Using genetic data to strengthen causal inference in observational research

Ten challenges for clinical translation in psychiatric genetics

Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.

Chronic neuropsychiatric sequelae of SARS-CoV-2: Protocol and methods from the Alzheimer's Association Global Consortium

Introduction

Coronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term.

Methods

This article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions.

Results

Successful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe.

Discussion

The Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection.

Key Points

The following review describes what is known so far in terms of molecular and epidemiological links among COVID-19, the brain, neurological symptoms, and AD and related dementias (ADRD)The primary objective of this large-scale collaboration is to clarify the pathogenesis of ADRD and to advance our understanding of the impact of a neurotropic virus on the long-term risk of cognitive decline and other CNS sequelae. No available evidence supports the notion that cognitive impairment after SARS-CoV-2 infection is a form of dementia (ADRD or otherwise). The longitudinal methodologies espoused by the consortium are intended to provide data to answer this question as clearly as possible controlling for possible confounders. Our specific hypothesis is that SARS-CoV-2 triggers ADRD-like pathology following the extended olfactory cortical network (EOCN) in older individuals with specific genetic susceptibility.The proposed harmonization strategies and flexible study designs offer the possibility to include large samples of under-represented racial and ethnic groups, creating a rich set of harmonized cohorts for future studies of the pathophysiology, determinants, long-term consequences, and trends in cognitive aging, ADRD, and vascular disease.We provide a framework for current and future studies to be carried out within the Consortium. and offers a "green paper" to the research community with a very broad, global base of support, on tools suitable for low- and middle-income countries aimed to compare and combine future longitudinal data on the topic.The Consortium proposes a combination of design and statistical methods as a means of approaching causal inference of the COVID-19 neuropsychiatric sequelae. We expect that deep phenotyping of neuropsychiatric sequelae may provide a series of candidate syndromes with phenomenological and biological characterization that can be further explored. By generating high-quality harmonized data across sites we aim to capture both descriptive and, where possible, causal associations.

The human genetic epidemiology of COVID-19

Human genetics can inform the biology and epidemiology of coronavirus disease 2019 (COVID-19) by pinpointing causal mechanisms that explain why some individuals become more severely affected by the disease upon infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Large-scale genetic association studies, encompassing both rare and common genetic variants, have used different study designs and multiple disease phenotype definitions to identify several genomic regions associated with COVID-19. Along with a multitude of follow-up studies, these findings have increased our understanding of disease aetiology and provided routes for management of COVID-19. Important emergent opportunities include the clinical translatability of genetic risk prediction, the repurposing of existing drugs, exploration of variable host effects of different viral strains, study of inter-individual variability in vaccination response and understanding the long-term consequences of SARS-CoV-2 infection. Beyond the current pandemic, these transferrable opportunities are likely to affect the study of many infectious diseases.

Estimating the causal effect of liability to disease on healthcare costs using Mendelian Randomization

Accurate measurement of the effects of disease status on healthcare costs is important in the pragmatic evaluation of interventions but is complicated by endogeneity bias. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We used a novel Mendelian Randomization analysis to model the causal impact on inpatient hospital costs of liability to six prevalent diseases and health conditions: asthma, eczema, migraine, coronary heart disease, Type 2 diabetes, and depression. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs on over 300,000 individuals. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex and methods robust to violations of the exclusion restriction. Results overall were imprecise and we could not rule out large effects of liability to disease on healthcare costs. In particular, genetic liability to coronary heart disease had substantial impacts on costs.

Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics

Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.

A Gentle Introduction to Instrumental Variables

Instrumental variables (IV) is a central strategy for identifying causal effects in absence of randomized experiments. Clinicians and epidemiologists may find the intuition of IV easy to grasp by comparison to randomized experiments. Randomization is an ideal IV because treatment is assigned randomly, and hence unaffected by everything else. IV methods in non-experimental settings mimic a randomized experiment by using a source of "as good as" random variation in treatment instead. The main challenge with IV designs is to find IVs that are as good as randomization. Discovering potential IVs require substantive knowledge and an understanding of IV design principles. Moreover, IV methods recover causal effects for a subset of the population who take treatment when induced by the IV. Sometimes these estimates are informative, other times their relevance is questionable. We provide an introduction to IV methods in clinical epidemiology. First, we introduce the main principles and assumptions. Second, we present practical examples based on mendelian randomization and provider preference IVs and refer to other common IVs in health. Third, practical steps in IV analysis are presented. Fourth, the promise and perils of IV methods are discussed. Finally, we suggest further readings.

GENETIC INFLUENCES ON PAIN MECHANISMS

OBJECTIVE

The aim: To review the available results for genetic influences on pain syndrome development.

PATIENTS AND METHODS

Materials and methods: In the period from 2009 to 2020, a total of 45 research papers describing the key points of genetic influences on pain mechanisms in both adults and children were published in Ukrainian and English and they are now included in the PubMed, EMBASE, Cochrane, and Google Scholar research databases.

CONCLUSION

Conclusions: Pain is a comprehensive characteristic of a person; therefore, it is inevitable that several genes with little individual effect interact with each other and environmental factors, influencing pain susceptibility and chronic pain syndrome manifestation. This requires searching for biomarkers for diagnosing and predicting the development of acute and chronic pain syndromes, especially in pediatric practice.

Mendelian randomization analysis of plasma levels of CD209 and MICB proteins and the risk of varicose veins of lower extremities

Varicose veins of lower extremities (VVs) are a highly prevalent condition, the pathogenesis of which is still not fully elucidated. Mendelian randomization (MR) can provide useful preliminary information on the traits that are potentially causally related to the disease. The aim of the present study is to replicate the effects of the plasma levels of MHC class I polypeptide-related sequence B (MICB) and cluster of differentiation 209 (CD209) proteins reported in a previous hypothesis-free MR study. We conducted MR analysis using a fixed effects inverse-variance weighted meta-analysis of Wald ratios method. For MICB and CD209, we used data from a large-scale genome-wide association study (GWAS) for plasma protein levels (N = 3,301). For VVs, we used GWAS data obtained in the FinnGen project (N = 128,698), the eMERGE network (phase 3, N = 48,429), and the UK Biobank data available in the Gene ATLAS (N = 452,264). The data used in the study were obtained in individuals of European descent. The results for MICB did not pass criteria for statistical significance and replication. The results for CD209 passed all statistical significance thresholds, indicating that the genetically predicted increase in CD209 level is associated with increased risk of VVs (βMR (SE) = 0.07 (0.01), OR (95% CI) = 1.08 (1.05-1.10), P-value = 5.9 ×10-11 in the meta-analysis of three cohorts). Our findings provide further support that CD209 can potentially be involved in VVs. In future studies, independent validation of our results using data from more powerful GWASs for CD209 measured by different methods would be beneficial.

