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BackgroundThe associations between height, socioeconomic position (SEP) and coronary heart disease (CHD) incidence are well established, but the contribution of genetic factors to these associations is still poorly understood. We used a polygenic score (PGS) for height to shed light on these associations.MethodsFinnish population-based health surveys in 1992–2011 (response rates 65–93%) were linked to population registers providing information on SEP and CHD incidence up to 2019. The participants (N=29 996; 54% women) were aged 25–75 at baseline, and there were 1767 CHD incident cases (32% in women) during 472 973 person years of follow-up. PGS-height was calculated based on 33 938 single-nucleotide polymorphisms, and residual height was defined as the residual of height after adjusting for PGS-height in a linear regression model. HRs of CHD incidence were calculated using Cox regression.ResultsPGS-height and residual height showed clear gradients for education, social class and income, with a larger association for residual height. Residual height also showed larger associations with CHD incidence (HRs per 1 SD 0.94 in men and 0.87 in women) than PGS-height (HRs per 1 SD 0.99 and 0.97, respectively). Only a small proportion of the associations between SEP and CHD incidence was statistically explained by the height indicators (6% or less).ConclusionsResidual height associations with SEP and CHD incidence were larger than for PGS-height. This supports the role of material and social living conditions in childhood as contributing factors to the association of height with both SEP and CHD risk.

BackgroundFixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age, therefore meta-analysing GWAS of age-diverse samples could be misleading. Meta-regression allows adjustment for study specific characteristics and models heterogeneity between studies. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes.MethodsWith simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of theFTOSNP rs9939609 on Body Mass Index (BMI).ResultsFixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both the main genetic effects and the age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. This is similar to the association that has been previously reported by a study that used individual participant data.ConclusionsGWAS using summary statistics from age-diverse samples should consider using meta-regression to explore age-varying genetic effects.KEY MESSAGESMeta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect for all studies. However, genetic effects may vary with age, therefore meta-analysing GWAS of age-diverse samples could produce misleading results.Meta-regression could be used to relate observed between-study heterogeneity to study characteristics such as age. Therefore, meta-regression could be used to combine summary level GWAS data to provide evidence for any age-varying genetic effects.This simulation study shows that when genetic effects vary with age, meta-regression provides unbiased estimates of main and age-varying genetic effects. The precision of the estimates depends on the number of studies included, and the diversity in age between them.The applied example using publicly available summary data, supported the simulation study.By applying meta-regression, we observed a previously reported age-varying association between each additional minor allele (A) of rs9939609 and BMI; an inverse at ages 0 to 3 and a positive association at ages 5.5 to 13.Similar association has been previously reported by a study that used individual participant data.

The prevalence of autistic traits and Attention Deficit Hyperactivity Disorder (ADHD) traits is known to vary by location. The genetic and environmental aetiology of these traits may also vary spatially, with geographical environments amplifying or masking genetic influences. To investigate this, we constructed polygenic risk scores for autism and ADHD in participants from the Avon Longitudinal Study of Parents and Children (N=4,255 to 6,165). We estimated the association between polygenic risk and autistic and ADHD trait scores across the area surrounding the city of Bristol in the southwest of the United Kingdom. Mapping the results shows how associations between polygenic risk and trait scores varied by location. Our maps suggest that there is evidence of spatial variation in genetic associations for all traits. For social autistic traits and an autistic trait mean factor score, patterns of association were consistent among risk scores constructed at different p-value thresholds. The patterns for ADHD traits were more variable. We examined similarities between these maps and those of environmental variables associated with the prevalence of autism and ADHD, and found that in many cases the spatial distributions were correlated. This finding of spatial variation in genetic associations for autism and ADHD traits will help us better understand the factors that contribute to the complex interplay between the environment and genetic influence.

Background Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals. Methods Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals [linkage disequilibrium score regression, genomic relatedness restricted maximum-likelihood (GREML) estimation] and family-based genomic designs (sibling regression, GREML-kinship, trio-genome-wide complex trait analysis, maternal-genome-wide complex trait analysis, relatedness disequilibrium regression). Results We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals. Conclusions Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why.

