Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations

The potential and translational application of infant genetic research

In the current genomic revolution, the infancy life stage is the most neglected. Although clinical genetics recognizes the value of early identification in infancy of rare genetic causes of disorders and delay, common genetic variation is almost completely ignored in research on infant behavioral and neurodevelopmental traits. In this Perspective, we argue for a much-needed surge in research on common genetic variation influencing infant neurodevelopment and behavior, findings that would be relevant for all children. We now see convincing evidence from different research designs to suggest that developmental milestones, skills and behaviors of infants are heritable and thus are suitable candidates for gene-discovery research. We highlight the resources available to the field, including genotyped infant cohorts, and we outline, with recommendations, special considerations needed for infant data. Therefore, infant genetic research has the potential to impact basic science and to affect educational policy, public health and clinical practice.

A mediation analysis framework based on variance component to remove genetic confounding effect

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Independent Prediction of Child Psychiatric Symptoms by Maternal Mental Health and Child Polygenic Risk Scores

OBJECTIVE

Prenatal maternal symptoms of depression and anxiety are associated with an increased risk for child socioemotional and behavioral difficulties, supporting the fetal origins of mental health hypothesis. However, to date, studies have not considered specific genomic risk as a possible confound.

METHOD

The Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 5,546) was used to test if child polygenic risk score for attention-deficit/hyperactivity disorder (ADHD), schizophrenia, or depression confounds or modifies the impact of prenatal maternal depression and anxiety on child internalizing, externalizing, and total emotional/behavioral symptoms from age 4 to 16 years. Longitudinal child and adolescent symptom data were analyzed in the ALSPAC cohort using generalized estimating equations. Replication analyses were done in an independent cohort (Prevention of Preeclampsia and Intrauterine Growth Restriction [PREDO] cohort; n = 514) from Finland, which provided complementary measures of maternal mental health and child psychiatric symptoms.

RESULTS

Maternal depression and anxiety and child polygenic risk scores independently and additively predicted behavioral and emotional symptoms from childhood through mid-adolescence. There was a robust prediction of child and adolescent symptoms from both prenatal maternal depression (generalized estimating equation estimate = 0.093, 95% CI 0.065-0.121, p = 2.66 × 10-10) and anxiety (generalized estimating equation estimate = 0.065, 95% CI 0.037-0.093, p = 1.62 × 10-5) after adjusting for child genomic risk for mental disorders. There was a similar independent effect of maternal depression (B = 0.156, 95% CI 0.066-0.246, p = .001) on child symptoms in the PREDO cohort. Genetically informed sensitivity analyses suggest that shared genetic risk only partially explains the reported association between prenatal maternal depression and offspring mental health.

CONCLUSION

These findings highlight the genomic contribution to the fetal origins of mental health hypothesis and further evidence that prenatal maternal depression and anxiety are robust in utero risks for child and adolescent psychiatric symptoms.

PLAIN LANGUAGE SUMMARY

Depression and anxiety affect approximately 15% of pregnant women, and children exposed to maternal depression or anxiety during pregnancy are at higher risk of developing mental health problems. However, the degree to which shared genetics explains the association between maternal and child mental health is unknown. In this study the authors generated polygenic risk scores (PRS), which provide a single measure of genetic risk for complex traits, to investigate the impact of shared genetic risk on the development of childhood mental health problems. Utilizing two longitudinal studies (n = 6,060), the authors found that PRS only partially explained the association between prenatal maternal depression and childhood mental health problems. These analyses show prenatal maternal depression remained a significant predictor of childhood mental health problems after accounting for shared genetic risk, further highlighting that prenatal maternal mental health is a robust predictor of child and adolescent mental health problems.