What have we learned from genome-wide association studies (GWAS) in Parkinson's disease?

After fifteen years of genome-wide association studies (GWAS) in Parkinson's disease (PD), what have we learned? Addressing this question will help catalogue the progress made towards elucidating disease mechanisms, improving the clinical utility of the identified loci, and envisioning how we can harness the strides to develop translational GWAS strategies. Here we review the advances of PD GWAS made to date while critically addressing the challenges and opportunities for next-generation GWAS. Thus, deciphering the missing heritability in underrepresented populations is currently at the reach of hand for a truly comprehensive understanding of the genetics of PD across the different ethnicities. Moreover, state-of-the-art GWAS designs hold a true potential for enhancing the clinical applicability of genetic findings, for instance, by improving disease prediction (PD risk and progression). Lastly, advanced PD GWAS findings, alone or in combination with clinical and environmental parameters, are expected to have the capacity for defining patient enriched cohorts stratified by genetic risk profiles and readily available for neuroprotective clinical trials. Overall, envisioning future strategies for advanced GWAS is currently timely and can be instrumental in providing novel genetic readouts essential for a true clinical translatability of PD genetic findings.

Incarceration, polygenic risk, and depressive symptoms among males in late adulthood

This study demonstrates how social and genetic factors jointly influence depression in late adulthood. We focus on the effect of incarceration, a major life event consistently found to be associated with mental health problems. Drawing on data from males in the Wisconsin Longitudinal Study and the Health and Retirement Study, we conduct a polygenic score analysis based on a genome-wide association study on depressive symptoms. Our analysis produces two important findings. First, incarceration experience mediates the association between the depression polygenic score and depressive symptoms in late adulthood (i.e., greater polygenic scores are associated with elevated incarceration risk, which increases depressive symptoms in late adulthood). Second, about one-fifth of the association between incarceration experience and late-adulthood depressive symptoms is accounted for by the depression polygenic score and childhood depression. These findings reveal complex biological and social mechanisms in the development of depression and, more broadly, provide important insights for causal inference in social science research.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update

BACKGROUND

The recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment.

METHODS

The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center.

RESULTS

Simulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 data instances. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes is associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation.

CONCLUSIONS

Our C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple log-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.

Informatics in Medical Product Regulation: The Right Drug at the Right Dose for the Right Patient

This chapter describes the role of regulatory medical product review in furthering precision medicine. Efficient data processing and appropriate analyses are needed to synthesize information and provide directions for use in a medical product label. We describe opportunities and challenges in outcome assessment through informatics, as bioengineered therapeutics are increasingly developed for the unmet needs of molecularly defined diseases. Data submission requirements and analytic principles are outlined, and regulatory resources and foundational law and statute are cited for the reader.

Phenome-wide screening of the putative causal determinants of depression using genetic data

Depression is one of the most common mental health disorders and one of the top causes of disability throughout the world. The present study sought to identify putative causal associations between depression and hundreds of complex human traits through a genome-wide screening of genetic data and a hypothesis-free approach. We leveraged genome-wide association studies (GWAS) summary statistics for depression and 1504 complex traits and investigated potential causal relationships using the latent causal variable method. We identified 559 traits genetically correlated with depression risk at FDR < 5%. Of these, 46 were putative causal genetic determinants of depression, including lifestyle factors, diseases of the nervous system, respiratory disorders, diseases of the musculoskeletal system, traits related to the health of the gastrointestinal system, obesity, vitamin D levels, and the use of prescription medications, among others. No phenotypes were identified as potential outcomes of depression. Our results suggest that genetic liability to multiple complex traits may contribute to a higher risk for depression. In particular, we show a putative causal genetic effect of pain, obesity, and inflammation on depression. These findings provide novel insights into the potential causal determinants of depression and should be interpreted as testable hypotheses for future studies to confirm, which may facilitate the design of new prevention strategies to reduce depression's burden.

Lifetime serum concentration of 25-hydroxyvitamin D 25(OH) is associated with hand grip strengths: insight from a Mendelian randomisation

Clinical trials have suggested that increased 25-hydroxyvitamin D (25(OH)D) has positive effect on hand grip strength. This Mendelian randomisation (MR) was implemented using summary-level data from the largest genome-wide association studies on vitamin D (n = 73,699) and hand grip strength. Inverse variance weighted method (IVW) was used to estimate the causal estimates. Weighted median (WM)-based method, MR-Egger and leave-one-out were applied as sensitivity analysis. Results showed that genetically higher-serum 25(OH)D levels had a positive effect on both right hand grip (IVW = Beta: 0.038, P = 0.030) and left hand grip (IVW = Beta: 0.034, P = 0.036). There was a low likelihood (statistically insignificant) of heterogeneity and pleiotropy, and the observed associations were not driven by single single-nucleotide polymorphisms. Furthermore, MR pleiotropy residual sum and outlier did not highlight any outliers. In conclusion, our results highlighted the causal and beneficial effect of serum 25(OH) D on right- and left-hand grip strengths.

Genetic Risk of Cardiovascular Disease Is Associated with Macular Ganglion Cell-Inner Plexiform Layer Thinning in an Early Glaucoma Cohort

PURPOSE

To evaluate the association between genetic risk for cardiovascular disease and retinal thinning in early glaucoma.

DESIGN

Prospective, observational genetic association study.

PARTICIPANTS

Multicohort study combining a cohort of patients with suspect and early manifest primary open-angle glaucoma (POAG), a cohort of patients with perimetric POAG, and an external normative control cohort.

METHODS

A cardiovascular disease genetic risk score was calculated for 828 participants from the Progression Risk of Glaucoma: Relevant SNPs [single nucleotide polymorphisms] with Significant Association (PROGRESSA) study. Participants were characterized as showing either predominantly macular ganglion cell-inner plexiform layer (GCIPL), predominantly peripapillary retinal nerve fiber layer (pRNFL) or equivalent macular GCIPL and pRNFL spectral-domain OCT thinning. The cardiovascular disease genetic risk scores for these groups were compared to an internal reference group of stable suspected glaucoma and of an external normative population. Replication was undertaken by comparing the phenotypes of participants from the Australia New Zealand Registry of Advanced Glaucoma (ANZRAG) with the normative control group.

