Search

Humans differ substantially in how strongly they respond to similar experiences. Theory suggests that such individual differences in susceptibility to environmental influences have a genetic basis. The present study investigated the genetic architecture of Environmental Sensitivity (ES) by estimating its heritability, exploring the presence of multiple heritable components and its genetic overlap with common personality traits. ES was measured with the Highly Sensitive Child (HSC) questionnaire and heritability estimates were obtained using classic twin design methodology in a sample of 2868 adolescent twins. Results indicate that the heritability of sensitivity was 0.47, and that the genetic influences underlying sensitivity to negative experiences are relatively distinct from sensitivity to more positive aspects of the environment, supporting a multi-dimensional genetic model of ES. The correlation between sensitivity, neuroticism and extraversion was largely explained by shared genetic influences, with differences between these traits mainly attributed to unique environmental influences operating on each trait.

Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits. The work of H. Tiemeier is further supported by a European Union’s Horizon 2020 research and innovation program (Contract grant number: 633595, DynaHealth) and a NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200). https://www.nwo.nl/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Birth weight (BW) is an important predictor of newborn survival and health and has associations with many adult health outcomes, including cardio-metabolic disorders, autoimmune diseases, and mental health. On average, twins have a lower BW than singletons as a result of a different pattern of fetal growth and shorter gestational duration. Therefore, investigations into the genetics of BW often exclude data from twins, leading to a reduction in sample size and remaining ambiguities concerning the genetic contribution to BW in twins. In this study, we carried out a genome-wide association meta-analysis of BW in 42 212 twin individuals and found a positive correlation of beta values (Pearson's r = 0.66, 95% confidence interval [CI]: 0.47-0.77) with 150 previously reported genome-wide significant variants for singleton BW. We identified strong positive genetic correlations between BW in twins and numerous anthropometric traits, most notably with BW in singletons (genetic correlation [rg] = 0.92, 95% CI: 0.66-1.18). Genetic correlations of BW in twins with a series of health-related traits closely resembled those previously observed for BW in singletons. Polygenic scores constructed from a genome-wide association study on BW in UK Biobank demonstrated strong predictive power in a target sample of Dutch twins and singletons. Together, our results indicate that a similar genetic architecture underlies BW in twins and singletons and that future genome-wide studies might benefit from including data from large twin registers. Birth weight (BW) is an important predictor of newborn survival and health and has associations with many adult health outcomes, including cardiometabolic disorders, autoimmune diseases and mental health. On average, twins have a lower BW than singletons as a result of a different pattern of fetal growth and shorter gestational duration. Therefore, investigations into the genetics of BW often exclude data from twins, leading to a reduction in sample size and remaining ambiguities concerning the genetic contribution to BW in twins. In this study, we carried out a genome-wide association meta-analysis of BW in 42 212 twin individuals and found a positive correlation of beta values (Pearson's r = 0.66, 95% confidence interval [CI]: 0.47-0.77) with 150 previously reported genome-wide significant variants for singleton BW. We identified strong positive genetic correlations between BW in twins and numerous anthropometric traits, most notably with BW in singletons (genetic correlation [rg] = 0.92, 95% CI: 0.66-1.18). Genetic correlations of BW in twins with a series of health-related traits closely resembled those previously observed for BW in singletons. Polygenic scores constructed from a genome-wide association study on BW in the UK Biobank demonstrated strong predictive power in a target sample of Dutch twins and singletons. Together, our results indicate that a similar genetic architecture underlies BW in twins and singletons and that future genome-wide studies might benefit from including data from large twin registers.

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes – child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely., Author summary An objective shared across the life, behavioural, and social sciences is to identify factors that increase risk for a particular disease or trait. However, identifying true risk factors is challenging. Often, a risk factor is statistically associated with a disease even if it is not really relevant, meaning that even successfully improving the risk factor will not impact the disease. One reason for the existence of such misleading associations stems from genetic confounding. This is when genetic factors influence directly both the risk factor and the disease, which generates a statistical association even in the absence of a true effect of the risk factor. Here, we propose a method to estimate genetic confounding and quantify its effect on observed associations. We show that a large part of the associations between maternal education and three child outcomes—educational achievement, body mass index and Attention-Deficit Hyperactivity Disorder—is explained by genetic confounding. Our findings can be applied to better understand the role of genetics in explaining associations of key risk factors with diseases and traits.