Examining intergenerational risk factors for conduct problems using polygenic scores in the Norwegian Mother, Father and Child Cohort Study

The aetiology of conduct problems involves a combination of genetic and environmental factors, many of which are inherently linked to parental characteristics given parents' central role in children's lives across development. It is important to disentangle to what extent links between parental heritable characteristics and children's behaviour are due to transmission of genetic risk or due to parental indirect genetic influences via the environment (i.e., genetic nurture). We used 31,290 genotyped mother-father-child trios from the Norwegian Mother, Father and Child Cohort Study (MoBa), testing genetic transmission and genetic nurture effects on conduct problems using 13 polygenic scores (PGS) spanning psychiatric conditions, substance use, education-related factors, and other risk factors. Maternal or self-reports of conduct problems at ages 8 and 14 years were available for up to 15,477 children. We found significant genetic transmission effects on conduct problems for 12 out of 13 PGS at age 8 years (strongest association: PGS for smoking, β = 0.07, 95% confidence interval = [0.05, 0.08]) and for 4 out of 13 PGS at age 14 years (strongest association: PGS for externalising problems, β = 0.08, 95% confidence interval = [0.05, 0.11]). Conversely, we did not find genetic nurture effects for conduct problems using our selection of PGS. Our findings provide evidence for genetic transmission in the association between parental characteristics and child conduct problems. Our results may also indicate that genetic nurture via traits indexed by our polygenic scores is of limited aetiological importance for conduct problems-though effects of small magnitude or effects via parental traits not captured by the included PGS remain a possibility.

Controlling for polygenic genetic confounding in epidemiologic association studies

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically-unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

Molecular Mechanisms of Glaucoma Pathogenesis with Implications to Caveolin Adaptor Protein and Caveolin-Shp2 Axis

Glaucoma is a common retinal disorder characterized by progressive optic nerve damage, resulting in visual impairment and potential blindness. Elevated intraocular pressure (IOP) is a major risk factor, but some patients still experience disease progression despite IOP-lowering treatments. Genome-wide association studies have linked variations in the Caveolin1/2 (CAV-1/2) gene loci to glaucoma risk. Cav-1, a key protein in caveolae membrane invaginations, is involved in signaling pathways and its absence impairs retinal function. Recent research suggests that Cav-1 is implicated in modulating the BDNF/TrkB signaling pathway in retinal ganglion cells, which plays a critical role in retinal ganglion cell (RGC) health and protection against apoptosis. Understanding the interplay between these proteins could shed light on glaucoma pathogenesis and provide potential therapeutic targets.

Disentangling the interplay between genes, cognitive skills, and educational level in adolescent and young adult smoking - The TRAILS study

Recent studies suggest that smoking and lower educational attainment may have genetic influences in common. However, little is known about the mechanisms through which genetics contributes to educational inequalities in adolescent and young adult smoking. Common genetic liabilities may underlie cognitive skills associated with both smoking and education, such as IQ and effortful control, in line with indirect health-related selection explanations. Additionally, by affecting cognitive skills, genes may predict educational trajectories and hereby adolescents' social context, which may be associated with smoking, consistent with social causation explanations. Using data from the Dutch TRAILS Study (N = 1581), we estimated the extent to which polygenic scores (PGSs) for ever smoking regularly (PGSSMOK) and years of education (PGSEDU) predict IQ and effortful control, measured around age 11, and whether these cognitive skills then act as shared predictors of smoking and educational level around age 16, 19, 22, and 26. Second, we assessed if educational level mediated associations between PGSs and smoking. Both PGSs were associated with lower effortful control, and PGSEDU also with lower IQ. Lower IQ and effortful control, in turn, predicted having a lower educational level. However, neither of these cognitive skills were directly associated with smoking behaviour after controlling for covariates and PGSs. This suggests that IQ and effortful control are not shared predictors of smoking and education (i.e., no indirect health-related selection related to cognitive skills). Instead, PGSSMOK and PGSEDU, partly through their associations with lower cognitive skills, predicted selection into a lower educational track, which in turn was associated with more smoking, in line with social causation explanations. Our findings suggest that educational differences in the social context contribute to associations between genetic liabilities and educational inequalities in smoking.

Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children

Importance

Children's exposure to screen time has been associated with poor mental health outcomes, yet the role of genetic factors remains largely unknown.

Objective

To assess the extent of genetic confounding in the associations between screen time and attention problems or internalizing problems in preadolescent children.

Design, Setting, and Participants

This cohort study analyzed data obtained between 2016 and 2019 from the Adolescent Brain Cognitive Development Study at 21 sites in the US. The sample included children aged 9 to 11 years of genetically assigned European ancestry with self-reported screen time. Data were analyzed between November 2021 and September 2023.

Exposure

Child-reported daily screen time (in hours) was ascertained from questionnaires completed by the children at baseline.

Main Outcomes and Measures

Child psychiatric problems, specifically attention and internalizing problems, were measured with the parent-completed Achenbach Child Behavior Checklist at the 1-year follow-up. Genetic sensitivity analyses model (Gsens) was used, which incorporated polygenic risk scores (PRSs) of both exposure and outcomes as well as either single-nucleotide variant (SNV; formerly single-nucleotide polymorphism)-based heritability or twin-based heritability to estimate genetic confounding.

Results

The 4262 children in the sample included 2269 males (53.2%) with a mean (SD) age of 9.9 (0.6) years. Child screen time was associated with attention problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD) and internalizing problems (β = 0.03 SD; 95% CI, 0.003-0.06 SD). The television time PRS was associated with child screen time (β = 0.18 SD; 95% CI, 0.14-0.23 SD), the attention-deficit/hyperactivity disorder PRS was associated with attention problems (β = 0.13 SD; 95% CI, 0.10-0.16 SD), and the depression PRS was associated with internalizing problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD). These PRSs were associated with cross-traits, suggesting genetic confounding. Estimates using PRSs and SNV-based heritability showed that genetic confounding accounted for most of the association between child screen time and attention problems and for 42.7% of the association between child screen time and internalizing problems. When PRSs and twin-based heritability estimates were used, genetic confounding fully explained both associations.

Conclusions and Relevance

Results of this study suggest that genetic confounding may explain a substantial part of the associations between child screen time and psychiatric problems. Genetic confounding should be considered in sociobehavioral studies of modifiable factors for youth mental health.

Genetic contributions of noncognitive skills to academic development

Noncognitive skills such as motivation and self-regulation, are partly heritable and predict academic achievement beyond cognitive skills. However, how the relationship between noncognitive skills and academic achievement changes over development is unclear. The current study examined how cognitive and noncognitive skills contribute to academic achievement from ages 7 to 16 in a sample of over 10,000 children from England and Wales. Noncognitive skills were increasingly predictive of academic achievement across development. Twin and polygenic scores analyses found that the contribution of noncognitive genetics to academic achievement became stronger over the school years. Results from within-family analyses indicated that associations with noncognitive genetics could not simply be attributed to confounding by environmental differences between nuclear families and are consistent with a possible role for evocative/active gene-environment correlations. By studying genetic effects through a developmental lens, we provide novel insights into the role of noncognitive skills in academic development.

Cross-National and Cross-Generational Evidence That Educational Attainment May Slow the Pace of Aging in European-Descent Individuals

OBJECTIVES

Individuals with more education are at lower risk of developing multiple, different age-related diseases than their less-educated peers. A reason for this might be that individuals with more education age slower. There are 2 complications in testing this hypothesis. First, there exists no definitive measure of biological aging. Second, shared genetic factors contribute toward both lower educational attainment and the development of age-related diseases. Here, we tested whether the protective effect of educational attainment was associated with the pace of aging after accounting for genetic factors.

METHODS

We examined data from 5 studies together totaling almost 17,000 individuals with European ancestry born in different countries during different historical periods, ranging in age from 16 to 98 years old. To assess the pace of aging, we used DunedinPACE, a DNA methylation algorithm that reflects an individual's rate of aging and predicts age-related decline and Alzheimer's disease and related disorders. To assess genetic factors related to education, we created a polygenic score based on the results of a genome-wide association study of educational attainment.

