Using genetic data to strengthen causal inference in observational research

Systematic Review and Meta-analysis of Genetically Informed Research: Associations Between Parent Anxiety and Offspring Internalizing Problems

OBJECTIVE

Parent anxiety is associated with offspring internalizing problems (emotional problems related to anxiety and depression). This may reflect causal processes, whereby exposure to parent anxiety directly influences offspring internalizing (and/or vice versa). However, parent-offspring associations could also be attributable to their genetic relatedness. A systematic review and meta-analysis were conducted to investigate whether exposure to parent anxiety is associated with offspring internalizing after controlling for genetic relatedness.

METHOD

A literature search across 5 databases identified 429 unique records. Publications were retained if they used a quasi-experimental design in a general population sample to control for participant relatedness in associations between parent anxiety and offspring internalizing outcomes. Publications were excluded if they involved an experimental exposure or intervention. Studies of prenatal and postnatal anxiety exposure were meta-analyzed separately. Pearson's correlation coefficient estimates (r) were pooled using multilevel random-effects models.

RESULTS

Eight publications were retained. Data were drawn from 4 population cohorts, each unique to a quasi-experimental design: adoption, sibling-comparison, children-of-twins or in vitro fertilization. Cohorts were located in northern Europe or America. Families were predominantly of European ancestry. Three publications (Nfamilies >11,700; offspring age range, 0.5-10 years) showed no association between prenatal anxiety exposure and offspring internalizing outcomes after accounting for participant relatedness (r = .04; 95% CI: -.07, .14). Six publications (Nfamilies >12,700; offspring age range, 0.75-22 years) showed a small but significant association between concurrent symptoms in parents and offspring after accounting for participant relatedness (r = .13; 95% CI: .04, .21).

CONCLUSION

Initial literature, derived from homogeneous populations, suggests that prenatal anxiety exposure does not cause offspring internalizing outcomes. However, postnatal anxiety exposure may be causally associated with concurrent offspring internalizing via nongenetic pathways. Longitudinal stability, child-to-parent effects, and the role of moderators and methodological biases require attention.

Editorial: The Value of Genetically Informative Designs to Understand Pathways of Intergenerational Transmission and Direction of Causality

One of the greatest challenges in the social, behavioral, and medical sciences is to determine the causality underlying associations between risk factors and behavioral or disease outcomes. An area in which insight into causality, and especially direction of causation of possible risk factors and outcomes, could have enormous (clinical) impact is the field of childhood and adolescent psychiatry. Abundant evidence shows that psychopathology runs in families, but the pathways underlying shared family risk are unclear. Large twin family studies provide robust estimates for the heritability of childhood and adolescent behavioral and emotional problems, but direct non-genetic effects from parent to offspring or vice versa cannot be excluded. Question remains as to whether there is a direct causal effect of parental mental health status on the mental health and well-being of their offspring above and beyond the transmission of genetic susceptibility. Genetically informed methods provide opportunities to tackle this causality challenge.¹.

The evolution of disgust for pathogen detection and avoidance

The behavioral immune system posits that disgust functions to protect animals from pathogen exposure. Therefore, cues of pathogen risk should be a primary driver influencing variation in disgust. Yet, to our knowledge, neither the relationship between current pathogen risk and disgust, nor the correlation between objective and perceived pathogen risk have been addressed using ecologically valid measures in a global sample. The current article reports two studies addressing these gaps. In Study 1, we include a global sample (n = 361) and tested the influence of both perceived pathogen exposure and an objective measure of pathogen risk-local communicable infectious disease mortality rates-on individual differences in pathogen and sexual disgust sensitivities. In Study 2, we first replicate Study 1's analyses in another large sample (n = 821), targeting four countries (US, Italy, Brazil, and India); we then replaced objective and perceived pathogen risk with variables specific to the SARS-CoV-2 pandemic. In Study 1, both local infection mortality rates and perceived infection exposure predicted unique variance in pathogen and sexual disgust. In Study 2, we found that perceived infection exposure positively predicted sexual disgust, as predicted. When substituting perceived and objective SARS-CoV-2 risk in our models, perceived risk of contracting SARS-CoV-2 positively predicted pathogen and sexual disgust, and state case rates negatively predicted pathogen disgust. Further, in both studies, objective measures of risk (i.e., local infection mortality and SARS-CoV-2 rates) positively correlated with subjective measures of risk (i.e., perceived infection exposure and perceived SARS-CoV-2 risk). Ultimately, these results provide two pieces of foundational evidence for the behavioral immune system: 1) perceptions of pathogen risk accurately assay local, objective mortality risk across countries, and 2) both perceived and objective pathogen risk explain variance in disgust levels.

The effects of eight serum lipid biomarkers on age-related macular degeneration risk: a Mendelian randomization study

BACKGROUND

Age-related macular degeneration (AMD) is a leading cause of vision loss. Whereas lipids have been studied extensively to understand their effects on cardiovascular diseases, their relationship with AMD remains unclear.

METHODS

Two-sample Mendelian randomization (MR) analyses were performed to systematically evaluate the causal relationships between eight serum lipid biomarkers, consisting of apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL-C), direct low-density lipoprotein cholesterol (LDL-C), lipoprotein A [Lp(a)], triglycerides (TG) and non-HDL cholesterol (non-HDL-C), and the risk of different AMD stages and subtypes. We derived 64-407 genetic instruments for eight serum lipid biomarkers in 419 649 participants of European descent from the UK Biobank cohort. We conducted genome-wide association studies (GWAS) for 12 711 advanced AMD cases [8544 choroidal neovascularization (CNV) and 2656 geographic atrophy (GA) specific AMD subtypes] and 5336 intermediate AMD cases with 14 590 controls of European descent from the International AMD Genomics Consortium.

RESULTS

Higher genetically predicted HDL-C and ApoA1 levels increased the risk of all AMD subtypes. LDL-C, ApoB, CHOL and non-HDL-C levels were associated with decreased risk of intermediate and GA AMD but not with CNV. Genetically predicted TG levels were associated with decreased risk of different AMD subtypes. Sensitivity analyses revealed no evidence for directional pleiotropy effects. In our multivariable MR analyses, adjusting for the effects of correlated lipid biomarkers yielded similar results.

CONCLUSION

These results suggest the role of lipid metabolism in drusen formation and particularly in AMD development at the early and intermediate stages. Mechanistic studies are warranted to investigate the utility of lipid pathways for therapeutic treatment in preventing AMD.