MAIN OUTCOME MEASURES

Spectral-domain OCT and Humphrey Visual Field (HVF) change.

RESULTS

After accounting for age, sex, and intraocular pressure (IOP), participants with predominantly macular GCIPL thinning showed a higher cardiovascular disease genetic risk score than reference participants (odds ratio [OR], 1.76/standard deviation [SD]; 95% confidence interval [CI], 1.18-2.62; P = 0.005) and than normative participants (OR, 1.32/SD; 95% CI, 1.12-1.54; P = 0.002). This finding was replicated by comparing ANZRAG participants with predominantly macular GCIPL change with the normative population (OR, 1.39/SD; 95% CI, 1.05-1.83; P = 0.022). Review of HVF data identified that participants with paracentral visual field defects also demonstrated a higher cardiovascular disease genetic risk score than reference participants (OR, 1.85/SD; 95% CI, 1.16-2.97; P = 0.010). Participants with predominantly macular GCIPL thinning exhibited a higher vertical cup-to-disc ratio genetic risk score (OR, 1.48/SD; 95% CI, 1.24-1.76; P < 0.001), but an IOP genetic risk score (OR, 1.12/SD; 95% CI, 0.95-1.33; P = 0.179) comparable with that of the normative population.

CONCLUSIONS

This study highlighted the relationship between cardiovascular disease and retinal thinning in suspect and manifest glaucoma cases.

SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson's disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.

Towards better hypothesis tests in oxytocin research: Evaluating the validity of auxiliary assumptions

Various factors have been attributed to the inconsistent reproducibility of human oxytocin research in the cognitive and behavioral sciences. These factors include small sample sizes, a lack of pre-registered studies, and the absence of overarching theoretical frameworks that can account for oxytocin's effects over a broad range of contexts. While there have been efforts to remedy these issues, there has been very little systematic scrutiny of the role of auxiliary assumptions, which are claims that are not central for testing a hypothesis but nonetheless critical for testing theories. For instance, the hypothesis that oxytocin increases the salience of social cues is predicated on the assumption that intranasally administered oxytocin increases oxytocin levels in the brain. Without robust auxiliary assumptions, it is unclear whether a hypothesis testing failure is due to an incorrect hypothesis or poorly supported auxiliary assumptions. Consequently, poorly supported auxiliary assumptions can be blamed for hypothesis failure, thereby safeguarding theories from falsification. In this article, I will evaluate the body of evidence for key auxiliary assumptions in human behavioral oxytocin research in terms of theory, experimental design, and statistical inference, and highlight assumptions that require stronger evidence. Strong auxiliary assumptions will leave hypotheses vulnerable for falsification, which will improve hypothesis testing and consequently advance our understanding of oxytocin's role in cognition and behavior.

Causal Inference with Genetic Data: Past, Present, and Future

The set of methods discussed in this collection has emerged from the convergence of two scientific fields-genetics and causal inference. In this introduction, we discuss relevant aspects of each field and show how their convergence arises from the natural experiments that genetics offer. We present introductory concepts useful to readers unfamiliar with genetically informed methods for causal inference. We conclude that existing applications and foreseeable developments should ensure that we rapidly reap the rewards of this relatively new field, not only in terms of our understanding of human disease and development, but also in terms of tangible translational applications.

A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk

Epidemiological and associative research from humans and animals identifies correlations between the environment and health impacts. The environment-health inter-relationship is effected through an individual's underlying genetic variation and mediated by mechanisms that include the changes to gene regulation that are associated with the diversity of phenotypes we exhibit. However, the causal relationships have yet to be established, in part because the associations are reduced to individual interactions and the combinatorial effects are rarely studied. This problem is exacerbated by the fact that our genomes are highly dynamic; they integrate information across multiple levels (from linear sequence, to structural organisation, to temporal variation) each of which is open to and responds to environmental influence. To unravel the complexities of the genomic basis of human disease, and in particular non-communicable diseases that are also influenced by the environment (e.g., obesity, type II diabetes, cancer, multiple sclerosis, some neurodegenerative diseases, inflammatory bowel disease, rheumatoid arthritis) it is imperative that we fully integrate multiple layers of genomic data. Here we review current progress in integrated genomic data analysis, and discuss cases where data integration would lead to significant advances in our ability to predict how the environment may impact on our health. We also outline limitations which should form the basis of future research questions. In so doing, this review will lay the foundations for future research into the impact of the environment on our health.

Polygenic Score for Physical Activity Is Associated with Multiple Common Diseases

INTRODUCTION

Genetic pleiotropy, in which the same genes affect two or more traits, may partially explain the frequently observed associations between high physical activity (PA) and later reduced morbidity or mortality. This study investigated associations between PA polygenic risk scores (PRS) and cardiometabolic diseases among the Finnish population.

METHODS

PRS for device-measured overall PA were adapted to a FinnGen study cohort of 218,792 individuals with genomewide genotyping and extensive digital longitudinal health register data. Associations between PA PRS and body mass index, diseases, and mortality were analyzed with linear and logistic regression models.

RESULTS

A high PA PRS predicted a lower body mass index (β = -0.025 kg·m-2 per one SD change in PA PRS, SE = 0.013, P = 1.87 × 10-80). The PA PRS also predicted a lower risk for diseases that typically develop later in life or not at all among highly active individuals. A lower disease risk was systematically observed for cardiovascular diseases (odds ratio [OR] per 1 SD change in PA PRS = 0.95, P = 9.5 × 10-19) and, for example, hypertension [OR = 0.93, P = 2.7 × 10-44), type 2 diabetes (OR = 0.91, P = 4.1 × 10-42), and coronary heart disease (OR = 0.95, P = 1.2 × 10-9). Participants with high PA PRS had also lower mortality risk (OR = 0.97, P = 0.0003).

CONCLUSIONS

Genetically less active persons are at a higher risk of developing cardiometabolic diseases, which may partly explain the previously observed associations between low PA and higher disease and mortality risk. The same inherited physical fitness and metabolism-related mechanisms may be associated both with PA levels and with cardiometabolic disease risk.

