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The American College of Cardiology / American Heart Association pooled cohort equations tool (ASCVD-PCE) is currently recommended to assess 10-year risk for atherosclerotic cardiovascular disease (ASCVD). ASCVD-PCE does not currently include genetic risk factors. Polygenic risk scores (PRSs) have been shown to offer a powerful new approach to measuring genetic risk for common diseases, including ASCVD, and to enhance risk prediction when combined with ASCVD-PCE. Most work to date, including the assessment of tools, has focused on performance in individuals of European ancestries. Here we present evidence for the clinical validation of a new integrated risk tool (IRT), ASCVD-IRT, which combines ASCVD-PCE with PRS to predict 10-year risk of ASCVD across diverse ethnicity and ancestry groups. We demonstrate improved predictive performance of ASCVD-IRT over ASCVD-PCE, not only in individuals of self-reported White ethnicities (net reclassification improvement [NRI]; with 95% confidence interval = 2.7% [1.1 to 4.2]) but also Black / African American / Black Caribbean / Black African (NRI = 2.5% [0.6-4.3]) and South Asian (Indian, Bangladeshi or Pakistani) ethnicities (NRI = 8.7% [3.1 to 14.4]). NRI confidence intervals were wider and included zero for ethnicities with smaller sample sizes, including Hispanic (NRI = 7.5% [-1.4 to 16.5]), but PRS effect sizes in these ethnicities were significant and of comparable size to those seen in individuals of White ethnicities. Comparable results were obtained when individuals were analyzed by genetically inferred ancestry. Together, these results validate the performance of ASCVD-IRT in multiple ethnicities and ancestries, and favor their generalization to all ethnicities and ancestries.

BackgroundIntegration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results.MethodsThe predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study (TEDS). GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed SNP-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression.ResultsGeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for Height (p=0.023) to 4% for Rheumatoid Arthritis (p=5.9×10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalised genes.ConclusionGeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

Risk-guided intervention in the US, informed by the pooled cohort equations (PCE) risk calculator, is the accepted practice for prevention of atherosclerotic cardiovascular disease (ASCVD). We recently showed that a polygenic risk score (PRS, summarising common genetic risk) can be integrated with PCE into an integrated risk tool (IRT) and we validated its utility across multiple contexts including sex, age, genetic ancestry and self-identified ethnicity (Weale et al 2021). The addition of PRS is always beneficial, but its contribution varies by context. When meta-analysed across multiple cohorts, the PRS is more predictive in men (odds ratio per standard deviation, ORsd=1.57) than women (ORsd=1.46). Across ethnicities, the group with the lowest effect size is African American (ORsd=1.14 in men, ORsd=1.11 in women), a result that predominantly reflects a lack of available training samples. The availability of a CVD IRT has practical clinical implications. Because the IRT substantially spreads the risk scores in a given patient stratum, the inclusion of genetic factors can result in some individuals crossing actionable risk thresholds in both directions. Using UK Biobank, a large UK population cohort aged 45-64 with extensive medical and genetic information, we show that the rates of up/down classification are 7.8%/4.2% in younger men (40-54yo), 1.9%/11.6% in older men (55-69yo), 1.0%/0.2% in younger women, and 6.5%/6.6% in older women. This implies a tendency to under-treat in younger and over-treat in older individuals under current guidelines. Model discrimination (Harrell’s C, a measure of area-under-the-curve for survival data) is also greatest in the younger (C=0.751/0.732 in men/women) vs older (C=0.666/0.697 in men/women) age group. Given the increased up-classification and discrimination in the younger group, the addition of genetics provides an opportunity for earlier intervention in those most at risk.