RESULTS

Across the 5 studies, and across the life span, higher educational attainment was associated with a slower pace of aging even after accounting for genetic factors (meta-analysis effect size = -0.20; 95% confidence interval [CI]: -0.30 to -0.10; p = .006). Further, this effect persisted after taking into account tobacco smoking (meta-analysis effect size = -0.13; 95% CI: -0.21 to -0.05; p = .01).

DISCUSSION

These results indicate that higher levels of education have positive effects on the pace of aging, and that the benefits can be realized irrespective of individuals' genetics.

Maternal postnatal depressive symptoms and offspring emotional and behavioral development at age 7 years in a U.K. birth cohort: The role of paternal involvement

There is considerable variability in developmental outcomes of children whose mothers experience depression. Few longitudinal studies have examined contributions of paternal involvement in the association between maternal postnatal depression (PND) and offspring development. We examined pathways from maternal PND at 8 weeks (Edinburgh Postnatal Depression Scale; total score) to offspring emotional and behavioral development at 7 years (Strengths and Difficulties Questionnaire; total score) through behavioral, affective, and cognitive dimensions of paternal involvement in a U.K.-based birth cohort (Avon Longitudinal Study of Parents and Children; n = 3,434). Analyses were adjusted for baseline confounders and paternal PND (Edinburgh Postnatal Depression Scale; total score) as an intermediate confounder. Maternal PND was strongly associated with offspring development, but this association was not mediated by the combination of all indirect pathways through various dimensions of paternal involvement. Only father-child conflict emerged as a risk factor for adverse offspring development and as a mediator in the association between maternal PND and offspring development (albeit the effect size was small). If found causal, interventions that reduce father-child conflict may reduce the risk of adverse development in offspring of mothers with PND. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A genetically informed Registered Report on adverse childhood experiences and mental health

Children who experience adversities have an elevated risk of mental health problems. However, the extent to which adverse childhood experiences (ACEs) cause mental health problems remains unclear, as previous associations may partly reflect genetic confounding. In this Registered Report, we used DNA from 11,407 children from the United Kingdom and the United States to investigate gene-environment correlations and genetic confounding of the associations between ACEs and mental health. Regarding gene-environment correlations, children with higher polygenic scores for mental health problems had a small increase in odds of ACEs. Regarding genetic confounding, elevated risk of mental health problems in children exposed to ACEs was at least partially due to pre-existing genetic risk. However, some ACEs (such as childhood maltreatment and parental mental illness) remained associated with mental health problems independent of genetic confounding. These findings suggest that interventions addressing heritable psychiatric vulnerabilities in children exposed to ACEs may help reduce their risk of mental health problems.

Gene-environment correlations and genetic confounding underlying the association between media use and mental health

The increase in online media use and mental health problems have prompted investigations into their association, although most literature is focussed on deleterious effects. We assessed the aetiology of media use and mental health associations (M age = 22.14, SD = 0.85) using twin (n = 4000 pairs) and polygenic score methods (n = 6000 unrelated individuals) in the Twins Early Development Study. Beyond the traditionally explored negative uses of online media (online victimisation and problematic internet use), we investigate general media uses such as posting online and watching videos and distinguish both positive (pro-social behaviour) and negative (anxiety, depression, peer and behaviour problems) mental health measures. Negative media use correlated with poor mental health (r = 0.11-0.32), but general media use correlated with prosocial behaviour (r = 0.20) and fewer behavioural problems (r =  - 0.24). Twin analyses showed that both general and negative media use were moderately heritable (ranging from 20 to 49%) and their associations with mental health were primarily due to genetic influences (44-88%). Genetic sensitivity analysis combining polygenic scores with heritability estimates also suggest genetic confounding. Results indicate research on the mental health impact of media use should adopt genetically informed designs to strengthen causal inference.