Meta-imputation of transcriptome from genotypes across multiple datasets by leveraging publicly available summary-level data

Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes. In TWAS, gene expression is often imputed from individual-level genotypes of regulatory variants identified from external resources, such as Genotype-Tissue Expression (GTEx) Project. In this setting, a straightforward approach to impute expression levels of a specific tissue is to use the model trained from the same tissue type. When multiple tissues are available for the same subjects, it has been demonstrated that training imputation models from multiple tissue types improves the accuracy because of shared eQTLs between the tissues and increase in effective sample size. However, existing joint-tissue methods require access of genotype and expression data across all tissues. Moreover, they cannot leverage the abundance of various expression datasets across various tissues for non-overlapping individuals. Here, we explore the optimal way to combine imputed levels across training models from multiple tissues and datasets in a flexible manner using summary-level data. Our proposed method (SWAM) combines arbitrary number of transcriptome imputation models to linearly optimize the imputation accuracy given a target tissue. By integrating models across tissues and/or individuals, SWAM can improve the accuracy of transcriptome imputation or to improve power to TWAS while only requiring individual-level data from a single reference cohort. To evaluate the accuracy of SWAM, we combined 49 tissue-specific gene expression imputation models from the GTEx Project as well as from a large eQTL study of Depression Susceptibility Genes and Networks (DGN) Project and tested imputation accuracy in GEUVADIS lymphoblastoid cell lines samples. We also extend our meta-imputation method to meta-TWAS to leverage multiple tissues in TWAS analysis with summary-level statistics. Our results capitalize on the importance of integrating multiple tissues to unravel regulatory impacts of genetic variants on complex traits.

The gene expression levels within a cell are affected by various factors, including DNA variation, cell type, cellular microenvironment, disease status, and other environmental factors surrounding the individual. The genetic component of gene expression is known to explain a substantial fraction of transcriptional variation among individuals and can be imputed from genotypes in a tissue-specific manner, by training from population-scale transcriptomic profiles designed to identify expression quantitative loci (eQTLs). Imputing gene expression levels is shown to help understand the genetic basis of human disease through Transcriptome-wide association analysis (TWAS) and Mendelian Randomization (MR). However, it has been unclear how to integrate multiple imputation models trained from individual datasets to maximize their accuracy without having to access individual genotypes and expression levels that are often protected for privacy concerns. We developed SWAM (Smartly Weighted Averaging across Multiple datasets), a meta-imputation framework which can accurately impute gene expression levels from genotypes by integrating multiple imputation models without requiring individual-level data. Our method examines the similarity or differences between resources and borrowing information most relevant to the tissue of interest. We demonstrate that SWAM outperforms existing single-tissue and multi-tissue imputation models and continue to increase accuracy when integrating additional imputation models.

Mendelian randomization study reveals a population-specific putative causal effect of type 2 diabetes in risk of cataract

BACKGROUND

The epidemiological association between type 2 diabetes and cataract has been well established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a putative causal relationship.

METHODS

We used East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase = 36 614, Ncontrol = 155 150) and cataract (Ncase = 24 622, Ncontrol = 187 831) to comprehensively investigate the shared genetics between the two diseases. We performed: (i) linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation pattern between type 2 diabetes and cataract; (ii) multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and (iii) summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the putative causality. Moreover, to investigate the extent of the population-specific genetic effect size underlying the shared genetics between type 2 diabetes and cataract, we applied the same analytical pipeline to perform a comparative analysis on European population-based GWAS of type 2 diabetes (Ncase = 62 892, Ncontrol = 596 424) and cataract (Ncase = 5045, Ncontrol = 356 096).

RESULTS

Using East Asian population-based GWAS summary data, we observed a strong genetic correlation [rg = 0.58, 95% confidence interval (CI) = 0.33, 0.83), P-value = 5.60 × 10-6] between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% CI = 1.06, 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (βSMR = -0.34, 95% CI = -0.46, -0.22, P-value = 6.41 × 10-8) and KCNK17 (βSMR = -0.07, 95% CI = -0.09, -0.05, P-value = 2.49 × 10-10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract. On the contrary, our comparative analysis on European population provided universally weak evidence on the genetic correlation and causal relationship between the two diseases.

CONCLUSIONS

Our results provided robust evidence supporting a putative causal effect of type 2 diabetes on the risk of cataract in East Asians, and revealed potential genetic heterogeneity in the shared genetics underlying type 2 diabetes and cataract between East Asians and Europeans. These findings posed new paths on guiding the prevention and early-stage diagnosis of cataract in type 2 diabetes patients.

Mendelian randomization analysis of the causal impact of body mass index and waist-hip ratio on rates of hospital admission

We analyze how measures of adiposity - body mass index (BMI) and waist hip ratio (WHR) - causally influence rates of hospital admission. Conventional analyses of this relationship are susceptible to omitted variable bias from variables that jointly influence both hospital admission and adipose status. We implement a novel quasi-Poisson instrumental variable model in a Mendelian randomization framework, identifying causal effects from random perturbations to germline genetic variation. We estimate the individual and joint effects of BMI, WHR, and WHR adjusted for BMI. We also implement multivariable instrumental variable methods in which the causal effect of one exposure is estimated conditionally on the causal effect of another exposure. Data on 310,471 participants and over 550,000 inpatient admissions in the UK Biobank were used to perform one-sample and two-sample Mendelian randomization analyses. The results supported a causal role of adiposity on hospital admissions, with consistency across all estimates and sensitivity analyses. Point estimates were generally larger than estimates from comparable observational specifications. We observed an attenuation of the BMI effect when adjusting for WHR in the multivariable Mendelian randomization analyses, suggesting that an adverse fat distribution, rather than a higher BMI itself, may drive the relationship between adiposity and risk of hospital admission.

Schizophrenia polygenic risk is associated with child mental health problems through early childhood adversity: evidence for a gene-environment correlation

Previous studies have shown that schizophrenia polygenic risk predicts a multitude of mental health problems in the general population. Yet it is unclear by which mechanisms these associations arise. Here, we explored a possible gene-environment correlation in the association of schizophrenia polygenic risk with mental health problems via childhood adversity. This study was embedded in the population-based Generation R Study, including N = 1901 participants with genotyping for schizophrenia polygenic risk, maternal reporting of childhood adversity, and Child Behaviour Checklist measurement of mental health problems. Independent replication was attempted in the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 3641). Associations were analysed with Poisson regression and statistical mediation analysis. Higher burden of schizophrenia polygenic risk was associated with greater exposure to childhood adversity (P-value threshold < 0.5: Generation R Study, OR = 1.08, 95%CI 1.02-1.15, P = 0.01; ALSPAC, OR = 1.02, 95%CI 1.01-1.03, P < 0.01). Childhood adversities partly explained the relationship of schizophrenia polygenic risk with emotional, attention, and thought problems (proportion explained, range 5-23%). Direct effects of schizophrenia polygenic risk and adversity on mental health outcomes were also observed. In summary, genetic liability to schizophrenia increased the risk for mental health problems in the general paediatric population through childhood adversity. Although this finding could result from a mediated causal relationship between genotype and mental health, we argue that these observations most likely reflect a gene-environment correlation, i.e. adversities are a marker for the genetic risk that parents transmit to children. These and similar recent findings raise important conceptual questions about preventative interventions aimed at reducing childhood adversities.