Background: Diverse behaviour problems in childhood correlate phenotypically, suggesting a general dimension of psychopathology that has been called the p factor. The shared genetic architecture between childhood psychopathology traits also supports a genetic p. This study systematically investigates the manifestation of this common dimension across self‐, parent‐ and teacher‐rated measures in childhood and adolescence. / Methods: The sample included 7,026 twin pairs from the Twins Early Development Study (TEDS). First, we employed multivariate twin models to estimate common genetic and environmental influences on p based on diverse measures of behaviour problems rated by children, parents and teachers at ages 7, 9, 12 and 16 (depressive traits, emotional problems, peer problems, autism traits, hyperactivity, antisocial behaviour, conduct problems and psychopathic tendencies). Second, to assess the stability of genetic and environmental influences on p across time, we conducted longitudinal twin modelling of the first phenotypic principal components of childhood psychopathological measures across each of the four ages. Third, we created a genetic p factor in 7,026 unrelated genotyped individuals based on eight polygenic scores for psychiatric disorders to estimate how a general polygenic predisposition to mostly adult psychiatric disorders relates to childhood p. / Results: Behaviour problems were consistently correlated phenotypically and genetically across ages and raters. The p factor is substantially heritable (50%–60%) and manifests consistently across diverse ages and raters. However, residual variation in the common factor models indicates unique contributions as well. Genetic correlations of p components across childhood and adolescence suggest stability over time (49%–78%). A polygenic general psychopathology factor derived from studies of psychiatric disorders consistently predicted a general phenotypic p factor across development (0.3%–0.9%). / Conclusions: Diverse forms of psychopathology generally load on a common p factor, which is highly heritable. There are substantial genetic influences on the stability of p across childhood. Our analyses indicate genetic overlap between general risk for psychiatric disorders in adulthood and p in childhood, even as young as age 7. The p factor has far‐reaching implications for genomic research and, eventually, for diagnosis and treatment of behaviour problems. /

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.

BackgroundOne goal of the DNA revolution is to predict problems in order to prevent them. We tested here if the prediction of behaviour problems from genome-wide polygenic scores (GPS) can be improved by creating composites across ages and across raters and by using a multi-GPS approach that includes GPS for adult psychiatric disorders as well as for childhood behaviour problems.MethodOur sample included 3,065 genotyped unrelated individuals from the Twins Early Development Study who were assessed longitudinally for hyperactivity, conduct, emotional problems and peer problems as rated by parents, teachers and children themselves. GPS created from 15 genome-wide association studies were used separately and jointly to test the prediction of behaviour problems composites (general behaviour problems, externalizing and internalizing) across ages (from age 2 to age 21) and across raters in penalized regression models. Based on the regression weights, we created multi-trait GPS reflecting the best prediction of behaviour problems. We compared GPS prediction to twin heritability using the same sample and measures.ResultsMulti-GPS prediction of behaviour problems increased from less than 2% of the variance for observed traits to up to 6% for cross-age and cross-rater composites. Twin study estimates of heritability mirrored patterns of multi-GPS prediction as they increased from less than 40% to up to 83%.ConclusionsThe ability of GPS to predict behaviour problems can be improved by using multiple GPS, cross-age composites and cross-rater composites, although the effect sizes remain modest, up to 6%. Our results can be used in any genotyped sample to create multi-trait GPS predictors of behaviour problems that will be more predictive than polygenic scores based on a single age, rater or GPS.Key pointsGenome-wide polygenic scores (GPS) can be used to predict behaviour problems in childhood, but the effect sizes are generally less than 3.5%.DNA-based prediction models of achieve greater accuracy if holistic approaches are employed, that is cross-trait, longitudinal and trans-situational approaches.The prediction of childhood behaviour problems can be improved by using multiple GPS to predict composites that aggregate behaviour problems across ages and across raters.Our results yield weights that can be applied to GPS in any study to create multi-trait GPS predictors of behaviour problems based on cross-age and cross-rater composites.As compared to individuals in the lowest multi-trait GPS decile, nearly three times as many individuals in the highest internalizing multi-trait GPS decile were diagnosed with anxiety disorder and 25% more individuals in the highest general behaviour problems and externalizing multi-trait GPS deciles have taken medication for mental health.

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

Neuropsychiatric disease has polygenic determinants but is often precipitated by environmental pressures, including adverse perinatal events. However, the way in which genetic vulnerability and early-life adversity interact remains obscure. We hypothesised that the extreme environmental stress of prematurity would promote neuroanatomic abnormality in individuals genetically vulnerable to psychiatric disorders. In 194 unrelated infants (104 males, 90 females), born before 33 weeks of gestation (mean gestational age 29.7 weeks), we combined Magnetic Resonance Imaging with a polygenic risk score (PRS) for five psychiatric pathologies to test the prediction that: deep grey matter abnormalities frequently seen in preterm infants are associated with increased polygenic risk for psychiatric illness. The variance explained by the PRS in the relative volumes of four deep grey matter structures (caudate nucleus, thalamus, subthalamic nucleus and lentiform nucleus) was estimated using linear regression both for the full, mixed ancestral, cohort and a subsample of European infants. Psychiatric PRS was negatively associated with lentiform volume in the full cohort (β = −0.24, p = 8 × 10−4) and a European subsample (β = −0.24, p = 8 × 10−3). Genetic variants associated with neuropsychiatric disease increase vulnerability to abnormal lentiform development after perinatal stress and are associated with neuroanatomic changes in the perinatal period.