Interplay of ADHD Polygenic Liability With Birth-Related, Somatic, and Psychosocial Factors in ADHD: A Nationwide Study

OBJECTIVE

Attention deficit hyperactivity disorder (ADHD) is a multifactorial neurodevelopmental disorder, yet the interplay between ADHD polygenic risk scores (PRSs) and other risk factors remains relatively unexplored. The authors investigated associations, confounding, and interactions of ADHD PRS with birth-related, somatic, and psychosocial factors previously associated with ADHD.

METHODS

Participants included a random general population sample (N=21,578) and individuals diagnosed with ADHD (N=13,697) from the genotyped Danish iPSYCH2012 case cohort, born between 1981 and 2005. The authors derived ADHD PRSs and identified 24 factors previously associated with ADHD using national registers. Logistic regression was used to estimate associations of ADHD PRS with each risk factor in the general population. Cox models were used to evaluate confounding of risk factor associations with ADHD diagnosis by ADHD PRS and parental psychiatric history, and interactions between ADHD PRS and each risk factor.

RESULTS

ADHD PRS was associated with 12 of 24 risk factors (odds ratio range, 1.03-1.30), namely, small gestational age, infections, traumatic brain injury, and most psychosocial risk factors. Nineteen risk factors were associated with ADHD diagnosis (odds ratio range, 1.20-3.68), and adjusting for ADHD PRS and parental psychiatric history led to only minor attenuations. Only the interaction between ADHD PRS and maternal autoimmune disease survived correction for multiple testing.

CONCLUSIONS

Higher ADHD PRS in the general population is associated with small increases in risk for certain birth-related and somatic ADHD risk factors, and broadly to psychosocial adversity. Evidence of gene-environment interaction was limited, as was confounding by ADHD PRS and family psychiatric history on ADHD risk factor associations. This suggests that the majority of the investigated ADHD risk factors act largely independently of current ADHD PRS to increase risk of ADHD.

The estimation of environmental and genetic parental influences

Parents share half of their genes with their children, but they also share background social factors and actively help shape their child's environment - making it difficult to disentangle genetic and environmental causes of parent-offspring similarity. While adoption and extended twin family designs have been extremely useful for distinguishing genetic and nongenetic parental influences, these designs entail stringent assumptions about phenotypic similarity between relatives and require samples that are difficult to collect and therefore are typically small and not publicly shared. Here, we describe these traditional designs, as well as modern approaches that use large, publicly available genome-wide data sets to estimate parental effects. We focus in particular on an approach we recently developed, structural equation modeling (SEM)-polygenic score (PGS), that instantiates the logic of modern PGS-based methods within the flexible SEM framework used in traditional designs. Genetically informative designs such as SEM-PGS rely on different and, in some cases, less rigid assumptions than traditional approaches; thus, they allow researchers to capitalize on new data sources and answer questions that could not previously be investigated. We believe that SEM-PGS and similar approaches can lead to improved insight into how nature and nurture combine to create the incredible diversity underlying human behavior.

Research Review: How to interpret associations between polygenic scores, environmental risks, and phenotypes

BACKGROUND

Genetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways (e.g. via environmental mediation) and biases.

METHODS

Here, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases.

RESULTS

Structural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter-intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene-environment correlations; and (c) adjusting an exposure-outcome association for a polygenic score can increase rather than decrease bias.

CONCLUSIONS

Strikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations.

Mother's and children's ADHD genetic risk, household chaos and children's ADHD symptoms: A gene-environment correlation study

BACKGROUND

Chaotic home environments may contribute to children's attention-deficit hyperactivity disorder (ADHD) symptoms. However, ADHD genetic risk may also influence household chaos. This study investigated whether children in chaotic households had more ADHD symptoms, if mothers and children with higher ADHD genetic risk lived in more chaotic households, and the joint association of genetic risk and household chaos on the longitudinal course of ADHD symptoms across childhood.

