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Intelligence and mental health are the core pillars of individual adaptation, growth, and opportunity. Here, we charted across childhood and adolescence the developmental interplay between the p-factor of psychopathology, which captures the experience of symptoms across the spectrum of psychiatric disorders, and the g-factor of general intelligence that describes the ability to think, reason, and learn.Our preregistered analyses included 7,433 twin pairs from the Twins Early Development Study (TEDS), who were born 1994 to 1996 in England and Wales. At the ages 7, 9, 12, and 16 years, the twins completed two to four intelligence tests, and multi-informant measures (i.e., self-, parent- and teacher-rated) of psychopathology were collected.Independent of their cross-sectional correlations, p- and g-factors were linked by consistent, bidirectional, and negative cross-lagged paths across childhood and adolescence (from −.07 to −.13 with 95% CIs from −.03 to −.15). The cross-lagged paths from intelligence to psychopathology were largely due to genetic influences, but the paths from psychopathology to intelligence were driven by environmental factors, and increasingly so with age.Our findings suggest that intelligence and psychopathology are developmentally intertwined due to fluctuating etiological processes. Understanding the interplay of g- and p-factors is key for improving children’s developmental outcomes.

Mental ill health is the largest single cause of disability in the UK (1). Research suggests that lifetime trajectories of common mental disorders are established by mid-adolescence, with half of all mental health problems starting by the age of 14 and three-quarters by 25 (2). According to the Mental Health of Children and Young People Survey, between 2017 and 2020, rates of probable mental disorders increased from 11.6% to 17.4% in the age group of 6-16 year-olds and by over 7% among 17-19 year-olds (10.1% vs 17.4%) in the UK (3). Although WHO estimates that roughly 10% of children and adolescents between 5 and 16 years have a diagnosable mental disorder (4), a recent study among 28,000 adolescents reported that the prevalence of mental health problems in children across many schools in England is much higher than previous estimates, with roughly two in five young people scoring above clinical cut-offs for emotional problems, conduct problems, and hyperactivity (5). However, only a fraction of them receives appropriate care. The Office of the Children's Commissioner found that from nearly 400,000 children and young people who were referred to NHS mental health services in 2018-2019, only 19% of children entered treatment within six weeks after the referral, while 33% stayed on the waiting list for over a year and 34% were not accepted into treatment (6). Mental health services further report that even among children with an official mental disorder diagnosis, only one in four children could access the necessary support in 2020 (7), while young people with psychological and emotional difficulties who do not meet the clinical criteria for a formal diagnosis are not eligible even for that, despite presenting a number of symptoms of physical and behavioural problems (8). It is unclear whether this increase in rates of mental disorders in youth is due to a genuine decline of mental health in children and young people, or it is instead due to greater awareness and less stigmatisation of reporting mental health problems for both children and their parents. Yet, the public mental health crisis is evident and tackling the causes and the development of effective early intervention and prevention programmes is urgent. The Children's Society's Good Childhood Report, the longest-standing report on the wellbeing of children in the UK, indicates that in 2022 a larger proportion of children aged 10 to 17 were unhappy with school (14.2%) than with the other nine aspects of life (e.g., home, family, friends) (9). Among the areas of school life, young people were most happy with the feeling of safety and relationships with their peers, and they felt least pleased with the amount of schoolwork and how much they were listened to in school (9). Similarly, Childline, a free counselling service for children in the UK, reported that children's primary concern was stress and anxiety related to schoolwork and exam performance (10,11). However, there is a lack of research exploring the role of the school context in young people's mental health. A systematic review of 48 studies reports strong evidence of the importance of the school climate, comprised of social connectedness, school safety, school connectedness, and the academic environment, for adolescents' mental health (12). Correspondingly, a qualitative study among adolescents and school staff across three secondary schools in the UK identified four dimensions of school culture (structure and context, organisational and academic, community, and safety and support) as important contributors to student mental health (13). A large longitudinal study of young people across 20 schools in England found strong and consistent associations between student-level and school-level (average student responses in each school) reports of positive school climate at baseline and better mental health outcomes at 36 months follow-up (14). However, these associations were compromised to borderline significant for student-level measures and non-significant for school-level climate after adjustment for baseline measures of student mental health (14). Thus, while the existing studies show that students' subjective perceptions of the school environment are significantly associated with their mental health, these reports are prone to bidirectional bias, so mental health outcomes measured after children and adolescents had been at the school for some time, may already have been influenced by the school climate, while mental disorders may, in turn, corrupt the perception of school environment. Furthermore, according to the NHS reports, children with a higher risk of having a mental disorder were twice as likely to miss more than 15 days of school than those from a lower risk group (3). Thus, having a mental disorder presents further risk of exacerbating worries about school and compromising the perception of school environments, making it more complicated to design effective intervention and prevention programmes. Additional objective measures of school-related risk factors, such as school-staff ratio, number of students eligible for free school meals (FSM), or Ofsted school rates, could provide valuable evidence for better understanding the associations between school environment and child and adolescent mental health. Indeed, a recent study across 97 schools in England which linked survey responses with the National Pupil Database (NPD) showed that having special educational needs (SEN) and being eligible for FSM significantly increased the odds of experiencing any of the four mental health problems, namely emotional symptoms, conduct problems, peer-relationship problems, and hyperactivity/inattention problems (5). Furthermore, students with a child in need status were also at significantly increased risk of experiencing all mental health problems except for emotional symptoms (5). However, the schools explained very little variance in mental health problems (intraclass correlations ≤0.02). Moreover, other studies have concluded that school composition and resources (e.g., staff-student ratio) were not associated with students' mental health (15). The mixed findings might indicate a complex relationship between the objective and subjective measures of school environment and their effects on young people's mental health; nevertheless, even fewer studies included both objective and subjective factors in one cohort. A large study that did use both the NPD data from 648 primary schools on school composition (number of children in the school, student gender and ethnicity ratio, and school deprivation indexed as the proportion of students eligible for FSM) and child-reported school climate explored their association revealed that among the school composition factors only school deprivation was associated with adolescents' mental health, such that young people in schools with higher proportions of students eligible for FSM had on average more behavioural symptoms and were more likely to have mental health difficulties above the clinical cut-off, whereas a subjectively-reported more positive school climate was associated with both lower levels of emotional and behavioural symptoms, and lower rates of clinically significant levels of mental health problems (16). The authors further explored the interaction between the school composition and school climate but found no significant effects on mental health outcomes (16). However, to date, there is no evidence whether the objective and subjective measures of school environment may complement each other and explain more variance of child and adolescent mental health problems. Even less is known about whether objective school-level characteristics provide different contexts and present different levels of risk to children with individual biological differences in mental health characteristics. Thus, studies exploring the association between school environment and child and adolescent mental health remain scarce and lack a comprehensive approach, considering both objective and subjective measures of school environment and the individual predisposition to mental disorders. Furthermore, they are often designed as cross-sectional studies, limiting the opportunities of exploring the cumulative risk of objective and subjective school environmental factors for young people's mental health across their development. Accordingly, the main goal of the present study is to clarify the longitudinal associations between school experiences and developmental psychopathology from childhood to emerging adulthood using both objective and subjective data. We have previously investigated mental health associations with subjective parent-, teacher-, and twin-reported measurements of school environment using the data from the Twin Early Development Study (TEDS), a cohort of twin pairs born in England and Wales between 1994 and 1996, utilising the data collected when twins were 7, 9, 12, 16, 18, and 21 years old (17). Our preliminary results show substantial associations between multiple school environment factors, such as classroom environment, homework and attitudes to school, and psychopathology (r~.25). They show additive effects over the years, possibly indicating that these environmental experiences might pose a cumulative risk for child mental health outcomes (Rimfeld et al., in preparation). Furthermore, prediction increased when previous school experience was added to the contemporary predictors in the model. We aim to extend these findings in the present study by focusing on the objective measurements of school environment, such as the number of absences, exam results, and school quality, by utilising data from the UK-wide registers, namely the NPD and the Ofsted ratings. Contrary to the subjective reports, these objective measures are not affected by the possible rater's bias due to existing mental health problems. Furthermore, decades of twin studies have shown that mental health problems are highly heritable (30-80%), thus, it is important to use genetically sensitive designs when exploring the links between school environment and mental health (18). Our preliminary results show that both mental health and educational experiences across the school years are highly heritable (~50%), and that genetic factors largely explain the correlation between subjectively reported school environment and psychopathology (~90%) (Rimfeld et al., in preparation). The present study will explore the links between objective school environment (National Pupil Database school records on school quality, Ofsted ratings) and mental health using a longitudinal genetically sensitive design (age 7-21) and will combine subjective (parent-, teacher- and self-reported school experiences) and objective school measures to explore the additive prediction from school experiences to mental health both contemporaneously and over time.