Highlights • We found that school enjoyment at age 13-14 was strongly associated with academic attainment at age 16. • Children experiencing depressive or externalising symptoms at age 10-11 were less likely to enjoy school and feel connected to staff and teachers at age 13-14. • We found evidence that school enjoyment partially mediated the association between earlier depressive and externalising symptoms and academic attainment. This suggests that children's experience of school could be a potentially important modifiable risk factor for low attainment in children with mental health problems., Background Previous research on the relationship between children's depressive and externalising symptoms, experience of school, and academic attainment is inconclusive. The aims of this study were (i) to test bidirectional associations between children's school experience and depressive and externalising symptoms at age 10-11 and 13-14, (ii) to ascertain whether school experience age 13-14 is associated with academic attainment age 16, and (iii) to test whether school experience mediates the relationship between depressive or externalising symptoms and attainment. Methods Data was used from the Avon Longitudinal Study of Parents and Children (n=6,409). A cross-lagged model was used to investigate bidirectional associations between school experience (enjoyment and connectedness) and depression and externalising at age 10-11 and 13-14. The same framework was used to test if school experience aged 13-14 mediated associations of depressive and externalising symptoms with later attainment. Results Depressive and externalising symptoms at 10-11 were negatively associated with school connectedness (depressive: standardised β=-0.06, CI: -0.11, 0.01; externalizing: β=-0.13, CI: -0.17, -0.08), and school enjoyment at 13-14 (depressive β=-0.04, -0.08, 0.03; externalising: β=-0.08, CI: -0.13, -0.03). School enjoyment at 13-14 was positively associated with attainment at 16 (β=0.10, CI: 0.04, 0.15), and partially mediated associations between depressive and externalising symptoms at 10-11 and attainment at 16 (depressive: proportion mediated 2.2%, CI: -1.5, 5.9; externalising: proportion mediated; 4.7%, CI: 0.7, 10.1,). Limitations Results may be subject to residual confounding. Conclusions School enjoyment is a potentially modifiable risk factor that may affect educational attainment of adolescents with depressive or externalising symptoms.

Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage., Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this.

Research at the intersection of social science and genomics, ‘sociogenomics’, is transforming our understanding of the interplay between genomics, individual outcomes and society. It has interesting and maybe unexpected implications for education research and policy. Here we review the growing sociogenomics literature and discuss its implications for educational researchers and policymakers. We cover key concepts and methods in genomic research into educational outcomes, how genomic data can be used to investigate social or environmental effects, the methodological strengths and limitations of genomic data relative to other observational social data, the role of intergenerational transmission and potential policy implications. The increasing availability of genomic data in studies can produce a wealth of new evidence for education research. This may provide opportunities for disentangling the environmental and genomic factors that influence educational outcomes and identifying potential mechanisms for intervention.

Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies., Family-based study designs have been applied to resolve confounding by population stratification, dynastic effects and assortative mating in genetic association analyses. Here, Brumpton et al. describe theory and simulations for overcoming such biases in Mendelian randomization through within-family studies.

Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual’s lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual’s lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.

Background Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g. weight measured once between ages 40 and 60 years). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time. Methods We propose an approach that emphasizes the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example. Results We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the underlying liability for the exposure, scaled to the effect of the liability on the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the effect of the liability on exposure is constant over time), as we illustrate by estimating the effect of body mass index measured at different ages on systolic blood pressure. Conclusion Univariable MR results should not be interpreted as time-point-specific direct or total causal effects, but as the effect of changing the liability for the exposure. Estimates of how the effects of a genetic variant on an exposure vary over time, together with biological knowledge that provides evidence regarding likely effective exposure periods, are required to interpret time-point-specific causal effects.

Noncognitive skills have been shown to associate with a range of health and socioeconomic outcomes. Many studies have relied on cross sectional data and have been unable to assess the longitudinal consistency of noncognitive skill measures. Using data from a UK birth cohort, we investigated a range of noncognitive skills: behavioural problems, social skills, communication, self-esteem, persistence, locus of control, empathy, impulsivity and personality. We assessed their consistency over a 17-year period throughout childhood and adolescence (age 6 months to 18 years), their genomic architecture, and their associations with socioeconomic outcomes. We found high longitudinal measurement consistency for behavioural and communication skills, but low consistency for other noncognitive skills, suggesting a high noise to signal ratio. We observed consistent non-zero heritability estimates and genetic correlations for only behavioural difficulties. Using aggregate measures of each skill over time, we found evidence of phenotypic correlations and heritability ($$h_{SNP}^2$$ h S N P 2 = 0.1–0.2) for behaviour, communication, self-esteem and locus of control. Associations between noncognitive skills and educational outcomes were observed for skills measured in mid to late childhood but these were at most a third of the size of IQ-education associations. These results suggest that measures designed to capture noncognitive skills may be subject to considerable response heterogeneity or measurement error. Aggregate measures that leverage repeat responses from longitudinal data may offer researchers more reliable measures that better identify underlying noncognitive skills than cross sectional measures.