This is what loneliness looks like: A mixed-methods study of loneliness in adolescence and young adulthood

The present study used quantitative and qualitative methods to explore how lonely young people are seen from others' perspectives, in terms of their personality, behaviour and life circumstances. Data were drawn from the Environmental Risk Longitudinal Twin Study, a cohort of 2,232 individuals born in the United Kingdom in the mid-1990s. When participants were aged 18, they provided self-reports of loneliness, and informant ratings of loneliness were provided by interviewers, as well as participants' parents and siblings. Interviewers further provided Big Five personality ratings, and detailed written notes in which they documented their perceptions of the participants and their reflections on the content of the interview. In the quantitative section of the paper, regression analyses were used to examine the perceptibility of loneliness, and how participants' loneliness related to their perceived personality traits. The informant ratings of participants' loneliness showed good agreement with self-reports. Furthermore, loneliness was associated with lower perceived conscientiousness, agreeableness and extraversion, and higher perceived neuroticism. Within-twin pair analyses indicated that these associations were partly explained by common underlying genetic influences. In the qualitative section of the study, the loneliest 5% of study participants (N=108) were selected, and thematic analysis was applied to the study' interviewers' notes about those participants. Three themes were identified and named: 'uncomfortable in own skin', 'clustering of risk', and 'difficulties accessing social resources'. These results add depth to the current conceptualisation of loneliness, and emphasise the complexity and intersectional nature of the circumstances severely lonely young adults live in.

Using Mendelian randomization as the cornerstone for causal inference in epidemiology

Mendelian randomization (MR) is attracting considerable critical attention. This paper aimed to explore the characteristics of the publications of MR, to reach an insight in this field and prospect the future trend. A bibliometric analysis was performed to identify published MR-related research. The articles were selected from the Web of Science Core Collection database. Excel 2019, VOSviewer 1.6.9, and CiteSpace 5.7.R3 were used to analyze the information. A total of 1783 papers of MR were identified, and the first included literature appeared in 2003. A total of 2829 institutions from 72 countries participated in the relevant research, while the UK contributed to 852 articles and were in a leading position. The most productive institution was the University of Bristol, and Smith GD who has posted the most articles (n=202) was also from there. The Int J Epidemiol (100 publications, 6861 citations) was the most prolific and high citation journal. Related topics of frontiers will still focus on coronary heart disease, diabetes, cancer, psychiatric disorder, body mass index, and lifestyle factors. We summarized the publication information of MR-related literature from 2003 to 2020, including country and institution of origin, authors, and publication journal. We analyzed former research hotspots in the field of MR and predicted future areas of interest. Exposures and outcomes detected in this paper will be the hotspots and frontiers of research in the next few years.

Parental feeding and childhood genetic risk for obesity: exploring hypothetical interventions with causal inference methods

BACKGROUND

Parental-feeding behaviors are common intervention targets for childhood obesity, but often only deliver small changes. Childhood BMI is partly driven by genetic effects, and the extent to which parental-feeding interventions can mediate child genetic liability is not known. Here we aim to examine how potential interventions on parental-feeding behaviors can mitigate some of the association between child genetic liability and BMI in early adolescence, using causal inference methods.

METHODS

Data from the Avon Longitudinal Study of Parents and Children were used to estimate an interventional disparity measure for a child polygenic score for BMI (PGS-BMI) on BMI at 12 years. The approach compares counterfactual outcomes for different hypothetical interventions on parental-feeding styles applied when children are 10-11 years (n = 4248). Results are presented as adjusted total association (Adj-Ta) between genetic liability (PGS-BMI) and BMI at 12 years, versus the interventional disparity measure-direct effect (IDM-DE), which represents the association that would remain, had we intervened on parental-feeding under different scenarios.

RESULTS

For children in the top quintile of genetic liability, an intervention shifting parental feeding to the levels of children with lowest genetic risk, resulted in a difference of 0.81 kg/m² in BMI at 12 years (Adj-Ta = 3.27, 95% CI: 3.04, 3.49; versus IDM-DE = 2.46, 95% CI: 2.24, 2.67).

CONCLUSIONS

Findings suggest that parental-feeding interventions have the potential to buffer some of the genetic liability for childhood obesity. Further, we highlight a novel way to analyze potential interventions for health conditions only using secondary data analyses, by combining methodology from statistical genetics and social epidemiology.

Genetic risk scores in life insurance underwriting

Genetic tests that predict the lifetime risk of common medical conditions are fast becoming more accurate and affordable. The life insurance industry is interested in using predictive genetic tests in the underwriting process, but more research is needed to establish whether this nascent form of genetic testing can refine the process over conventional underwriting factors. Here, we perform Cox regression of survival on a battery of genetic risk scores for common medical conditions and mortality risks in the Health and Retirement Study, without returning results to participants. Adjusted for covariates in a relevant insurance scenario, the scores could improve mortality risk classification by identifying 2.6 years shorter median lifespan in the highest decile of total genetic liability. We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.

Protecting against researcher bias in secondary data analysis: challenges and potential solutions

Analysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society's most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data.

Polygenic scores: prediction versus explanation

During the past decade, polygenic scores have become a fast-growing area of research in the behavioural sciences. The ability to directly assess people's genetic propensities has transformed research by making it possible to add genetic predictors of traits to any study. The value of polygenic scores in the behavioural sciences rests on using inherited DNA differences to predict, from birth, common disorders and complex traits in unrelated individuals in the population. This predictive power of polygenic scores does not require knowing anything about the processes that lie between genes and behaviour. It also does not mandate disentangling the extent to which the prediction is due to assortative mating, genotype-environment correlation, or even population stratification. Although bottom-up explanation from genes to brain to behaviour will remain the long-term goal of the behavioural sciences, prediction is also a worthy achievement because it has immediate practical utility for identifying individuals at risk and is the necessary first step towards explanation. A high priority for research must be to increase the predictive power of polygenic scores to be able to use them as an early warning system to prevent problems.

