Greater Adolescent Cognitive Ability Is Linked to Lower Risk of Cognitive Impairment in Later Life

There have been few investigations of the role that adolescent cognitive ability plays in predicting later-life cognitive impairment, and the mechanisms, such greater life course educational exposure, that might underlie these connections. This knowledge gap is due, in part, to a lack of cohorts with early-life cognitive assessment who are followed to later adulthood. We capitalized on data from the 1960 Project Talent (PT) high school cohort (n>360,000) and two recent follow-ups, the Project Talent Twin & Sibling (PTTS; n=2,491 in 2014) Study and the Project Talent Aging Study (PTAS; n=6,421 in 2018), to examine these potential links. In 1960, ability was assessed in multiple cognitive domains (e.g., general aptitude, quantitative, reasoning). Participants/proxies reporting 2 or more symptoms of cognitive impairment in 2018 on the AD8 Dementia Screener were classified as having a positive screen. Binary logistic generalized estimating equations with race, sex, and adolescent family SES covariates, indicated that in multiple cognitive domains, higher ability in adolescence predicted lower odds of a positive AD8 screen in later life (ORs of 0.80 - 0.85). The effects were only slightly attenuated with inclusion of life course educational attainment. Sibling models found a similar pattern of associations and effect sizes, indicating that the association is not attributable to shared family and genetic background. These findings indicate that higher cognitive ability as indicated by better performance in multiple cognitive domains in adolescence may be protective against cognitive impairment five decades later and life course educational attainment only partially mediates this association.

Interpreting Polygenic Score Effects in Sibling Analysis

Researchers often claim that sibling analysis can be used to separate causal genetic effects from the assortment of biases that contaminate most downstream genetic studies. Indeed, typical results from sibling models show large (>50%) attenuations in the associations between polygenic scores and phenotypes compared to non-sibling models, consistent with researchers' expectations about bias reduction. This paper explores these expectations by using family (quad) data and simulations that include indirect genetic effect processes and evaluates the ability of sibling models to uncover direct genetic effects. We find that sibling models, in general, fail to uncover direct genetic effects; indeed, these models have both upward and downward biases that are difficult to sign in typical data. When genetic nurture effects exist, sibling models create 'measurement error' that attenuate associations between polygenic scores and phenotypes. As the correlation between direct and indirect effect changes, this bias can increase or decrease. Our findings suggest that interpreting results from sibling analysis aimed at uncovering direct genetic effects should be treated with caution.

Mediation Analysis in Sibling Designs: An Application to the Effect of Prenatal Antidepressant Exposure on Toddler Depression Mediated by Gestational Age at Birth

Background: Mediation analysis requires strong assumptions of no unmeasured confounding. Sibling designs offer a method for controlling confounding shared within families, but no previous research has done mediation analysis using sibling models. Methods: We demonstrate the validity of the sibling mediation approach using simulation, and show its application using the example of prenatal antidepressant exposure and toddler anxiety and depression, with gestational age at birth as a mediator. We used data from the Norwegian Mother and Child Cohort Study, a cohort comprising 41% of births in Norway between 1999 and 2008 to identify 91,333 pregnancies, of which 25,776 were part of sibling groups. Results: In simulations, sibling models were less biased than cohort models in cases where non-shared confounding was weaker than shared confounding, and when stronger non-shared confounding was controlled, but more biased otherwise. In the full cohort, the estimated mean difference in depression/anxiety scale z-scores for natural direct effects (NDE) were 0.31 (95% confidence interval 0.23 to 0.39) and 0.14 (95% CI 0.03 to 0.24), without and with adjustment for non-shared confounders, respectively. The natural indirect effect was 0.01 (95% CI 0.00 to 0.02) after adjustment. Adjustment for shared and non-shared confounding showed similar point estimates with wider confidence intervals (NDE 0.18, 95% CI -0.21 to 0.47; NIE -0.01, 95% CI -0.06 to 0.06).Conclusions: Findings suggest that the modest association between prenatal antidepressant exposure and anxiety/depression is not mediated by gestational age and is likely explained by both shared confounders and non-shared confounders, and chance.

