Margot P. van de Weijer

Loneliness and social isolation are important public health concerns due to their associations with a range of health outcomes. However, it is difficult to ascertain whether any effects are biased by confounding and reverse causation. We use a triangulation approach combining observational, sibling control, and Mendelian Randomisation analyses (a genetically informed analysis), to draw robust conclusions about these relationships. Using a combination of UK Biobank data (N = 8,004 to 414,432) and genome-wide association studies (N = 17,526 to 2,083,151), we examine relationships between loneliness and social isolation and outcomes related to physical health, mental health and wellbeing and general health (e.g., multimorbidity capturing both mental and physical health). We find evidence for effects of loneliness and social isolation on poorer mental health and wellbeing and of loneliness on poorer general health. We do not find evidence of effects on specific physical health outcomes; however, these effects cannot definitively be ruled out.

Mendelian imputation is a promising method for imputing missing parental genotypes and estimating direct and indirect genetic effects. While the method has clear scientific benefits, so far there has been little reflection on its legal and ethical implications. In this perspective, we discuss these considerations and provide recommendations on transparent use of this method. We argue that imputed genotypes should be considered identifiable, personal data, and that using these data requires informed consent. We distinguish different scenarios where consent may be missing, such as an individual previously not having been contacted for consent, or a person who was contacted but did not reply or did not wish to participate. Informed consent is important across all these scenarios as it allows individuals to decide whether they want to participate in a study. In addition, transparent communication on how people’s data are used is important for public perception of science, and a failure to communicate this may contribute to mistrust. We discuss potential harmful applications of Mendelian imputation, and how robust regulatory frameworks guiding Institutional Review Boards on novel advanced methods such as Mendelian imputation are currently missing. This complicates decision making, especially when researchers argue for public interest as a lawful basis for foregoing consent. We end with a set of recommendations for (1) future genetic research projects for which Mendelian imputation will be possible; (2) research that has already been conducted using this method; and (3) the inclusion of ethical considerations in the publication of novel techniques/analyses.

This editorial perspective focuses on the complex relationship of cigarette smoking and e‐cigarette use (‘vaping’) with mental health problems. It is challenging to reliably determine the causal nature of these associations because both (e‐)smoking and mental health problems generally arise during adolescence, and both are highly multifactorial in their aetiology. While there is now scientific consensus that cigarette smoking is a causal risk factor for mental health problems, there is still a scarcity of causal research and conclusions with respect to e‐cigarette use. In order to more reliably determine whether and how (e‐)smoking affects mental health, it is important to better understand the potential causal pathways. Here, we discuss the main biological mechanisms that might explain causal effects of smoking and e‐cigarettes on mental health, including (neuro‐)inflammation, oxidative stress and nicotine binding. We showcase informative studies that have been conducted using sophisticated causally informative study designs and identify in which areas robust causal knowledge is especially lacking. In future work, evidence ‘triangulation’, where different types of research methods are integrated to look for converging results, seems to be the most promising approach to obtain reliable causal evidence.

Wellbeing is associated with both behavioral phenotypes as well as several key life outcomes, such as health, employment, and coping with stressful events. These phenotypes associated with wellbeing could be potential indicators of differential epigenetic patterns between individuals that differ in their levels of wellbeing. We performed the largest epigenome-wide (EWAS) meta-analysis of wellbeing to date by combining whole blood DNA methylation data (Illumina 450k array) from 13 cohorts from Europe, Australia, and the USA ( N = 10,757 participants). After correcting for smoking and BMI, no epigenome-wide significant methylation sites were identified. We tested whether a weighted methylation score (MS) based on leave-one-cohort-out EWAS meta-analysis summary statistics predicted wellbeing in an independent cohort, and whether prediction was significant over and above the polygenic score (PGS) for wellbeing. The MS was associated with wellbeing (variance explained=0.22%, p =0.03) and was no longer significant after adding the polygenic score (PGS; variance explained=0.43%, p =0.0046, MS; variance explained=0.07%, p =0.2842). We further compared DNA methylation levels in 16 pairs of monozygotic twins discordant for wellbeing. These analyses revealed no significant within-pair DNA methylation differences at the top-sites from the meta-analysis or in MS. Our results suggest that larger EWAS meta-analyses with uniform phenotype assessment are required to identify methylation sites associated with wellbeing.

Mendelian imputation is a promising method for imputing missing parental genotypes and estimating direct genetic effects. While the method has clear scientific benefits, so far there has been little reflection on its legal and ethical implications. In this perspective, we discuss these considerations and provide recommendations on transparent use of this method. We argue that imputed genotypes should be considered identifiable, personal data, and that using these data requires informed consent. We distinguish different scenarios where consent may be missing, such as an individual previously not having been contacted for consent, or a person who was contacted but did not reply or did not wish to participate. Informed consent is important across all these scenarios as it allows individuals to decide whether they want to participate in a study. In addition, transparent communication on how people’s data are used is important for public perception of science, and a failure to communicate this may contribute to mistrust. We discuss potential harmful applications of Mendelian imputation, and how robust regulatory frameworks guiding Institutional Review Boards on novel advanced methods such as Mendelian imputation are currently missing. This complicates decision making, especially when researchers argue for public interest as a lawful basis for foregoing consent. We end with a set of recommendations for 1) future genetic research projects for which Mendelian imputation will be possible; 2) research that has already been conducted using this method; and 3) the inclusion of ethical considerations in the publication of novel techniques/analyses.