Computational Tools for Causal Inference in Genetics

The advent of large-scale, phenotypically rich, and readily accessible data provides an unprecedented opportunity for epidemiologists, statistical geneticists, bioinformaticians, and also behavioral and social scientists to investigate the causes and consequences of disease. Computational tools and resources are an integral component of such endeavors, which will become increasingly important as these data continue to grow exponentially. In this review, we have provided an overview of computational software and databases that have been developed to assist with analyses in causal inference. This includes online tools that can be used to help generate hypotheses, publicly accessible resources that store summary-level information for millions of genetic markers, and computational approaches that can be used to leverage this wealth of data to study causal relationships.

Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes - child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely.

Twins and Causal Inference: Leveraging Nature's Experiment

In this review, we discuss how samples comprising monozygotic and dizygotic twin pairs can be used for the purpose of strengthening causal inference by controlling for shared influences on exposure and outcome. We begin by briefly introducing how twin data can be used to inform the biometric decomposition of population variance into genetic, shared environmental, and nonshared environmental influences. We then discuss how extensions to this model can be used to explore whether associations between exposure and outcome survive correction for shared etiology (common causes). We review several analytical approaches that can be applied to twin data for this purpose. These include multivariate structural equation models, cotwin control methods, direction of causation models (cross-sectional and longitudinal), and extended family designs used to assess intergenerational associations. We conclude by highlighting some of the limitations and considerations that researchers should be aware of when using twin data for the purposes of interrogating causal hypotheses.

Therapy response prediction in major depressive disorder: current and novel genomic markers influencing pharmacokinetics and pharmacodynamics

Major depressive disorder is connected with high rates of functional disability and mortality. About a third of the patients are at risk of therapy failure. Several pharmacogenetic markers especially located in CYP450 genes such as CYP2D6 or CYP2C19 are of relevance for therapy outcome prediction in major depressive disorder but a further optimization of predictive tools is warranted. The article summarizes the current knowledge on pharmacogenetic variants, therapy effects and side effects of important antidepressive therapeutics, and sheds light on new methodological approaches for therapy response estimation based on genetic markers with relevance for pharmacokinetics, pharmacodynamics and disease pathology identified in genome-wide association study analyses, highlighting polygenic risk score analysis as a tool for further optimization of individualized therapy outcome prediction.

Are there causal relationships between attention-deficit/hyperactivity disorder and body mass index? Evidence from multiple genetically informed designs

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) and body mass index (BMI) are associated. However, it remains unclear whether this association reflects causal relationships in either direction or confounding. Here, we implemented genetically informed methods to examine bidirectional causality and potential confounding.

METHODS

Three genetically informed methods were employed: (i) cross-lagged twin-differences analyses to assess bidirectional effects of ADHD symptoms and BMI at ages 8, 12, 14 and 16 years in 2386 pairs of monozygotic twins from the Twins Early Development Study (TEDS); (ii) within- and between-family ADHD and BMI polygenic score (PS) analyses in 3320 pairs of dizygotic TEDS twins; and (iii) two-sample bidirectional Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) on ADHD (N = 55,374) and BMI (N = 806,834).

RESULTS

Mixed results were obtained across the three methods. Twin-difference analyses provided little support for cross-lagged associations between ADHD symptoms and BMI over time. PS analyses were consistent with bidirectional relationships between ADHD and BMI, with plausible time-varying effects from childhood to adolescence. MR findings also suggested bidirectional causal effects between ADHD and BMI. Multivariable MR indicated the presence of substantial confounding in bidirectional relationships.

CONCLUSIONS

The three methods converged to highlight multiple sources of confounding in the association between ADHD and BMI. PS and MR analyses suggested plausible causal relationships in both directions. Possible explanations for mixed causal findings across methods are discussed.

A web-based survey on various symptoms of computer vision syndrome and the genetic understanding based on a multi-trait genome-wide association study

A variety of eye-related symptoms due to the overuse of digital devices is collectively referred to as computer vision syndrome (CVS). In this study, a web-based survey about mind and body functions, including eye strain, was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, a multi-trait genome-wide association study (GWAS), a multivariate analysis on individual-level multivariate data, was performed based on the structural equation modeling methodology assuming a causal pathway for a genetic variant to influence each symptom via a single common latent variable. Twelve loci containing lead variants with a suggestive level of significance were identified. Two loci showed relatively strong signals and were associated with TRABD2B relative to the Wnt signaling pathway and SDK1 having neuronal adhesion and immune functions, respectively. By utilizing publicly available eQTL data, colocalization between GWAS and eQTL signals for four loci was detected, and a locus on 2p25.3 showed a strong colocalization (PPH₄ > 0.9) on retinal MYT1L, known to play an important role in neuronal differentiation. This study suggested that the use of multivariate questionnaire data and multi-trait GWAS can lead to biologically reasonable findings and enhance our genetic understanding of complex relationships among symptoms related to CVS.

Causal Inference Methods to Integrate Omics and Complex Traits

Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.

The epidemiology of cognitive development

The epidemiology of cognitive development is an approach essentially based on large observational studies, which examines individual differences in cognitive abilities throughout childhood and their determinants. Although different in terms of methodology and main interests from developmental psychology, cognitive epidemiology offers complementary viewpoints on cognitive development and addresses fundamental research questions of interest to developmental psychologists. The present paper depicts the contributions of the epidemiological approach to the field of cognitive development and highlights the methodological advances that have made such contributions possible. We discuss the stability and developmental trajectories of cognitive functions, their main predictors, the complex interplay between environmental and genetic predictors, and the relationships between the different domains of cognition from birth to adulthood.

Identifying risk factors involved in the common versus specific liabilities to substance use: A genetically informed approach

Individuals most often use several rather than one substance among alcohol, cigarettes or cannabis. This widespread co-occurring use of multiple substances is thought to stem from a common liability that is partly genetic in origin. Genetic risk may indirectly contribute to a common liability to substance use through genetically influenced mental health vulnerabilities and individual traits. To test this possibility, we used polygenic scores indexing mental health and individual traits and examined their association with the common versus specific liabilities to substance use. We used data from the Avon Longitudinal Study of Parents and Children (N = 4218) and applied trait-state-occasion models to delineate the common and substance-specific factors based on four classes of substances (alcohol, cigarettes, cannabis and other illicit substances) assessed over time (ages 17, 20 and 22). We generated 18 polygenic scores indexing genetically influenced mental health vulnerabilities and individual traits. In multivariable regression, we then tested the independent contribution of selected polygenic scores to the common and substance-specific factors. Our results implicated several genetically influenced traits and vulnerabilities in the common liability to substance use, most notably risk taking (bstandardised = 0.14; 95% confidence interval [CI] [0.10, 0.17]), followed by extraversion (bstandardised = -0.10; 95% CI [-0.13, -0.06]), and schizophrenia risk (bstandardised = 0.06; 95% CI [0.02, 0.09]). Educational attainment (EA) and body mass index (BMI) had opposite effects on substance-specific liabilities such as cigarette use (bstandardised-EA = -0.15; 95% CI [-0.19, -0.12]; bstandardised-BMI = 0.05; 95% CI [0.02, 0.09]) and alcohol use (bstandardised-EA = 0.07; 95% CI [0.03, 0.11]; bstandardised-BMI = -0.06; 95% CI [-0.10, -0.02]). These findings point towards largely distinct sets of genetic influences on the common versus specific liabilities.

