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C M Middeldorp,
M C T Slof-Op 't Landt,
S E Medland,
C E M van Beijsterveldt,
M Bartels,
G Willemsen,
J-J Hottenga,
E J C de Geus,
H E D Suchiman, C V Dolan,
M C Neale,
P E Slagboom,
D I Boomsma
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ABSTRACT: There are two major hypotheses regarding the etiology of anxiety and depression: the mono-amine hypothesis and the hypothesis of an abnormal stress response acting partly via reduced neurogenesis. Association studies have focused on genes involved in these processes, but with inconclusive results. This study investigated the effect of 45 single nucleotide polymorphisms (SNPs) in genes encoding for serotonin receptors 1A, 1D, 2A, catechol-O-methyltransferase (COMT), tryptophane hydroxylase type 2 (TPH2), brain derived neurotrophic factor (BDNF), PlexinA2 and regulators of G-protein-coupled signaling (RGS) 2, 4, 16. Anxious depression (A/D) symptoms were assessed five times in 11 years in over 11 000 adults with 1504 subjects genotyped and at age 7, 10, 12 and during adolescence in over 20 000 twins with 1078 subjects genotyped. In both cohorts, a longitudinal model with one latent factor loading on all A/D measures over time was analysed. The genetic association effect modeled at the level of this latent factor was 60% and 70% heritable in the children and adults, respectively, and explained around 50% of the total phenotypic variance. Power analyses showed that the samples contained 80% power to detect an effect explaining between 1.4% and 3.6% of the variance. However, no SNP showed a consistent effect on A/D. To conclude, this longitudinal study in children and adults found no association of SNPs in the serotonergic system or core regulators of neurogenesis with A/D. Overall, there has been no convincing evidence, so far, for a role of genetic variation in these pathways in the development of anxiety and depression.
Genes Brain and Behavior 10/2010; 9(7):808-16. · 3.48 Impact Factor
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ABSTRACT: There is increasing interest in methods to disentangle the relationship between genotype and (endo)phenotypes in human complex traits. We present a population-based method of increasing the power and cost-efficiency of studies by selecting random individuals with a particular genotype and then assessing the accompanying quantitative phenotypes. Using statistical derivations, power- and cost graphs we show that such a "forward genetics" approach can lead to a marked reduction in sample size and costs. This approach is particularly apt for implementing in epidemiological studies for which DNA is already available but the phenotyping costs are high.
Behavior Genetics 03/2010; 40(4):564-71. · 2.52 Impact Factor
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ABSTRACT: In most assessment instruments, distinct items are designed to measure a trait, and the sum score of these items serves as an approximation of an individual's trait score. In interpreting group differences with respect to sum scores, the instrument should measure the same underlying trait across groups (e.g., male/female, young/old). Differences with respect to the sum score should accurately reflect differences in the latent trait of interest. A necessary condition for this is that the instrument is measurement invariant. In the current study, the authors illustrate a stepwise approach for testing measurement invariance with respect to sex in a four-item instrument designed to assess disordered eating behavior in a large epidemiological sample (1,195 men and 1,507 women). This approach can be applied to other phenotypes for which group differences are expected. Any analysis of such variables may be subject to measurement bias if a lack of measurement invariance between grouping variables goes undetected.
Assessment 09/2009; 16(4):415-23. · 2.01 Impact Factor
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ABSTRACT: The goal of the present study is to examine genetic and environmental influences on maternal and teacher ratings of Attention Problems (AP) in 7-year-old children. Teachers completed the Teacher Report Form (N=2259 pairs), and mothers the Child Behavior Checklist (N=2057 pairs). Higher correlations were found in twins rated by the same teacher than in twins rated by different teachers. This can be explained by rater bias or by a greater environmental sharing in twins, who are in the same classroom. We further found that 41% of the variation in maternal and teacher ratings is explained by a common factor. The heritability of this common factor is 78%. The heritabilities of the rater specific factors of mothers and teachers are 76% and 39%, respectively. Because Attention Problems that are persistent over situations may indicate more serious behavior problems than context dependent Attention Problems, we believe that gene finding strategies should focus on this common phenotype.
Behavior Genetics 12/2006; 36(6):833-44. · 2.52 Impact Factor
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ABSTRACT: There is increasing evidence that behavioral problems are common in very young children, yet little is known about the etiology of individual differences in these problems. It is unclear to what degree environmental and genetic factors influence the development of early child psychopathology. In this paper, we focus on the following issues. Firstly, to what degree do genetic and environmental factors influence variation in behavioral problems? Secondly, to what degree are these underlying etiological factors moderated by sex and informant? We investigate these issues by analyzing Child Behavior Checklist (CBCL) data on 9689 3-year-old twin pairs.
