The p factor: genetic analyses support a general dimension of psychopathology in childhood and adolescence

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.

Twins Early Development Study: A Genetically Sensitive Investigation into Behavioral and Cognitive Development from Infancy to Emerging Adulthood

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

Using DNA to predict behaviour problems from preschool to adulthood

Background: One 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. Method: Our 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, externalising, and internalising) across ages (from age 2 to 21) and across raters in penalised 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. Results: Multi-GPS prediction of behaviour problems increased from <2% of the variance for observed traits to up to 6% for cross-age and cross-rater composites. Twin study estimates of heritability, although to a lesser extent, mirrored patterns of multi-GPS prediction as they increased from <40% to 83%. Conclusions: The 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 approach 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

Sub-types of insomnia in adolescents: insights from a quantitative/ molecular twin study

Background: Insomnia with short sleep duration has been postulated as more severe than that accompanied by normal/long sleep length. While the short duration subtype is considered to have greater genetic influence than the other subtype, no studies have addressed this question. This study aimed to compare these subtypes in terms of: 1) the heritability of insomnia symptoms; 2) polygenic scores (PGS) for insomnia symptoms and sleep duration; 3) the associations between insomnia symptoms and a wide variety of traits/disorders. Methods: The sample comprised 4,000 pairs of twins aged 16 from the Twins Early Development Study. Twin models were fitted to estimate the heritability of insomnia in both groups. PGS were calculated for self-reported insomnia and sleep duration and compared among participants with short and normal/long sleep duration. Results: Heritability was not significantly different in the short sleep duration group (A=0.13 [95%CI=0.01, 0.32]) and the normal/long sleep duration group (A=0.35 [95%CI=0.29, 0.40]). Shared environmental factors accounted for a substantial proportion of the variance in the short sleep duration group (C=0.19 [95%CI= 0.05, 0.32]) but not in the normal/long sleep duration group (C=0.00 [95%CI=0.00, 0.04]). PGS did not differ significantly between groups although results were in the direction expected by the theory. Our results also showed that insomnia with short (as compared to normal/long) sleep duration had a stronger association with anxiety and depression (p<.05) - although not once adjusting for multiple testing. Conclusions: We found mixed results in relation to the expected differences between the insomnia subtypes in adolescents. Future research needs to further establish cut-offs for ‘short’ sleep at different developmental stages and employ objective measures of sleep

Comparing Within- and Between-Family Polygenic Score Prediction

Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating

The psychopathology p factor: will it revolutionise the science and practice of child and adolescent psychiatry?

The psychopathology p factor has emerged from a series of strong empirical studies, largely in the adult psychiatry literature. Here, some of the recent findings relating to the p factor in children and adolescents are considered and the implications for child and adolescent psychiatry are discussed. Is it essential to covary for ‘p’ when we study specific domains of psychopathology? Do neurodevelopmental conditions make up part of the psychopathology p factor? How do we treat the ‘p factor’ in clinics? This editorial considers some of the contributions from this issue of Journal of Child Psychology and Psychiatry together with the wider literature that speak to these issues

Genomweite polygene Werte revolutionieren die Intelligenzforschung

Intelligence — the ability to learn, reason and solve problems — predicts all important life outcomes and is highly heritable. Recent genome-wide association studies have identified inherited genome sequence differences that account for five percent of the variance in intelligence and thus, for ten percent of its heritability. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of DNA variants

Using DNA to predict behaviour problems from preschool to adulthood

Background: One 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. Method: Our 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, externalising, and internalising) across ages (from age 2 to 21) and across raters in penalised 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. Results: Multi-GPS prediction of behaviour problems increased from <2% of the variance for observed traits to up to 6% for cross-age and cross-rater composites. Twin study estimates of heritability, although to a lesser extent, mirrored patterns of multi-GPS prediction as they increased from <40% to 83%. Conclusions: The 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 approach 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

Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction

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

Phenotypic and genetic evidence for a unifactorial structure of spatial abilities

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