Suicidal ideation and attempt in adolescents exposed to maternal smoking across pregnancy and childhood: A 20-year prospective cohort study

BACKGROUND

Few studies investigated the association between maternal smoking during pregnancy and offspring suicide risk, none considering postnatal smoking exposure. We investigated associations between maternal smoking patterns during the pre- and postnatal periods and adolescent suicidal ideation and attempt.

METHODS

We identified longitudinal patterns of maternal smoking from the prenatal period to the end of childhood (children's age 12 years, 10 assessments) among participants in the Québec Longitudinal Study of Child Development (N = 1623). We estimated associations between maternal smoking patterns and offspring self-reported suicidal ideation and attempt (ages 13-20). Background confounding factors (e.g., socioeconomic, familial, mental health) were controlled using propensity score inverse-probability weighting (IPW).

RESULTS

Participants reporting suicidal ideation and attempt were 9.3% and 8.4%, respectively. We identified four maternal smoking patterns: non-smoking (66.5%), increasing (5.5%), decreasing (9.3%), persistent (18.5%). Children exposed to persistent (OR=2.92, CI=1.99-4.30) and increasing (OR=2.06, CI=1.13-3.74) maternal smoking were more likely to attempt suicide, compared to non-exposed children. Accounting for confounding factors using IPW fully explained the association between increasing smoking and suicide attempt (OR=0.95, CI=0.39-2.09) but only reduced the association between persistent exposure and suicide attempt (OR=2.30, CI=1.04-4.99). No increased suicide attempt risk was found for children of mothers with a decreased smoking pattern. We found no associations for suicidal ideation.

LIMITATIONS

Propensity score cannot account for unmeasured confounding factors; attrition limits generalizability.

CONCLUSIONS

Offspring of mothers who smoked persistently and heavily prenatally and postnatally were at increased risk of suicide attempt in adolescence. Future studies should elucidate biological and psychosocial mechanisms potentially at play in these associations.

Prenatal medication exposure and epigenetic outcomes: a systematic literature review and recommendations for prenatal pharmacoepigenetic studies

When used during pregnancy, analgesics and psychotropics pass the placenta to enter the foetal circulation and may induce epigenetic modifications. Where such modifications occur and whether they disrupt normal foetal developme nt, are currently unanswered questions. This field of prenatal pharmacoepigenetics has received increasing attention, with several studies reporting associations between in utero medication exposure and offspring epigenetic outcomes. Nevertheless, no recent systematic review of the literature is available. Therefore, the objectives of this review were to (i) provide an overview of the literature on the association of prenatal exposure to psychotropics a nd analgesics with epigenetic outcomes, and (ii) suggest recommendations for future studies within prenatal pharmacoepigenetics. We performed systematic literature searches in five databases. The eligible studies assessed human prenatal exposure to psychotropics or analgesics, with epigenetic analyses of offspring tissue as an outcome. We identified 18 eligible studies including 4,419 neonates exposed to either antidepressants, antiepileptic drugs, paracetamol, acetylsalicylic acid, or methadone. The epigenetic outcome in all studies was DNA methylation in cord blood, placental tissue or buccal cells. Although most studies found significant differences in DNA methylation upon medication exposure, almost no differences were persistent across studies for similar medications and sequencing methods. The reviewed studies were challenging to compare due to poor transparency in reporting, and heterogeneous methodology, design, genome coverage, and statistical modelling. We propose 10 recommendations for future prenatal pharmacoepigenetic studies considering both epidemiological and epigenetic perspectives. These recommendations may improve the quality, comparability, and clinical relevance of such studies. PROSPERO registration ID: CRD42020166675.

Genomics of hypertension: the road to precision medicine

The known genetic architecture of blood pressure now comprises >30 genes, with rare variants resulting in monogenic forms of hypertension or hypotension and >1,477 common single-nucleotide polymorphisms (SNPs) being associated with the blood pressure phenotype. Monogenic blood pressure syndromes predominantly involve the renin-angiotensin-aldosterone system and the adrenal glucocorticoid pathway, with a smaller fraction caused by neuroendocrine tumours of the sympathetic and parasympathetic nervous systems. The SNPs identified in genome-wide association studies (GWAS) as being associated with the blood pressure phenotype explain only approximately 27% of the 30-50% estimated heritability of blood pressure, and the effect of each SNP on the blood pressure phenotype is small. A paucity of SNPs from GWAS are mapped to known genes causing monogenic blood pressure syndromes. For example, a GWAS signal mapped to the gene encoding uromodulin has been shown to affect blood pressure by influencing sodium homeostasis, and the effects of another GWAS signal were mediated by endothelin. However, the majority of blood pressure-associated SNPs show pleiotropic associations. Unravelling these associations can potentially help us to understand the underlying biological pathways. In this Review, we appraise the current knowledge of blood pressure genomics, explore the causal pathways for hypertension identified in Mendelian randomization studies and highlight the opportunities for drug repurposing and pharmacogenomics for the treatment of hypertension.

Achieved educational attainment, inherited genetic endowment for education, and obesity

This study investigates two sources of education effects on obesity - achieved educational attainment and inherited genetic endowment for education. In doing so, we accomplish two goals. First, we assess the role of genetic confounding in the association between education and health. Second, we consider the heterogeneity in the extent to which genetic potential for education is realized, and we examine its impact on obesity. Data come from the National Longitudinal Study of Adolescent to Adult Health. Using a polygenic score approach, we find that, net of genetic confounding, holding a college degree is associated with a lower likelihood of obesity. Moreover, among individuals who hold a college degree, those with a high education polygenic score (a greater genetic propensity to succeed in education) are less likely to be obese than those with a relatively low education polygenic score. However, when individuals with a high education polygenic score do not have a college degree, their risk of obesity is similar to that of non-college-educated individuals with a low education polygenic score, suggesting that the effect of genetic endowment for education on obesity is conditional on college education.

