comprehension (once Information was effectively removed from the model). Yet, males outperformed females on the factors Working Memory and Perceptual Organization, and females outperformed males on Perceptual Speed. These sex difference on the level of the first order common factors were however not attributable to sex differences in g. Summarizing, the present study showed that males and females do differ with respect to specific cognitive abilities, but that g cannot be viewed as the source of these differences. © 2005 Elsevier Inc. All rights reserved.

The executive functions of inhibition and shifting were studied in arithmetic-disabled children, reading-disabled children, reading plus arithmetic-disabled children, and controls (N = 74). Measures involved the rapid naming of objects, digits, letters, or quantities with or without additional task requirements that reflected inhibition or shifting. Also, the Making Trails task, reflecting shifting, was administered. For tasks without executive demands, arithmetic-disabled children were slower in the naming of digits and quantities, whereas reading-disabled children were slower in the naming of digits and letters. For the executive tasks, arithmetic-disabled children as well as reading plus arithmetic-disabled children were impaired on the Making Trails task and on an object naming task that required both inhibition and shifting. Reading-disabled children exhibited no problems in executive functioning. Furthermore, it was shown that reading plus arithmetic-disabled children experienced the combination of problems that characterize children with a single learning deficit.