Mathematics anxiety (MA) is a partially overlaps with general anxiety, but is an independent phenotype, characterised by negative emotional reactions experienced in anticipation of or during mathematics-related activities (Malanchini et al., 2017). Research has shown that, independent of the effects of general anxiety, students who report high levels of MA tend to exhibit lower levels of maths achievement (Zhang et al., 2019). While this association (~ r = -0.3) is now well-replicated, research suggests that the relationship between MA and maths achievement is more complex, with the effects of MA on performance also dependent on levels of mathematics motivation (MM). Individuals with high levels of MM value mathematic abilities highly, are willing to embrace maths challenges, and are keen to perform well in the subject (Gottfried et al., 2007). Some studies have demonstrated that, alongside high reported levels of MM, moderate levels of MA might be beneficial for students’ mathematics achievement (Wang et al., 2015, 2018). Research has also suggested that MA might also lead to lower participation in STEM (Science, Technology, Engineering and Mathematics), regardless of achievement levels (Ahmed, 2018; Choe et al., 2019). In both compulsory and post-compulsory education settings, MA and MM have been found to predict STEM avoidance in course enrolment (Daker et al., 2021; Meece et al., 1990), but fewer studies have considered the potential effects of MA and MM on STEM avoidance in wider career choices. In a UK sample of twin pairs aged 16 to 21 from the Twins Early Development Study (TEDS), we will investigate the relationships between MA, MM and STEM career choices, and whether these are affected by previous mathematics achievement. Evidence also suggests that both motivation and anxiety levels are associated with socioeconomic status (SES), with research indicating that a disproportionate number of students with low educational motivation may be from low SES backgrounds (De Clercq et al., 2017; Rogelberg et al., 2021), and that children from lower SES backgrounds exhibit higher levels of anxiety than those from higher SES backgrounds (Zhu et al., 2019). This study will, therefore, also account for the effects of SES on the relationships between the stated variables. There are approximately 10,000 twin pairs actively involved in the TEDS. We will use all data available at 16-21 years for our chosen variables, but the final sample will be lower than this. First, we will assess and report the relationships between all variables (self-reported MA at 18 (both corrected and uncorrected for general anxiety), self-reported MM at 16, GCSE mathematics achievement at 16, STEM career choice at 21 and SES at 16). Then, we will investigate the extent to which self-reported MA (both corrected and uncorrected for general anxiety) and self-reported MM predict STEM career choice and take a stepwise approach to adding covariates into this model (maths achievement at 16 and SES). As previous studies have suggested that the highest achieving students might exhibit both high levels of MM and moderate to low levels of MA, we hypothesise that both MA and MM will be significant independent predictors of STEM career choice, with the expectation that higher levels of MM will predict a greater likelihood of STEM career choice, and higher levels of MA will predict a lower likelihood of STEM career choice. We also hypothesise that both previous maths achievement and SES will attenuate the associations between MA, MM and STEM career choice. Several studies have reported that female students tend to report higher levels of MA (Malanchini et al., 2017, 2020) and lower levels of MM (Wang et al., 2018). One study investigating whether the outcomes of MA differed by sex found that MA and self-perceived mathematics ability predict each other only in male students, suggesting that MA and MM may have a negative reciprocal relationship in boys, but not girls (Wang et al., 2020). Boys have been shown to identify more with mathematics, and feel more obligated to perform well than girls (Cvencek et al., 2011). Experiencing MA may therefore be more threatening to male student identity than female student identity, and might result in lower MM. Conversely, it may be harder for boys to reconcile lower MM with the societal expectation that they should perform well in mathematics, and this conflict could result in increased MA compared to girls with low MM (Wang et al., 2020). As a result, differences in MA and MM might go on to have a greater impact on the STEM-related career choices of male participants compared to female participants, with lower MM and higher MA resulting in a lower probability of STEM career choice for males than females. We hypothesise that, when doing sex-stratified analyses of the associations above, MA and MM are better predictors of STEM career choice in males than females. We will also use genetically sensitive designs (twin analyses) to understand the aetiology of the relationships between each of the stated traits. We will assess the heritability of all traits, and use multivariate genetic analyses (Cholesky decomposition) to explore the aetiology of covariance between the traits. Using a genetically sensitive framework will allow us to investigate the origins of individual differences in MA, MM and STEM degree choice, as well as the origins of the co-variation between these traits. We will assess the extent to which genes, shared environments, and individual-specific environments contribute to the aetiology of these traits, and determine whether the phenotypic correlations found between traits are reflected in their aetiology. Malanchini et al. (2020) found moderate estimates of heritability across of mathematics-related traits including mathematics anxiety, attitudes, ability and achievement, as well as considerable genetic overlap between these traits. We therefore hypothesise that all variables in the current study will be moderately heritable, and that genetic factors will largely explain the covariance between traits. The study results would advance our understanding of the complex emotion-motivation processes underlying STEM avoidance in young adults and might provide important insights for interventions targeted at increasing participation in STEM fields.