Common genetic polymorphisms contribute to the association between chronic lymphocytic leukaemia and non-melanoma skin cancer

BACKGROUND

Epidemiological studies have demonstrated a positive association between chronic lymphocytic leukaemia (CLL) and non-melanoma skin cancer (NMSC). We hypothesized that shared genetic risk factors between CLL and NMSC could contribute to the association observed between these diseases.

METHODS

We examined the association between (i) established NMSC susceptibility loci and CLL risk in a meta-analysis including 3100 CLL cases and 7667 controls and (ii) established CLL loci and NMSC risk in a study of 4242 basal cell carcinoma (BCC) cases, 825 squamous cell carcinoma (SCC) cases and 12802 controls. Polygenic risk scores (PRS) for CLL, BCC and SCC were constructed using established loci. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Higher CLL-PRS was associated with increased BCC risk (OR4th-quartile-vs-1st-quartile = 1.13, 95% CI: 1.02-1.24, Ptrend = 0.009), even after removing the shared 6p25.3 locus. No association was observed with BCC-PRS and CLL risk (Ptrend = 0.68). These findings support a contributory role for CLL in BCC risk, but not for BCC in CLL risk. Increased CLL risk was observed with higher SCC-PRS (OR4th-quartile-vs-1st-quartile = 1.22, 95% CI: 1.08-1.38, Ptrend = 1.36 × 10-5), which was driven by shared genetic susceptibility at the 6p25.3 locus.

CONCLUSION

These findings highlight the role of pleiotropy regarding the pathogenesis of CLL and NMSC and shows that a single pleiotropic locus, 6p25.3, drives the observed association between genetic susceptibility to SCC and increased CLL risk. The study also provides evidence that genetic susceptibility for CLL increases BCC risk.

Complex Linkage Disequilibrium Effects in HLA-DPB1 Expression and Molecular Mismatch Analyses of Transplantation Outcomes

BACKGROUND

HLA molecular mismatch (MM) is a risk factor for de novo donor-specific antibody (dnDSA) development in solid organ transplantation. HLA expression differences have also been associated with adverse outcomes in hematopoietic cell transplantation. We sought to study both MM and expression in assessing dnDSA risk.

METHODS

One hundred three HLA-DP-mismatched solid organ transplantation pairs were retrospectively analyzed. MM was computed using amino acids (aa), eplets, and, supplementarily, Grantham/Epstein scores. DPB1 alleles were classified as rs9277534-A (low-expression) or rs9277534-G (high-expression) linked. To determine the associations between risk factors and dnDSA, logistic regression, linkage disequilibrium (LD), and population-based analyses were performed.

RESULTS

A high-risk AA:GX (recipient:donor) expression combination (X = A or G) demonstrated strong association with HLA-DP dnDSA (P = 0.001). MM was also associated with HLA-DP dnDSA when evaluated by itself (eplet P = 0.007, aa P = 0.003, Grantham P = 0.005, Epstein P = 0.004). When attempting to determine the relative individual effects of the risk factors in multivariable analysis, only AA:GX expression status retained a strong association (relative risk = 18.6, P = 0.007 with eplet; relative risk = 15.8, P = 0.02 with aa), while MM was no longer significant (eplet P = 0.56, aa P = 0.51). Importantly, these risk factors are correlated, due to LD between the expression-tagging single-nucleotide polymorphism and polymorphisms along HLA-DPB1.

CONCLUSIONS

The MM and expression risk factors each appear to be strong predictors of HLA-DP dnDSA and to possess clinical utility; however, these two risk factors are closely correlated. These metrics may represent distinct ways of characterizing a common overlapping dnDSA risk profile, but they are not independent. Further, we demonstrate the importance and detailed implications of LD effects in dnDSA risk assessment and possibly transplantation overall.

Do ADHD-impulsivity and BMI have shared polygenic and neural correlates?

There is an extensive body of literature linking ADHD to overweight and obesity. Research indicates that impulsivity features of ADHD account for a degree of this overlap. The neural and polygenic correlates of this association have not been thoroughly examined. In participants of the IMAGEN study, we found that impulsivity symptoms and body mass index (BMI) were associated (r = 0.10, n = 874, p = 0.014 FWE corrected), as were their respective polygenic risk scores (PRS) (r = 0.17, n = 874, p = 6.5 × 10-6 FWE corrected). We then examined whether the phenotypes of impulsivity and BMI, and the PRS scores of ADHD and BMI, shared common associations with whole-brain grey matter and the Monetary Incentive Delay fMRI task, which associates with reward-related impulsivity. A sparse partial least squared analysis (sPLS) revealed a shared neural substrate that associated with both the phenotypes and PRS scores. In a last step, we conducted a bias corrected bootstrapped mediation analysis with the neural substrate score from the sPLS as the mediator. The ADHD PRS associated with impulsivity symptoms (b = 0.006, 90% CIs = 0.001, 0.019) and BMI (b = 0.009, 90% CIs = 0.001, 0.025) via the neuroimaging substrate. The BMI PRS associated with BMI (b = 0.014, 95% CIs = 0.003, 0.033) and impulsivity symptoms (b = 0.009, 90% CIs = 0.001, 0.025) via the neuroimaging substrate. A common neural substrate may (in part) underpin shared genetic liability for ADHD and BMI and the manifestation of their (observable) phenotypic association.

Causal inference with observational data: the need for triangulation of evidence

The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias - either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation - the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.

Family-Based Designs that Disentangle Inherited Factors from Pre- and Postnatal Environmental Exposures: In Vitro Fertilization, Discordant Sibling Pairs, Maternal versus Paternal Comparisons, and Adoption Designs

Identifying environmental risk and protective exposures that have causal effects on health is an important scientific goal. Many environmental exposures are nonrandomly allocated and influenced by dispositional factors including inherited ones. We review family-based designs that can separate the influence of environmental exposures from inherited influences shared between parent and offspring. We focus on prenatal exposures. We highlight that the family-based designs that can separate the prenatal environment from inherited confounds are different to those that are able to pull apart later-life environmental exposures from inherited confounds. We provide a brief review of the literature on maternal smoking during pregnancy and offspring attention-deficit/hyperactivity disorder (ADHD) and conduct problems; these inconsistencies in the literature make a review useful and this illustrates that results of family-based genetically informed studies are inconsistent with a causal interpretation for this exposure and these two offspring outcomes.