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Mendelian Randomisation (MR) is an increasingly popular approach that estimates the causal effect of risk factors on complex human traits. While it has seen several extensions that relax its basic assumptions, most suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritabilities, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results with other MR methods, LHC-MR unravels new mechanisms (how disease diagnosis might lead to improved lifestyle) and reveals new causal effects (e.g. HDL cholesterol being protective against high systolic blood pressure), hidden from standard MR methods due to a heritable confounder of opposite effect direction.

Good Theories in Need of Better Data: Combining Clinical and Social Psychological Approaches to Study the Mechanisms Linking Relationships and Health

The study of intimate relationships and health is a fast-growing discipline with numerous well-developed theories, many of which outline specific interpersonal behaviors and psychological pathways that may give rise to good or poor health. In this article, we argue that the study of relationships and health can move toward interrogating these mechanisms with greater precision and detail, but doing so will require a shift in the nature of commonly used research methods in this area. Accordingly, we draw heavily on the science of behavior change and discuss six key methodologies that may galvanize the mechanistic study of relationships and health: dismantling studies, factorial studies, experimental therapeutics, experimental mediation research, multiple assessments, and recursive modeling. We provide empirical examples for each strategy and outline new ways in which a given approach may be used to study the mechanisms linking intimate relationships and health. We conclude by discussing the key challenges and limitations for using these research strategies as well as novel ideas about how to integrate this work into existing paradigms within the field.

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Numerous studies have found evidence that GWAS loci experience negative selection, which increases in intensity with the effect size of identified variants. However, there is also accumulating evidence that this selection is not entirely mediated by the focal trait and contains a substantial pleiotropic component. Understanding how selective constraint shapes phenotypic variation requires advancing models capable of balancing these and other components of selection, as well as empirical analyses capable of inferring this balance and how it is generated by the underlying biology. We first review the classic theory connecting phenotypic selection to selection at individual loci as well as approaches and findings from recent analyses of negative selection in GWAS data. We then discuss geometric theories of pleiotropic selection with the potential to guide future modeling efforts. Recent findings revealing the nature of pleiotropic genetic variation provide clues to which genetic relationships are important and should be incorporated into analyses of selection, while findings that effect sizes vary between populations indicate that GWAS measurements could be misleading if effect sizes have also changed throughout human history.

Comparing Genetic and Socioenvironmental Contributions to Ethnic Differences in C-Reactive Protein

C-reactive protein (CRP) is a routinely measured blood biomarker for inflammation. Elevated levels of circulating CRP are associated with response to infection, risk for a number of complex common diseases, and psychosocial stress. The objective of this study was to compare the contributions of genetic ancestry, socioenvironmental factors, and inflammation-related health conditions to ethnic differences in C-reactive protein levels. We used multivariable regression to compare CRP blood serum levels between Black and White ethnic groups from the United Kingdom Biobank (UKBB) prospective cohort study. CRP serum levels are significantly associated with ethnicity in an age and sex adjusted model. Study participants who identify as Black have higher average CRP than those who identify as White, CRP increases with age, and females have higher average CRP than males. Ethnicity and sex show a significant interaction effect on CRP. Black females have higher average CRP levels than White females, whereas White males have higher average CRP than Black males. Significant associations between CRP, ethnicity, and genetic ancestry are almost completely attenuated in a fully adjusted model that includes socioenvironmental factors and inflammation-related health conditions. BMI, smoking, and socioeconomic deprivation all have high relative effects on CRP. These results indicate that socioenvironmental factors contribute more to CRP ethnic differences than genetics. Differences in CRP are associated with ethnic disparities for a number of chronic diseases, including type 2 diabetes, essential hypertension, sarcoidosis, and lupus erythematosus. Our results indicate that ethnic differences in CRP are linked to both socioenvironmental factors and numerous ethnic health disparities.

High-dimensional generalized propensity score with application to omics data

Propensity score (PS) methods are popular when estimating causal effects in non-randomized studies. Drawing causal conclusion relies on the unconfoundedness assumption. This assumption is untestable and is considered more plausible if a large number of pre-treatment covariates are included in the analysis. However, previous studies have shown that including unnecessary covariates into PS models can lead to bias and efficiency loss. With the ever-increasing amounts of available data, such as the omics data, there is often little prior knowledge of the exact set of important covariates. Therefore, variable selection for causal inference in high-dimensional settings has received considerable attention in recent years. However, recent studies have focused mainly on binary treatments. In this study, we considered continuous treatments and proposed the generalized outcome-adaptive LASSO (GOAL) to select covariates that can provide an unbiased and statistically efficient estimation. Simulation studies showed that when the outcome model was linear, the GOAL selected almost all true confounders and predictors of outcome and excluded other covariates. The accuracy and precision of the estimates were close to ideal. Furthermore, the GOAL is robust to model misspecification. We applied the GOAL to seven DNA methylation datasets from the Gene Expression Omnibus database, which covered four brain regions, to estimate the causal effects of epigenetic aging acceleration on the incidence of Alzheimer's disease.

Combining multivariate genomic approaches to elucidate the comorbidity between autism spectrum disorder and attention deficit hyperactivity disorder

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two highly heritable neurodevelopmental disorders. Several lines of evidence point towards the presence of shared genetic factors underlying ASD and ADHD. We conducted genomic analyses of common risk variants (i.e. single nucleotide polymorphisms, SNPs) shared by ASD and ADHD, and those specific to each disorder.

METHODS

With the summary data from two GWAS, one on ASD (N = 46,350) and another on ADHD (N = 55,374) individuals, we used genomic structural equation modelling and colocalization analysis to identify SNPs shared by ASD and ADHD and SNPs specific to each disorder. Functional genomic analyses were then conducted on shared and specific common genetic variants. Finally, we performed a bidirectional Mendelian randomization analysis to test whether the shared genetic risk between ASD and ADHD was interpretable in terms of reciprocal relationships between ASD and ADHD.

