High mathematics and reading performance:How important are environmental influences?

Earlier findings of international comparisons on school achievement are often interpreted to mean that there is only a small percentage of excellent students in the Netherlands. Inspired by research in behaviour genetics, it was investigated whether Dutch high-scoring children are less sensitive to environmental influences than the non-high-scoring students. To test this, the reading and mathematics scores from high-scoring and non-high-scoring students participating in the Programme for International Student Achievement (PISA) 2012, the Trends in International Mathematics and Science Study (TIMSS) 2011 and the Progress in International Reading and Literacy Study (PIRLS) 2011 were analyzed. Contrary to our expectations, the results suggest that high-scoring children are as sensitive to school influences as are non-high-scoring students, but more sensitive to the influence of individual socioeconomic status

Psychometric modelling of longitudinal genetically informative twin data

The often-used A(C)E model that decomposes phenotypic variance into parts due to additive genetic and environmental influences can be extended to a longitudinal model when the trait has been assessed at multiple occasions. This enables inference about the nature (e.g., genetic or environmental) of the covariance among the different measurement points. In the case that the measurement of the phenotype relies on self-report data (e.g., questionnaire data), often, aggregated scores (e.g., sum–scores) are used as a proxy for the phenotype. However, earlier research based on the univariate ACE model that concerns a single measurement occasion has shown that this can lead to an underestimation of heritability and that instead, one should prefer to model the raw item data by integrating an explicit measurement model into the analysis. This has, however, not been translated to the more complex longitudinal case. In this paper, we first present a latent state twin A(C)E model that combines the genetic twin model with an item response theory (IRT) model as well as its specification in a Bayesian framework. Two simulation studies were conducted to investigate 1) how large the bias is when sum–scores are used in the longitudinal A(C)E model and 2) if using the latent twin model can overcome the potential bias. Results of the first simulation study (e.g., AE model) demonstrated that using a sum–score approach leads to underestimated heritability estimates and biased covariance estimates. Surprisingly, the IRT approach also lead to bias, but to a much lesser degree. The amount of bias increased in the second simulation study (e.g., ACE model) under both frameworks, with the IRT approach still being the less biased approach. Since the bias was less severe under the IRT approach than under the sum–score approach and due to other advantages of latent variable modelling, we still advise researcher to adopt the IRT approach. We further illustrate differences between the traditional sum–score approach and the latent state twin A(C)E model by analyzing data of a two-wave twin study, consisting of the answers of 8,016 twins on a scale developed to measure social attitudes related to conservatism

Cognitive impairment three months after surgery is an independent predictor of survival time in glioblastoma patients

Purpose Cognitive functioning is increasingly investigated for its prognostic value in glioblastoma (GBM) patients, but the association of cognitive status during early adjuvant treatment with survival time is unclear. The aim of this study was to determine whether cognitive performance three months after surgical resection predicted survival time, while using a clinically intuitive time ratio (TR) statistic. Methods Newly diagnosed patients with GBM undergoing resection between November 2010 and February 2018 completed computerized cognitive assessment 3 months after surgery with the CNS Vital Signs battery (8 measures). The association of cognitive performance (continuous Z scores and dichotomous impairment status; impaired vs. unimpaired) with survival time was assessed with multivariate Accelerated Failure Time (AFT) models that also included clinical prognostic factors and covariates related to cognitive performances. Results 114 patients were included in the analyses (median survival time 16.4 months). Of the clinical factors, postoperative Karnofsky Performance Status (TR 1.51), surgical (TR 2.20) and non-surgical (TR 1.94) salvage treatment, and pre-surgical tumor volume (cm3, TR 1.003) were significant independent predictors of survival time. Independently of the base model factors and covariates, impairment on Stroop test I and Stroop test III estimated 23% and 26% reduction of survival time (TR 0.77, TR 0.74) respectively, as compared to unimpaired performance. Conclusion These findings suggest that impaired performances on tests of executive control and processing speed in the early phase of adjuvant treatment can reflect a worse prognostic outlook rather than an early treatment effect, and their assessment might allow for early refinement of current prognostic stratification. This study was approved by the local Medical Ethics Committee Brabant (file number NL41351.008.12)

Nature, nurture, and item response theory: a psychometric approach to behaviour genetics

This dissertation discusses a number of psychometric issues that require special attention in the analysis of genetically-informative data, such as data on twins. These include heterogeneous measurement error, scaling and scale transformation, and harmonization of phenotypes. It is shown how ignoring these issues can result in spurious findings of genotype by environment interaction. Multilevel item response theory models are proposed that can help solve these problems

Can We Validate the Results of Twin Studies? A Census-Based Study on the Heritability of Educational Achievement

As for most phenotypes, the amount of variance in educational achievement explained by SNPs is lower than the amount of additive genetic variance estimated in twin studies. Twin-based estimates may however be biased because of self-selection and differences in cognitive ability between twins and the rest of the population. Here we compare twin registry based estimates with a census-based heritability estimate, sampling from the same Dutch birth cohort population and using the same standardized measure for educational achievement. Including important covariates (i.e., sex, migration status, school denomination, SES, and group size), we analyzed 893,127 scores from primary school children from the years 2008–2014. For genetic inference, we used pedigree information to construct an additive genetic relationship matrix. Corrected for the covariates, this resulted in an estimate of 85%, which is even higher than based on twin studies using the same cohort and same measure. We therefore conclude that the genetic variance not tagged by SNPs is not an artifact of the twin method itself