A genetically-informed study disentangling the relationships between tobacco smoking, cannabis use, alcohol consumption, substance use disorders and respiratory infections, including COVID-19

Background

Observational studies suggest smoking, cannabis use, alcohol consumption, cannabis use, and substance use disorders (SUDs) may play a role in the susceptibility for respiratory infections and disease, including coronavirus 2019 (COVID-2019). However, causal inference is challenging due to comorbid substance use.

Methods

Using genome-wide association study data of European ancestry (data from >1.7 million individuals), we performed single-variable and multivariable Mendelian randomization to evaluate relationships between smoking, cannabis use, alcohol consumption, SUDs, and respiratory infections.

Results

Genetically predicted lifetime smoking was found to be associated with increased risk for hospitalized COVID-19 (odds ratio (OR)=4.039, 95% CI 2.335-6.985, P-value=5.93×10-7) and very severe hospitalized COVID-19 (OR=3.091, 95% CI, 1.883-5.092, P-value=8.40×10-6). Genetically predicted lifetime smoking was also associated with increased risk pneumoniae (OR=1.589, 95% CI, 1.214-2.078, P-value=7.33×10-4), lower respiratory infections (OR=2.303, 95% CI, 1.713-3.097, P-value=3.40×10-8), and several others. Genetically predicted cannabis use disorder (CUD) was associated with increased bronchitis risk (OR=1.078, 95% CI, 1.020-1.128, P-value=0.007).

Conclusions

We provide strong genetic evidence showing smoking increases the risk for respiratory infections and diseases even after accounting for other substance use and abuse. Additionally, we provide find CUD may increase the risk for bronchitis, which taken together, may guide future research SUDs and respiratory outcomes.

Investigating the genetic and environmental aetiologies of non-suicidal and suicidal self-harm: a twin study

BACKGROUND

Self-harm is a major health concern, not only as a signal of distress but also as a strong predictor of later suicide. Self-harm can be further refined into suicidal self-harm (SSH, i.e. suicide attempt) and non-suicidal self-harm (NSSH). Understanding the aetiologies of NSSH and SSH can help inform suicide prevention strategies. Using a twin design, we investigated the phenotypic and aetiological relationships between NSSH and SSH, and their aetiological overlap with mental health problems.

METHODS

We analysed data from the Twins Early Development Study using structural equation modelling. At age 21 years, 9063 twins (62.4% female) answered questions related to self-harm. At age 16 years, 19 self- or parent-reported mental health measures were administered, including measures of internalising and externalising problems, psychotic-like experiences and substance abuse.

RESULTS

Prevalences for NSSH and SSH were 21.9% and 10.5%, respectively. Additive genetic factors explained half of the variance in NSSH (55%) and SSH (50%), with the rest explained by non-shared environmental factors. Phenotypically, NSSH and SSH were strongly correlated (r = 0.87) with their correlation explained by genetic (57%) and non-shared environmental (43%) factors. We found no evidence that NSSH and SSH differed in their phenotypic and aetiological relationships with mental health measures.

CONCLUSION

Our findings suggest no aetiological difference between NSSH and SSH. NSSH and SSH should be regarded as two different ends of a continuum, rather than as two distinct categories.

Assessment and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

Hypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power, sample overlap or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments across human phenome. We developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed causal architecture plot. We apply this pipeline to body mass index (BMI) and lipid traits as exemplars of traits where there is strong prior expectation for causal effects, and to dental caries and periodontitis as exemplars of traits where there is a need for causal inference. The results for lipids and BMI suggest that these traits are best viewed as contributing factors on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. The automated pipeline is implemented in the Complex-Traits Genetics Virtual Lab ( https://vl.genoma.io ) and results are available in https://view.genoma.io . We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a new way visualizing the overall causal map of the human phenome.

Investigating the effects of genetic risk of schizophrenia on behavioural traits

To characterise the trait-effects of increased genetic risk for schizophrenia, and highlight potential risk mediators, we test the association between schizophrenia polygenic risk scores (PRSs) and 529 behavioural traits (personality, psychological, lifestyle, nutritional) in the UK Biobank. Our primary analysis is performed on individuals aged 38–71 with no history of schizophrenia or related disorders, allowing us to report the effects of schizophrenia genetic risk in the sub-clinical general population. Higher schizophrenia PRSs were associated with a range of traits, including lower verbal-numerical reasoning (P = 6 × 10^–61), higher nervous feelings (P = 1 × 10⁻⁴⁶) and higher self-reported risk-taking (P = 3 × 10⁻³⁸). We follow-up the risk-taking association, hypothesising that the association may be due to a genetic propensity for risk-taking leading to greater migration, urbanicity or drug-taking — reported environmental risk factors for schizophrenia, and all positively associated with risk-taking in these data. Next, to identify potential disorder or medication effects, we compare the PRS–trait associations in the general population to the trait values in 599 medicated and non-medicated individuals diagnosed with schizophrenia in the biobank. This analysis highlights, for example, levels of BMI, physical activity and risk-taking in cases in the opposite directions than expected from the PRS–trait associations in the general population. Our analyses offer simple yet potentially revealing insights into the possible causes of observed trait–disorder associations, which can complement approaches such as Mendelian Randomisation. While we urge caution in causal interpretations in PRS cross-trait studies that are highly powered to detect weak horizontal pleiotropy or population structure, we propose that well-designed polygenic score analyses have the potential to highlight modifiable risk factors that lie on the path between genetic risk and disorder.

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

Non-genetic risk and protective factors and biomarkers for neurological disorders: a meta-umbrella systematic review of umbrella reviews

BACKGROUND

The etiologies of chronic neurological diseases, which heavily contribute to global disease burden, remain far from elucidated. Despite available umbrella reviews on single contributing factors or diseases, no study has systematically captured non-purely genetic risk and/or protective factors for chronic neurological diseases.

METHODS

We performed a systematic analysis of umbrella reviews (meta-umbrella) published until September 20th, 2018, using broad search terms in MEDLINE, SCOPUS, Web of Science, Cochrane Database of Systematic Reviews, Cumulative Index to Nursing and Allied Health Literature, ProQuest Dissertations & Theses, JBI Database of Systematic Reviews and Implementation Reports, DARE, and PROSPERO. The PRISMA guidelines were followed for this study. Reference lists of the identified umbrella reviews were also screened, and the methodological details were assessed using the AMSTAR tool. For each non-purely genetic factor association, random effects summary effect size, 95% confidence and prediction intervals, and significance and heterogeneity levels facilitated the assessment of the credibility of the epidemiological evidence identified.

