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.
•Starting from middle childhood, studies point to a strong genetic basis in the association between cognitive ability and academic performance.•Over development, genetic influences are amplified by ...environmental experiences trhigh gene-environment interplay.•The strong stability and heritability of academic performance is not driven entirely by cognitive ability.•Other educationally-relevant noncognitive characteristics contribute to accounting for the genetic variation in academic performance beyond cognitive ability.•Overall, genetic research has provided compelling evidence that has resulted in greatly advancing our knowledge and understanding of the association between cognitive ability and learning.•Considering both cognitive and noncognitive skills as well as their biological and environmental underpinnings will be fundamental in moving towards a comprehensive, evidence-based model of education.
Cognitive ability and educational success predict positive outcomes across the lifespan, from higher earnings to better health and longevity. The shared positive outcomes associated with cognitive ability and education are emblematic of the strong interconnections between them. Part of the observed associations between cognitive ability and education, as well as their links with wealth, morbidity and mortality, are rooted in genetic variation. The current review evaluates the contribution of decades of behavioural genetic research to our knowledge and understanding of the biological and environmental basis of the association between cognitive ability and education. The evidence reviewed points to a strong genetic basis in their association, observed from middle childhood to old age, which is amplified by environmental experiences. In addition, the strong stability and heritability of educational success are not driven entirely by cognitive ability. This highlights the contribution of other educationally relevant noncognitive characteristics. Considering both cognitive and noncognitive skills as well as their biological and environmental underpinnings will be fundamental in moving towards a comprehensive, evidence-based model of education.
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.
The increasing availability of genotype data in longitudinal population‐ and family‐based samples provides opportunities for using polygenic scores (PGS) to study developmental questions in child and ...adolescent psychology and psychiatry. Here, we aim to provide a comprehensive overview of how PGS can be generated and implemented in developmental psycho(patho)logy, with a focus on longitudinal designs. As such, the paper is organized into three parts: First, we provide a formal definition of polygenic scores and related concepts, focusing on assumptions and limitations. Second, we give a general overview of the methods used to compute polygenic scores, ranging from the classic approach to more advanced methods. We include recommendations and reference resources available to researchers aiming to conduct PGS analyses. Finally, we focus on the practical applications of PGS in the analysis of longitudinal data. We describe how PGS have been used to research developmental outcomes, and how they can be applied to longitudinal data to address developmental questions.
Background
Genetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the ...associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways (e.g. via environmental mediation) and biases.
Methods
Here, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases.
Results
Structural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter‐intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene–environment correlations; and (c) adjusting an exposure‐outcome association for a polygenic score can increase rather than decrease bias.
Conclusions
Strikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations.
Many mental health conditions present a spectrum of social difficulties that overlaps with social behaviour in the general population including shared but little characterised genetic links. Here, we ...systematically investigate heterogeneity in shared genetic liabilities with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), bipolar disorder (BP), major depression (MD) and schizophrenia across a spectrum of different social symptoms. Longitudinally assessed low-prosociality and peer-problem scores in two UK population-based cohorts (4-17 years; parent- and teacher-reports; Avon Longitudinal Study of Parents and Children(ALSPAC): N ≤ 6,174; Twins Early Development Study(TEDS): N ≤ 7,112) were regressed on polygenic risk scores for disorder, as informed by genome-wide summary statistics from large consortia, using negative binomial regression models. Across ALSPAC and TEDS, we replicated univariate polygenic associations between social behaviour and risk for ADHD, MD and schizophrenia. Modelling variation in univariate genetic effects jointly using random-effect meta-regression revealed evidence for polygenic links between social behaviour and ADHD, ASD, MD, and schizophrenia risk, but not BP. Differences in age, reporter and social trait captured 45-88% in univariate effect variation. Cross-disorder adjusted analyses demonstrated that age-related heterogeneity in univariate effects is shared across mental health conditions, while reporter- and social trait-specific heterogeneity captures disorder-specific profiles. In particular, ADHD, MD, and ASD polygenic risk were more strongly linked to peer problems than low prosociality, while schizophrenia was associated with low prosociality only. The identified association profiles suggest differences in the social genetic architecture across mental disorders when investigating polygenic overlap with population-based social symptoms spanning 13 years of child and adolescent development.