Supplemental Digital Content is available in the text., Background: There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment. Methods: Using the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals. Results: The novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7–7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%–15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6–19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period. Conclusions: An IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person’s polygenic risk.

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E–06), PCDH7 (P = 2.0E–06), and IPO13 (P = 2.5E–06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range $$\left| {r_g} \right|$$ r g : 0.19–1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = ~−0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range $$\left| {r_g} \right|$$ r g : 0.46–0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

BACKGROUND: Aggression in children has genetic and environmental causes. Studies of aggression can pool existing datasets to include more complex models of social effects. Such analyses require large datasets with harmonized outcome measures. Here, we made use of a reference panel for phenotype data to harmonize multiple aggression measures in school-aged children to jointly analyze data from five large twin cohorts.METHODS: Individual level aggression data on 86,559 children (42,468 twin pairs) were available in five European twin cohorts measured by different instruments. A phenotypic reference panel was collected which enabled a model-based phenotype harmonization approach. A bi-factor integration model in the integrative data analysis framework was developed to model aggression across studies while adjusting for rater, age, and sex. Finally, harmonized aggression scores were analyzed to estimate contributions of genes, environment, and social interaction to aggression. The large sample size allowed adequate power to test for sibling interaction effects, with unique dynamics permitted for opposite-sex twins.RESULTS: The best-fitting model found a high level of overall heritability of aggression (~60%). Different heritability rates of aggression across sex were marginally significant, with heritability estimates in boys of ~64% and ~58% in girls. Sibling interaction effects were only significant in the opposite-sex twin pairs: the interaction effect of males on their female co-twin differed from the effect of females on their male co-twin. An aggressive female had a positive effect on male co-twin aggression, whereas more aggression in males had a negative influence on a female co-twin.CONCLUSIONS: Opposite-sex twins displayed unique social dynamics of aggressive behaviors in a joint analysis of a large, multinational dataset. The integrative data analysis framework, applied in combination with a reference panel, has the potential to elucidate broad, generalizable results in the investigation of common psychological traits in children.

BackgroundThere is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment.MethodsThis research has been conducted using the UK Biobank (UKB) resource. We developed our own polygenic risk score (PRS) for coronary artery disease (CAD), using novel and established methods to combine published genomewide association study (GWAS) data with data from 114,196 UK Biobank individuals, also leveraging a large resource of other GWAS datasets along with functional information, to aid in the identification of causal variants, and thence define weights for > 8M genetic variants. We utilised a further 60,000 UKB individuals to develop an integrated risk tool (IRT) that combined our PRS with established risk tools (either the American Heart Association/American College of Cardiology’s pooled cohort equations (PCE) or the UK’s QRISK3) which was then tested in an additional, independent, set of 212,563 UKB individuals. We evaluated prediction performance in individuals of European ancestry, both as a whole and stratified by age and sex.FindingsThe novel CAD PRS showed superior predictive power for CAD events, compared to other published PRSs. As an individual risk factor, it has similar predictive power to each of systolic blood pressure, HDL cholesterol, and LDL cholesterol, but is more predictive than total cholesterol and smoking history. Our novel CAD PRS is largely uncorrelated with PCE, QRISK3, and family history, and, when combined with PCE into an integrated risk tool, had superior predictive accuracy. In individuals reclassified as high risk, CAD event rates were markedly and significantly higher compared to those reclassified as low risk. Overall, 9.7% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, in contrast to 3.7% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.7% (95% CI 4.4−7.0), but when individuals were stratified into four age-by-sex subgroups the improvement was larger for all subgroups (range 7.7%−17.3%), with best performance in younger middle-aged men aged 40–54yo (17.3%, 95% CI 13.0–21.5). Broadly similar results were found using a different risk tool (QRISK3), and also for cardiovascular disease events defined more broadly.InterpretationAn integrated risk tool that includes polygenic risk outperforms current, clinical risk stratification tools, and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person’s polygenic risk.FundingGenomics plc

Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7 , but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

BackgroundMaternal smoking during pregnancy (MSDP) has been linked to offspring's externalizing problems. It has been argued that socio-demographic factors (e.g. maternal age and education), co-occurring environmental risk factors, or pleiotropic genetic effects may account for the association between MSDP and later outcomes. This study provides a comprehensive investigation of the association between MSDP and a single harmonized component of externalizing: aggressive behaviour, measured throughout childhood and adolescence.MethodsData came from four prospective twin cohorts – Twins Early Development Study, Netherlands Twin Register, Childhood and Adolescent Twin Study of Sweden, and FinnTwin12 study – who collaborate in the EU-ACTION consortium. Data from 30 708 unrelated individuals were analysed. Based on item level data, a harmonized measure of aggression was created at ages 9–10; 12; 14–15 and 16–18.ResultsMSDP predicted aggression in childhood and adolescence. A meta-analysis across the four samples found the independent effect of MSDP to be 0.4% (r = 0.066), this remained consistent when analyses were performed separately by sex. All other perinatal factors combined explained 1.1% of the variance in aggression across all ages and samples (r = 0.112). Paternal smoking and aggressive parenting strategies did not account for the MSDP-aggression association, consistent with the hypothesis of a small direct link between MSDP and aggression.ConclusionsPerinatal factors, including MSDP, account for a small portion of the variance in aggression in childhood and adolescence. Later experiences may play a greater role in shaping adolescents’ aggressive behaviour.

A large number of studies document that children differ in the degree they are shaped by their developmental context with some being more sensitive to environmental influences than others. Multiple theories suggest that Environmental Sensitivity is a common trait predicting the response to negative as well as positive exposures. However, most research to date has relied on more or less proximal markers of Environmental Sensitivity. In this paper we introduce a new questionnaire-the Highly Sensitive Child (HSC) scale-as a promising self-report measure of Environmental Sensitivity. After describing the development of the short 12-item HSC scale for children and adolescents, we report on the psychometric properties of the scale, including confirmatory factor analysis and test-retest reliability. After considering bivariate and multivariate associations with well-established temperament and personality traits, we apply Latent Class Analysis to test for the existence of hypothesized sensitivity groups. Analyses are conducted across 5 studies featuring 4 different U.K.-based samples ranging in age from 8-19 years and with a total sample size of N = 3,581. Results suggest the 12-item HSC scale is a psychometrically robust measure that performs well in both children and adolescents. Besides being relatively independent from other common traits, the Latent Class Analysis suggests that there are 3 distinct groups with different levels of Environmental Sensitivity-low (approx. 25-35%), medium (approx. 41-47%), and high (20-35%). Finally, we provide exploratory cut-off scores for the categorization of children into these different groups which may be useful for both researchers and practitioners. (PsycINFO Database Record

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. Here we report a meta- A nalysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10-8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10-6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10-6). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10-29). These findings provide new insight into the genetic architecture of intelligence.

A genome-wide polygenic score (GPS), derived from a 2013 genome-wide association study (N=127,000), explained 2% of the variance in total years of education (EduYears). In a follow-up study (N=329,000), a new EduYears GPS explains up to 4%. Here, we tested the association between this latest EduYears GPS and educational achievement scores at ages 7, 12 and 16 in an independent sample of 5825 UK individuals. We found that EduYears GPS explained greater amounts of variance in educational achievement over time, up to 9% at age 16, accounting for 15% of the heritable variance. This is the strongest GPS prediction to date for quantitative behavioral traits. Individuals in the highest and lowest GPS septiles differed by a whole school grade at age 16. Furthermore, EduYears GPS was associated with general cognitive ability (~3.5%) and family socioeconomic status (~7%). There was no evidence of an interaction between EduYears GPS and family socioeconomic status on educational achievement or on general cognitive ability. These results are a harbinger of future widespread use of GPS to predict genetic risk and resilience in the social and behavioral sciences.