RESULTS

We identified 2797 potentially relevant reviews, and 14 umbrella reviews (203 unique meta-analyses) were eligible. The median number of primary studies per meta-analysis was 7 (interquartile range (IQR) 7) and that of participants was 8873 (IQR 36,394). The search yielded 115 distinctly named non-genetic risk and protective factors with a significant association, with various strengths of evidence. Mediterranean diet was associated with lower risk of dementia, Alzheimer disease (AD), cognitive impairment, stroke, and neurodegenerative diseases in general. In Parkinson disease (PD) and AD/dementia, coffee consumption, and physical activity were protective factors. Low serum uric acid levels were associated with increased risk of PD. Smoking was associated with elevated risk of multiple sclerosis and dementia but lower risk of PD, while hypertension was associated with lower risk of PD but higher risk of dementia. Chronic occupational exposure to lead was associated with higher risk of amyotrophic lateral sclerosis. Late-life depression was associated with higher risk of AD and any form of dementia.

CONCLUSIONS

We identified several non-genetic risk and protective factors for various neurological diseases relevant to preventive clinical neurology, health policy, and lifestyle counseling. Our findings could offer new perspectives in secondary research (meta-research).

Common health conditions in childhood and adolescence, school absence, and educational attainment: Mendelian randomization study

Good health is positively related to children's educational outcomes, but relationships may not be causal. Demonstrating a causal influence would strongly support childhood and adolescent health as important for education policy. We applied genetic causal inference methods to assess the causal relationship of common health conditions at age 10 (primary/elementary school) and 13 (mid-secondary/mid-high school) with educational attainment at 16 and school absence at 14-16. Participants were 6113 children from the Avon Longitudinal Study of Parents and Children (ALSPAC). Exposures were symptoms of attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), depression, asthma, migraines and BMI. Genetic liability for these conditions and BMI was indexed by polygenic scores. In non-genetic, multivariate-adjusted models, all health conditions except asthma and migraines were associated with poorer attainment and greater school absence. School absence substantially mediated effects of BMI (39.9% for BMI at 13) and migraines (72.0% at 10), on attainment with more modest mediation for emotional and neurodevelopmental conditions. In genetic models, a unit increase in standardized BMI at 10 predicted a 0.19 S.D. decrease (95% CI: 0.11, 0.28) in attainment at 16, equivalent to around a 1/3 grade lower in all subjects, and 8.7% more school absence (95% CI:1.8%,16.1%). Associations were similar at 13. Genetic liability for ADHD predicted lower attainment but not more absence. Triangulation across multiple approaches supports a causal, negative influence on educational outcomes of BMI and ADHD, but not of ASD, depression, asthma or migraine. Higher BMI in childhood and adolescence may causally impair educational outcomes.

Investigating causality between liability to ADHD and substance use, and liability to substance use and ADHD risk, using Mendelian randomization

Attention-deficit hyperactivity disorder (ADHD) has consistently been associated with substance use, but the nature of this association is not fully understood. To inform intervention development and public health messages, a vital question is whether there are causal pathways from ADHD to substance use and/or vice versa. We applied bidirectional Mendelian randomization, using summary-level data from the largest available genome-wide association studies (GWAS) on ADHD, smoking (initiation, cigarettes per day, cessation, and a compound measure of lifetime smoking), alcohol use (drinks per week, alcohol problems, and alcohol dependence), cannabis use (initiation), and coffee consumption (cups per day). Genetic variants robustly associated with the "exposure" were selected as instruments and identified in the "outcome" GWAS. Effect estimates from individual genetic variants were combined with inverse-variance weighted regression and five sensitivity analyses (weighted median, weighted mode, MR-Egger, generalized summary data-based MR, and Steiger filtering). We found evidence that liability to ADHD increases likelihood of smoking initiation and heaviness of smoking among smokers, decreases likelihood of smoking cessation, and increases likelihood of cannabis initiation. There was weak evidence that liability to ADHD increases alcohol dependence risk but not drinks per week or alcohol problems. In the other direction, there was weak evidence that smoking initiation increases ADHD risk, but follow-up analyses suggested a high probability of horizontal pleiotropy. There was no clear evidence of causal pathways between ADHD and coffee consumption. Our findings corroborate epidemiological evidence, suggesting causal pathways from liability to ADHD to smoking, cannabis use, and, tentatively, alcohol dependence. Further work is needed to explore the exact mechanisms mediating these causal effects.

On the Development of OCD

OCD most often arises before adulthood with adolescence being a particularly vulnerable period. This is also a time when both brain and cognition undergo fundamental developmental change and reorganisation. However, the neurocognitive mechanisms that drive the emergence of OCD during development are still largely unknown. In this chapter, I review the relatively sparse literature on the developmental aspects of OCD and I discuss the symptomatic, cognitive and neural patterns in OCD and the developing mind. I highlight how we need to understand the emergence of cognitive impairments and neural alteration in a developmental context if we want to understand more about the mechanisms that give rise to OCD. Moreover, I outline how we best approach this challenge to overcome the current limitations in research.

The Role of Eating Behaviours in Genetic Susceptibility to Obesity

PURPOSE OF REVIEW

Eating behaviours are hypothesised to be the behavioural expression of genetic risk of obesity. In this review, we summarise findings from behavioural genetic research on the association between genetic risk for obesity and validated psychometrics measures of eating behaviours in children and adults (published in the past 10 years).

RECENT FINDINGS

Twin studies have produced some evidence for a shared genetic aetiology underlying body mass index and eating behaviours. Studies using measured genetic susceptibility to obesity have suggested that increased genetic liability for obesity is associated with variation in obesogenic eating behaviours such as emotional and uncontrolled eating. More research on this topic is needed. Especially longitudinal studies using genetically sensitive designs to investigate the direction of genetic pathways between genetic liability of eating behaviours to weight and vice versa, as well as the potential subsequent link to eating disorders.

Volitional eye movement control and ADHD traits: a twin study

BACKGROUND

Top-down volitional command of eye movements may serve as a candidate endophenotype of ADHD, an important function underlying goal-directed action in everyday life. In this twin study, we examined the relation between performance on a response inhibition eye-tracking paradigm and parent-rated ADHD traits in a population-based twin sample. We hypothesized that altered eye movement control is associated with the severity of ADHD traits and that this association is attributable to genetic factors.

