Biomedical subjects

Conor V Dolan

Publications and source records attributed to Conor V Dolan.

15 recordsLinked to original sources

PubMed2026

Genetics of major depressive disorder in a homogeneous population with uniform phenotyping.

Harmonized phenotyping and diverse population-specific studies are crucial for advancing gene discovery in psychiatric genetics. We conducted a genome-wide association (GWAS) mega-analysis of DSM-defined lifetime major depressive disorder (MDD) in 64 941 participants (25.7% cases) from the Dutch BIObanks Netherlands Internet Collaboration (BIONIC) consortium. Liability-scale SNP-based heritability was 12.0% (SE = 1.4%) as estimated by LDSC (assuming a lifetime prevalence of 15%) and 26.6% (SE = 1.1%) when estimated by LDAK-REML on individual-level genotype data, indicating substantial common-variant signal in this clinically harmonized sample. The genetic correlation with the latest major depression GWAS from the Psychiatric Genomics Consortium (PGC-MD) was high (rG = 0.89, SE = 0.048). Polygenic scores (PGSs) based on BIONIC predicted depression in UK Biobank, and PGSs derived from PGC-MD predicted MDD in BIONIC, supporting transferability of depression polygenic signal across cohorts and phenotype definitions. Within-family PGS analyses in twins suggested that the observed prediction was not primarily driven by detectable family-level confounding, and twin concordance for MDD increased with polygenic burden. We identified one genome-wide significant locus, indexed by rs3818852 in PALMD, but this finding currently lacks independent replication and should be interpreted cautiously. Finally, genetic correlation and latent causal variable analyses identified multiple traits showing shared or directionally consistent genetic associations with MDD. Together, these findings underscore the value of clinically harmonized phenotyping in regional biobank collaborations for studying the genetic architecture of MDD.

Humans↗

PubMed2006

Age-related change in executive function: developmental trends and a latent variable analysis.

This study examined the developmental trajectories of three frequently postulated executive function (EF) components, Working Memory, Shifting, and Inhibition of responses, and their relation to performance on standard, but complex, neuropsychological EF tasks, the Wisconsin Card Sorting Task (WCST), and the Tower of London (ToL). Participants in four age groups (7-, 11-, 15-, and 21-year olds) carried out nine basic experimental tasks (three tasks for each EF), the WCST, and the ToL. Analyses were done in two steps: (1) analyses of (co)variance to examine developmental trends in individual EF tasks while correcting for basic processing speed, (2) confirmatory factor analysis to extract latent variables from the nine basic EF tasks, and to explain variance in the performance on WCST and ToL, using these latent variables. Analyses of (co)variance revealed a continuation of EF development into adolescence. Confirmatory factor analysis yielded two common factors: Working Memory and Shifting. However, the variables assumed to tap Inhibition proved unrelated. At a latent level, again correcting for basic processing speed, the development of Shifting was seen to continue into adolescence, while Working Memory continued to develop into young-adulthood. Regression analyses revealed that Working Memory contributed most strongly to WCST performance in all age groups. These results suggest that EF component processes develop at different rates, and that it is important to recognize both the unity and diversity of EF component processes in studying the development of EF.

Adolescent↗

PubMed2006

Why g is not an adaptation: a comment on Kanazawa (2004).

In S. Kanazawa's evolutionary theory of general intelligence (g), g is presented as a species-typical information-processing mechanism. This conceptualization of g departs radically from the accepted conceptualization of g as a source of individual differences that is manifest in the positive manifold. Kanazawa's theory is thus problematic in the sense that it concerns a purely hypothetical, and empirically unsupported, conceptualization of g. The authors argue that an evolutionary account of g should address it as a source of individual differences--that is, in a manner that is consistent with the empirical support for g.

Adaptation, Psychological↗

PubMed2006

A dynamical model of general intelligence: the positive manifold of intelligence by mutualism.

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.

Humans↗

PubMed2006

Detecting genotype-environment interaction in monozygotic twin data: comparing the Jinks and Fulker test and a new test based on Marginal Maximum Likelihood estimation.

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.

Biometry↗

PubMed2006

A test of the equal environment assumption (EEA) in multivariate twin studies.