The genetic and environmental contributions to the variation and longitudinal stability in childhood aggressive behavior were assessed in two large twin cohorts, the Netherlands Twin Register (NTR), and the Twins Early Development Study (TEDS; United Kingdom). In NTR, maternal ratings on aggression from the Child Behavior Checklist (CBCL) were available for 10,765 twin pairs at age 7, for 8,557 twin pairs at age 9/10, and for 7,176 twin pairs at age 12. In TEDS, parental ratings of conduct disorder from the Strength and Difficulty Questionnaire (SDQ) were available for 6,897 twin pairs at age 7, for 3,028 twin pairs at age 9 and for 5,716 twin pairs at age 12. In both studies, stability and heritability of aggressive behavioral problems was high. Heritability was on average somewhat, but significantly, lower in TEDS (around 60%) than in NTR (between 50% and 80%) and sex differences were slightly larger in the NTR sample. In both studies, the influence of shared environment was similar: in boys shared environment explained around 20% of the variation in aggression across all ages while in girls its influence was absent around age 7 and only came into play at later ages. Longitudinal genetic correlations were the main reason for stability of aggressive behavior. Individual differences in CBCL-Aggressive Behavior and SDQ-Conduct disorder throughout childhood are driven by a comparable but significantly different genetic architecture. © 2016 Wiley Periodicals, Inc.

Common genetic influences offer a partial explanation for comorbidity between different psychiatric disorders1,2,3. However, the genetics underlying co-development—the cross-domain co-occurrence of patterns of change over time—of psychiatric symptoms during childhood and adolescence has not been well explored. Here, we show genetic influence on joint symptom trajectories of parent-reported conduct and emotional problems (overall N = 15,082) across development (4–16 years) using both twin- and genome-wide polygenic score analyses (genotyped N = 2,610). Specifically, we found seven joint symptom trajectories, including two characterized by jointly stable and jointly increasing symptoms of conduct and emotional problems, respectively (7.3% of the sample, collectively). Twin modelling analyses revealed substantial genetic influence on trajectories (heritability estimates range of 0.41–0.78). Furthermore, individuals’ risk of being classified in the most symptomatic trajectory classes was significantly predicted by polygenic scores for years-of-education-associated alleles and depressive symptoms-associated alleles. Complementary analyses of child self-reported symptoms across late childhood and early adolescence yielded broadly similar results. Taken together, our results indicate that genetic factors are involved in the co-development of conduct and emotional problems across childhood and adolescence, and that individuals with co-developing symptoms across multiple domains may represent a clinical subgroup characterized by increased levels of genetic risk.

Childhood aggression and its resulting consequences inflict a huge burden on affected children, their relatives, teachers, peers and society as a whole. Aggression during childhood rarely occurs in isolation and is correlated with other symptoms of childhood psychopathology. In this paper, we aim to describe and improve the understanding of the co-occurrence of aggression with other forms of childhood psychopathology. We focus on the co-occurrence of aggression and other childhood behavioural and emotional problems, including other externalising problems, attention problems and anxiety-depression. The data were brought together within the EU-ACTION (Aggression in Children: unravelling gene-environment interplay to inform Treatment and InterventiON strategies) project. We analysed the co-occurrence of aggression and other childhood behavioural and emotional problems as a function of the child's age (ages 3 through 16years), gender, the person rating the behaviour (father, mother or self) and assessment instrument. The data came from six large population-based European cohort studies from the Netherlands (2x), the UK, Finland and Sweden (2x). Multiple assessment instruments, including the Child Behaviour Checklist (CBCL), the Strengths and Difficulties Questionnaire (SDQ) and Multidimensional Peer Nomination Inventory (MPNI), were used. There was a good representation of boys and girls in each age category, with data for 30,523 3- to 4-year-olds (49.5% boys), 20,958 5- to 6-year-olds (49.6% boys), 18,291 7- to 8-year-olds (49.0% boys), 27,218 9- to 10-year-olds (49.4% boys), 18,543 12- to 13-year-olds (48.9% boys) and 10,088 15- to 16-year-olds (46.6% boys). We replicated the well-established gender differences in average aggression scores at most ages for parental ratings. The gender differences decreased with age and were not present for self-reports. Aggression co-occurred with the majority of other behavioural and social problems, from both externalising and internalising domains. At each age, the co-occurrence was particularly prevalent for aggression and oppositional and ADHD-related problems, with correlations of around 0.5 in general. Aggression also showed substantial associations with anxiety-depression and other internalizing symptoms (correlations around 0.4). Co-occurrence for self-reported problems was somewhat higher than for parental reports, but we found neither rater differences, nor differences across assessment instruments in co-occurrence patterns. There were large similarities in co-occurrence patterns across the different European countries. Finally, co-occurrence was generally stable across age and sex, and if any change was observed, it indicated stronger correlations when children grew older. We present an online tool to visualise these associations as a function of rater, gender, instrument and cohort. In addition, we present a description of the full EU-ACTION projects, its first results and the future perspectives.