Sibling Models, Categorical Outcomes, and the Intra-Class Correlation

In sibling models with categorical outcomes the question arises of how best to calculate the intraclass correlation, ICC. We show that, for this purpose, the random effects linear probability model is preferable to a random effects non-linear probability model, such as a logit or probit. This is because, for a binary outcome, the ICC derived from a random effects linear probability model is a non-parametric estimate of the ICC, equivalent to a statistic called Cohen’s κ. Furthermore, because κ can be calculated when the outcome has more than two categories, we can use the random effects linear probability model to compute a single ICC in cases with more than two outcome categories. Lastly, ICCs are often compared between groups to show the degree to which sibling differences vary between groups: we show that when the outcome is categorical these comparisons are invalid. We suggest alternative measures for this purpose.

Interpreting Mendelian randomization estimates of the effects of categorical exposures such as disease status and educational attainment

Mendelian randomization has been previously used to estimate the effects of binary and ordinal categorical exposures - e.g. type 2 diabetes or educational attainment defined by qualification - on outcomes. Binary and categorical phenotypes can be modelled in terms of liability, an underlying latent continuous variable with liability thresholds separating individuals into categories. Genetic variants typically influence an individual’s categorical exposure via their effects on liability, thus Mendelian randomization analyses with categorical exposures will capture effects of liability which act independent of exposure category.We discuss how groups where the categorical exposure is invariant can be used to detect liability effects acting independently of exposure category. For example, associations between an adult educational attainment polygenic score (PGS) and BMI measured before the minimum school leaving age (e.g. age 10), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher education PGS is strongly associated with lower smoking initiation and higher glasses use at age 15. These associations were replicated in sibling models. An orthogonal approach using the raising of the school leaving age (ROSLA) policy change found that individuals who chose to remain in education to age 16 before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to 16 after the reform, and had higher income, decreased cigarette smoking, higher glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment associates with health and social outcomes independent of years in full-time education.Mendelian randomization studies with non-continuous exposures should be interpreted in terms of liability, which may affect the outcome via changes in exposure category and/or independently.

Genetic evidence for a large overlap and potential bidirectional causal effects between resilience and well-being

Resilience and well-being are strongly related. People with higher levels of well-being are more resilient after stressful life events or trauma and vice versa. Less is known about the underlying sources of overlap and causality between the constructs. In a sample of 11.304 twins and 2.572 siblings from the Netherlands Twin Register, we investigated the overlap and possible direction of causation between resilience (i.e. the absence of psychiatric symptoms despite negative life events) and well-being (i.e. satisfaction with life) using polygenic score (PGS) prediction, twin-sibling modelling, and the Mendelian Randomization Direction of Causality (MR-DoC) model. Longitudinal twin-sibling models showed significant phenotypic correlations between resilience and well-being (.41/.51 at time 1 and 2). Well-being PGS were predictive for both well-being and resilience, indicating that genetic factors influencing well-being also predict resilience. Twin-sibling modeling confirmed this genetic correlation (.71) and showed a strong environmental correlation (.93). In line with causality, both genetic (51%) and environmental (49%) factors contributed significantly to the covariance between resilience and well-being. Furthermore, the results of within-subject and MZ twin differences analyses were in line with bidirectional causality. Additionally, we used the MR-DoC model combining both molecular and twin data to test causality, while correcting for pleiotropy. We confirmed the causal effect from well-being to resilience, with the direct effect of well-being explaining 11% (T1) and 20% (T2) of the variance in resilience. Data limitations prevented us to test the directional effect from resilience to well-being with the MR-DoC model. To conclude, we showed a strong relation between well-being and resilience. A first attempt to quantify the direction of this relationship points towards a bidirectional causal effect. If replicated, the potential mutual effects can have implications for interventions to lower psychopathology vulnerability, as resilience and well-being are both negatively related to psychopathology.