Background\ud \ud Adolescence marks a period where depression will commonly onset. Twin studies show that genetic influences play a role in how depression develops and changes across adolescence. Recent genome‐wide association studies highlight that common genetic variants – which can be combined into polygenic risk scores (PRS) – are also implicated in depression. However, the role of PRS in adolescent depression and changes in adolescent depression is not yet understood. We aimed to examine associations between PRS for five psychiatric traits and depressive symptoms measured across adolescence using cross‐sectional and growth‐curve models. The five PRS were as follows: depression (DEP), major depressive disorder (MDD), anxiety (ANX), neuroticism (NEU) and schizophrenia (SCZ).\ud Methods\ud \ud We used data from over 6,000 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC) to examine associations between the five PRS and self‐reported depressive symptoms (Short Mood and Feelings Questionnaire) over 9 occasions from 10 to 24 years. The PRS were created from well‐powered genome‐wide association studies conducted in adult populations. We examined cross‐sectional associations between the PRS at each age and then again with longitudinal trajectories of depressive symptoms in a repeated measures framework using multilevel growth‐curve analysis to examine the severity and the rate of change.\ud Results\ud \ud There was strong evidence that higher PRS for DEP, MDD and NEU were associated with worse depressive symptoms throughout adolescence and into young adulthood in our cross‐sectional analysis, with consistent associations observed across all nine occasions. Growth‐curve analyses provided stronger associations (as measured by effect sizes) and additional insights, demonstrating that individuals with higher PRS for DEP, MDD and NEU had steeper trajectories of depressive symptoms across development, all with a greater increasing rate of change during adolescence. Evidence was less consistent for the ANX and SCZ PRS in the cross‐sectional analysis, yet there was some evidence for an increasing rate of change in adolescence in the growth‐curve analyses with the ANX PRS.\ud Conclusions\ud \ud These results show that common genetic variants as indexed by varying psychiatric PRS show patterns of specificity that influence both the severity and rate of change in depressive symptoms throughout adolescence and then into young adulthood. Longitudinal data that make use of repeated measures designs have the potential to provide greater insights how genetic factors influence the onset and persistence of adolescent depression.

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BackgroundMendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g., weight measured once between ages 40 and 60). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time.MethodsWe propose an approach that emphasises the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example.ResultsWe show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the liability as induced by a specific genotype that gives rise to the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the liability of exposure is constant over time), as we demonstrate by estimating the effect of BMI measured at different ages on systolic blood pressure.ConclusionsPractitioners should not interpret MR results as timepoint-specific direct or total causal effects, but as the effect of changing the liability that causes the entire exposure trajectory. Estimates of how the effects of a genetic variant on an exposure vary over time are needed to interpret timepoint-specific causal effects.

BackgroundHigher BMI in childhood is associated with emotional and behavioural problems, but these associations may not be causal. Results of previous genetic studies imply causal effects but may reflect influence of demography and the family environment.MethodsThis study used data on 40,949 8-year-old children and their parents from the Norwegian Mother, Father and Child Cohort Study (MoBa) and Medical Birth Registry of Norway (MBRN). We investigated the impact of BMI on symptoms of depression, anxiety, and attention-deficit hyperactivity disorder (ADHD) at age 8. We applied within-family Mendelian randomization, which accounts for familial effects by controlling for parental genotype.ResultsWithin-family Mendelian randomization estimates using genetic variants associated with BMI in adults suggested that a child’s own BMI increased their depressive symptoms (per 5kg/m2increase in BMI, beta=0.26 S.D., CI=-0.01,0.52, p=0.06) and ADHD symptoms (beta= 0.38 S.D., CI=0.09,0.63, p=0.009). These estimates also suggested maternal BMI, or related factors, may independently affect a child’s depressive symptoms (per 5kg/m2increase in maternal BMI, beta=0.11 S.D., CI:0.02,0.09, p=0.01). However, within-family Mendelian randomization using genetic variants associated with retrospectively-reported childhood body size did not support an impact of BMI on these outcomes. There was little evidence from any estimate that the parents’ BMI affected the child’s ADHD symptoms, or that the child’s or parents’ BMI affected the child’s anxiety symptoms.ConclusionsWe found inconsistent evidence that a child’s BMI affected their depressive and ADHD symptoms, and little evidence that a child’s BMI affected their anxiety symptoms. There was limited evidence of an influence of parents’ BMI. Genetic studies in samples of unrelated individuals, or using genetic variants associated with adult BMI, may have overestimated the causal effects of a child’s own BMI.FundingThis research was funded by the Health Foundation. It is part of the HARVEST collaboration, supported by the Research Council of Norway. Individual co-author funding: the European Research Council, the South-Eastern Norway Regional Health Authority, the Research Council of Norway, Helse Vest, the Novo Nordisk Foundation, the University of Bergen, the South-Eastern Norway Regional Health Authority, the Trond Mohn Foundation, the Western Norway Regional Health Authority, the Norwegian Diabetes Association, the UK Medical Research Council. The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit.