We used a case-control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (∼170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population. usc Refereed/Peer-reviewed

One of the best predictors of children’s educational achievement is their family’s socioeconomic status (SES), but the degree to which this association is genetically mediated remains unclear. For 3000 UK-representative unrelated children we found that genome-wide single-nucleotide polymorphisms could explain a third of the variance of scores on an age-16 UK national examination of educational achievement and half of the correlation between their scores and family SES. Moreover, genome-wide polygenic scores based on a previously published genome-wide association meta-analysis of total number of years in education accounted for ~3.0% variance in educational achievement and ~2.5% in family SES. This study provides the first molecular evidence for substantial genetic influence on differences in children’s educational achievement and its association with family SES.

High intelligence (general cognitive ability) is fundamental to the human capital that drives societies in the information age. Understanding the origins of this intellectual capital is important for government policy, for neuroscience, and for genetics. For genetics, a key question is whether the genetic causes of high intelligence are qualitatively or quantitatively different from the normal distribution of intelligence. We report results from a sibling and twin study of high intelligence and its links with the normal distribution. We identified 360,000 sibling pairs and 9000 twin pairs from 3 million 18-year-old males with cognitive assessments administered as part of conscription to military service in Sweden between 1968 and 2010. We found that high intelligence is familial, heritable, and caused by the same genetic and environmental factors responsible for the normal distribution of intelligence. High intelligence is a good candidate for “positive genetics” — going beyond the negative effects of DNA sequence variation on disease and disorders to consider the positive end of the distribution of genetic effects., Highlights • High intelligence is as familial and heritable as the rest of the distribution. • There are no substantial genetic effects unique to high intelligence. • There are no substantial environmental effects unique to high intelligence. • Studying the “positive” end of trait dimensions is rare, and potentially very useful.

Although gene–environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between trait-associated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample (n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children’s developmental outcomes—independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age (R2 = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding (R2 = 0.021; P = 7e-30), maternal smoking during pregnancy (R2 = 0.008; P = 5e-13), parental smacking (R2 = 0.01; P = 4e-15), household income (R2 = 0.032; P = 1e-22), watching television (R2 = 0.034; P = 5e-47), and maternal education (R2 = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children’s inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

Using twin (6,105 twin pairs) and genomic (5,825 unrelated individuals taken from the twin sample) analyses, we tested for genetic influences on the parent-offspring correspondence in educational attainment. Genetics accounted for nearly half of the variance in intergenerational educational attainment. A genomewide polygenic score (GPS) for years of education was also associated with intergenerational educational attainment: The highest and lowest GPS means were found for offspring in stably educated families (i.e., who had taken A Levels and had a university-educated parent; M = 0.43, SD = 0.97) and stably uneducated families (i.e., who had not taken A Levels and had no university-educated parent; M = −0.19, SD = 0.97). The average GPSs fell in between for children who were upwardly mobile (i.e., who had taken A Levels but had no university-educated parent; M = 0.05, SD = 0.96) and children who were downwardly mobile (i.e., who had not taken A Levels but had a university-educated parent; M = 0.28, SD = 1.03). Genetic influences on intergenerational educational attainment can be viewed as an index of equality of educational opportunity.