Educational and occupational success, jointly referred to as socioeconomic status, are associated with many important life outcomes, including physical and mental health, life satisfaction, happiness, and longevity (1–4). Individual differences in socioeconomic outcomes (combined socioeconomic status, or employment status, educational attainment and income separately) are often assumed to be environmental in origin, however, twin and family-based research has shown that both genetic and environmental factors play an important role in explaining individual variation (5–8). The heritability (the proportion of individual differences explained by inherited DNA differences) of educational attainment as well as occupational status is around 50%, with the rest of the variation explained by environmental factors (5,8,9). Heritability has traditionally been calculated by comparing the similarity of identical and non-identical twin pairs. It is also possible to estimate heritability directly from DNA, by comparing the phenotypic, or trait, similarity between people with their genetic similarity, calculated using hundreds of thousands of DNA variants, or SNPs (single nucleotide polymorphisms). These SNP-based heritabilities have also been shown to be substantial for socioeconomic outcomes, around 20% on average (7,10–12). SNP heritability for complex traits, like educational and occupational outcomes, is roughly half of that of family-based heritability (13). The reasons for this are that SNP heritability is calculated using genotyped genetic markers, so can only index the variation captured by genetic markers genotyped on a SNP array (SNP chip). As these arrays only uses common SNPs, this excludes rare and structural variants, and it only estimates additive genetic effects (14). The missing heritability, the gap between the family-based heritability and SNP heritability, has been shown to be almost recovered for height and body-mass-index (BMI) by including rare variants in a model using whole-genome sequence data (15). It remains to be seen if the missing heritability in other complex traits, for example for educational status and occupational status, is also explained by the rare variants, which would indicate that the heritability of complex traits is explained mainly by additive genetic effects. Alternatively, it has been argued that twin heritability overestimates the true heritability (16), and this explains (or, rather, explains away) the missing heritability. Another way to estimate genetic influence on complex traits is to use genome-wide polygenic scores (GPS). GPS use summary statistics of GWA (Genome-wide Association) studies that aim to identify specific genetic markers associated with a phenotype of interest, by aggregating thousands of SNP associations across the genome to predict the trait of interest (17). This prediction using GPS serves as an index that summarizes the current prediction of additive genetic influences of the trait of interest (18). These GPS heritabilities for socioeconomic variables have also been shown to be substantial, around 11% on average (19,20). However, all these heritability estimates may be different in varied environments and may change with major changes in environment, for example with societal changes such as changes in policy (5,21). Paradoxically, heritability for educational outcomes tends to be higher when environmental variation is attenuated. That is, with less variation in the environment, a larger proportion of individual differences is explained by genetic factors (22). Thus heritability could be used as an index of equal opportunities (18,23). In addition, research has shown that individual differences in socioeconomic outcomes are explained both by inherited DNA differences but also by non-transmitted alleles operating via parental genotypes. That is, part of the variance in educational attainment is explained by so-called genetic nurture; that is, parents’ genetic variants not transmitted to their children still predict educational attainment in offspring (24,25). If parents’ non-transmitted genetics predict their offspring’s achievement, this link must happen through parent-driven environmental processes, independent of child’s own biology. The environmental experiences that parents create for their children are associated with their offspring educational outcomes, are a clear example of passive gene-environment correlation. The gene-environment correlation is a well-studied area in quantitative genetic research (26,27), but often limited to between family variance. However, DNA data is allowing us to get closer to this phenomenon and quantify the genetic nurture component in several social outcomes. Genetic nurture has been shown to be substantial for educational outcomes (25) and to a smaller degree for depressive symptoms (28). It is a plausible hypothesis meriting major research interest to study how genetic nurture is influenced by changes in the larger social environment. A related issue here concerns the locus of the heritability of socioeconomic outcomes. It is well-established, for example, that educational achievement is explained by a package of heritable cognitive and non-cognitive traits (29). However, it is not known what proportion of the putative change in heritability of socioeconomic outcomes is explained by cognitive and non-cognitive factors following a major societal change in the environment. Furthermore, it is not known how the change in social environment reflects genetic nurture, that is the proportion of variance in offspring social outcomes that is explained by parental genotypes, independent of child’s biology, and the proportion of this genetic nurture that is explained by cognitive or non-cognitive genetic variances in parental genotypes. A newly developed genomic method, GWAS-by-subtraction, allows researchers to investigate these outstanding research questions by partitioning the SNP associations of educational attainment into cognitive and non-cognitive parts of variance (30). Educational attainment has also been shown to be associated with fecundity (the ability to produce many offspring): a more educated people tend to have children later in life and as a result, or in addition to that, tends to have fewer offspring. The relationship between educational attainment and fecundity could result in a decline of educationally relevant genetic variants in the population. Although this effect in one generation is small, it could lead to a significant decline over many generations (31). We have previously shown a massive shift in the aetiology of social outcomes in Estonia following an abrupt social change - the shift from communism to capitalism following the collapse of the Soviet Union. DNA differences explained twice as much variance in social outcomes (occupational status, educational attainment) independent Estonia compared to when Estonia was occupied by the Soviet Union (32). We also reported interesting sex differences, where the shift was much larger for females compared to males, but we were cautious with our conclusions due to limited power in the sex-stratified analyses. We also tested the decline of educationally relevant genetic variants in the population, but did not find evidence for the hypothesis. In addition, we did not see a decrease in average EduYears GPS across birth cohorts, but we did not account for potential differences in fecundity (32). The Estonian Biobank sample, used in our previous study, has grown from 50,000 DNA donors, making up 5% of the adult population, to 205,000 DNA donors, making up 20% of the adult population in Estonia, thus offering an opportunity to replicate and extend our previous research.

The aim of the proposed research is to close a major gap in the cognitive and educational literature by applying a genetically informative cross-lagged model (the ACE cross-lagged model; Malanchini, Wang et al., 2017) to investigate reciprocal causal links between cognitive ability and academic achievement over development. By adopting a genetically informative framework we will be able to investigate the extent to which the developmental links between cognitive skills and academic achievement are due to genetic and environmental factors.

We have known for decades that school achievement is highly heritable. In fact, during the primary school years, it has been found to be even more heritable than general cognitive ability, intelligence (Kovas et al., 2013). The heritability of educational attainment is constantly high across academic subjects, across ages, and across raters (teacher ratings; exam scores), with average heritability around 60% (SNP-based heritability around 30%) (Rimfeld et al., 2018). This high heritability reflects the involvement of many heritable cognitive abilities as well as non-cognitive traits, from personality and self-efficacy through to behavioural problems and health (Krapohl et al., 2014); all of which can be used to predict school performance. The strongest predictors of school achievement to date include family socioeconomic factors (SES; family income, occupation etc.) and DNA (von Stumm et al., 2019). We have previously shown that we can use DNA to predict school performance across school years, with the highest prediction at age 16 GCSE (General Certificate of Secondary Education) performance, where DNA differences between students, aggregated into a genome-wide polygenic score (GPS), explain 16% of the variance in exam results (Allegrini et al., 2019; Selzam et al., 2017). The GPS derived from the summary statistics of a Genome-Wide Association (GWA) study of adult years of education (Lee et al., 2018), is currently the most powerful polygenic scores in the behavioural sciences. Family SES also predicts academic performance across school years, explaining up to 23% of individual differences in school performance at age 16 (von Stumm et al., 2019). Furthermore, research shows that SES and GPS act additively, indicating that the joint prediction of SES and GPS is particularly dramatic for students at the extremes of the distribution (von Stumm et al., 2019); however, there are many students who underperform or exceed their expectations in these extremes groups (SES; GPS). However, it is not clear what predicts underachievement, that is, why students with high GPS and high or low SES underperform; and overachievement, when children with low GPS and high or low SES perform very well. Of particular interest here is to study the group of young people with low GPS and high SES or vice versa, that is studying why students with a high genetic score from low SES families may not reach their full potential. With the current project, we aim to identify children at the extremes of both GPS and SES, using +/-1.5, +/-2 and +/-3 SD as cut-off criteria. We will then assess if these identified children are performing as expected or perform below or above the expectations using both the exam grades and teacher ratings. We will then use longitudinal data collected at TEDS to predict over and underachievement in academic performance, using both cognitive and non-cognitive predictors including the p- factor (general factor of psychopathology) as well as school-related variables (self-reported data about school environment; objectively collected school environment measures, such as school type, Ofsted ratings, school and classroom sizes). In addition, we will assess whether teachers undermark/undergrade students from low SES backgrounds assessing the differences between teacher grades and standardized test scores. We have previously shown that there is a high correlation between teacher grades and exam scores in the general population (Rimfeld et al., 2019), but with the current project, we will assess if this also applies to the extremes of the distribution. We will also use intelligence scores (using both phenotypic scores and IQ GPS) to assess if intelligence can explain the discrepancy between GPS and SES and school performance.