Rater Bias and Psychometric Models were fitted to CBCL/2-3 data obtained from mothers and fathers to determine the genetic and environmental contributions to the five CBCL syndromes:aggressive, oppositional, overactive, withdrawn, and anxious/depressed behavior.
Parental ratings are influenced by aspects of the child's behavior that are experienced in the same way by both parents and by aspects of the child's behavior that are experienced uniquely by each parent. There is evidence for high genetic contributions to all CBCL syndromes. Shared and non-shared environmental influences play significant roles as well. One exception is overactive behavior, which is influenced by genetic and non-shared environmental influences only.
Variation in behavior problems in the very young shows high heritability. Individual raters offer unique perspectives that can have an impact on estimates of problem behavior and genetic architecture. Therefore, multi-informant approaches in the assessment of the very young will be useful to clinicians and researchers alike.
Behavior Genetics 12/2004; 34(6):571-83. · 2.52 Impact Factor
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ABSTRACT: INTRODUCTION The structure of individual differences in cognitive abilities during development has received considerable attention in cognitive developmental theory (Schaie, 1994; Vernon, 1976). One important hypothesis, dating back to Garrett (1946; see also Carroll, 1993; Reinert, 1970; Wohlwill, 1973), states that cognitive abilities become increasingly more differentiated during development. In operational terms, this means that the intercorrelations among psychometric measures of ability decrease during normal cognitive development in children. So far, support for this hypothesis has been poor. In an early review of about 60 factor analytic studies, Reinert (1970) suggested that a trend toward increased differentiation was present. However, this conclusion was based on the selection of studies that actually reported a change. In a more recent review, Carroll (1993) failed to find clear evidence for the differentiation of abilities. More recent cross-sectional studies of the cha
07/2004;
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ABSTRACT: The differentiation hypothesis in cognitive development states that cognitive abilities become progressively more independent as children grow older. Studies of phenotypic development in children have generally failed to produce convincing support for this hypothesis. The aim of the present study is to investigate the issue of differentiation at the genetic and environmental level. Six psychometric measures assessing verbal and nonverbal cognitive abilities were administered to 209 Dutch twin pairs at ages 5, 7, and 10 years. Longitudinal results provided little evidence for the differentiation hypothesis. Stability in subtest performance is due mainly to genetic influences. The shared environment contribution to phenotypic stability is small. The unique environment contributes to age-specific variance only.
Behavior Genetics 08/2003; 33(4):367-81. · 2.52 Impact Factor
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ABSTRACT: Sibling interaction effects are suggested by a difference in phenotypic variance between monozygotic (MZ) twins and dizygotic (DZ) twins, and a pattern of twin correlations that is inconsistent with additive genetic influences. Notably, negative sibling interaction will result in MZ correlations which are more than twice as high as DZ correlations, a pattern also seen in the presence of genetic dominance. Negative sibling interaction effects have been reported in most genetic studies on Attention Deficit Hyperactivity Disorder (ADHD) and related phenotypes, while the presence of genetic dominance is not always considered in these studies. In the present paper the statistical power to detect both negative sibling interaction effects and genetic dominance is explored. Power calculations are presented for univariate models including sources of variation due to additive genetic influences, unique environmental influences, dominant genetic influences and a negative sibling interaction (i.e., contrast effect) between phenotypes of twins. Parameter values for heritability and contrast effects are chosen in accordance with published behavior genetic studies on ADHD and associated phenotypes. Results show that when both genetic dominance and contrast effects are truly present and using a classical twin design, genetic dominance is more likely to go undetected than the contrast effect. Failure to detect the presence of genetic dominance consequently gives rise to slightly biased estimates of additive genetic effects, unique environmental effects, and the contrast effect. Contrast effects are more easily detected in the absence of genetic dominance. If the significance of the contrast effect is evaluated while also including genetic dominance, small contrast effects are likely to go undetected, resulting in a relatively large bias in estimates of the other parameters. Alternative genetic designs, such as adding pairs of unrelated siblings reared together to a classical twin design, or adding non-twin siblings to twin pairs, greatly enhances the statistical power to detect contrast effects as well as the power to distinguish between genetic dominance and contrast effects.