METHODS

A total of 640 twins (320 pairs, 50% monozygotic) aged 9-14 years) from the Child and Adolescent Twin Study in Sweden (CATSS) participated. Twins performed the antisaccade task indexing inhibitory alterations as either direction errors (following exogenous cues rather than instructions) or premature anticipatory eye movements (failure to wait for cues). We calculated the associations of eye movement control and ADHD traits using linear regression mixed-effects models and genetic and environmental influences with multivariate twin models.

RESULTS

Premature anticipatory eye movements were positively associated with inattentive traits (β = .17; 95% CI: 0.04, 0.31), while controlling for hyperactive behaviors and other covariates. Both premature anticipatory eye movements and inattention were heritable (h²  = 0.40, 95% CI: 0.22, 0.56; h²  = 0.55; 95% CI: 0.42, 0.65; respectively), and their genetic correlation was small but statistically significant (r = .19, 95% CI: 0.02, 0.36). However, the genetic correlation did not remain significant after adjusting for covariates (age, sex, hyperactivity traits, IQ). No link was found between direction errors and ADHD traits.

CONCLUSIONS

This study indicates that there is a specific, genetically influenced, relation between top-down eye movement control and the inattentive traits typical of ADHD.

A Mendelian randomization study identified obesity as a causal risk factor of uterine endometrial cancer in Japanese

Causal inference is one of the challenges in epidemiologic studies. Gynecologic diseases have been reported to have association with obesity, however the causality remained controversial except for uterine endometrial cancer. We conducted two-sample Mendelian randomization (MR) analysis using the large-scale genome-wide association study (GWAS) results of gynecologic diseases and body mass index (BMI) in the Japanese population to assess causal effect of BMI on gynecologic diseases. We first conducted GWAS of ovarian cancer, uterine endometrial cancer, uterine cervical cancer, endometriosis, and uterine fibroid (n = 647, 909, 538, 5236, and 645 cases, respectively, and 39 556 shared female controls), and BMI (81 610 males and non-overlapping 23 924 females). We then applied two-sample MR using 74 BMI-associated variants as instrumental variables. We observed significant causal effect of increased BMI on uterine endometrial cancer (β = 0.735, P = .0010 in inverse variance-weighted analysis), which is concordant with results of European studies. Causal effect of obesity was not apparent in the other gynecologic diseases tested. Our MR analyses provided strong evidence of the causal role of obesity in gynecologic diseases etiology, and suggested a possible preventive effect of intervention for obesity.

Genome-wide association study of self-reported walking pace suggests beneficial effects of brisk walking on health and survival

Walking is a simple form of exercise, widely promoted for its health benefits. Self-reported walking pace has been associated with a range of cardiorespiratory and cancer outcomes, and is a strong predictor of mortality. Here we perform a genome-wide association study of self-reported walking pace in 450,967 European ancestry UK Biobank participants. We identify 70 independent associated loci (P < 5 × 10^-8), 11 of which are novel. We estimate the SNP-based heritability as 13.2% (s.e. = 0.21%), reducing to 8.9% (s.e. = 0.17%) with adjustment for body mass index. Significant genetic correlations are observed with cardiometabolic, respiratory and psychiatric traits, educational attainment and all-cause mortality. Mendelian randomization analyses suggest a potential causal link of increasing walking pace with a lower cardiometabolic risk profile. Given its low heritability and simple measurement, these findings suggest that self-reported walking pace is a pragmatic target for interventions aiming for general benefits on health.

A longitudinal twin study of victimization and loneliness from childhood to young adulthood

The present study used a longitudinal and discordant twin design to explore in depth the developmental associations between victimization and loneliness from mid-childhood to young adulthood. The data were drawn from the Environmental Risk (E-Risk) Longitudinal Twin Study, a birth cohort of 2,232 individuals born in England and Wales during 1994-1995. Diverse forms of victimization were considered, differing across context, perpetrator, and timing of exposure. The results indicated that exposure to different forms of victimization was associated with loneliness in a dose-response manner. In childhood, bullying victimization was uniquely associated with loneliness, over and above concurrent psychopathology, social isolation, and genetic risk. Moreover, childhood bullying victimization continued to predict loneliness in young adulthood, even in the absence of ongoing victimization. Within-twin pair analyses further indicated that this longitudinal association was explained by genetic confounds. In adolescence, varied forms of victimization were correlated with young adult loneliness, with maltreatment, neglect, and cybervictimization remaining robust to controls for genetic confounds. These findings indicate that vulnerability to loneliness in victimized young people varies according to the specific form of victimization in question, and also to the developmental period in which it was experienced.

Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

Mendelian randomization while jointly modeling cis genetics identifies causal relationships between gene expression and lipids

Inference of causality between gene expression and complex traits using Mendelian randomization (MR) is confounded by pleiotropy and linkage disequilibrium (LD) of gene-expression quantitative trait loci (eQTL). Here, we propose an MR method, MR-link, that accounts for unobserved pleiotropy and LD by leveraging information from individual-level data, even when only one eQTL variant is present. In simulations, MR-link shows false-positive rates close to expectation (median 0.05) and high power (up to 0.89), outperforming all other tested MR methods and coloc. Application of MR-link to low-density lipoprotein cholesterol (LDL-C) measurements in 12,449 individuals with expression and protein QTL summary statistics from blood and liver identifies 25 genes causally linked to LDL-C. These include the known SORT1 and ApoE genes as well as PVRL2, located in the APOE locus, for which a causal role in liver was not known. Our results showcase the strength of MR-link for transcriptome-wide causal inferences.

Mendelian randomization is a useful tool to infer causal relationships between traits, but can be confounded by the presence of pleiotropy. Here, the authors have developed MR-link, a Mendelian randomization method which accounts for unobserved pleiotropy and linkage disequilibrium between instrumental variables.

RIPK1 gene variants associate with obesity in humans and can be therapeutically silenced to reduce obesity in mice

Obesity is a major public health burden worldwide and is characterized by chronic low-grade inflammation driven by the cooperation of the innate immune system and dysregulated metabolism in adipose tissue and other metabolic organs. Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is a central regulator of inflammatory cell function that coordinates inflammation, apoptosis and necroptosis in response to inflammatory stimuli. Here we show that genetic polymorphisms near the human RIPK1 locus associate with increased RIPK1 gene expression and obesity. We show that one of these single nucleotide polymorphisms is within a binding site for E4BP4 and increases RIPK1 promoter activity and RIPK1 gene expression in adipose tissue. Therapeutic silencing of RIPK1 in vivo in a mouse model of diet-induced obesity dramatically reduces fat mass, total body weight and improves insulin sensitivity, while simultaneously reducing macrophage and promoting invariant natural killer T cell accumulation in adipose tissue. These findings demonstrate that RIPK1 is genetically associated with obesity, and reducing RIPK1 expression is a potential therapeutic approach to target obesity and related diseases.