The winding road to adulthood is the subtitle of the field-defining book on emerging adulthood by Jeffrey Arnett (Arnett, 2014). The book documents the recent societal changes – for example, insecure employment, unstable relationships, difficulties in owning a home -- that have prolonged the transition from adolescence to adulthood, a process which he called emerging adulthood. The transition is marked by instability, self-focus, feeling in-between, exploring identity in work and love, and an optimistic sense of possibilities. More than 3,000 papers, primarily in psychology and psychiatry, have investigated emerging adulthood. Most of these are normative, describing average changes that take place during the twenties. Less is known about individual differences, which are a key feature of emerging adulthood because the transition to adulthood is much less lock-stepped than in the past. Genetic and environmental analyses of individual differences in emerging adulthood have been reported for domains that are assessed throughout the life course (Bergen, Gardner, & Kendler, 2007) such as psychopathology (e.g.,(Polderman et al., 2015)), personality (e.g. (Turkheimer, Pettersson, & Horn, 2014)) and cognitive abilities (e.g., (Briley & Tucker-Drob, 2013)). However, much less is known about the genetic and environmental aetiology of individual differences in traits especially salient to emerging adulthood. The great heterogeneity of experience during emerging adulthood suggests the hypothesis that environmental differences are especially important. A refinement of this hypothesis of heightened environmental influence is that any residual effect of family environment is overwhelmed as emerging adults make their own way in the world. The instability and uncertainty of emerging adulthood produces idiosyncratic experiences that nudge individuals down different paths. Such experiences are not likely to be shared by siblings, which in behavioural genetics is called non-shared environment. A less obvious possibility is that genetic influence is greater and shared environmental influence weaker in emerging adulthood, which is the trend found in the few relevant twin studies of emerging adulthood (Koenig et al., 2008; Kornadt et al., 2018; Hufer et al., 2019). Genotypes become phenotypes by selecting, modifying and creating experiences in part on the basis of their genetic propensities, called gene-environment correlation. For this reason, it is possible that the instability and uncertainties of emerging adulthood create more opportunities for individuals to make choices and select increasingly diverse life experiences that are correlated with their genotypes. The current study will first explore who the emerging adults are, what their life goals are, their purpose in life, relationships, attitudes of marriage, money and occupation (see methods). We will then systematically investigate the genetic and environmental influences on a broad range of traits relevant to emerging adulthood. We will use the classical twin design to investigate the aetiology of these traits in the Twins Early Development Study (Rimfeld et al., 2019), assessed when the twins were in their early twenties. In addition, we will explore how these psychological traits during emerging adulthood relate to core functional outcomes, focusing on physical health (health factor computed from variables such as over the counter painkillers taken, hospitalizations, self-reported health, see methods), mental health (defined here as p-factor, or common psychology factor, see methods) and wellbeing (financial wellbeing, satisfaction with relationships, lovelife, see methods) and educational outcomes (defined here as the highest educational level achieved by emerging adulthood). References Arnett, J. J. (2014). Emerging Adulthood: The Winding Road from the Late Teens through the Twenties. In Emerging Adulthood: The Winding Road from the Late Teens through the Twenties. Oxford University Press, Oxford, England. Bergen, S. E., Gardner, C. O., & Kendler, K. S. (2007). Age-related changes in heritability of behavioral phenotypes over adolescence and young adulthood: A meta-analysis. Twin Research and Human Genetics. https://doi.org/10.1375/twin.10.3.423 Briley, D. a, & Tucker-Drob, E. M. (2013). Explaining the increasing heritability of cognitive ability across development: a meta-analysis of longitudinal twin and adoption studies. Psychological Science, 24, 1704–1713. https://doi.org/10.1177/0956797613478618 Hatemi, P. K., & McDermott, R. (2012). The genetics of politics: Discovery, challenges, and progress. Trends in Genetics. https://doi.org/10.1016/j.tig.2012.07.004 Hufer, A., Kornadt, A. E., Kandler, C., & Riemann, R. (2020). Genetic and environmental variation in political orientation in adolescence and early adulthood: A nuclear twin family analysis. Journal of Personality and Social Psychology. https://doi.org/10.1037/pspp0000258 Koenig, L. B., McGue, M., & Iacono, W. G. (2008). Stability and Change in Religiousness During Emerging Adulthood. Developmental Psychology. https://doi.org/10.1037/0012-1649.44.2.532 Kornadt, A. E., Hufer, A., Kandler, C., & Riemann, R. (2018). On the genetic and environmental sources of social and political participation in adolescence and early adulthood. PLoS ONE. https://doi.org/10.1371/journal.pone.0202518 Polderman, T. J. C., Benyamin, B., De Leeuw, C. A., Sullivan, P. F., Van Bochoven, A., Visscher, P. M., & Posthuma, D. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics, 47(7), 702–709. https://doi.org/10.1038/ng.3285 Rimfeld, K., Malanchini, M., Spargo, T., Spickernell, G., Selzam, S., McMillan, A., … Plomin, R. (2019). Twins Early Development Study: A Genetically Sensitive Investigation into Behavioral and Cognitive Development from Infancy to Emerging Adulthood. Twin Research and Human Genetics. https://doi.org/10.1017/thg.2019.56 Turkheimer, E., Pettersson, E., & Horn, E. E. (2014). A phenotypic null hypothesis for the genetics of personality. Annual Review of Psychology, 65, 515–540. https://doi.org/10.1146/annurev-psych-113011-143752

This is a 4 year research program funded by Sir Henry Wellcome Postdoctoral Research Fellowship. This project aims to identify the associations between educational experiences and mental health establishing both aetiology and causal direction, using epidemiological and genetic tools in studies from the UK, Sweden and Estonia. Finally, I aim to develop individualised prediction models for adverse mental health outcomes using both genetic and environmental risk factors.

Callous-unemotional (CU) traits develop during childhood and predict negative mental health outcomes including psychopathy. Negative parenting has been repeatedly related to CU traits. However, since individual differences in CU traits are partly heritable, genetically-informed designs are required to examine the degree to which parenting contributes to CU traits as an environmental factor, and the degree to which gene-environment correlation, including child-driven effects on parenting, is at play. This study will involve participants from the Twin Early Development Study (TEDS), a community-based study of twins born in England and Wales between 1994–1996. Using longitudinal cross-lagged twin models, we will study genetic and environmental effects unique and common to negative parenting and CU traits, effects contributing to stability of both phenotypes over time, and cross-lagged (longitudinal parent- and child-driven) effects. Results will illuminate how parent-child relationships evolve from childhood through adolescence and contribute to developmental trajectories of CU traits.