Similarities between parents and offspring arise from nature and nurture. Beyond this simple dichotomy, recent genomic studies have uncovered "genetic nurture" effects, whereby parental genotypes influence offspring outcomes via environmental pathways rather than genetic transmission. Such genetic nurture effects also need to be accounted for to accurately estimate "direct" genetic effects (i.e., genetic effects on a trait originating in the offspring). Empirical studies have indicated that genetic nurture effects are particularly relevant to the intergenerational transmission of risk for child educational outcomes, which are, in turn, associated with major psychological and health milestones throughout the life course. These findings have yet to be systematically appraised across contexts. We conducted a systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes. A total of 12 studies comprising 38,654 distinct parent(s)-offspring pairs or trios from 8 cohorts reported 22 estimates of genetic nurture effects. Genetic nurture effects on offspring's educational outcomes (βgenetic nurture = 0.08, 95% CI [0.07, 0.09]) were smaller than direct genetic effects (βdirect genetic = 0.17, 95% CI [0.13, 0.20]). Findings were largely consistent across studies. Genetic nurture effects originating from mothers and fathers were of similar magnitude, highlighting the need for a greater inclusion of fathers in educational research. Genetic nurture effects were largely explained by observed parental education and socioeconomic status, pointing to their role in environmental pathways shaping child educational outcomes. Findings provide consistent evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes, in addition to effects due to genetic transmission.

Background Higher BMI in childhood predicts subsequent neurodevelopmental and emotional problems, but it is unclear if associations are causal. Observational studies are vulnerable to reverse causation and confounding. Mendelian randomization (MR) studies with unrelated individuals can also suffer from familial biases, such as dynastic effects (“genetic nurture”). Methods We apply within-family MR (WFMR) to overcome these biases. We used genetic information from 26,370 family trios in the Norwegian Mother, Father and Child Cohort Study (MoBa) to construct BMI polygenic scores in children and both parents. By using all three polygenic scores to instrument BMI, we avoided familial biases affecting previous studies. Results Multivariable-adjusted and conventional MR models implied an impact of children’s BMI on depressive, ADHD, and autism symptoms. In conventional MR models, a 5kg/m2 increase in BMI corresponded to depressive symptoms 0.49 SD higher (95%CI: 0.24-0.73), and ADHD symptoms 0.49 SD higher (95%CI: 0.28-0.70). WFMR estimates were less precise but gave little evidence of causal impacts of children’s BMI. Maternal BMI was positively associated with children’s depressive (0.16 SD per 5kg/m2, 95%CI: 0.04-0.28) and autism symptoms, and paternal BMI with children’s ADHD symptoms. Conclusions Compared to conventional MR models, MR models accounting for parental genotype found less evidence of causal effects of children’s own BMI on emotional and neurodevelopmental symptoms. The discrepancy may suggest an influence of family or population-level effects. Key messages The influence of children’s own BMI on emotional and neurodevelopmental problems may have been overstated. Parental BMI, familial or population level effects may influence these outcomes.

Background Non-random sampling could bias estimates of association in observational studies but the extent to which this occurs in studies of SARS-CoV-2 infection/COVID-19 severity is not well established. Methods Using ALSPAC and UK Biobank we investigated pre-pandemic predictors of selection (i.e. having data on SARS-CoV-2 infection and COVID-19 severity from self-report and/or health record linkage). We conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to selection when estimating the association of BMI with SARS-CoV-2 infection and COVID-19 severity. Results A broad range of characteristics related to selection in both cohorts, sometimes in opposite directions. We found bias in all simulated scenarios, mostly of small magnitude. Both the direction and magnitude of bias was influenced by the presence of an effect of BMI on SARS-CoV-2 infection and COVID-19 severity and the control group definition used (e.g. assuming no effect of BMI on SARS-CoV-2 infection our main simulation showed bias equivalent to an estimated odds ratio of 0.99 when using non-infected controls but 1.16 when using controls combining non-infected and non-assessed). Conclusions Despite small amounts of bias in most scenarios, a control group definition including those non-assessed (e.g. non-tested) can induce more bias. In large samples such as UK Biobank the statistical power means incorrect conclusions could be made. Key messages Observational studies estimating associations of factors with SARS-CoV-2 or COVID-19 may be biased due to non-random selection into the analytic sample.

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

• Nosocomial transmission of SARS-CoV-2 between healthcare workers is evident. • Ethnicity is associated with SARS-CoV-2 antibody seroprevalence. • Socioeconomic factors influence risk of infection with SARS-CoV-2. • SARS-CoV-2 screening strategies must adapt to local healthcare contexts. • Collider bias can affect the interpretation of observational cohort data.