It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education (EduYears) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.

Objective Advanced paternal age (APA) at conception has been linked with autism and schizophrenia in offspring, neurodevelopmental disorders that affect social functioning. The current study explored the effects of paternal age on social development in the general population. Method We used multilevel growth modelling to investigate APA effects on socioemotional development from early childhood until adolescence, as measured by the Strengths and Difficulties Questionnaire (SDQ) in the Twins Early Development Study (TEDS) sample. We also investigated genetic and environmental underpinnings of the paternal age effects on development, using ACE (Additive genetics, Common environment, unique Environment) and gene–environment (GxE) models. Results In the general population, both very young and advanced paternal ages were associated with altered trajectory of social development (intercept: p=.01; slope: p=.03). No other behavioural domain was affected by either young or advanced age at fatherhood, suggesting specificity of paternal age effects. Increased importance of genetic factors in social development was recorded in the offspring of older but not very young fathers, suggesting distinct underpinnings of the paternal age effects at these two extremes. Conclusion Our findings highlight that the APA-related deficits that lead to autism and schizophrenia are likely continuously distributed in the population.

Juvenile obesity is associated with adverse health outcomes. Understanding genetic and environmental influences on body mass index (BMI) during adolescence could inform interventions. We investigated independent and interactive effects of parenting, socioeconomic status (SES) and polygenic risk on BMI pre-adolescence, and on the rate of change in BMI across adolescence. Genome-wide genotype data, BMI and child perceptions of parental warmth and punitive discipline were available at 11 years old, and parental SES was available from birth on 3,414 unrelated participants. Linear models were used to test the effects of social environment and polygenic risk on pre-adolescent BMI. Change in BMI across adolescence was assessed in a subset (N = 1943). Sex-specific effects were assessed. Higher genetic risk was associated with increased BMI pre-adolescence and across adolescence (p

General cognitive ability (intelligence) is one of the most heritable behavioural traits and most predictive of socially important outcomes and health. We hypothesized that some of the missing heritability of IQ might lie hidden in the human leukocyte antigen (HLA) region, which plays a critical role in many diseases and traits but is not well tagged in conventional GWAS. Using a uniquely powered design, we investigated whether fine-mapping of the HLA region could narrow the missing heritability gap. Our case-control design included 1,393 cases with extremely high intelligence scores (top 0.0003 of the population equivalent to IQ > 147) and 3,253 unselected population controls. We imputed variants in 200 genes across the HLA region, one SNP (rs444921) reached our criterion for study-wide significance. SNP-based heritability of the HLA variants was small and not significant (h2 = 0.3%, SE = 0.2%). A polygenic score from the case-control genetic association analysis of SNPs in the HLA region did not significantly predict individual differences in intelligence in an independent unselected sample. We conclude that although genetic variation in the HLA region is important to the aetiology of many disorders, it does not appear to be hiding much of the missing heritability of intelligence.