Purpose The ability to identify children early in development who are at substantial risk for language/literacy difficulties would have great benefit both for the children and for the educational and therapeutic institutions that serve them. Information that is relatively easily available prior to the age of 3 years, such as late talking, family history of language/literacy difficulties, and socioeconomic status, have some but very limited predictive power. Here, we examine whether the inclusion of a DNA-based genome-wide polygenic score that has been shown to capture children's genetic propensity for educational attainment (EA3) adds enough prediction to yield a clinically useful score. Method Data are longitudinal scores of 1,420 children from the Twins Early Development Study, who were assessed at ages 2 and 3 years on language and nonverbal ability and at 12 years of age on oral language, word decoding, and reading comprehension. Five risk factors were examined: expressive vocabulary, nonverbal ability (these two from parent report), family history, mothers' education, and EA3. Analyses were conducted both for continuous and categorically defined measures of risk and outcome. Results Language and literacy abilities at 12 years of age were significantly but modestly predicted by the risk factors, with a small but significant added prediction from EA3. Indices of diagnostic validity for poor outcomes, such as sensitivity and area under the curve statistics, were poor in all cases. Conclusions We conclude that, at present, clinically useful prediction from toddlerhood remains an unattained goal. Supplemental Material https://doi.org/10.23641/asha.12170331

Objectives Individual adolescent psychotic‐like experiences (PLEs) are associated with schizophrenia risk factors. As DSM‐5 schizophrenia requires the co‐occurrence of at least two psychotic symptoms, we investigated whether co‐occurring adolescent PLEs have stronger associations with schizophrenia risk factors, lower quality of life and functioning, and have higher heritability, than individual PLEs. Methods Participants were 9646 16‐year‐old twins from the longitudinal Twins Early Development Study. We investigated co‐occurrence of high questionnaire scores for three PLE combinations: (1) paranoia and hallucinations; (2) paranoia or hallucinations, and cognitive disorganization; and (3) paranoia or hallucinations, and negative symptoms, and their associations with 11 schizophrenia‐relevant variables by regression analysis and structural equation twin modeling. Results Against expectation, none of the co‐occurring PLEs had the nominally strongest associations significantly more often than individual PLEs. Co‐occurring PLEs had the strongest associations with bullying victimization, cannabis use and lower life satisfaction, but individual PLEs had the strongest associations with cognitive function variables. Obstetric complications were most associated with negative symptoms. Secondary analysis revealed that co‐occurrence of cognitive disorganization and negative symptoms had the nominally strongest associations with most schizophrenia‐relevant variables overall and relatively high heritability (67%). Conclusions Focusing on co‐occurrence enhances some individual PLE associations but obscures others. The combination of subjective cognitive disorganization plus observed negative symptoms showed a broad range of enhanced associations with schizophrenia‐relevant variables. Future research could investigate associations with other risk factors and the ability of this PLE combination to predict onset of schizophrenia., HIGHLIGHTS Focusing on co‐occurring psychotic‐like experiences (PLEs) in adolescence enhances some individual PLE associations but may obscure othersWhile victimization, cannabis use, and lower life satisfaction have broad associations with PLE combinations, cognitive variables are most associated with cognitive disorganization and negative symptoms, and obstetric complications are most associated with negative symptomsThe combination of subjective cognitive disorganization and observed negative symptoms has a relatively broad range of enhanced associations with schizophrenia‐relevant variables and relatively high heritability

Polygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement. Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance. Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE = .38; 95% CIs = .30, .45). We estimated that 40% (95% CIs = 31%, 50%) of the polygenic scores effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs = 12%, 25%) of environmental effects were accounted for by the polygenic model, indicating genetic confounding. Lastly, we did not find evidence that GxE effects significantly contributed to multivariable prediction. Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence., Author summary Our study investigates the complex interplay between genetic and environmental contributions underlying educational achievement (EA). Polygenic scores are becoming increasingly powerful predictors of EA. While emerging evidence indicates that polygenic scores are not pure measures of genetic predisposition, previous quantitative genetics findings indicate that measures of the environment are themselves heritable. In this regard it is unclear how such measures of individual predisposition jointly combine to predict EA. We investigate this question in a representative UK sample of 7,026 16-year-olds where we provide substantive results on gene-environment correlation and interaction underlying variation in EA. We show that polygenic score and environmental prediction models of EA overlap substantially. Polygenic scores effects on EA are partly accounted for by their correlation with environmental effects; similarly, environmental effects on EA are linked to polygenic scores effects. Nonetheless, jointly considering polygenic scores and measured environments significantly improves prediction of EA. We also find that, although correlation between polygenic scores and measured environments is substantial, interactions between them do not play a significant role in the prediction of EA. Our findings have relevance for genomic and environmental prediction models alike, as they show the way in which individuals’ genetic predispositions and environmental effects are intertwined. This suggests that both genetic and environmental effects must be taken into account in prediction models of complex behavioral traits such as EA.