Child educational development is associated with major psychological, social, economic and health milestones throughout the life course. Understanding the early origins of educational inequalities and their reproduction across generations is therefore crucial. Recent genomic studies provide novel insights in this regard, uncovering “genetic nurture” effects, whereby parental genotypes influence offspring’s educational development via environmental pathways rather than genetic transmission. These findings have yet to be systematically appraised. We conducted the first systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes and investigate key moderators. Twelve studies comprising 38,654 distinct parent(s)-offspring pairs or trios from eight cohorts were included, from which we derived 22 estimates of genetic nurture effects. Multilevel random effects models showed that the effect of genetic nurture on offspring’s educational outcomes (βgenetic nurture = 0.08, 95% CI [0.07, 0.09]) was about half the size of direct genetic effects (βdirect genetic = 0.17, 95% CI [0.13, 0.20]). Maternal and paternal genetic nurture effects were similar in magnitude, suggesting comparable roles of mothers and fathers in determining their children’s educational outcomes. Genetic nurture effects were largely explained by parental educational level and family socioeconomic status, suggesting that genetically influenced environments play an important role in shaping child educational outcomes. Even after accounting for genetic transmission, we provide evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes. Further exploring these downstream environmental pathways may inform educational policies aiming to break the intergenerational cycle of educational underachievement and foster social mobility.Public Significance StatementThis meta-analysis demonstrates that parents’ genetics influence their children’s educational outcomes through the rearing environments that parents provide. This “genetic nurture” effect is largely explained by family socioeconomic status and parental education level, is similar for mothers and fathers (suggesting that both parents equally shape their children’s educational outcomes) and is about half the size of direct genetic effects on children’s educational outcomes. Interventions targeting such environmental pathways could help to break the intergenerational cycle of educational underachievement and foster social mobility.

Pleural empyema represents a significant healthcare burden due to extended hospital admissions and potential requirement for surgical intervention. This study aimed to assess changes in incidence and management of pleural empyema in England over the past 10 years and the potential impact of influenza on rates. Hospital Episode Statistics data were used to identify patients admitted to English hospitals with pleural empyema between 2008 and 2018. Linear regression was used to analyse the relationship between empyema rates and influenza incidence recorded by Public Health England. The relationship between influenza and empyema was further explored using serological data from a prospective cohort study of patients presenting with pleural empyema. Between April 2008 and March 2018 there were 55 530 patients admitted with pleural empyema. There was male predominance (67% versus 33%), which increased with age. Cases have increased significantly from 4447 in 2008 to 7268 in 2017. Peaks of incidence correlated moderately with rates of laboratory-confirmed influenza in children and young adults (r=0.30). For nine of the 10 years studied, the highest annual point incidence of influenza coincided with the highest admission rate for empyema (with a 2-week lag). In a cohort study of patients presenting to a single UK hospital with pleural empyema/infection, 24% (17 out of 72) had serological evidence of recent influenza infection, compared to 7% in seasonally matched controls with simple parapneumonic or cardiogenic effusions (p, Rates of pleural empyema are increasing in English hospitals without any improvement in patient outcomes. This study also found that male predominance of empyema increased with age, and rates increased in winter and during peaks of influenza. https://bit.ly/3oiJ870

Structured summaryBackgroundHealthcare workers (HCWs) are at increased risk of infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There are limited data exploring the relative impact of geographical and socioeconomic factors on risk of SARS-CoV-2 infection among HCWs.AimTo estimate and explore SARS-CoV-2 IgG antibody seroprevalence in HCWs and support staff at a hospital in South West England.MethodsWe conducted a nested cross-sectional study from May-July 2020. Inverse probability weighted regression was used to estimate seroprevalence of SARS-CoV-2 and associations with demographic and socioeconomic risk factors that were robust to selection into testing.FindingsAttendance for testing varied by demographic and socioeconomic factors. The overall rate of SARS-CoV-2 IgG seroprevalence among tested staff was 9.3% (638/6858). The highest seroprevalence was found in wards associated with SARS-CoV-2 outbreaks and among junior staff with patient-facing roles. Black, Asian and Minority Ethnic (BAME) staff had increased odds of SARS-CoV-2 seroprevalence (adjusted OR: 1.99, 95%CI: 1.69, 2.34; pConclusionTransmission between staff groups is evident within the healthcare setting. BAME individuals were at increased risk of infection with SARS-CoV-2. Work role, area of residence, and neighbourhood deprivation all contribute to SARS-CoV-2 infection risk. As hospitals introduce routine staff SARS-CoV-2 testing they should consider differential uptake of testing among staff groups.

Population stratification has recently been demonstrated to bias genetic studies even in relatively homogeneous populations such as within the British Isles. A key component to correcting for stratification in genome-wide association studies (GWAS) is accurately identifying and controlling for the underlying structure present in the sample. Meta-analysis across cohorts is increasingly important for achieving very large sample sizes, but comes with the major disadvantage that each individual cohort corrects for different population stratification. Here we demonstrate that correcting for structure against an external reference adds significant value to meta-analysis. We treat the UK Biobank as a collection of smaller studies, each of which is geographically localised. We provide software to standardize an external dataset against a reference, provide the UK Biobank principal component loadings for this purpose, and demonstrate the value of this with an analysis of the geographically sampled ALSPAC cohort.

Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

In this paper, we review and demonstrate how population phenomena can inflate genotype-phenotype associations., Heritability, genetic correlation, and genetic associations estimated from samples of unrelated individuals are often perceived as confirmation that genotype causes the phenotype(s). However, these estimates can arise from indirect mechanisms due to population phenomena including population stratification, dynastic effects, and assortative mating. We introduce these, describe how they can bias or inflate genotype-phenotype associations, and demonstrate methods that can be used to assess their presence. Using data on educational achievement and parental socioeconomic position as an exemplar, we demonstrate that both heritability and genetic correlation may be biased estimates of the causal contribution of genotype. These results highlight the limitations of genotype-phenotype estimates obtained from samples of unrelated individuals. Use of these methods in combination with family-based designs may offer researchers greater opportunities to explore the mechanisms driving genotype-phenotype associations and identify factors underlying bias in estimates.

Background: Adolescence marks a period where depression will commonly onset and previous research using twin studies has suggested that genetic influences play a role in how depression develops and changes across adolescence. Recent genome-wide association studies have also shown that common genetic variants — which can be brought together as polygenic risk scores (PRS) — are also implicated in depression. However, the role of PRS in adolescent depression and changes in adolescent depression is not yet understood. The aim of this study was to examine the association between a PRS for depressive symptoms and depressive symptoms across adolescence and young adulthood, and how polygenic risk is associated with changes in depressive symptoms using two methods: cross-sectional analysis and multilevel growth curve modelling to examine the rate of change over time. Methods: Using data from over 6000 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC) we examined associations between genetic liability to depressive symptoms (polygenic risk score (PRS) for depressive symptoms) and self-reported depressive symptoms (short mood and feelings questionnaire over 9 occasions from 10-24 years). We examined cross-sectional associations at each age and trajectories of depressive symptoms in a repeated measures framework using growth curve analysis. Results: The PRS was associated with depressive symptoms throughout adolescence and young adulthood in both cross-sectional and growth curve analyses, though associations were stronger in the latter analyses. Growth curve analyses also provided additional insights, demonstrating that individuals with a higher PRS had steeper trajectories of depressive symptoms across adolescence with a greater increasing rate of change. Conclusions: In a longitudinal study spanning adolescence to early adult life, these results show that common genetics variants as indexed by a PRS for depressive symptoms influence both the severity of rate of change in adolescent depressive symptoms. Longitudinal data that make use of repeated measures designs have the potential to provide insights into the factors that influence the onset and persistence of adolescent depression.

Purpose: Previous studies have demonstrated positive associations between myopia and environmental risk factors such as urbanization. However, these have failed to account for the clustering of individuals within geographical areas, opening analyses to theoretical and statistical limitations. We demonstrate how a multilevel modelling approach can provide a more nuanced understanding of the relationship between geography and myopia. We examined longitudinal associations between onset of myopia and urban/rural status or population density. Methods: Data were collected over 5 visits during an 8-year period for a UK cohort of 3,512 children. Associations between incident myopia (spherical equivalent ≤ -1.00 diopters) and both urban/rural status and population density were examined using discrete time multilevel hazard models which allow the partitioning of variance into different neighborhood and school areas. Results : There was evidence for an association between myopia and higher population density (Hazard Ratio = 1.14; 95% CI = 1.032 to 1.26) after adjustment for a range of risk factors. There was no strong evidence that urban/rural status was associated with incident myopia. Only a minor amount of variation in myopia was attributable to geographical areas (Conclusion: Our findings contrast with previous studies and raise the possibility that some of the results reported may have been driven by confounding bias whereby geographical differences in myopia are driven by lifestyle factors that are correlated with geographical setting.

The increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How accurately polygenic scores predict an individual pupil's educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study with data linkage to national schooling records, we investigated how accurately polygenic scores for education predicted pupils' test score achievement. We also assessed the performance of polygenic scores over and above phenotypic data that are available to schools. Across our sample, there was high overlap between the polygenic score and achievement distributions, leading to poor predictive accuracy at the individual level. Prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. Conditional on prior achievement, polygenic scores failed to accurately predict later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for accurately predicting individual educational performance or for personalised education.The way that people learn in school and other educational settings differs from person to person for a wide range of reasons. Over the past 15 years there has been a shift in the way that children are taught in the UK and some other countries. Education has become more focused on the students as individuals, recognising that different people learn in different ways and at different speeds. This has led to the idea of ‘personalised education’, a way of tailoring students’ learning to suit their individual needs and differences. One way that individuals differ is in the genetic material they inherit from their parents. Except for identical twins, no two people have completely identical genomes. It is now easier and cheaper to study the genetic material of individuals than ever before. This has led to a lot of research investigating how our DNA relates to our health, education and other aspects of life. Some researchers and politicians are now suggesting that individuals’ genetic data should be routinely collected by organisations so that their education or health care can be personalised. However, it remains unclear whether this genetic personalisation would be more useful than demographic or socioeconomic data – such as sex, age and family background – that is already available. To investigate whether an individual’s DNA could be used to predict how well they will perform in school, Morris et al. combined genetic data and school test results from a group of 3,500 UK children born in the early 1990s. This revealed that the genetic data did not predict how the children would perform throughout their time at school as accurately as more general information about their family background and other socioeconomic factors. The findings of Morris et al. suggest that knowledge of students’ DNA is unlikely to help educators who want to identify individuals who need extra help or will be at the top of the class. More research is needed on larger groups of children from a broader range of backgrounds, but it is unclear whether a student's DNA will ever be useful for tailoring their education. Currently, it appears that DNA would be less useful for personalising education than easily measured information like test results taken in primary school, education of the child's parents and other social data.

Mendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trøndelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

Non-cognitive skills have previously been associated with a range of health and socioeconomic outcomes, though there has been considerable heterogeneity in published research. Many studies have used cross sectional data and therefore the longitudinal consistency of measures designed to capture non-cognitive skills is poorly understood. Using data from a UK cohort, we assess the consistency of non-cognitive skills over a 17-year period throughout childhood and adolescence, their genomic architecture, and their associations with socioeconomic outcomes. We find that longitudinal measurement consistency is high for behavioural and communication skills but low for other non-cognitive skills, implicating a high noise to signal ratio for many non-cognitive skills. Consistent non-zero heritability estimates and genetic correlations applied to cross-sectional measures are observed only for behavioural difficulties. When aggregating across multiple measurements, we find evidence of low heritabilityfor behaviour, communication, self-esteem and locus of control. We find weak correlations between aggregate measures of skills, further supporting cross-sectional measurement error in the non-cognitive measures. Associations between non-cognitive skills and educational outcomes are observed for skills measured in mid to late childhood and these are at most a third of the size of IQ-education associations. These results suggest that individual measures designed to capture non-cognitive skills may be subject to considerable measurement error and provide unreliable indicators of children’s skills. However, aggregate measures that leverage longitudinal data may more reliably identify underlying non-cognitive traits.

Research into health disparities has long recognized the importance of residential mobility as a crucial factor in determining health outcomes. However, a lack of connectivity between the health and mobility literatures has led to a stagnation of theory and application on the health side, which lacks the detail and temporal perspectives now seen as critical to understanding residential mobility decisions. Through a critical re-think of mobility processes with respect to health outcomes and an exploitation of longitudinal analytical techniques, we argue that health geographers have the potential to better understand and identify the relationship that residential mobility has with health.

Value-added measures of educational progress have been used by education researchers and policy-makers to assess the performance of teachers and schools, contributing to performance-related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a UK birth cohort to investigate how successfully value-added measures control for genetic differences between pupils. We use raw value-added, contextual value-added (which additionally controls for background characteristics) and teacher-reported value-added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value-added measures but explain up to 20% of the variation in contextual value-added measures (95% CI = 6.06% to 35.71%). Value-added measures built from teacher-rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross-sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value-added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research.

TM is funded by an Economic and Social Research Council PhD studentship in Advanced Quantitative Methods (ES/J50015X/1). DM and MvH are supported by a European Research Council grant under the European Union's Seventh Framework Programme (FP/2007-2013) Agreement n.615159 (ERC Consolidator Grant DEPRIVEDHOODS, Socio-spatial inequality, deprived neighbourhoods, and neighbourhood effects). The UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. Neighbourhood effects studies have demonstrated an association between area deprivation and smoking behaviour whereby people living in deprived neighbourhoods are more likely to smoke than those in non-deprived neighbourhoods. This evidence though is based largely upon data that ignores long term exposures to neighbourhood contexts and is confounded by neighbourhood selection bias. In this study, we investigate the temporal ordering of exposure to neighbourhood deprivation throughout childhood and whether associations between neighbourhood deprivation and cigarette smoking are due to compositional or contextual neighbourhood effects. Data come from a UK cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). We use longitudinal measures of neighbourhood deprivation and self-reported smoking behaviour for 2744 children to examine the influence of neighbourhood deprivation on smoking status and smoking heaviness at age 17. Our results demonstrate that children who are born into and grow up in deprived neighbourhoods are up to twice as likely to be smokers at age 17 than those in non-deprived neighbourhoods. These associations are largely due to family socioeconomic position and the intergenerational transmission of smoking behaviour from parents to children; compositional rather than direct contextual ‘neighbourhood effects’. Our findings highlight the importance of considering longitudinal exposure to neighbourhood deprivation over cross sectional exposure. In conclusion, we find that it is the family rather than the neighbourhood into which a child is born that determines their smoking behaviour. Publisher PDF