The COVID-19 pandemic has devastated the lives of many, both for those infected by the virus as well as those spared from the infection. The COVID-19 pandemic has changed the everyday lives of all, through full or partial forced isolation (lockdown), closure of schools, public spaces and associated economic consequences. It is already well documented that COVID-19 pandemic may have substantial and long-lasting consequences for the mental health of many 1–4. This pandemic might worsen mental health on average in the population, but young people are likely to be affected disproportionately 5–8. Young people’s mental health has been found to be worse following the pandemic-related lockdown compared to the adult population even after the first restrictions were lifting, suggesting that there could be long-lasting consequences of pandemic 9. Young adulthood (the period between early twenties and early thirties) is a time marked by instability and fragility in several aspects of life and a critical age for the development of psychopathologies 10–12. Even before COVID-19, Public Health England reported that 14.3M days of work are lost every year to stress, depression and anxiety. The ~12M young adults living in the UK make up over a third of the current workforce and nearly half of the workforce for the next two decades. The instability and financial hardship generated by this crisis are likely to exacerbate the struggles of young adults, with cascading effects on the health, wellbeing and economic stability of the nation. The majority of the research to date focuses on average psychological changes during the COVID-19 crisis and the unprecedented global experience of lockdown, but this crisis is likely to affect individuals differently 13. Individual differences are likely to be large and include both negative changes, e.g., increased anxiety and depression, and positive changes, e.g., increased wellbeing 14. It is important to study individual differences as well as average differences (means) in response to the pandemic and extraordinary experience of lockdown. A fundamental aspect of individual differences often ignored when studying the response to COVID-19 crises is genetic variation. Yet, it has been demonstrated over decades of twin studies that inherited DNA differences contribute substantially to most psychological traits 15,16. Even more importantly, research has shown that sensitivity to environmental changes have a heritable component 17. It is thus reasonable to assume that the response to the COVID-19 crisis is also partially driven by genetic variation between individuals. Our focus here is on individual differences in mental health outcomes before and during the ongoing crisis and the genetic and environmental origins of these differences as assessed by the classical twin method that compares the resemblance of identical and non-identical twins. We will also investigate factors influencing changes in mental health using genome-wide polygenic scores (GPS), aggregate scores capturing genetic predisposition of risk and protective factors based on previous genome-wide association study (GWAS) 18. Lockdown measures could be especially detrimental for young adults with existing psychological and psychiatric vulnerabilities 8,14,19. Here we can capitalise on the data collected at TEDS (the Twins Early Development Study 20 over two decades to identify young adults already experiencing mental health problems prior to the COVID-19 pandemic and assess the change in their mental health symptoms while taking into account the genetic factors. One month after COVID-19 lockdown (20-30 April 2020), we assessed a dozen psychological measures for more than 3000 UK-representative young adult twins in their mid-twenties, participating since infancy in the longitudinal Twins Early Development Study (TEDS; Rimfeld et al., 2019). They were assessed on the same traits in a three-wave longitudinal study collected in April, July and October 2020, although the focus of the present study in on mental health outcomes. In addition, we collected data on measures developed specifically to quantify the impact of COVID-19 pandemic (the CoRonavIruS Health Impact Survey (CRISIS; 21). These data will allow us to study mental health before and during the pandemic using genetically sensitive designs. We will also test the extent to which self-reported data on COVID worries, life changes and mood states during pandemic (CRISIS measures) explain change in mental health measured over the first seven months of the pandemic. Our first study (Rimfeld et al. under review) compared a wide range of psychological measures (30 different traits) from T1 (2018) to T2 (April 2020) and found modest and inconsistent mean changes in these traits after one month of the COVID-19 lockdown. The largest negative effects of the pandemic were reduced volunteering and achievement motivation and increased hyperactivity-inattention. However, there were as many positive changes in response to the pandemic, most notably, reduced verbal peer victimisation. It is, however, possible that one month of lockdown was an insufficient timeframe to examine the negative consequences of this unprecedented restriction. Here, we use longitudinal data collected over four waves (2018 and three post-pandemic waves of assessment one, four and seven months after lockdown began in March 2020). The focus of the present study is on the aetiology of individual differences in mental health changes in young adults, as a function of the COVID-19 pandemic. We will: 1) describe changes in means from 2018 and during the pandemic, but our focus is on individual differences and their genetic and environmental origins at each wave of assessment as well as change in individual differences (we will also report these changes in extreme, especially vulnerable groups); 2) analyse change and continuity using longitudinal modelling (latent growth curve modelling) and the twin design (Cholesky decomposition of latent growth curve modelling); 3) assess the extent to which changes in mental health can be predicted by self-reported COVID worries, mood states, life changes and COVID impact (family, symptoms, living conditions) (derived from CRISIS 21 ) and polygenic scores (derived from both psychiatric and cognitive GWA summary statistics).

Summary: This study will examine the extent to which different aspects of spatial ability (i.e., navigation, object manipulation and visualisation), distilled from a battery of 16 tests, can predict STEM (science, technology, engineering and mathematics) educational outcomes, post-secondary degree choice and STEM-related non-cognitive factors, and the extent of their prediction after accounting for general cognitive ability. The project will leverage data from the longitudinal Twins Early Development Study (TEDS), including a comprehensive battery of spatial ability tests collected when the twins were 18 years old. We will employ factor analysis, linear and multiple regression within a structural equation model (SEM) framework to: a) produce latent variables for the three factors of spatial ability (as previously described in Malanchini et al., 2020a), b) analyse the association between these variables and key STEM-related education and life outcomes, c) examine the specific predictive ability of spatial skills beyond general cognitive ability. Finally, we will explore the aetiology driving the associations between spatial skills and educational and life outcomes by conducting bivariate and trivariate Cholesky decompositions. Background: STEM (science, technology, engineering and mathematics) education is becoming increasingly important in our increasingly technological world. For governments and businesses, higher student engagement in STEM represents an investment in human capital development; at the individual level, success in STEM is associated with a myriad of positive life outcomes, including income, well-being, and all-cause mortality (Goodman, 2019; Betts and Rose, 2001, Dreary, Hill and Gale, 2021). Given this, it is important to first understand the factors that predict both success in STEM education and one’s decision to pursue STEM careers—the basic knowledge necessary to increase overall engagement in STEM as well as reduce disparities in STEM outcomes. As with achievement in education more broadly, success in STEM subjects at school varies in the population. Research has shown that much of the individual variation in educational achievement can be accounted for by inherited DNA differences between individuals (see Malanchini, Rimfeld, Allegrini, Ritchie & Plomin, 2020 for a review). Both cognitive ability and non-cognitive factors (e.g., self-efficacy, motivation and mathematics anxiety) have been shown to contribute to the heritability of educational achievement, with shared genetic influences between the cognitive and non-cognitive factors explaining around 75% of achievement’s total heritability (Krapohl et al., 2014). Compared to the extant literature on educational achievement more broadly, however, there has been little genetically-informed work focusing specifically on STEM achievement and its predictors. A recent study exploring the genetic and environmental overlap between emotions, attitudes and performance (i.e. non-cognitive factors) in mathematics found a strong genetic overlap between these mathematics-related traits (Malanchini et al., 2020b). This provides a first line of evidence for delving deeper into the investigation of how students develop, select and modify STEM-related attitudes and experiences, partly based on their genetic predispositions. In addition to these non-cognitive factors, previous literature has shown that spatial ability (a domain of cognitive ability defined broadly as one’s ability to manipulate mental representations of objects) predicts STEM outcomes (Wai, Lubinsky and Penbow, 2009). However, theoretical discordances with regards to the architecture of the construct have stymied its adoption in psychological and educational longitudinal research. Specifically, it is unknown whether the different subdomains of spatial ability represent a single construct or several different ones. In an effort to elucidate this architecture, Malanchini et al. (2020c) employed factor analysis on a comprehensive novel battery of 16 spatial ability tests and found evidence for a hierarchical model in which 3 factors emerged (navigation, object manipulation and visualisation). All 3 factors were highly intercorrelated (mainly for genetic reasons) and loaded substantially onto a general factor. This unitary factor of spatial ability was found to be highly heritable at 84%. Critically, 45% of this genetic influence was independent of genetic influence on general cognitive ability. This finding supports previous work finding that spatial ability may be distinguishable, both phenotypically and genetically, from general cognitive ability (Rimfeld et al., 2017). In other words, spatial ability can be thought of as a cognitive skill in its own right, distinct from—and thus relatively untapped by—measures of general cognitive ability. The finding that spatial ability is characterized by a multi-factorial structure that appears to be distinct from general cognitive ability presents exciting new avenues of inquiry for understanding the role of spatial skills in propelling individuals on different STEM trajectories. Principally, it allows us to explore the heterogeneity (or lack thereof) in predictive value amongst spatial domains: does, for example, object manipulation predict certain STEM outcomes better than navigation? Furthermore, a paucity of research has examined the role of spatial ability for STEM outcomes within a developmental context. Fundamentally, no research to date has investigated the contribution of genetic and environmental factors to their association, not least with a comprehensive battery of spatial ability tests tapping multiple domains of spatial ability. The present study will leverage this unique, novel, and comprehensive battery of spatial tests alongside longitudinal data from the Twins Early Development Study (TEDS) to investigate the specific role of the three subdomains of spatial ability in predicting STEM outcomes (beyond general cognitive ability) within a developmental context, and to explore the aetiology underlying these associations. For comparison, we will also associate spatial ability with non-STEM educational outcomes (e.g. English and humanities grades).