METHODS

Participants were mothers and children from the Environmental Risk (E-Risk) Longitudinal Twin Study, a UK population-representative birth cohort of 2,232 twins. Children's ADHD symptoms were assessed at ages 5, 7, 10 and 12 years. Household chaos was rated by research workers at ages 7, 10 and 12, and by mother's and twin's self-report at age 12. Genome-wide ADHD polygenic risk scores (PRS) were calculated for mothers (n = 880) and twins (n = 1,999); of these, n = 871 mothers and n = 1,925 children had information on children's ADHD and household chaos.

RESULTS

Children in more chaotic households had higher ADHD symptoms. Mothers and children with higher ADHD PRS lived in more chaotic households. Children's ADHD PRS was associated with household chaos over and above mother's PRS, suggesting evocative gene-environment correlation. Children in more chaotic households had higher baseline ADHD symptoms and a slower rate of decline in symptoms. However, sensitivity analyses estimated that gene-environment correlation accounted for a large proportion of the association of household chaos on ADHD symptoms.

CONCLUSIONS

Children's ADHD genetic risk was independently associated with higher levels of household chaos, emphasising the active role of children in shaping their home environment. Our findings suggest that household chaos partly reflects children's genetic risk for ADHD, calling into question whether household chaos directly influences children's core ADHD symptoms. Our findings highlight the importance of considering parent and child genetic risk in relation to apparent environmental exposures.

Associations Between Pregnancy-Related Predisposing Factors for Offspring Neurodevelopmental Conditions and Parental Genetic Liability to Attention-Deficit/Hyperactivity Disorder, Autism, and Schizophrenia: The Norwegian Mother, Father and Child Cohort Study (MoBa)

Importance

Several maternal exposures during pregnancy are considered predisposing factors for offspring neurodevelopmental conditions. However, many of these exposures may be noncausal and biased by maternal genetic liability.

Objective

To assess whether pregnancy-related predisposing factors for offspring neurodevelopmental conditions are associated with maternal genetic liability for attention-deficit/hyperactivity disorder (ADHD), autism, and schizophrenia and to compare associations for maternal genetic liability with those for paternal genetic liability, which could indicate that paternal exposures are not suitable negative controls for maternal exposures.

Design, Setting, and Participants

The Norwegian Mother, Father and Child Cohort Study (MoBa) is a population-based pregnancy cohort that recruited parents from June 1999 to December 2008. Polygenic scores (PGS) for ADHD, autism, and schizophrenia were derived in mothers and fathers. The associations between maternal PGS and 37 pregnancy-related measures were estimated, and these results were compared with those from paternal PGS predicting paternal measures during the mother's pregnancy. Analysis took place between March 2021 and March 2022.

Exposures

PGS for ADHD, autism, and schizophrenia, calculated (using discovery effect size estimates and threshold of P < .05) from the largest available genome-wide association studies.

Main Outcomes and Measures

Self-reported pregnancy-related measures capturing lifestyle behaviors, metabolism, infectious and autoimmune diseases, other physical health conditions, and medication use.

Results

Data were available for up to 14 539 mothers (mean [SD] age, 30.00 [4.45] years) and 14 897 fathers (mean [SD] age, 32.46 [5.13] years) of European ancestry. Modest but robust associations were observed between specific pregnancy-related measures and maternal PGS, including ADHD PGS with asthma (odds ratio [OR], 1.15 [95% CI, 1.06-1.25]), smoking (OR, 1.26 [95% CI, 1.19-1.33]), prepregnancy body mass index (β, 0.25 [95% CI, 0.18-0.31]), pregnancy weight gain (β, 0.20 [95% CI, 0.10-0.30]), taking folate (OR, 0.92 [95% CI, 0.88-0.96]), and not taking supplements (OR, 1.09 [95% CI, 1.04-1.14]). Schizophrenia PGS was associated with coffee consumption (OR, 1.09 [95% CI, 1.05-1.12]), smoking (OR, 1.12 [95% CI, 1.06-1.19]), prepregnancy body mass index (β, -0.18 [95% CI, -0.25 to -0.11]), and pregnancy weight gain (β, 0.17 [95% CI, 0.07-0.27]). All 3 PGSs associated with symptoms of depression/anxiety (ADHD: OR, 1.15 [95% CI, 1.09-1.22]; autism: OR, 1.13 [95% CI, 1.06-1.19]; schizophrenia: OR, 1.13 [95% CI, 1.07-1.20]). Associations were largely consistent for maternal and paternal PGS, except ADHD PGS and smoking (fathers: OR, 1.13 [95% CI, 1.09-1.17]).