In the classic twin design, estimation of genetic and environmental effects is based on the assumption that environmental influences are shared to the same extent by monozygotic and dizygotic twins (equal environment assumption, EEA). We explore the conditions in which the EEA can be tested based on multivariate phenotypic data. We focus on the test whether the correlation between shared environmental factors in dizygotic twins (r(C)) is less than 1. First, model identification was investigated analytically in Maple and Mx. Second, statistical power was examined in Mx. Third, the amount of bias caused by violation of the EEA was evaluated. Finally, applications to empirical data concern spatial ability in adolescents and aggression in children. Bivariate and trivariate models include several instances in which the EEA can be tested. The number of twin pairs that is needed to detect violation of the EEA with a statistical power of .80 (alpha = .05) varied between 508 and 3576 pairs for the situations considered. The bias in parameter estimates, given misspecification, ranged from 5% to 34% for additive genetic effects, and from 4% to 34% for shared environmental effects. Estimates of the nonshared environmental effects were not biased. The EEA was not violated for spatial ability or aggression. Multivariate data provide sufficient information to test the validity of the EEA. The number of twin pairs that is needed is no greater than the number typically available in most twin registries. The analysis of spatial ability and aggression indicated no detectable violation of the EEA.

Aggression↗

PubMed2006

Phenotypic factor analysis of family data: correction of the bias due to dependency.

Twin registries form an exceptionally rich source of information that is largely unexploited for phenotypic analyses. One obstacle to straightforward phenotypic statistical analysis is the inherent dependency, which is due to the clustering of cases within families. The present simulation study gauges the degree of the bias produced by the dependency of family data on the estimates of standard errors and chi-squared, when they are treated as independent observations in a phenotypic model, and assesses the efficiency of an estimator, which corrects for dependency. When family-clustered data are used for phenotypic analysis, in treating individuals as independent, and using standard maximum likelihood estimation, there is a tendency for the chi-square statistic to be overestimated, and the standard errors of the parameters to be underestimated. The bias increases with family resemblance, due to heritability or shared environment. The source of family resemblance -- either heritability (h(2)) and/or shared environment (c(2)) -- interacts with the composition of the sample. In the absence of c(2), samples with twins, parents and spouses show the lowest bias, whereas in the presence of c(2) samples with only twins show the lowest bias. In all conditions the bias remained below 15%. The use of the 'complex option' available in Mplus (clustering corrected robust maximum likelihood estimation) reduces the bias to the levels observed when only independent cases are considered. Thus with the use of robust estimates the bias due to family dependency becomes practically negligible in all conditions of dependency. In conclusion, the present study shows that the bias due to dependency in family data does not form a serious obstacle to phenotypic data analysis.

Chi-Square Distribution↗

PubMed2005

Genetic and environmental contributions to loneliness in adults: the Netherlands twin register study.

Heritability estimates based on two small studies in children indicate that the genetic contribution to individual differences in loneliness is approximately 50%. Heritability estimates of complex traits such as loneliness may change across the lifespan, however, as the frequency, duration, and range of exposure to environmental influences accrues, or as the expression of genetic factors changes. We examined data on loneliness from 8,387 young adult and adult Dutch twins who had participated in longitudinal survey studies. A measure of loneliness was developed based on factor analyses of items of the YASR (The Young Adult Self Report). Variation in loneliness was analyzed with genetic structural equation models. The estimate of genetic contributions to variation in loneliness in adults was 48%, which is similar to the heritability estimates found previously in children. There was no evidence for sex or age differences in genetic architecture. Sex differences in prevalence were significant, but we did not see an association with age or birth cohort. All resemblance between twin relatives was explained by shared genes, without any suggestion of a contribution of shared environmental factors.

Adult↗

PubMed2005

Stereotype threat and group differences in test performance: a question of measurement invariance.

Studies into the effects of stereotype threat (ST) on test performance have shed new light on race and sex differences in achievement and intelligence test scores. In this article, the authors relate ST theory to the psychometric concept of measurement invariance and show that ST effects may be viewed as a source of measurement bias. As such, ST effects are detectable by means of multi-group confirmatory factor analysis. This enables research into the generalizability of ST effects to real-life or high-stakes testing. The modeling approach is described in detail and applied to 3 experiments in which the amount of ST for minorities and women was manipulated. Results indicate that ST results in measurement bias of intelligence and mathematics tests.

Adolescent↗

PubMed2005

Problems with using sum scores for estimating variance components: contamination and measurement noninvariance.

Twin studies of complex traits, such as behavior or psychiatric diagnoses, frequently involve univariate analysis of a sum score derived from multiple items. In this article, we show that absence of measurement invariance across zygosity can bias estimates of genetic and environmental components of variance. Specifically, if the item responses are considered as multiple indicators of a latent factor, and the aim is to partition the variance in the latent factor, then the factor loadings relating the items to the factor should be equal for monozygotic (MZ) and dizygotic (DZ) twins. While it seems unlikely, a priori, that these loadings should differ as a function of zygosity, certain special measurement situations are cause for concern. Ratings by parents, or self-ratings of phenotypes which are more easily observed in others than via introspection, may be tainted by the co-twin's phenotype to a greater extent in MZ than DZ pairs. We also show that the analysis of sum scores typically biases both MZ and DZ correlations compared to the true latent trait correlation. These two sources of bias are quantified for a range of values and are shown to be especially acute for sum scores based on binary items. Solutions to these problems include formal tests for measurement invariance across zygosity prior to analysis of the sum or scale scores, and multivariate genetic analysis at the individual item or symptom level.