RESULTS

We found that 37.5% of the SNPs associated with ASD (at p < 1e-6) colocalized with ADHD SNPs and that 19.6% of the SNPs associated with ADHD colocalized with ASD SNPs. We identified genes mapped to SNPs that are specific to ASD or ADHD and that are shared by ASD and ADHD, including two novel genes INSM1 and PAX1. Our bidirectional Mendelian randomization analyses indicated that the risk of ASD was associated with an increased risk of ADHD and vice versa.

CONCLUSIONS

Using multivariate genomic analyses, the present study uncovers shared and specific genetic variants associated with ASD and ADHD. Further functional investigation of genes mapped to those shared variants may help identify pathophysiological pathways and new targets for treatment.

Smoking is significantly associated with increased risk of COVID-19 and other respiratory infections

Observational studies suggest smoking, cannabis use, alcohol consumption, and substance use disorders (SUDs) may impact risk for respiratory infections, including coronavirus 2019 (COVID-2019). However, causal inference is challenging due to comorbid substance use. Using summary-level European ancestry data (>1.7 million participants), we performed single-variable and multivariable Mendelian randomization (MR) to evaluate relationships between substance use behaviors, COVID-19 and other respiratory infections. Genetic liability for smoking demonstrated the strongest associations with COVID-19 infection risk, including the risk for very severe respiratory confirmed COVID-19 (odds ratio (OR) = 2.69, 95% CI, 1.42, 5.10, P-value = 0.002), and COVID-19 infections requiring hospitalization (OR = 3.49, 95% CI, 2.23, 5.44, P-value = 3.74 × 10-8); these associations generally remained robust in models accounting for other substance use and cardiometabolic risk factors. Smoking was also strongly associated with increased risk of other respiratory infections, including asthma-related pneumonia/sepsis (OR = 3.64, 95% CI, 2.16, 6.11, P-value = 1.07 × 10-6), chronic lower respiratory diseases (OR = 2.29, 95% CI, 1.80, 2.91, P-value = 1.69 × 10-11), and bacterial pneumonia (OR = 2.14, 95% CI, 1.42, 3.24, P-value = 2.84 × 10-4). We provide strong genetic evidence showing smoking increases the risk for COVID-19 and other respiratory infections even after accounting for other substance use behaviors and cardiometabolic diseases, which suggests that prevention programs aimed at reducing smoking may be important for the COVID-19 pandemic and have substantial public health benefits.

The roots of paternal depression: Experienced and nonexperienced trauma or Folie a Deux?

The transition to fatherhood may be challenged with anxiety and trepidation. A high prevalence has been found for paternal depression and it is reactive to maternal depression. This review aims to address potential sources of paternal depression, which may have adverse consequences on child development. We describe through three hypotheses how fathers may be at risk of depression during the transition to fatherhood: (1) psychological (interacting with ecological systems); (2) brain functional∖structural changes; and (3) (epi)genomic. We propose that paternal stressful experiences during the transition to fatherhood may be the source for paternal depression through direct stressful paternal experiences or via (potential, currently debated) nonexperienced (by the father) epigenomic transgenerational transmission. On the other hand, we suggest that resilient fathers may undergo a transient dysphoric period affected by identifying with the newborn's vulnerability as well as with the mother's postpartum vulnerability resulting in "paternity blues." In accordance with recent views on paternal "heightened sensitivity" toward the infant, we propose that the identification of both parents with the vulnerability of the newborn creates a sensitive period of Folie a Deux (shared madness) which may be a healthy transient, albeit a quasi-pathological period, recruited by the orienting response of the newborn for survival.

Little Evidence of Modified Genetic Effect of rs16969968 on Heavy Smoking Based on Age of Onset of Smoking

INTRODUCTION

Tobacco smoking is the leading cause of preventable death globally. Smoking quantity, measured in cigarettes per day, is influenced both by the age of onset of regular smoking (AOS) and by genetic factors, including a strong effect of the nonsynonymous single-nucleotide polymorphism rs16969968. A previous study by Hartz et al. reported an interaction between these two factors, whereby rs16969968 risk allele carriers who started smoking earlier showed increased risk for heavy smoking compared with those who started later. This finding has yet to be replicated in a large, independent sample.

METHODS

We performed a preregistered, direct replication attempt of the rs16969968 × AOS interaction on smoking quantity in 128 383 unrelated individuals from the UK Biobank, meta-analyzed across ancestry groups. We fit statistical association models mirroring the original publication as well as formal interaction tests on multiple phenotypic and analytical scales.

RESULTS

We replicated the main effects of rs16969968 and AOS on cigarettes per day but failed to replicate the interaction using previous methods. Nominal significance of the rs16969968 × AOS interaction term depended strongly on the scale of analysis and the particular phenotype, as did associations stratified by early/late AOS. No interaction tests passed genome-wide correction (α = 5e-8), and all estimated interaction effect sizes were much smaller in magnitude than previous estimates.

CONCLUSIONS

We failed to replicate the strong rs16969968 × AOS interaction effect previously reported. If such gene-moderator interactions influence complex traits, they likely depend on scale of measurement, and current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected.

IMPLICATIONS

We failed to replicate the strong rs16969968 × AOS interaction effect on smoking quantity previously reported. If such gene-moderator interactions influence complex traits, current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected. Furthermore, many potential interaction effects are likely to depend on the scale of measurement employed.

Two genetic analyses to elucidate causality between body mass index and personality

BACKGROUND/OBJECTIVES

Many personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality.

SUBJECTS/METHODS

We employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits neuroticism, worry, and depressive affect and used to predict BMI in an independent sample (N = 3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a direction of causation (DoC) analysis, twin data from five countries (N = 5424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation.

RESULTS

In Mendelian randomization, the allele score for BMI predicted domain-general (β = 0.05; 95% CI: 0.02, 0.08; P = 0.003) and eating-specific PPS (β = 0.06; 95% CI: 0.03, 0.09; P < 0.001). The allele score for worry also predicted BMI (β = -0.05; 95% CI: -0.08, -0.02; P < 0.001), while those for neuroticism and depressive affect did not (P ≥ 0.459). In DoC, BMI similarly predicted domain-general (β = 0.21; 95% CI:, 0.18, 0.24; P < 0.001) and eating-specific personality traits (β = 0.19; 95% CI:, 0.16, 0.22; P < 0.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI > ~25).

CONCLUSIONS

Although both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.