Conclusions and Relevance

In this study, genetic liability to neurodevelopmental conditions that is passed from mothers to children was associated with several pregnancy-related factors and may therefore confound associations between these pregnancy-related factors and offspring neurodevelopment that have previously been thought to be causal. It is crucial that future study designs account for genetic confounding to obtain valid causal inferences so that accurate advice can be given to pregnant individuals.

Polygenic and socioeconomic risk for high body mass index: 69 years of follow-up across life

Genetic influences on body mass index (BMI) appear to markedly differ across life, yet existing research is equivocal and limited by a paucity of life course data. We thus used a birth cohort study to investigate differences in association and explained variance in polygenic risk for high BMI across infancy to old age (2-69 years). A secondary aim was to investigate how the association between BMI and a key purported environmental determinant (childhood socioeconomic position) differed across life, and whether this operated independently and/or multiplicatively of genetic influences. Data were from up to 2677 participants in the MRC National Survey of Health and Development, with measured BMI at 12 timepoints from 2-69 years. We used multiple polygenic indices from GWAS of adult and childhood BMI, and investigated their associations with BMI at each age. For polygenic liability to higher adult BMI, the trajectories of effect size (β) and explained variance (R2) diverged: explained variance peaked in early adulthood and plateaued thereafter, while absolute effect sizes increased throughout adulthood. For polygenic liability to higher childhood BMI, explained variance was largest in adolescence and early adulthood; effect sizes were marginally smaller in absolute terms from adolescence to adulthood. All polygenic indices were related to higher variation in BMI; quantile regression analyses showed that effect sizes were sizably larger at the upper end of the BMI distribution. Socioeconomic and polygenic risk for higher BMI across life appear to operate additively; we found little evidence of interaction. Our findings highlight the likely independent influences of polygenic and socioeconomic factors on BMI across life. Despite sizable associations, the BMI variance explained by each plateaued or declined across adulthood while BMI variance itself increased. This is suggestive of the increasing importance of chance ('non-shared') environmental influences on BMI across life.

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.

Inequalities in body mass index, diet and physical activity in the UK: Longitudinal evidence across childhood and adolescence

We use longitudinal data across a key developmental period, spanning much of childhood and adolescence (age 5 to 17, years 2006-2018) from the UK Millennium Cohort Study, a nationally representative study with an initial sample of just over 19,000. We first examine the extent to which inequalities in overweight, obesity, BMI and body fat over this period are consistent with the evolution of inequalities in health behaviours, including exercise and healthy diet markers (i.e., skipping breakfast) (n = 7,220). We next study the links between SES, health behaviours and adiposity (BMI, body fat), using rich models that account for the influence of a range of unobserved factors that are fixed over time. In this way, we improve on existing estimates measuring the relationship between SES and health behaviours on the one hand and adiposity on the other. The advantage of the individual fixed effects models is that they exploit within-individual changes over time to help mitigate biases due to unobserved fixed characteristics (n = 6,883). We observe stark income inequalities in BMI and body fat in childhood (age 5), which have further widened by age 17. Inequalities in obesity, physical activity, and skipping breakfast are observed to widen from age 7 onwards. Ordinary Least Square estimates reveal the previously documented SES gradient in adiposity, which is reduced slightly once health behaviours including breakfast consumption and physical activity are accounted for. The main substantive change in estimates comes from the fixed effects specification. Here we observe mixed findings on the SES associations, with a positive association between income and adiposity and a negative association with wealth. The role of health behaviours is attenuated but they remain important, particularly for body fat.