Genetic nurturing, missing heritability, and causal analysis in genetic statistics

Correlation between genotypes and phenotypes can be produced by genetic nurturing, namely the effect of parents’ genotypes on their offspring’s phenotypes through the parents’ phenotypes. Population subdivision and assortative mating can give rise to correlations between genotypes and phenotypes similar to those due to genetic nurturing. Variances and correlations may not reveal causal relationships in the presence of these complexities. We analyze mechanistic models of genetic nurturing, population subdivision, and assortative mating and compare these with results obtained within the framework of modern causal analysis. Our results clarify statistical signals emanating from correlations between nontransmitted alleles and offspring phenotypes and reveal difficulties with standard linear models in the interpretation of heritability, in particular, the concept of missing heritability.

Genetic nurturing, the effect of parents’ genotypes on offspring phenotypes through parental phenotypic transmission, can be modeled in terms of gene–culture interactions. This paper first uses a simple one-locus, two-phenotype gene–culture cotransmission model to compute the effect of genetic nurturing in terms of regression of children’s phenotypes on transmitted and nontransmitted alleles. With genetic nurturing, interpreting heritability and hence the meaning of “missing heritability” becomes problematic. Other factors, for example, population subdivision and assortative mating, generate similar signals to those of genetic nurturing, namely, correlation between parents’ nontransmitted alleles and children’s phenotypes. Corrections must be made for these to isolate the signal of genetic nurturing. Finally, a unified causal framework is constructed for genetic nurturing, population subdivision, and assortative mating. Causal and noncausal paths from transmitted and nontransmitted alleles to children’s phenotypes are identified and investigated in the presence of genetic nurturing, population subdivision, and assortative mating. Using causal analysis, assumptions made in inferring direct and indirect effects are then clarified and evaluated in a broader causal context.

Risk factors for disruptive behaviours: protocol for a systematic review and meta-analysis of quasi-experimental evidence

INTRODUCTION

Disruptive behaviour disorders, including oppositional defiant disorder and conduct disorder, are a common set of diagnoses in childhood and adolescence, with global estimates of 5.7%, 3.6% and 2.1% for any disruptive disorder, oppositional defiant disorder and conduct disorder, respectively. There are high economic and social costs associated with disruptive behaviours and the prevalence of these disorders has increased in recent years. As such, disruptive behaviours represent an escalating major public health concern and it is important to understand what factors may influence the risk of these behaviours. Such research would inform interventions that aim to prevent the development of disruptive behaviours. The current review will identify the most stringent evidence of putative risk factors for disruptive behaviour from quasi-experimental studies, which enable stronger causal inference.

METHODS AND ANALYSIS

The review will be carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. An electronic search of references published between 1 January 1980 and 1 March 2020 will be conducted using Medline, Embase, PsycINFO and Web of Science. Initial abstract and title screening, full-text screening and data extraction will be completed independently by two reviewers using Evidence for Policy and Practice Information (EPPI)-Reviewer 4 software. Quasi-experimental studies in the English language examining the association between any putative risk factor and a clearly defined measure of disruptive behaviour (eg, a validated questionnaire measure) will be included. We will conduct meta-analyses if we can pool a minimum of three similar studies with the same or similar exposures and outcomes.

ETHICS AND DISSEMINATION

The proposed review does not require ethical approval. The results will help to identify risk factors for which there is strong evidence of causal effects on disruptive behaviours and also highlight potential risk factors that require further research. The findings will be disseminated via publication in a peer-reviewed scientific journal and through presentations at international meetings and conferences.

PROSPERO REGISTRATION NUMBER

CRD42020169313.

Identifying Psychological Pathways to Polyvictimization: Evidence from a Longitudinal Cohort Study of Twins from the United Kingdom

OBJECTIVES

Examine the extent to which cognitive/psychological characteristics predict later polyvictimization. We employ a twin-based design that allows us to test the social neurocriminology hypothesis that environmental factors influence brain-based characteristics and influence behaviors like victimization.

METHODS

Using data from the Environmental Risk Longitudinal Twin Study (N = 1986), we capitalize on the natural experiment embedded in a discordant-twin design that allows for the adjustment of family environments and genetic factors.

RESULTS

The findings indicate that self-control, as well as symptoms of conduct disorder and anxiety, are related to polyvictimization even after adjusting for family environments and partially adjusting for genetic influences. After fully adjusting for genetic factors, only self-control was a statistically significant predictor of polyvictimization.

CONCLUSION

The findings suggest polyvictimization is influenced by cognitive/psychological characteristics that individuals carry with them across contexts. Policies aimed at reducing victimization risks should consider interventions that address cognitive functioning and mental health.

Mechanisms linking parental educational attainment with child ADHD, depression, and academic problems: a study of extended families in The Norwegian Mother, Father and Child Cohort Study

BACKGROUND

Low educational attainment in parents is associated with child psychopathology. It is not clear whether the associations are due to risk factors that family members share or due to effects of maternal or paternal education on the offspring. We investigate whether associations between maternal and paternal educational attainment and child symptoms of attention deficit/hyperactivity disorder (ADHD), depression, and academic problems are due to shared genetic factors, shared family environmental factors, or effects of the parental phenotype educational attainment itself.

METHODS

This study is based on the Norwegian Mother, Father and Child Cohort Study (MoBa). The sample comprised 34,958 children (17,128 girls) in 28,372 extended-family units. We used data from related nuclear families linked by siblings in the parent generation. We applied a quasi-experimental extended children-of-twins design that included siblings in both generations and took account of nonrandom mating by including partners. Educational attainment was self-reported by mothers and fathers. Mothers reported children's symptoms of ADHD, symptoms of depression, and academic problems by questionnaire when the children were 8 years old.

RESULTS

Children of lowly educated parents scored higher on all outcomes and had an approximate doubling of the risk of high symptom levels. The association between maternal and paternal educational attainment and child symptoms of ADHD and academic problems persisted after controlling for shared genetic and family environmental factors. Phenotypic transmission to depression was weaker and in the best fitting model fully explained by genetic factors shared by the two generations.

CONCLUSIONS

Associations between educational attainment of mothers and fathers and child symptoms of ADHD and academic problems could not be ascribed to shared familial risk factors, whereas associations with symptoms of depression could. Parental education or resources and behaviors resulting from low education might be targets of interventions aimed at reducing symptoms of ADHD and academic problems.

Tutorial: a guide to performing polygenic risk score analyses

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.