We examine whether salivary DNA-methylation and polygenic scores of an inflammatory marker (hs-CRP) are associated with child and adolescent mental health and whether molecular profiles and mental health phenotypes are socially stratified. Public registration: https://doi.org/10.17605/OSF.IO/TA29G

Although other research focuses on average psychological changes during the COVID-19 crisis and the unprecedented global experience of lockdown, our focus is on individual differences in psychological traits before and after the crisis and the genetic and environmental origins of these differences as assessed by the classical twin method that compares the resemblance of identical and non-identical twins. One month after COVID-19 lockdown (20-30 April 2020), we assessed a dozen psychological measures for more than 3000 UK-representative young adult twins in their mid-twenties, participants since infancy in the longitudinal Twins Early Development Study (TEDS; Rimfeld et al., 2019). They were assessed on the same traits in 2018 (T1), which made it possible to compare results before and after lockdown (T2) for a wide range of traits such as anxiety and depression, relationships, physical activity, online behaviour, volunteering, self-harm and drug use, traits especially important during this emerging adulthood developmental phase. We will describe changes in means from T1 to T2, but our focus is on individual differences and their genetic and environmental origins at T1 and T2 and in changes from T1 to T2. We operationalise change by regressing T1 from T2 so that T2 scores are independent of scores at T1, which we refer to as ‘change scores’. We will use the twin method to estimate genetic and environmental influences on variance at T1, at T2, and on T2 change scores.

The goal of genome-wide association (GWA) studies is to identify inherited DNA variants associated with complex traits and common disorders. Achieving this goal has facilitated research on topics such as tracing how genetic risk unfolds developmentally, gene-environment interaction and correlation, and multivariate issues of genetic heterogeneity and co-morbidity. Eventually, DNA prediction will advance clinical work by identifying problems on the basis of causes rather than symptoms, by moving away from diagnoses towards dimensions, by switching from one-size-fit-all treatments to individually tailored treatments, and by focusing on prevention rather than treatment (Plomin, 2018). Individual DNA variants such as single-nucleotide polymorphisms (SNPs) predict miniscule amounts of variance, but thousands of SNP associations aggregated in genome-wide polygenic scores (GPS) can predict substantial amounts of variance (GPS heritability) for some traits. The most predictive GPS for behavioural traits are for educational achievement and general cognitive ability (g) (up to 16% and 11% GPS heritability, respectively; Allegrini et. al., 2019) and for anthropometric traits such as height (up to 40%; Lello et. al., 2018) and body mass index (BMI, 13%; Khera et. al., 2019; Yengo et. al., 2018). However, GPS heritability appears to be much lower for behaviour problems such as symptoms of autism (2.5%; Grove et. al., 2019) and hyperactivity/inattention (0.5%; Chang et. al., 2020) and psychotic-like experiences (1%; Pain et. al., 2018). A recent meta-analysis also showed low GPS heritability for internalising problems and hyperactivity/inattention across development (

Background The statistics of mental health problems in the UK portrays a dark picture: 1 in 10 children have a diagnosable mental health disorder, approximately three children in every classroom (Green, McGinnity, Meltzer, Ford, & Goodman, 2009; Murphy & Fonagy, 2012). These mental health problems may have a long shadow later in life for children (Kelly-Irving et al., 2013; Kessler, Foster, Saunders, & Stang, 1995); they also affect children's families (Bogels & Brechman-Toussaint, 2006) as well as the overall economy (Prince et al., 2007). In addition to being associated with adverse life outcomes, these mental health problems can lead to self-harm and suicide (Fombonne, Wostear, Cooper, Harrington, & Rutter, 2001). Furthermore, studies have shown that the earlier these symptoms appear, the worse the outcome tends to be (Otto et al., 2001). It is of vital importance to identify children who are at greatest risk of developing mental health problems as early as possible because earlier intervention or prevention tends to be most effective (Colizzi, Lasalvia, & Ruggeri, 2020). One way to identify children at highest risk of developing mental health problems is to study their vulnerability to any mental health problems rather than focusing on the risk of a specific disease, taking into account both genetic and environmental risk factors. Recent research has provided empirical evidence for a general factor of psychopathology (p-factor), indicating that the range of psychopathology symptoms, from internalizing to externalizing problems, can be summarized by a common latent factor that explains a large part of the variance. This p-factor, capturing the vulnerability to mental health problems, emerges taking into account dimensionality of disorders (quantitative continuum of symptoms) as well as diagnoses, persistence and co-occurrence of the range of disorders (Allegrini et al., 2020; Caspi et al., 2014; Martel et al., 2017), both during childhood and adulthood. The p-factor is predictive of multiple negative outcomes, general wellbeing and impairment throughout life (Caspi & Moffitt, 2018). The p-factor is analogous to the g-factor (general cognitive ability, intelligence) indicating that a common factor characterizes a range of cognitive abilities, although there is some specificity for verbal, non-verbal and spatial abilities (Carroll, 1993). Similarly, there is some specificity for psychopathology symptoms after accounting for the p-factor (Allegrini et al., 2020; Selzam, Coleman, Caspi, Moffitt, & Plomin, 2018). An important factor to consider is what explains the wide individual differences in p-factor. Individual differences in mental health problems are explained by both genetic and environmental factors. Twin and family studies have shown that virtually all dimensions of psychopathology are explained by substantial genetic influence, with heritability estimates ranging from 30-80% (Knopik, Neiderhiser, DeFries, & Plomin, 2017). Behavioural genetic and genomic studies have also indicated substantial genetic correlations across dimensions of psychopathology, as well as across medical diagnoses, implying that shared genetic factors contribute to the co-occurrence of the range of dimensions of psychopathological traits and diagnosed mental health disorders (Allegrini et al., 2020; Knopik et al., 2017). The common p-factor is also found to be highly heritable (50-60%) during childhood and adolescence and highly heritable across raters (Allegrini et al., 2020). Great progress has been made in identifying specific genetic markers associated with a range of mental health problems (Visscher et al., 2017). Individually these markers are not very useful because their effect sizes are very small. However, markers can be aggregated in genome-wide polygenic scores (GPS) to predict psychopathology symptoms across development (Middeldorp & Wray, 2018). The predictive ability of GPS constructed from genome-wide association (GWA) studies of a range of mental health traits has been shown to be significant, but the predictive accuracy is still modest. The goal of the proposed study is to boost the predictive power of GPS by focusing on the p factor. Multivariate prediction models that leverage joint prediction of multiple GWA summary statistics have been shown to be effective in boosting power and predictive accuracy of GPS (Allegrini et al., 2020, 2019; Grotzinger et al., 2019; Maier et al., 2018; Turley et al., 2018). There are several multivariate methods available for researchers to use (Genomic SEM, MTAG, SMTpred). The predictive accuracy for the cognitive traits has been shown to be similar between these different methods (Allegrini et al., 2019). However, to our knowledge it has not been tested empirically for mental health outcomes, that is if the different multivariate methods differ for the prediction for psychopathology symptoms (and diagnoses of mental health problems) across development. The current project will investigate how the genetics of the p-factor can best be captured using methods leveraging GWA summary statistics -- both multi-trait GWA prediction (e.g. Genomic SEM; MTAG) and multi-polygenic score models (e.g. multiple regression; prediction models using the elastic net). We will test whether the way genomic p is constructed leads to differences in prediction in both the dimensional p- factor (continuous measures of psychopathology) and clinical outcomes (diagnoses by a psychiatrist; p-factor) during childhood and early adulthood. We will systematically test the multivariate prediction of GPS (using both multi-GPS and multi-trait GWA prediction) to the p-factor from childhood to emerging adulthood (using data from self-, parent-, teacher-ratings). This project will leverage the most recent GWA summary statistics of psychiatric problems in adulthood. In addition, the project will attempt to boost the power to predict the p factor by including GPS of nonpsychiatric traits, most notably educational attainment and intelligence. It has been shown both phenotypically and genetically that educational attainment and intelligence are predictors of psychiatric problems both during childhood and in adulthood (Harden et al., 2019; Plomin & Deary, 2015). We will test the extent to which polygenic scores derived from GWA summary statistics from intelligence, educational attainment and personality-related phenotypes like neuroticism and risk-taking will increase the prediction of the p-factor. We will test the prediction in both dimensional/quantitative measures of mental health as well as clinical outcomes of mental health using multivariate genomic prediction methods building on work by Mallard and colleagues (2019), which suggests that diagnostic criteria in adulthood are only moderately related to self-reported symptoms (Mallard et al., 2019). Here we will test the prediction of p- factor in self-, parent- and teacher ratings, as well as clinical diagnoses across childhood and adulthood. Finally, we will study the specificity of psychiatric problems after accounting for genomic p factor. There are symptom-specific genomic factors, or residual variance in psychopathology after controlling for multivariate GPS predictions (genomic p-factor) (Grotzinger et al., 2019). Here we will test how this symptom-specific variance could increase prediction of specific symptoms of psychopathology (internalizing, externalizing and neurodevelopmental disorders) during childhood and emerging adulthood, over and above the genomic p factor. Genomic SEM will be employed to fit the common pathway model to the multivariate data using summary statistics of adult psychiatric GWA, testing for both common and specific factors of psychopathology. Using the summary statistics derived from Genomic SEM, we will construct GPS to test the variance explained in the phenotypic p-factor and the specific psychopathology symptoms from childhood to emerging adulthood in two representative samples from the UK and Sweden. Our project will take a systematic look at how the genetics of p can best be aggregated to predict the predisposition to psychopathology from childhood to early adulthood. This prediction could potentially be useful in order to identify children at greatest risk who would benefit most from early prevention and intervention. Additionally, the future potential impact of this work would allow using the genomic p factor in studies as a control variable. When studies explore the environmental risk factors of psychopathology, genetic p allows to partially control for genetic confounding, thus getting a cleaner measure of non-genetic, environmental risk factors of adverse mental health.