The Twins Early Development Study (TEDS) is a longitudinal twin study that recruited over 16,000 twin-pairs born between 1994 and 1996 in England and Wales through national birth records. More than 10,000 of these families are still engaged in the study. TEDS was and still is a representative sample of the population in England and Wales. Rich cognitive and emotional/behavioral data have been collected from the twins from infancy to emerging adulthood, with data collection at first contact and at ages 2, 3, 4, 7, 8, 9, 10, 12, 14, 16, 18 and 21, enabling longitudinal genetically sensitive analyses. Data have been collected from the twins themselves, from their parents and teachers, and from the UK National Pupil Database. Genotyped DNA data are available for 10,346 individuals (who are unrelated except for 3320 dizygotic co-twins). TEDS data have contributed to over 400 scientific papers involving more than 140 researchers in 50 research institutions. TEDS offers an outstanding resource for investigating cognitive and behavioral development across childhood and early adulthood and actively fosters scientific collaborations.

Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods. Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioural sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status.

To understand the regulation of A2a adenosine receptor (A2a-R) response, we examined the molecular mechanisms underlying the desensitization of A2a response in rat pheochromocytoma PC12 cells, which possess an A2a-R identical with the A2a receptor we recently cloned from rat brain. Prolonged exposure of PC12 cells to adenosine agonists significantly inhibited the response of the cells to subsequent stimulation with an A2a-selective adenosine agonist (CGS21680). No significant change in the number of binding sites and affinity for CGS21680 was observed in desensitized cells, nor did we find any significant change in the transcript level of A2a-R in cells pretreated with adenosine agonists. However, the basal adenylyl cyclase activity and the cyclase activities stimulated by adenosine agonists, by GTP gamma S, and by forskolin were reduced in desensitized cells. Prolonged exposure of PC12 cells to dibutyryl-cAMP did not significantly change either the basal or the adenosine agonist-evoked adenylyl cyclase activity. Therefore, elevation of cellular cAMP content is by itself not sufficient to produce the observed reductions of adenylyl cyclase activity with A2a desensitization. Inhibition of adenylyl cyclase activity in desensitized cells occurred after short-term (30 min) incubation with CGS21680 and could be blocked by the adenosine antagonist 8-cyclopentyl-1,3-dipropylxanthine. Gs alpha protein levels did not significantly change after a 30-min exposure to CGS21680. In contrast, long-term exposure (12-20 hr) of PC12 cells to adenosine agonists resulted in a slight further reduction of adenylyl cyclase activity and a consistent decline in the Gs alpha protein level. In addition, long-term incubation with adenosine agonists or with forskolin-enhanced phosphodiesterase (PDE) activity in the cytosolic and membrane fractions by 57 +/- 9% and 53 +/- 18%, respectively. Hydrolysis of cAMP was significantly faster in agonist-desensitized cells than in control cells. PDE might therefore play an important role in desensitization of the A2a response in PC12 cells. Polymerase chain reaction-based analysis of the mRNA for A2a-R and A2b-R indicated that both A2a-R and A2b-R were present in PC12 cells; the A2b response was also diminished in A2a-desensitized cells. Our data suggest that inhibition of adenylyl cyclase after short-term agonist treatment, down-regulation of Gs alpha protein level after long-term agonist treatment, and activation of PDE after long-term agonist treatment account for desensitization of the A2a-mediated response in PC12 cells.