Ubiquitous associations have been detected between different types of childhood psychopathology and polygenic risk scores based on adult psychiatric disorders and related adult outcomes, indicating ...that genetic factors partly explain the association between childhood psychopathology and adult outcomes. However, these analyses in general do not take into account the correlations between the adult trait and disorder polygenic risk scores. This study aimed to further clarify the influence of genetic factors on associations between childhood psychopathology and adult outcomes by accounting for these correlations. Using a multivariate multivariable regression, we analyzed associations of childhood attention‐deficit/hyperactivity disorder (ADHD), internalizing, and social problems, with polygenic scores (PGS) of adult disorders and traits including major depression, bipolar disorder, subjective well‐being, neuroticism, insomnia, educational attainment, and body mass index (BMI), derived for 20,539 children aged 8.5–10.5 years. After correcting for correlations between the adult phenotypes, major depression PGS were associated with all three childhood traits, that is, ADHD, internalizing, and social problems. In addition, BMI PGS were associated with ADHD symptoms and social problems, while neuroticism PGS were only associated with internalizing problems and educational attainment PGS were only associated with ADHD symptoms. PGS of bipolar disorder, subjective well‐being, and insomnia were not associated with any childhood traits. Our findings suggest that associations between childhood psychopathology and adult traits like insomnia and subjective well‐being may be primarily driven by genetic factors that influence adult major depression. Additionally, specific childhood phenotypes are genetically associated with educational attainment, BMI and neuroticism.
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.
Although earlier research has shown that individual differences on the spectrum of attention deficit hyperactivity disorder (ADHD) are highly heritable, emerging evidence suggests that symptoms are ...associated with complex interactions between genes and environmental influences. This study investigated whether a genetic predisposition Note that the term ‘genetic predisposition’ was used in this manuscript to refer to an estimate based on twin modeling (an individual’s score on the latent trait that resembles additive genetic influences) in the particular population being examined. for the symptom dimensions
hyperactivity
and
inattention
determines the extent to which unique-environmental influences explain variability in these symptoms. To this purpose, we analysed a sample drawn from the Twins Early Development Study (TEDS) that consisted of item-level scores of 2168 16-year-old twin pairs who completed both the
Strengths and Difficulties Questionnaire
(SDQ; Goodman, in J Child Psychol Psychiatry 38:581–586, 1997) and the
Strength and Weaknesses of ADHD Symptoms and Normal Behavior
(SWAN; Swanson, in Paper presented at the meeting of the American Psychological Association, Los Angeles, 1981) questionnaire. To maximize the psychometric information to measure ADHD symptoms, psychometric analyses were performed to investigate whether the items from the two questionnaires could be combined to form two longer subscales. In the estimation of genotype-environment interaction, we corrected for error variance heterogeneity in the measurement of ADHD symptoms through the application of item response theory (IRT) measurement models. A positive interaction was found for both hyperactivity (e.g.,
β
1
= 2.20 with 95% highest posterior density interval equal to 1.79;2.65 and effect size equal to 3.00) and inattention (e.g.,
β
1
= 2.16 with 95% highest posterior density interval equal to 1.56;2.79 and effect size equal to 3.07). These results indicate that unique-environmental influences were more important in creating individual differences in both hyperactivity and inattention for twins with a genetic predisposition for these symptoms than for twins without such a predisposition.
Background Autism and different neurodevelopmental conditions frequently co-occur, as do their symptoms at sub-diagnostic threshold levels. Overlapping traits and shared genetic liability are ...potential explanations. Methods In the population-based Norwegian Mother, Father, and Child Cohort study (MoBa), we leverage item-level data to explore the phenotypic factor structure and genetic architecture underlying neurodevelopmental traits at age 3 years (N = 41,708-58,630) using maternal reports on 76 items assessing children's motor and language development, social functioning, communication, attention, activity regulation, and flexibility of behaviors and interests. Results We identified 11 latent factors at the phenotypic level. These factors showed associations with diagnoses of autism and other neurodevelopmental conditions. Most shared genetic liabilities with autism, ADHD, and/or schizophrenia. Item-level GWAS revealed trait-specific genetic correlations with autism (items r.sub.g range = - 0.27-0.78), ADHD (items r.sub.g range = - 0.40-1), and schizophrenia (items r.sub.g range = - 0.24-0.34). We find little evidence of common genetic liability across all neurodevelopmental traits but more so for several genetic factors across more specific areas of neurodevelopment, particularly social and communication traits. Some of these factors, such as one capturing prosocial behavior, overlap with factors found in the phenotypic analyses. Other areas, such as motor development, seemed to have more heterogenous etiology, with specific traits showing a less consistent pattern of genetic correlations with each other. Conclusions These exploratory findings emphasize the etiological complexity of neurodevelopmental traits at this early age. In particular, diverse associations with neurodevelopmental conditions and genetic heterogeneity could inform follow-up work to identify shared and differentiating factors in the early manifestations of neurodevelopmental traits and their relation to autism and other neurodevelopmental conditions. This in turn could have implications for clinical screening tools and programs.
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