Behavior Genetics 06/2003; 33(3):247-55. · 2.52 Impact Factor
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ABSTRACT: Through the application of finite mixture distribution models, we investigated the existence of distinct modes of behavior in learning a simple discrimination. The data were obtained in a repeated measures study in which subjects aged 6 to 10 years carried out a simple discrimination learning task. In contrast to distribution models of exclusively rational learners or exclusively incremental learners, a mixture distribution model of rational learners and slow learners was found to fit the data of all measurement occasions and all age groups. Hence, the finite mixture distribution analysis provides strong support for the existence of distinct modes of learning behavior. The results of a second experiment support this conclusion by crossvalidation of the models that fit the data of the first experiment. The effect of verbally labeling the values on the relevant stimulus dimension and the consistency of behavior over measurement occasions are related to the mixture model estimates.
Memory & Cognition 08/2001; 29(5):659-77. · 1.92 Impact Factor
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ABSTRACT: Estimated generalized least squares (EGLS) electromagnetic source analysis is used to downweight noisy and correlated data. Standard EGLS requires many trials to accurately estimate the noise covariances and, thus, the source parameters. Alternatively, the noise covariances can be modeled parametrically. Only the parameters of the model describing the noise covariances need to be estimated and, therefore, less trials are required. This method is referred to as parametric egls (PEGLS). In this paper, PEGLS is developed and its performance is tested in a simulation study and in a pseudoempirical study.
IEEE Transactions on Biomedical Engineering 07/2001; 48(6):737-41. · 2.28 Impact Factor
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ABSTRACT: In a longitudinal study of Dutch adolescent and young adult twins, their parents and their siblings, questionnaire data were collected on depression, anxiety and correlated personality traits, such as neuroticism. Data were collected by mailed surveys in 1991, 1993, 1995 and 1997. A total of 13,717 individuals from 3344 families were included in the study. To localise quantitative trait loci (QTLs) involved in anxiety and depression, the survey data were used to select the most informative families for a genome-wide search. For each individual a genetic factor score was computed, based on a genetic multivariate analysis of anxiety, depression, neuroticism and somatic anxiety. A family was selected if at least two siblings (or DZ twins) had extreme factor scores. Both discordant (high-low) and concordant (high-high and low-low) pairs were included in the selected sample. Once an extreme sibling pair was selected, all family members (parents and additional siblings of the selected pair) who had at least once returned a questionnaire booklet were asked to provide a DNA sample. In total, 2724 individuals from 563 families (1007 parents and 1717 offspring) were approached and 1975 individuals from 479 families (643 patients and 1332 offspring) complied by returning a buccal swab for DNA isolation. All offspring from selected families were asked to participate in a psychiatric interview and in a 24-hour ambulatory assessment of cardiovascular parameters and cortisol. The interview consisted of the WHO-Composite International Diagnostic Interview and was administered to 1253 offspring. In this paper we describe the genetic-epidemiological analyses of the survey data on anxiety, somatic anxiety, neuroticism and depression. We detail how these data were used to select families for the QTL study and discuss strategies that may help elucidate the molecular pathways leading from genes to anxious depression.
Twin Research 01/2001; 3(4):323-34.
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ABSTRACT: In instantaneous encephalogram or magnetoencephalogram (EEG/MEG)
source analysis, ordinary least squares estimation (OLS) requires that
the spatial noise is homoscedastic and uncorrelated over sensors. In
spatio-temporal analysis OLS also requires that the noise is
homoscedastic and uncorrelated in time (over samples). Generally, these
assumptions are violated and, as a consequence, OLS can give rise to
inaccuracies in the estimates of location and moment parameters of
sources underlying the EEG/MEG. To improve these estimates of the
sources, the generalized least squares (GLS) was developed, which uses
the spatial or spatio-temporal noise covariances. In GLS these noise
covariances are estimated from trial variation around the mean.
Therefore, GLS requires many trials to accurately estimate the spatial
noise covariances and thus the source parameters. Alternatively, with
maximum likelihood (ML) the spatial or spatio-temporal noise covariances
can be modeled parametrically. Here, only the model parameters
describing the noise covariances need to be estimated. Consequently,
fewer trials are required to obtain accurate noise covariances and
consequently accurate source parameters. In this paper ML estimation for
spatio-temporal analysis is derived, and it is shown that the noise and
source parameters can be estimated separately. Furthermore, the
likelihood ratio function is proposed to estimate the spatial or
spatio-temporal noise covariance model parameters, which can also be
used to test whether the model is adequate
Sensor Array and Multichannel Signal Processing Workshop. 2000. Proceedings of the 2000 IEEE; 02/2000
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ABSTRACT: The genetic and environmental factor structures of intellectual abilities in 5-year-old Dutch twins were examined. Six subtests of the RAKIT, a Dutch intelligence test, were administered to 209 twin pairs. The subtests were categorized as either verbal or nonverbal. The genetic covariance structure displayed a two-common factor structure including specific factors to account for subtest residual variance. The correlation between the genetic Verbal and genetic Nonverbal factors did not differ significantly from zero. The shared environmental influence displayed a single-common factor structure. Unique environmental influences did not contribute to the covariance between subtests and were specific in origin. Estimates of heritability of the subtests ranged from 15% to 56%. Shared environmental influences were significantly present, but were modest in magnitude. The phenotypic data was best described by an oblique two-factor model. This model was not mirrored in the factor structures found for either the genetic or environmental covariances.