In this study, we estimate (i) the SNP heritability of educational attainment at three time points throughout the compulsory educational lifecourse; (ii) the SNP heritability of value-added measures of educational progress built from test data; and (iii) the extent to which value-added measures built from teacher rated ability may be biased due to measurement error. We utilise a genome wide approach using generalized restricted maximum likelihood (GCTA-GREML) to determine the total phenotypic variance in educational attainment and value-added measures that is attributable to common genetic variation across the genome within a sample of unrelated individuals from a UK birth cohort, the Avon Longitudinal Study of Parents and Children. Our findings suggest that the heritability of educational attainment measured using point score test data increases with age from 47% at age 11 to 61% at age 16. We also find that genetic variation does not contribute towards value-added measures created only from educational attainment point score data, but it does contribute a small amount to measures that additionally control for background characteristics (up to 20.09% [95%CI: 6.06 to 35.71] from age 11 to 14). Finally, our results show that value-added measures built from teacher rated ability have higher heritability than those built from exam scores. Our findings suggest that the heritability of educational attainment increases through childhood and adolescence. Value-added measures based upon fine grain point scores may be less prone to between-individual genomic differences than measures that control for students’ backgrounds, or those built from more subjective measures such as teacher rated ability.

Research has suggested that children who move home report poorer mental health than those who remain residentially stable. However, many previous studies have been based on cross sectional data and have failed to consider major life events as confounders. This study uses longitudinal data from ALSPAC, a UK population based birth cohort study, and employs within-between random effect models to decompose the association between moving in childhood and poor mental health. Results suggest that while unobserved between-individual differences between mobile and non-mobile children account for a large portion of this association, within-individual differences remain and indicate that moving may have a detrimental impact upon subsequent mental health. There is heterogeneity in children's response to moving, suggesting that a dichotomy of movers vs stayers is overly simplistic.

ObjectiveDespite differences in obesity and ill health between urban and rural areas in the UK being well documented, very little is known about differences in dietary patterns across these areas. The present study aimed to examine whether urban/rural status is associated with dietary patterns in a population-based UK cohort study of children.DesignDietary patterns were obtained using principal components analysis and cluster analysis of 3 d diet records collected from children at 10 years of age. Rurality was obtained from the 2001 UK Census urban/rural indicator at the time of dietary assessment. General linear models were used to examine the relationship between rurality and dietary pattern scores from principal components analysis; multinomial logistic regression was used to assess the association between rurality and dietary clusters.SettingThe Avon Longitudinal Study of Parents and Children (ALSPAC), South West England.SubjectsChildren (n 5677) aged 10 years (2817 boys and 2860 girls).ResultsAfter adjustment, increases in rurality were associated with increased scores on the ‘health awareness’ dietary pattern (β=0·35; 95 % CI 0·14, 0·56; Pβ=−0·39; 95 % CI −0·59, −0·19; PP=0·02) compared with those in the most urban areas.ConclusionsThere is evidence to suggest that differences exist in dietary patterns between rural and urban areas. Similar results were found using two different methods of dietary pattern analysis, showing that children residing in rural households were more likely to consume healthier diets than those in urban households.

Background A large literature exists suggesting that amongst adolescents a greater number of transitions through housing and neighbourhood environments (residential mobility) leads to increased participation in risky health behaviours such as cannabis use. However, previous research has failed to adequately account for underlying selection differences between mobile and non-mobile adolescents. Given that these groups differ across a range of characteristics these findings may therefore be subject to considerable selection bias. The aim of this study was to determine if the relationship between residential mobility and cannabis use in adolescence are spuriously driven by selection effects and therefore if conventional models produce biased results. Methods In this study we utilise data from 4767 adolescents in a UK cohort, The Avon Longitudinal Study of Parents and Children. First we employ a multilevel version of a conventional logistic regression analysis to determine the extent to which adolescent residential mobility is associated with cannabis use. Secondly, we utilise a between-within multilevel model to decompose this relationship into between-child and within-child effects in order to determine the extent to which cannabis use in adolescence is driven by underlying selection differences between mobile and non-mobile adolescent groups (the between-child part) or by changes in residential mobility for individuals (the within-child part). All analyses are conducted in Stata and MLwiN. Results Consistent with previous research we find a positive association between cumulative residential mobility and cannabis use with conventional logistic regression models (Odds Ratio: 2.56, 95% Confidence Interval: 1.20–5.78), implying that adolescents who experience a residential change are more likely to use cannabis than those who remain residentially stable. However, decomposing this relationship into its between- and within-child components reveals that a conventional model is underspecified and misleading; we find that differences in cannabis use between mobile and non-mobile adolescents are due to underlying selection differences between these groups (between-child log odds: 3.56, standard error: 1.22), not by a change in status of residential mobility (within-child log odds: 1.33, standard error: 1.02). Conclusion Our findings suggest that transitions through housing and neighbourhood environments in adolescence do not have a causal effect on cannabis use, but that the two are spuriously driven by selection effects. A number of limitations must be acknowledged; only a small number of variables could be decomposed into between- and within-effects; school moves were unavailable; and reason for moving was unknown. Future research into differences in health behaviour participation between groups should take account of selection bias in order to determine accurate effect estimation.