Genetic contributions to autism spectrum disorder

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous (de novo). More than 100 risk genes have been implicated by rare, often de novo, potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse, each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Climate Change and Children’s Mental Health: A Developmental Perspective

Climate change is a major global public-health challenge that will have wide-ranging impacts on human psychological health and well-being. Children and adolescents are at particular risk because of their rapidly developing brain, vulnerability to disease, and limited capacity to avoid or adapt to threats and impacts. They are also more likely to worry about climate change than any other age group. Drawing on a developmental life-course perspective, we show that climate-change-related threats can additively, interactively, and cumulatively increase psychopathology risk from conception onward; that these effects are already occurring; and that they constitute an important threat to healthy human development worldwide. We then argue that monitoring, measuring, and mitigating these risks is a matter of social justice and a crucial long-term investment in developmental and mental health sciences. We conclude with a discussion of conceptual and measurement challenges and outline research priorities going forward.

Contribution of birth weight to mental health, cognitive and socioeconomic outcomes: two-sample Mendelian randomisation

BACKGROUND

Low birth weight is associated with adult mental health, cognitive and socioeconomic problems. However, the causal nature of these associations remains difficult to establish owing to confounding.

AIMS

To estimate the contribution of birth weight to adult mental health, cognitive and socioeconomic outcomes using two-sample Mendelian randomisation, an instrumental variable approach strengthening causal inference.

METHOD

We used 48 independent single-nucleotide polymorphisms as genetic instruments for birth weight (genome-wide association studies' total sample: n = 264 498) and considered mental health (attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, post-traumatic stress disorder (PTSD), schizophrenia, suicide attempt), cognitive (intelligence) and socioeconomic (educational attainment, income, social deprivation) outcomes.

RESULTS

We found evidence for a contribution of birth weight to ADHD (OR for 1 s.d. unit decrease (~464 g) in birth weight, 1.29; 95% CI 1.03-1.62), PTSD (OR = 1.69; 95% CI 1.06-2.71) and suicide attempt (OR = 1.39; 95% CI 1.05-1.84), as well as for intelligence (β = -0.07; 95% CI -0.13 to -0.02) and socioeconomic outcomes, i.e. educational attainment (β = -0.05; 95% CI -0.09 to -0.01), income (β = -0.08; 95% CI -0.15 to -0.02) and social deprivation (β = 0.08; 95% CI 0.03-0.13). However, no evidence was found for a contribution of birth weight to the other examined mental health outcomes. Results were consistent across a wide range of sensitivity analyses.

CONCLUSIONS

These findings support the hypothesis that birth weight could be an important element on the causal pathway to mental health, cognitive and socioeconomic outcomes.

A genetically informed study on the association of cannabis, alcohol, and tobacco smoking with suicide attempt

Use of substances such as cannabis, alcohol, and tobacco, has been associated with increased risk of suicide attempt in several observational studies. However, establishing whether these associations are causal is challenging when using observational designs. To evaluate the potential causal contributions of cannabis use, alcohol use, and tobacco smoking to suicide attempt, we applied two-sample Mendelian randomization, an instrumental variable approach using single-nucleotide polymorphisms (SNPs) as instrumental variables for three exposures: lifetime cannabis use (yes/no; 42 instrument SNPs; GWAS sample size [N] = 162,082), alcohol use (drinks-per-week; 53 instrument SNPs; N = 941,280), and tobacco smoking (initiation, yes/no; 156 instrument SNPs; N = 1,232,091; heaviness; 27 instrument SNPs; N = 337,334). The main outcome was suicide attempt measured from hospital records (N = 50,264). All data come from publicly available summary statistics of genome-wide association studies of participants of European ancestry. We found evidence supporting a possible causal role of cannabis (OR = 1.18; 95% CI = 1.01-1.37, P = 0.032), alcohol (OR = 1.95; 95% CI = 1.15-3.32, P = 0.013), and smoking (initiation, OR = 1.90; 95% CI = 1.54-2.34, P < 0.001; heaviness, OR = 2.13; 95% CI = 1.13-3.99; P = 0.019) on suicide attempt. Using multivariable Mendelian randomization, we found that only cannabis showed a direct pathway to suicide attempt (P = 0.001), suggesting that the effect of alcohol and smoking was mediated by the other substance use phenotypes. No evidence was found for reverse causation, i.e., associations of suicide attempt on cannabis (P = 0.483), alcohol (P = 0.234), smoking initiation (P = 0.144), and heaviness (P = 0.601). In conclusion, evidence from this quasi-experimental study based on genetic data from large-scale GWASs are consistent with a causal role of cannabis, alcohol, and tobacco smoking on suicide attempt.

Triangulating Evidence through the Inclusion of Genetically Informed Designs

Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.

Association between adiposity and cardiovascular outcomes: an umbrella review and meta-analysis of observational and Mendelian randomization studies

AIMS

The aim of this study was to investigate the causal relationship and evidence of an association between increased adiposity and the risk of incident cardiovascular disease (CVD) events or mortality.

METHODS AND RESULTS 

Observational (informing association) and Mendelian randomization (MR) (informing causality) studies were assessed to gather mutually complementary insights and elucidate perplexing epidemiological relationships. Systematic reviews and meta-analyses of observational and MR studies that were published until January 2021 and evaluated the association between obesity-related indices and CVD risk were searched. Twelve systematic reviews with 53 meta-analyses results (including over 501 cohort studies) and 12 MR studies were included in the analysis. A body mass index (BMI) increase was associated with higher risks of coronary heart disease, heart failure, atrial fibrillation, all-cause stroke, haemorrhagic stroke, ischaemic stroke, hypertension, aortic valve stenosis, pulmonary embolism, and venous thrombo-embolism. The MR study results demonstrated a causal effect of obesity on all indices but stroke. The CVD risk increase for every 5 kg/m2 increase in BMI varied from 10% [relative risk (RR) 1.10; 95% confidence interval (CI) 1.01-1.21; certainty of evidence, low] for haemorrhagic stroke to 49% (RR 1.49; 95% CI 1.40-1.60; certainty of evidence, high) for hypertension. The all-cause and CVD-specific mortality risks increased with adiposity in cohorts, but the MR studies demonstrated no causal effect of adiposity on all-cause mortality.

CONCLUSION 

High adiposity is associated with increased CVD risk despite divergent evidence gradients. Adiposity was a causal risk factor for CVD except all-cause mortality and stroke. Half (49%; 26/53) of the associations were supported by high-level evidence. The associations were consistent between sexes and across global regions. This study provides guidance on how to integrate evidence from observational (association) and genetics-driven (causation) studies accumulated to date, to enable a more reliable interpretation of epidemiological relationships.