Behavior Genetics 02/2000; 30(1):29-40. · 2.52 Impact Factor
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ABSTRACT: The genetic and environmental factor structures of intellectual abilities in 5-year-old Dutch twins were examined. Six subtests of the RAKIT, a Dutch intelligence test, were administered to 209 twin pairs. The subtests were categorized as either verbal or nonverbal. The genetic covariance structure displayed a two-common factor structure including specific factors to account for subtest residual variance. The correlation between the genetic Verbal and genetic Nonverbal factors did not differ significantly from zero. The shared environmental influence displayed a single-common factor structure. Unique environmental influences did not contribute to the covariance between subtests and were specific in origin. Estimates of heritability of the subtests ranged from 15% to 56%. Shared environmental influences were significantly present, but were modest in magnitude. The phenotypic data was best described by an oblique two-factor model. This model was not mirrored in the factor structures found for either the genetic or environmental covariances.
Behavior Genetics 12/1999; 30(1):29-40. · 2.52 Impact Factor
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ABSTRACT: Sib pair-selection strategies, designed to identify the most informative sib pairs in order to detect a quantitative-trait locus (QTL), give rise to a missing-data problem in genetic covariance-structure modeling of QTL effects. After selection, phenotypic data are available for all sibs, but marker data-and, consequently, the identity-by-descent (IBD) probabilities-are available only in selected sib pairs. One possible solution to this missing-data problem is to assign prior IBD probabilities (i.e., expected values) to the unselected sib pairs. The effect of this assignment in genetic covariance-structure modeling is investigated in the present paper. Two maximum-likelihood approaches to estimation are considered, the pi-hat approach and the IBD-mixture approach. In the simulations, sample size, selection criteria, QTL-increaser allele frequency, and gene action are manipulated. The results indicate that the assignment of prior IBD probabilities results in serious estimation bias in the pi-hat approach. Bias is also present in the IBD-mixture approach, although here the bias is generally much smaller. The null distribution of the log-likelihood ratio (i.e., in absence of any QTL effect) does not follow the expected null distribution in the pi-hat approach after selection. In the IBD-mixture approach, the null distribution does agree with expectation.
The American Journal of Human Genetics 02/1999; 64(1):268-80. · 10.60 Impact Factor
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Psychophysiology 06/1990; 27(3):360-1. · 3.29 Impact Factor
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ABSTRACT: Moment expressions for individual factor scores can serve as simple tests for the presence of a particular class of interaction factors that are disguised as pure genetic and/or environmental factors. That is, individual genetic and environmental factor scores may be used to construct fourth-order moments of these factors in order to test whether a common genetic or environmental factor in the multivariate genetic factor model is in fact of the interactive origin concerned. Expected fourth-order moments are derived for cases with and without interaction. Application of fourth-order moments of factor scores to detect interactive origin of common factors is illustrated with simulated twin data.
Genetic Epidemiology 02/1990; 7(1):93-100. · 3.44 Impact Factor
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ABSTRACT: Moment expressions for individual factor scores can serve as simple tests for the presence of a particular class of interaction factors that are disguised as pure genetic and/or environmental factors. That is, individual genetic and environmental factor scores may be used to construct fourth-order moments of these factors in order to test whether a common genetic or environmental factor in the multivariate genetic factor model is in fact of the interactive origin concerned. Expected fourth-order moments are derived for cases with and without interaction. Application of fourth-order moments of factor scores to detect interactive origin of common factors is illustrated with simulated twin data.
Genetic Epidemiology 12/1989; 7(1):93 - 100. · 3.44 Impact Factor
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ABSTRACT: A method is introduced to test the hypothesis that both the phenotypic means and the phenotypic covariances can be modeled with the same common genetic and environmental factors. LISREL can be used to implement the method. An illustration is given with simulated twin data.
Behavior Genetics 02/1989; 19(1):51-62. · 2.52 Impact Factor
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