Mathematical Ability and Socio-Economic Background: IRT Modeling to Estimate Genotype by Environment Interaction

Bias in Gene-by-Environment Interaction Effects with Sum Scores; An Application to Well-being Phenotypes

In the current study, we investigated the influence of using skewed sum scores on estimated gene-by-environment interaction effects (GxE) for life satisfaction and happiness with perceived social support. To this end, we analyzed item-level data from a large adult twin sample (Ns between 3610 and 11,305) of the Netherlands Twin Register. Item response theory (IRT) models were incorporated in unmeasured (univariate) GxE models, and measured GxE models (with social support as moderator). We found that skewness introduced spurious GxE effects, with the largest effect for the most skewed variable (social support). Finally, in the IRT model for life satisfaction, but not for happiness, heritability estimates decreased with higher social support, while this was not observed when analyzing sum scores. Together, our results indicate that IRT can be used to address psychometric issues related to the use of sum scores, especially in the context of GxE, for complex traits like well-being.

Modification of TSH-related genetic effects by indicators of socioeconomic position

OBJECTIVE

Thyroid-stimulating hormone (TSH) is influenced by genetic and environmental factors such as socioeconomic position (SEP). However, interactions between TSH-related genetic factors and indicators of SEP have not been investigated to date. The aim of the study was to determine whether education and income as SEP indicators may interact with TSH-related genetic effect allele sum scores (GESTSH_2013 and GESTSH_2020) based on two different GWAS meta-analyses that affect TSH values in a population-based study.

METHODS

In 4085 participants of the Heinz Nixdorf Recall Study associations between SEP indicators, GESTSH and TSH were quantified using sex- and age-adjusted linear regression models. Interactions between SEP indicators and GESTSH were assessed by GESTSH × SEP interaction terms, single reference joint effects and calculating genetic effects stratified by SEP group.

RESULTS

Participants within the highest education group showed the strongest genetic effect with on average 1.109-fold (95% CI: 1.067-1.155) higher TSH values per GESTSH_2013 SD, while in the lowest education group, the genetic effect was less strong (1.061-fold (95% CI: 1.022-1.103)). In linear regression models including interaction terms, some weak indication for a positive GESTSH_2013 by education interaction was observed showing an interaction effect size estimate of 1.005 (95% CI: 1.000-1.010) per year of education and GESTSH_2013 SD. No indication for interaction was observed for using income as SEP indicator. Using the GESTSH_2020, similar results were observed.

CONCLUSION

Our results gave some indication that education may affect the expression of TSH-related genetic effects. Stronger genetic effects in high-education groups may be explained by environmental factors that have an impact on gene expression and are more prevalent in high SEP groups.

Family socioeconomic status amplifies unique environmental influences on the dynamics of adolescent daily positive affective process

BACKGROUND AND OBJECTIVE

Affective processes as complex dynamical systems happen in people's daily lives. Affective dynamics characterizing unique features of individual affective systems are linked with long-term developmental outcomes. This study investigated genetic and environmental contributions, and the moderation by family socioeconomic status (SES), to two affective dynamics characterizing affective system sensitivity to the relative levels and change in affect, respectively.

METHODS

Using a sample of 490 17-year-old twins (41.1% males, 93.9% White) in a 40-day-long daily diary design, dynamical systems modeling estimated individual affective dynamics of positive and negative affect. Twin modeling examined genetic and environmental contributions to affective dynamics as well as the moderation of SES.

RESULTS

For both positive and negative affect, system sensitivity to the relative levels of affect was predominantly under unique environmental influences with negligible genetic influences; system sensitivity to the change in affect was affected by both genetic and unique environmental influences. SES amplifies unique environmental influences on sensitivity to the change in positive affect.

CONCLUSIONS

These findings highlight different genetic and environmental origins of different adolescent affective dynamics in their daily lives, and suggest that macro contexts could influence micro timescale affective dynamics.

Infant Motor Milestones and Childhood Overweight: Trends over Two Decades in A Large Twin Cohort

BACKGROUND

Poor motor skill competence may influence energy balance with childhood overweight as a result. Our aim was to investigate whether the age of motor milestone achievement has changed over the past decades and whether this change may contribute to the increasing trend observed in childhood overweight.

METHODS

Motor skill competence was assessed in children from the Young Netherlands Twin Register born between 1987 and 2007. Follow-up ranged from 4 up to 10 years. Weight and height were assessed at birth, 6 months, 14 months, and 2, 4, 7, and 10 years.

RESULTS

Babies born in later cohorts achieved their motor milestones 'crawling', 'standing', and 'walking unassisted' later compared to babies born in earlier cohorts (N = 18,514, p < 0.001). The prevalence of overweight at age 10 was higher in later cohorts (p = 0.033). The increase in overweight at age 10 was not explained by achieving motor milestones at a later age and this persisted after adjusting for gestational age, sex, and socioeconomic status.

CONCLUSION

Comparing children born in 1987 to those born in 2007, we conclude that children nowadays achieve their motor milestones at a later age. This does not however, explain the increasing trend in childhood overweight.

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.