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.\ud \ud 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.\ud \ud Results: Multi-GPS prediction of behaviour problems increased from

Supplemental Material, sj-docx-1-spp-10.1177_19485506211036610 for Genetic and Environmental Factors of Non-Ability-Based Confidence by Randi L. Vogt, Anqing Zheng, Daniel A. Briley, Margherita Malanchini, K. Paige Harden and Elliot M. Tucker-Drob in Social Psychological and Personality Science

Supporting info item, per2202-Open_Practices_Disclosure_Form for Genetic and Environmental Influences on Achievement Goal Orientations Shift with Age by Zheng Anqing, Briley Daniel A., Malanchini Margherita, Tackett Jennifer L., Harden K. Paige, Tucker–Drob Elliot M. and Wrzus Cornelia in European Journal of Personality

Supporting info item, per2202-supp-0001-Supplementary_materials for Genetic and Environmental Influences on Achievement Goal Orientations Shift with Age by Zheng Anqing, Briley Daniel A., Malanchini Margherita, Tackett Jennifer L., Harden K. Paige, Tucker–Drob Elliot M. and Wrzus Cornelia in European Journal of Personality

Extant literature has established a consistent association between aspects of reading motivation, such as enjoyment and self-perceived ability, and reading achievement, in that more motivated readers are generally more skilled readers. However, the developmental etiology of this relation is yet to be investigated. The present study explores the development of the motivation-achievement association and its genetic and environmental underpinnings. Applying cross-lagged design in a sample of 13,825 twins, we examined the relative contribution of genetic and environmental factors to the association between reading enjoyment and self-perceived ability and reading achievement. Children completed a reading comprehension task and self-reported their reading enjoyment and perceived ability twice in middle childhood: when they were 9-10 and 12 years old. Results showed a modest reciprocal association over time between reading motivation (enjoyment and perceived ability) and reading achievement. Reading motivation at age 9-10 statistically predicted the development of later achievement, and similarly, reading achievement at age 9-10 predicted the development of later motivation. This reciprocal association was observed beyond the stability of the variables and their contemporaneous correlation and was largely explained by genetic factors. United Kingdom MRC [G0500079]; National Institute of Child Health and Human Development (NICHD) [HD038075, HD059215, HD 075460] The first two authors contributed equally to this work. We thank the TEDS families, research staff and funding agencies for their ongoing contribution and support in this work. TEDS is supported by a program grant (G0500079) from United Kingdom MRC. Our work on reading development is also supported by the National Institute of Child Health and Human Development (NICHD) Grants HD038075, HD059215, and HD 075460. The content of this publication is solely the responsibility of the authors, and does not represent the official views of the funding agencies.

The most consistent sex differences in cognition are found for spatial ability, in which males, on average, outperform females. Utilizing a twin design, two studies have shown that females with male co-twins perform better than females with female co-twins on a mental rotation task. According to the Twin Testosterone Transfer hypothesis (TTT) this advantage is due to in-uterine transmission of testosterone from males to females. The present study tested the TTT across 14 different spatial ability measures, including mental rotation tasks, in a large sample of 19–21-year-old twins. Males performed significantly better than females on all spatial tasks, with effect sizes ranging from η 2 = 0.02 to η 2 = 0.16. Females with a male co-twin outperformed females with a female co-twin in two of the tasks. The effect sizes for both differences were negligible (η 2

Little is known about the etiology of developmental change and continuity in educational achievement. Here, we study achievement from primary school to the end of compulsory education for 6000 twin pairs in the UK-representative Twins Early Development Study sample. Results showed that educational achievement is highly heritable across school years and across subjects studied at school (twin heritability ~60%; SNP heritability ~30%); achievement is highly stable (phenotypic correlations ~0.70 from ages 7 to 16). Twin analyses, applying simplex and common pathway models, showed that genetic factors accounted for most of this stability (70%), even after controlling for intelligence (60%). Shared environmental factors also contributed to the stability, while change was mostly accounted for by individual-specific environmental factors. Polygenic scores, derived from a genome-wide association analysis of adult years of education, also showed stable effects on school achievement. We conclude that the remarkable stability of achievement is largely driven genetically even after accounting for intelligence., Learning: High stability of school achievement Twin studies have shown that individual differences in school achievement are to a large extent (around 60%) explained by genetic differences. However, little is known about age-to-age stability and change in school achievement. A team led by Kaili Rimfeld used school achievement data from primary school to the end of compulsory education for 6000 twin pairs in the UK-representative Twins Early Development Study sample. Results showed that school achievement is highly heritable across the school years and across subjects studied at school, that school achievement is highly stable, and that this stability is largely explained by genetic factors. The finding of genetically driven stability of school achievement should provide additional motivation to identify children in need of interventions as early as possible, as the problems are likely to remain throughout the school years.

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.

Individuals differ in their level of general anxiety as well as in their level of anxiety towards specific activities, such as mathematics and spatial tasks. Both specific anxieties correlate moderately with general anxiety, but the aetiology of their association remains unexplored. Moreover, the factor structure of spatial anxiety is to date unknown. The present study investigated the factor structure of spatial anxiety, its aetiology, and the origins of its association with general and mathematics anxiety in a sample of 1,464 19-21-year-old twin pairs from the UK representative Twins Early Development Study. Participants reported their general, mathematics and spatial anxiety as part of an online battery of tests. We found that spatial anxiety is a multifactorial construct, including two components: navigation anxiety and rotation/visualization anxiety. All anxiety measures were moderately heritable (30% to 41%), and non-shared environmental factors explained the remaining variance. Multivariate genetic analysis showed that, although some genetic and environmental factors contributed to all anxiety measures, a substantial portion of genetic and non-shared environmental influences were specific to each anxiety construct. This suggests that anxiety is a multifactorial construct phenotypically and aetiologically, highlighting the importance of studying anxiety within specific contexts.