The topicof assortative (nonrandom)matingmight seemesoteric or even salacious. For example, in lectures you have to point out to students that random mating is not about promiscuity. In this issue of JAMA Psychiatry, Nordsletten and colleagues1 report the first general population study to date of assortativemating for psychiatric disorders, which may help to solve 3 puzzles in psychiatric genetics: Why are psychiatric disorders so highly heritable when they are associatedwith reduced fecundity?Whyare somepsychiatric disorders somuchmorehighlyheritable thanothers?Why is there so much genetic comorbidity across psychiatric disorders? The research capitalizes on the powerful population registers in Sweden, which contain diagnostic information, includingpsychiatric diagnoses, onall individuals admitted toSwedish hospitals since 1973. The registers yield huge samples of cases (eg, more than 70000 individuals diagnosed as having schizophrenia). Using other registers to track couples via their children, the investigators were able to measure assortative mating levels within and between 11 psychiatric disorders. Althoughyoucanseeassortativemating forphysical traits, likeheight andweight,withyourowneyes, the correlationbetween spouses is only approximately0.20 for these traits. For personality, assortative mating is even lower at approximately 0.10. In contrast, Nordsletten and colleagues1 find an amazing amount of assortativematingwithin psychiatric disorders. Spouse tetrachoric correlations are greater than 0.40 for attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia. The next highest spouse correlation emerged for substance abuse (range, 0.36-0.39). Assortative mating was significant but far less substantial for other disorders, such as affective disorders (range, 0.14-0.19). Thesefindingsare intriguing inrelationtothefirst2puzzles about heritability of psychiatric disorders. The significance of assortativemating for polygenic traits, suchaspsychiatric disorders, is that it increases additive genetic variance generation after generation until equilibrium is reached. Additive genetic variance refers to genetic effects that breed true from parents to offspring because they involve independent effects of alleles that “addup.”This additivevariance is in contrast tononadditiveeffectsofdominancewithina locusorepistasis across loci in which the effects of alleles or loci interact. Unlike inbreeding, which reduces heterozygosity across the genome, assortative mating is trait specific. That is, it increasesadditivegeneticvariance (changinggenotypic frequencies but not allelic frequencies) only for genes associatedwith the trait forwhichmatesassort andother traits genetically correlated with that trait. As a consequence, assortative mating increases the contribution of additive genetic variance (narrowheritability) for any trait on which it acts. This boost to heritability from assortative mating could help to explain why psychiatric disorders have such high heritability despite reduced fecundity. Assortative mating could also contribute to the second question about why some psychiatric disorders—most notably,ADHD,ASD,andschizophrenia—aremoreheritable in twin studies than other disorders, such as the affective disorders. The answer from the article by Nordsletten and colleagues1 could be that the former show twice asmuch assortativemating as the latter. However, these solutions are not quite as clear cut as they may seem. The argument goes that the abundant assortative mating for ADHD, ASD, and schizophrenia increases additive genetic variance. However, twin studies suggest that nonadditive genetic influence is greater for these same 3 disorders than for other disorders in that dizygotic twins aremuch less thanhalf as similar asmonozygotic twins.2Most notably, concordance forASD is approximately60%formonozygotic twins and approximately 5% for dizygotic twins.2 Nonetheless, it is possible that these 3 disorders are so highly heritable because they include injections of both additive genetic variance from assortative mating and unusually high nonadditive genetic variance. Without assortative mating, these disorders might show little additive genetic variance. Another issue that needs to be resolved involves DNAbased heritability estimates, which only detect additive genetic variance. From the findings by Nordsletten and colleagues1 alone, one would predict that DNA-based heritability estimates should be greater for ADHD, ASD, and schizophrenia than for affective disorders, but the results so far suggest otherwise.3 Nonetheless, the dust is yet to settle on the explosionof researchestimatingheritabilityusingDNAalone. It is crucial to resolve this issue because genome-wide association studies typically only search for additive genetic effects. In other words, additive genetic variance is the ceiling both for DNA-based heritability estimates and for identifying DNA variants in genome-wide association studies. Understanding its extent and causes is key to psychiatric genetics research. The third genetic puzzle is why there is so much genetic comorbidity across psychiatric disorders. A major discovery from genome-wide association studies is that genetic variRelated article page 354 Opinion

Correction to: Molecular Psychiatry (2017) 22: 267–272; doi: 10.1038/mp.2016.107 Following publication, the authors noticed that some information in the Acknowledgements section was presented incorrectly or omitted. The updated Acknowledgements section follows. We gratefully acknowledge the ongoing contribution of the participants in the Twins Early Development Study (TEDS) and their families.