Humans↗

PubMed2004

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.

Computer Simulation↗

PubMed2004

Implications of absence of measurement invariance for detecting sex limitation and genotype by environment interaction.

Using univariate sum scores in genetic studies of twin data is common practice. This practice precludes an investigation of the measurement model relating the individual items to an underlying factor. Absence of measurement invariance across a grouping variable such as gender or environmental exposure refers to group differences with respect to the measurement model. It is shown that a decomposition of a sum score into genetic and environmental variance components leads to path coefficients of the additive genetic factor that are biased differentially across groups if individual items are non-invariant. The arising group differences in path coefficients are identical to what is known as "scalar sex limitation" when gender is the grouping variable, or as "gene by environment interaction" when environmental exposure is the grouping variable. In both cases the interpretation would be in terms of a group-specific effect size of the genetic factor. This interpretation may be incorrect if individual items are non-invariant.

Algorithms↗

PubMed2003

Weak measurement invariance with respect to unmeasured variables: an implication of strict factorial invariance.

Measurement bias refers to systematic differences across subpopulations in the relation between observed test scores and the latent variant underlying the test scores. Comparisons of subpopulations with the same score on the latent variable can be expected to have the same observed test score. Measurement invariance is therefore one of the key issues in psychological testing. It has been established that strict factorial invariance (SFI) with respect to a selection variable V almost certainly implies weak measurement invariance with respect to V: given SFI, means and variances of observed scores do not depend on V. It is shown that this result can be extended. SFI in groups derived by selection on V has implications not only for V but also for potentially biasing variables W, if W and the selection variable V and/or if W and the factor underlying the observed test scores are statistically dependent. Given SFI with respect to V and prior knowledge concerning these dependencies, it is not necessary to measure and model variables W in order to exclude them as potentially biasing variables if the investigation focuses on groups selected on V.

Analysis of Variance↗

PubMed2002

A framework for ML estimation of parameters of (mixtures of) common reaction time distributions given optional truncation or censoring.

We present a framework for distributional reaction time (RT) analysis, based on maximum likelihood (ML) estimation. Given certain information relating to chosen distribution functions, one can estimate the parameters of these distributions and of finite mixtures of these distributions. In addition, left and/or right censoring or truncation may be imposed. Censoring and truncation are useful methods by which to accommodate outlying observations, which are a pervasive problem in RT research. We consider five RT distributions: the Weibull, the ex-Gaussian, the gamma, the log-normal, and the Wald. We employ quasi-Newton optimization to obtain ML estimates. Multicase distributional analyses can be carried out, which enable one to conduct detailed (across or within subjects) comparisons of RT data by means of loglikelihood difference tests. Parameters may be freely estimated, estimated subject to boundary constraints, constrained to be equal (within or over cases), or fixed. To demonstrate the feasibility of ML estimation and to illustrate some of the possibilities offered by the present approach, we present three small simulation studies. In addition, we present three illustrative analyses of real data.

Humans↗

PubMedDate not supplied

The relations between DISC-IV DSM diagnoses of ADHD and multi-informant CBCL-AP syndrome scores.

BACKGROUND: Previous studies have examined the relation between attention problems (APs) obtained with the Child Behavior Checklist (CBCL) and attention deficit hyperactivity disorder (ADHD) assessed with the Diagnostic and Statistical Manual of Mental Disorders (DSM). We will examine this relation across sex using multi-informant data. METHODS: Parents of 12538 twins, aged 7, 10, and 12 years, and teachers of twins, aged 10 years, completed the questionnaires. The mothers of a sample of 283 boys and 291 girls who scored either low or high on longitudinal maternal CBCL-AP were interviewed. RESULTS: Children with a low AP score obtained a negative ADHD diagnosis in 96% of cases. Children with a high AP score obtained a positive diagnosis in 36% (girls) and 59% (boys) of cases. The association between paternal and maternal AP ratings and ADHD was the same, whereas the association between teacher AP ratings and ADHD was low. CONCLUSIONS: The association between AP and ADHD is higher in boys than girls, possibly because of a bias toward the male manifestation of ADHD.

Attention↗