Spatial abilities – defined broadly as the capacity to manipulate mental representations of objects and the relations between them – have been studied widely, but with little agreement reached concerning their nature or structure. Two major putative spatial abilities are “mental rotation” (rotating mental models) and “visualisation” (complex manipulations, such as identifying objects from incomplete information), but inconsistent findings have been presented regarding their relationship to one another. Similarly inconsistent findings have been reported for the relationship between two- and three-dimensional stimuli.Behavioural genetic methods offer a largely untapped means to investigate such relationships. 1,265 twin pairs from the Twins Early Development Study completed the novel “Bricks” test battery, designed to tap these abilities in isolation. The results suggest substantial genetic influence unique to spatial ability as a whole, but indicate that dissociations between the more specific constructs (rotation and visualisation, in 2D and 3D) disappear when tested under identical conditions: they are highly correlated phenotypically, perfectly correlated genetically (indicating that the same genetic influences underpin performance), and are related similarly to other abilities. This has important implications for the structure of spatial ability, suggesting that the proliferation of apparent sub-domains may sometimes reflect idiosyncratic tasks rather than meaningful dissociations.

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EEG correlates of mathematical and trait anxiety level were studied in 52 healthy Russian-speakers during execution of error-recognition tasks with lexical, arithmetic and algebraic conditions. Event-related spectral perturbations were used as a measure of brain activity. The ERSP plots revealed alpha/beta desynchronizations within a 500-3000 ms interval after task onset and slow-wave synchronization within an interval of 150-350 ms. Amplitudes of these intervals reflected the accuracy of error recognition, and were differently associated with the three conditions. The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16- 20 Hz) frequency bands. In theta band the effects of mathematical anxiety were stronger expressed in lexical, than in arithmetic and algebraic condition. The mathematical anxiety effects in theta band were associated with differences between anterior and posterior cortical areas, whereas the effects of trait anxiety were associated with inter-hemispherical differences. In beta1 and beta2 bands effects of trait and mathematical anxiety were directed oppositely. The trait anxiety was associated with increase of amplitude of desynchronization, whereas the mathematical anxiety was associated with decrease of this amplitude. The effect of mathematical anxiety in beta2 band was insignificant for lexical condition but was the strongest in algebraic condition. EEG correlates of anxiety in theta band could be interpreted as indexes of task emotionality, whereas the reaction in beta2 band is related to tension of intellectual resources., {"references":["M. H. Ashcraft, M.H. (2002). Math anxiety: Personal, educational, and\ncognitive consequences. Directions in Psychological Science, 2002, vol.\n11, pp. 181-185.","C. D. Spielberger, C.D. Trait-state anxiety and motor behavior. Journal\nof Motor Behavior, 1971, vol. 3, pp. 265-279.","J. A. Gray, & N. McNaughton, N. (2000). The neuropsychology of\nanxiety (2nd ed). Oxford University Press, 2000. p. 443.","M. W. Eysenck, N. Derakshan, R. Santos, M. G. Calvo Anxiety and\ncognitive performance: attentional control theory. Emotion, 2007. vol. 7.\nN 2. pp. 336–356.","M. H. Ashcraft, J. A. Krause Working memory, math performance, and\nmath anxiety, Psychon Bull Rev, 2007, vol. 14, № 2, pp. 243-248.","A. N. Savostyanov, A. C. Tsai, A. Yu. Zhigalov, E. A. Levin, J. D. Lee\nand M. Liou Trait Anxiety and Neurophysiology of Executive Control in\nthe Stop-Signal Paradigm, in Trait Anxiety, Edited by Anna S. Morales.\n- New York: Nova Science Publishers, 2011. – pp. 191-222.","Y. L. Khanin Short management to application of Ch.D. Spilberger's\nscale of reactive and personal anxiety /Y. L. Khanin. - L, 1976. - 198 p.\nAmerican Psychiatry Association. Diagnostic and statistical Manual of\nMental Disorders.","L. Alexander, & C. Martray The development of an abbreviated version\nof the Mathematics Anxiety Rating Scale, Measurement and Evaluation\nin Counseling and Development, 1989. – vol. 22, № 3, pp. 143–150.","A. Delorme, S. Makeig EEGLAB: an open source toolbox for analysis of\nsingle-trial EEG dynamics including independent component analysis, J.\nNeurosci. Methods, 2004, vol. 134, № 1, pp. 9–21.\n[10] S. Makeig, A. J. Bell, T. P. Jung, T. J. Sejnowski Independent\ncomponent analysis of electroencephalografic data Adv. Neural Inf.\nProcess. Syst., 1996, vol. 8, pp. 145–151.\n[11] W. Klimesch, EEG alpha and theta oscillations reflect cognitive and\nmemory performance: a review and analysis, Brain Research Reviews,\n1999, vol. 29, № 2-3, pp. 169-195.\n[12] L. I. Aftanas, A. A. Varlamov, S. V. Pavlov, V. P. Makhnev and N. V.\nReva, Affective picture processing: event-related synchronization within\nindividually defined human theta band is modulated by valence\ndimension, Neuroscience Letters, 2001, vol. 303, № 2, pp. 115-118.\n[13] R. Adolphs, Neural systems for recognizing emotion, Current Opinion in\nNeurobiology, 2002, vol. 12, № 2, pp. 169-177.\n[14] L. X. Blonder, D. Bowers and K. M. Heilman, The Role of the Right-\nHemisphere in Emotional Communication, Brain, 1999, vol. 114, pp.\n1115-1127.\n[15] E. Basar (Ed.), Brain Functions and Oscillations. II. Integrative Brain\nFunction. Neurophysiology and Cognitive Processes, Springer, Berlin,\nHeidelberg, 1999."]}

Educational achievement has traditionally been closely associated with general cognitive ability (g). Although g explains a substantial portion of variance in educational attainment, several non-cognitive factors have been found to relate to achievement beyond g. The present thesis focuses on exploring the association between achievement and two such factors: motivation and anxiety. The five empirical chapters included in the present thesis address several questions regarding the relation between motivation, anxiety and achievement, which to date remained unexplored. The present thesis includes data from two samples: the Twins Early Development Study (TEDS), a large-scale developmental twin sample from the United Kingdom (UK), and a sample of students attending the first year of secondary school in the UK, who contributed data longitudinally. The results showed that academic anxiety and motivation are domain-specific constructs. This observed domain-specificity of motivation and anxiety was also found to apply to their association with academic achievement. Motivation and anxiety constructs were moderately heritable, and the remaining variance explained by nonshared, individual specific, environmental influences. The cross-sectional and longitudinal links between motivation, anxiety and achievement were largely due to genetic influences common to all measures within a specific academic domain. The present thesis also explored the directionality of effects in the longitudinal associations between educational achievement and motivation; partly supporting the view of reciprocal links between the two constructs in several academic domains. However, a reciprocal relation between motivation and achievement was not observed in the domain of second language in a sample of naïve learners. The results of the present thesis have important implication for future research and practice. For example, it is argued that future interventions aimed at reducing the academic anxiety should consider three main factors: (1) its domain specific; (2) the directionality of effects in its association with achievement; (3) possible factors moderating or mediating the association between anxiety and achievement (i.e. motivation).

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used Genomic Structural Equation Modeling and prior genome-wide association studies (GWAS) of educational attainment (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability.We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Non-cognitive genetics were enriched in the same brain tissues and cell types as cognitive performance but showed different associations with gray-matter brain volumes. Non-cognitive genetics were further distinguished by associations with personality traits, less risky behavior,and increased risk for certain psychiatric disorders.For socioeconomic success and longevity, non-cognitive and cognitive-performance genetics demonstrated similar-magnitude associations. By conducting a GWAS of a phenotype that was not directly measured, we offer a first view of genetic architecture of non-cognitive skills influencing educational success.