Environmental measures used widely in the behavioral sciences show nearly as much genetic influence as behavioral measures, a critical finding for interpreting associations between environmental factors and children's development. This research depends on the twin method that compares monozygotic and dizygotic twins, but key aspects of children's environment such as socioeconomic status (SES) cannot be investigated in twin studies because they are the same for children growing up together in a family. Here, using a new technique applied to DNA from 3000 unrelated children, we show significant genetic influence on family SES, and on its association with children's IQ at ages 7 and 12. In addition to demonstrating the ability to investigate genetic influence on between-family environmental measures, our results emphasize the need to consider genetics in research and policy on family SES and its association with children's IQ., Highlights • Twin research shows genetic influence on within-family environments. • Twin research cannot address genetic influence on children's family-level SES. • A new DNA-based method (GCTA) shows genetic influence on family-level SES. • GCTA shows that genes drive the correlation between family SES and children's IQ. • Heritability can be viewed as an index of meritocratic social mobility.

We have previously shown that individual differences in educational achievement are highly heritable in the early and middle school years in the UK. The objective of the present study was to investigate whether similarly high heritability is found at the end of compulsory education (age 16) for the UK-wide examination, called the General Certificate of Secondary Education (GCSE). In a national twin sample of 11,117 16-year-olds, heritability was substantial for overall GCSE performance for compulsory core subjects (58%) as well as for each of them individually: English (52%), mathematics (55%) and science (58%). In contrast, the overall effects of shared environment, which includes all family and school influences shared by members of twin pairs growing up in the same family and attending the same school, accounts for about 36% of the variance of mean GCSE scores. The significance of these findings is that individual differences in educational achievement at the end of compulsory education are not primarily an index of the quality of teachers or schools: much more of the variance of GCSE scores can be attributed to genetics than to school or family environment. We suggest a model of education that recognizes the important role of genetics. Rather than a passive model of schooling as instruction (instruere, ‘to build in’), we propose an active model of education (educare, ‘to bring out’) in which children create their own educational experiences in part on the basis of their genetic propensities, which supports the trend towards personalized learning.

The aetiology of individual differences in educational attainment and occupational status includes genetic as well as environmental factors1–5 and can change as societies change3,6,7. The extent of genetic influence on these social outcomes can be viewed as an index of success in achieving meritocratic values of equality of opportunity by rewarding talent and hard work, which are to a large extent influenced by genetic factors, rather than rewarding environmentally driven privilege. To the extent that the end of the Soviet Union and the independence of Estonia led to an increase in meritocratic selection of individuals in education and occupation, genetic influence should be higher in the post-Soviet era than in the Soviet era. Here we confirmed this hypothesis: DNA differences (single-nucleotide polymorphisms) explained twice as much variance in educational attainment and occupational status in the post-Soviet era compared with the Soviet era in both polygenic score analyses and single-nucleotide polymorphism heritability analyses of 12,500 Estonians. Our results demonstrate a change in the extent of genetic influence in the same population following a massive and abrupt social change—in this case, the shift from a communist to a capitalist society.

Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, the behavioral phenome. For 3152 unrelated 16-year-old individuals representative of the United Kingdom, we created 13 GPS from the largest GWAS for psychiatric disorders (for example, schizophrenia, depression and dementia) and cognitive traits (for example, intelligence, educational attainment and intracranial volume). The behavioral phenome included 50 traits from the domains of psychopathology, personality, cognitive abilities and educational achievement. We examined phenome-wide profiles of associations for the entire distribution of each GPS and for the extremes of the GPS distributions. The cognitive GPS yielded stronger predictive power than the psychiatric GPS in our UK-representative sample of adolescents. For example, education GPS explained variation in adolescents’ behavior problems (~0.6%) and in educational achievement (~2%) but psychiatric GPS were associated with neither. Despite the modest effect sizes of current GPS, quantile analyses illustrate the ability to stratify individuals by GPS and opportunities for research. For example, the highest and lowest septiles for the education GPS yielded a 0.5 s.d. difference in mean math grade and a 0.25 s.d. difference in mean behavior problems. We discuss the usefulness and limitations of GPS based on adult GWAS to predict genetic propensities earlier in development.