Background: There is growing evidence that smoking increases the risk of developing psychiatric disorders, but the underlying mechanisms are largely unknown. We examine brain structure as a potential pathway between smoking and psychiatric disease liability. Methods: We test associations between smoking (initiation, cigarettes per day, cessation, lifetime use) and depression, bipolar disorder, and schizophrenia, with and without correcting for volume of the amygdala, hippocampus, lateral and medial orbitofrontal cortex, superior frontal context, and cortical thickness and surface area. We use three methods that use summary statistics of genome-wide association studies to investigate genome-wide and local genetic overlap (genomic structural equation modeling, local analysis of (co)variant association), as well as causal associations (Mendelian randomization). Results: While we find causal effects of smoking on brain volume in different brain areas, and with psychiatric disorders, brain volume did not seem to mediate the effect of smoking on psychiatric disorders. Conclusions: While these findings are limited by characteristics of the included summary statistics (e.g. sample size), we conclude that brain volume of these areas is unlikely to explain a substantial part of any effect of smoking on psychiatric disorders. Nevertheless, genetic methods are valuable tools for exploring other potential mechanisms, such as brain functional connectivity, foregoing the need to collect all phenotypes in one dataset.

Objective Observational studies consistently link physical activity (PA) to better brain health; psychiatric, neurocognitive, and mental well-being outcomes. While PA intervention studies generally find positive mental health effects, it is unclear whether day-to-day PA, not manipulated through an intervention, is beneficial. Design/Data Systematic review of Mendelian Randomization (MR) studies using genetic instrumental variables to assess causal effects of PA on brain health. In Embase and medRxiv, 557 articles were identified, of which 35 met inclusion criteria. Study quality was determined based on a MR-specific scoring system. Eligibility criteria MR studies exploring any PA exposure (self-reported PA, sedentary behaviours, strength-related traits) on mental health, cognition, or cognition-based neurodegenerative outcomes (e.g., Alzheimer’s). Results Of 35 studies, 43% received a low quality score and 57% a moderate score. There was no consistent evidence for causal effects of day-to-day PA. For sedentary behaviours (e.g., TV-watching) there was consistent evidence for increasing effects on depression. For strength-related traits (e.g., grip-strength) there was consistent evidence for protective effects, particularly on cognitive outcomes. For depression specifically, device-measured PA more often showed protective effects on depression than self-reported PA. Conclusion MR studies on PA and brain health are generally lacking in quality, due to low sample sizes and/or poorly measured variables. Results are mixed with the most consistent evidence indicating that better physical strength and less sedentary behaviour are beneficial for brain health. Since core MR assumptions are difficult to fulfill for highly complex PA traits, triangulation with other methods and improved phenotyping is needed in future work. What is already known? The prevalence of the most common mental health disorders (i.e., depression, anxiety) has been increasing in the last 20 years in the United States and Europe. bservational studies report a consistent association between physical activity and brain health outcomes, but the high risk of confounding precludes a causal interpretation. Physical activity intervention studies find positive mental health effects, but do not show whether day-to-day physical activity, not manipulated through an intervention, is beneficial. What are the new findings? verall, the Mendelian randomization literature offers limited support for a broadly protective effect of physical activity. Self-reported physical activity showed inconsistent associations with depression, schizophrenia, and bipolar disorder, possibly due to misclassification and confounding. behaviours were more consistently associated with negative mental health outcomes, particularly depression. Strength-related traits, such as handgrip strength and appendicular lean mass, showed the most consistent and strong, protective effects, especially on cognitive outcomes. Randomization relies on core assumptions that are difficult to fulfill for highly complex traits, like physical activity. Triangulation with other methods and improved phenotyping is needed in future work.

Individual sensitivity to environmental exposures may be genetically influenced. This genotype-by-environment interplay implies differences in phenotypic variance across genotypes, but these variants have proven challenging to detect. Genome-wide association studies of monozygotic twin differences are conducted through family-based variance analyses, which are more robust to the systemic biases that impact population-based methods. We combined data from 21,792 monozygotic twins (10,896 pairs) from 11 studies to conduct one of the largest genome-wide association study meta-analyses of monozygotic phenotypic differences, in children, adolescents and adults separately, for seven psychiatric and neurodevelopmental phenotypes: attention deficit hyperactivity disorder symptoms, autistic traits, anxiety and depression symptoms, psychotic-like experiences, neuroticism and wellbeing. The proportions of phenotypic variance explained by single-nucleotide polymorphisms in these phenotypes were estimated (h ² = 0–18%), but were imprecise. We identified 13 genome-wide significant associations (single-nucleotide polymorphisms, genes and gene sets), including genes related to stress reactivity for depression, growth factor-related genes for autistic traits and catecholamine uptake-related genes for psychotic-like experiences. This is the largest genetic study of monozygotic twins to date by an order of magnitude, evidencing an alternative method to study the genetic architecture of environmental sensitivity. The statistical power was limited for some analyses, calling for better-powered future studies.