BackgroundDiverse behaviour problems in childhood correlate phenotypically, suggesting a general dimension of psychopathology that has been called the p factor. The shared genetic architecture between childhood psychopathology traits also supports a genetic p. This study systematically investigates the manifestation of this common dimension across self-, parent- and teacher-rated measures in childhood and adolescence.MethodsThe sample included 7,026 twin pairs from the Twins Early Development Study (TEDS). First, we employed multivariate twin models to estimate common genetic and environmental influences on p based on diverse measures of behaviour problems rated by children, parents and teachers at ages 7, 9, 12 and 16 (depressive symptoms, emotional problems, peer problems, autistic symptoms, hyperactivity, antisocial, conduct and psychopathic symptoms). Second, to assess the stability of genetic and environmental influences on p across time, we conducted longitudinal twin modelling of the first phenotypic principal components of childhood psychopathological measures across each of the four ages. Third, we created a genetic p factor in 7,026 unrelated genotyped individuals based on eight polygenic scores for adult psychiatric disorders to estimate how a general polygenic predisposition to adult psychiatric disorders relates to childhood p.ResultsBehaviour problems were consistently correlated phenotypically and genetically across ages and raters. The p factor is substantially heritable (50-60%), and manifests consistently across diverse ages and raters. Genetic correlations of p components across childhood and adolescence suggest stability over time (49-78%). A polygenic general psychopathology factor, derived from studies of adult psychiatric disorders consistently predicted a general phenotypic p factor across development.ConclusionsDiverse forms of psychopathology consistently load on a common p factor, which is highly heritable. There are substantial genetic influences on the stability of p across childhood. Our analyses indicate genetic overlap between general risk for psychiatric disorders in adulthood and p in childhood, even as young as age 7. The p factor has far-reaching implications for genomic research and, eventually, for diagnosis and treatment of behaviour problems.

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 parental feeding practices (PFPs) of excessive restriction of food intake (‘restriction’) and pressure to increase food consumption (‘pressure’) have been argued to causally influence child weight in opposite directions (high restriction causing overweight; high pressure causing underweight). However child weight could also ‘elicit’ PFPs. A novel approach is to investigate gene-environment correlation between child genetic influences on BMI and PFPs. Genome-wide polygenic scores (GPS) combining BMI-associated variants were created for 10,346 children (including 3,320 DZ twin pairs) from the Twins Early Development Study using results from an independent genome-wide association study meta-analysis. Parental ‘restriction’ and ‘pressure’ were assessed using the Child Feeding Questionnaire. Child BMI standard deviation scores (BMI-SDS) were calculated from children’s height and weight at age 10. Linear regression and fixed family effect models were used to test between- (n = 4,445 individuals) and within-family (n = 2,164 DZ pairs) associations between the GPS and PFPs. In addition, we performed multivariate twin analyses (n = 4,375 twin pairs) to estimate the heritabilities of PFPs and the genetic correlations between BMI-SDS and PFPs. The GPS was correlated with BMI-SDS (β = 0.20, p = 2.41x10-38). Consistent with the gene-environment correlation hypothesis, child BMI GPS was positively associated with ‘restriction’ (β = 0.05, p = 4.19x10-4), and negatively associated with ‘pressure’ (β = -0.08, p = 2.70x10-7). These results remained consistent after controlling for parental BMI, and after controlling for overall family contributions (within-family analyses). Heritabilities for ‘restriction’ (43% [40–47%]) and ‘pressure’ (54% [50–59%]) were moderate-to-high. Twin-based genetic correlations were moderate and positive between BMI-SDS and ‘restriction’ (rA = 0.28 [0.23–0.32]), and substantial and negative between BMI-SDS and ‘pressure’ (rA = -0.48 [-0.52 - -0.44]. Results suggest that the degree to which parents limit or encourage children’s food intake is partly influenced by children’s genetic predispositions to higher or lower BMI. These findings point to an evocative gene-environment correlation in which heritable characteristics in the child elicit parental feeding behaviour., Author summary It is widely believed that parents influence their child’s BMI via certain feeding practices. For example, rigid restriction has been argued to cause overweight, and pressuring to eat to cause underweight. However, recent longitudinal research has not supported this model. An alternative hypothesis is that child BMI, which has a strong genetic basis, evokes parental feeding practices (‘gene-environment correlation’). To test this, we applied two genetic methods in a large sample of 10-year-old children from the Twins Early Development Study: a polygenic score analysis (DNA-based score of common genetic variants associated with BMI in genome-wide meta-analyses), and a twin analysis (comparing resemblance between identical and non-identical twin pairs). Polygenic scores correlated positively with parental restriction of food intake (‘restriction’; β = 0.05, p = 4.19x10-4), and negatively with parental pressure to increase food intake (‘pressure’; β = -0.08, p = 2.70x10-7). Associations were unchanged after controlling for all genetic and environmental effects shared within families. Results from twin analyses were consistent. ‘Restriction’ (43%) and ‘pressure’ (54%) were substantially heritable, and a positive genetic correlation between child BMI and ‘restriction’ (rA = 0.28), and negative genetic correlation between child BMI and ‘pressure’ (rA = -0.48) emerged. These findings challenge the prevailing view that parental behaviours are the sole cause of child BMI by supporting an alternate hypothesis that child BMI also causes parental feeding behaviour.

It has recently been proposed that a single dimension, called thepfactor, can capture a person’s liability to mental disorder. Relevant to thephypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time we compare genetic correlations from different methods and examine their support for a geneticpfactor. We tested the hypothesis of a geneticpfactor by using principal component analysis on matrices of genetic correlations between major psychiatric disorders estimated by three methods – family study, Genome-wide Complex Trait Analysis, and Linkage-Disequilibrium Score Regression – and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7,026 unrelated individuals. All disorders loaded on a first unrotated principal component, which accounted for 57%, 43%, 34% and 19% of the variance respectively for each method. Our results showed that all four methods provided strong support for a geneticpfactor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.

1.AbstractBackgroundNeuropsychiatric disease has polygenic determinants but is often precipitated by environmental pressures, including adverse perinatal events. However, the way in which genetic vulnerability and early-life adversity interact remains obscure. Preterm birth is associated with abnormal brain development and psychiatric disease. We hypothesised that the extreme environmental stress of premature extra-uterine life could contribute to neuroanatomic abnormality in genetically vulnerable individuals.MethodsWe combined Magnetic Resonance Imaging (MRI) and genome-wide single nucleotide polymorphism (SNP) data from 194 infants, born before 33 weeks of gestation, to test the prediction that: the characteristic deep grey matter abnormalities seen in preterm infants are associated with polygenic risk for psychiatric illness. Summary statistics from a meta-analysis of SNP data for five psychiatric disorders were used to compute individual polygenic risk scores (PRS). The variance explained by the PRS in the relative volumes of four deep grey matter structures (caudate nucleus, thalamus, subthalamic nucleus and lentiform nucleus) was estimated using linear regression both for the full, mixed-ancestral, cohort and a subsample of European infants.ResultsThe PRS was negatively associated with: lentiform volume in the full cohort (β=−0.24, p=8×10−4) and the European subsample (β=−0.24, p=8×10−3); and with subthalamic nuclear volume in the full cohort (β=−0.18, p=0.01) and the European subsample (β=−0.26, p=3×10−3).ConclusionsGenetic variants associated with neuropsychiatric disease increase vulnerability to abnormal deep grey matter development and are associated with neuroanatomic changes in the perinatal period. This suggests a mechanism by which perinatal adversity leads to later neuropsychiatric disease in genetically predisposed individuals.

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.

Spatial abilities encompass several skills differentiable from general cognitive ability (g). Importantly, spatial abilities have been shown to be significant predictors of many life outcomes, even after controlling for g. To date, no studies have analyzed the genetic architecture of diverse spatial abilities using a multivariate approach. We developed “gamified” measures of diverse putative spatial abilities. The battery of 10 tests was administered online to 1,367 twin pairs (age 19–21) from the UK-representative Twins Early Development Study (TEDS). We show that spatial abilities constitute a single factor, both phenotypically and genetically, even after controlling for g. This spatial ability factor is highly heritable (69%). We draw three conclusions: (i) The high heritability of spatial ability makes it a good target for gene-hunting research; (ii) some genes will be specific to spatial ability, independent of g; and (iii) these genes will be associated with all components of spatial ability.

We document the growth in published papers on behavioral genetics for 5-year intervals from 1960 through 2014. We used 1861 papers published in Behavior Genetics to train our search strategy which, when applied to Ovid PsychINFO, selected more than 45,000 publications. Five trends stand out: (1) the number of behavioral genetic publications has grown enormously; nearly 20,000 papers were published in 2010–2014. (2) The number of human quantitative genetic (QG) publications (e.g., twin and adoption studies) has steadily increased with more than 3000 papers published in 2010–2014. (3) The number of human molecular genetic (MG) publications increased substantially from about 2000 in 2000–2004 to 5000 in 2005–2009 to 9000 in 2010–2014. (4) Nonhuman publications yielded similar trends. (5) Although there has been exponential growth in MG publications, both human and nonhuman QG publications continue to grow. A searchable resource of this corpus of behavioral genetic papers is freely available online at http://www.teds.ac.uk/public_datasets.html and will be updated annually. Electronic supplementary material The online version of this article (doi:10.1007/s10519-016-9786-2) contains supplementary material, which is available to authorized users.

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.

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.