Re-application to university after rejection – The role of mental health and education-linked genes in predicting persistence in pursuing educational goals

Re-application to university after rejection – The role of mental health and education-linked genes in predicting persistence in pursuing educational goals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Re-application to university after rejection – The role of mental health and education-linked genes in predicting persistence in pursuing educational goals Henrik Dobewall, Hannu Lehti, Outi Sirniö, Maria Vaalavuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6717200/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Socioeconomic disparities in university enrollment may stem from lower re-application rates after rejection among individuals from less educated families. We investigate the role of mental health and education-linked genes alongside family background in predicting re-application to university. The study uses longitudinal register data from 14,079 individuals in Finland, data on psychiatric diagnoses and medication, and parental polygenic scores to assess the genetic endowment to high educational attainment (EA-PGS) combined with application records (4,430 rejected). Discrete-time hazard models are employed. Poor mental health predicted whether a person applied to university and experienced rejection but not discontinuation. Individuals with a higher EA-PGS were more likely to re-apply after rejection, suggesting a genetic component in persistence. However, neither mental health nor genes explained the social origin gap in re-application rates. No gene-environment interactions were found. The study informs debates on educational equality and shows how genes express themselves in smaller or bigger decisions. Scientific community and society/Social sciences/Sociology Biological sciences/Psychology/Human behaviour Scientific community and society/Social sciences/Education Figures Figure 1 Figure 2 Introduction Educational pathways consist of several transitions that young people make as they progress through the education system. These pathways reflect both the opportunities and constraints shaped by individuals’ socioeconomic background and the characteristics of the prevailing educational system. Educational transitions, e.g., which school to attend, which track to choose, and whether to continue education after each level is accomplished, have been shown to be partially dependent on individuals’ family socioeconomic background 1 – 3 , even in egalitarian welfare states like Finland 4 , 5 . Each transition consists of two processes, namely application to an institution and the institution’s decision about the application, and social origin differences in the former which are driven by educational expectations and aspirations. To better understand the mechanisms underlying educational inequalities, the focus should be placed on individuals’ educational decision-making processes and behaviors that stem from their socially and genetically inherited background. Two recent Finnish studies have identified that the social origin gap in university enrollment is partly driven by applicants with less educated parents being less likely to continue applying for study places and enroll at university after an initial rejection compared to individuals with more highly educated parents 5 , 6 . We build on this work and its findings but consider the potential contribution of mental health and genetic endowments in individuals’ persistence in pursuing educational goals and ask whether family socioeconomic background moderates the influence of genes during this educational transition. Our first focus is on how experiencing poor mental health, in addition to family background and major life events, interplay with genetic endowments in social stratification via university re-application behavior. Despite mental illness having a significant impact on individuals’ educational aspirations and decisions 7 , 8 , to our knowledge, the association between poor mental health and university application behavior has not been studied before. The role of genetic endowment in re-application behavior is currently unexplored even though the link between genes and intelligence, educational attainment, and educational choices is widely acknowledged 9 – 15 . Persistence, understood as a motivational personality trait, is a genetically influenced tendency to continue working toward goals despite obstacles or initial failure and reflects an individual's ability to maintain effort and commitment over time 16 – 18 . Coined as grit in the sociological literature 19 , it is strongly associated with educational outcomes even after accounting for individual differences in intelligence 20 , 21 . The possibility to measure the genetic component of complex traits introduces new perspectives on equality of (educational) opportunities. While the concept has long guided normative theories of justice, its meaning had become contested in light of recent findings in the field of sociogenomics 22 , 23 . In genetic terminology, educational choices can be understood as active gene–environment correlations, which occur when individuals select or create environments that align with their genetically influenced traits 24 . Therefore, also application behavior should be at least indirectly influenced by genetic endowments. However, the returns for status attainment of educational goal striving appear to be more pronounced among the socioeconomically advantaged 25 . It is therefore possible that accounting for education-linked genes alters the effect of family background on discontinuation. Health selection effects in educational outcomes are widely recognized, while individuals from lower socioeconomic backgrounds are disproportionately diagnosed with a mental illness 26 , 27 . Register-based studies conducted in Nordic welfare states have found that being diagnosed with a mental illness and the use of psychotropic medication in adolescence predict lower academic performance and educational attainment 28 , 29 . Young individuals with attention-deficit/hyperactivity disorder, for instance, are more likely to experience academic underachievement, educational difficulties, and increased grade retention due to impairments in body functions, activity limitations, and restrictions in social participation 30 . Poor mental health is further inversely associated not only with educational attainment but also with labor market outcomes in adulthood 31 – 33 . We, therefore, add to the literature by investigating the role of experiencing poor mental health during the transition from secondary level to university. We expect mental illness to be negatively associated with university application outcomes in general, but potentially even more strongly associated with discontinuation once rejected ( hypothesis 1 ). Until recently, it has been considered that the link between family background and educational choices is due to various socio-cultural and economic resources that parents provide their children 34 , while the exploitation of genetic data has directed our gaze to the influence of an individual’s genotype and its potential interaction with the environment 35 – 37 . Indeed, a recent study by Okbay 14 found that a polygenic score summing up the combined effect of many genetic variants to estimate the likelihood of achieving high educational attainment explains up to 16 percent of the variance in educational attainment in between-family genome-wide association studies (GWAS). This is comparable to the variance accounted for by parental background measured jointly with education, income, and unemployment 38 , 39 . The genetic effects in educational attainment identifiable by within-family GWAS, at the moment, remain comparatively smaller 40 . To better understand the mechanisms that drive educational attainment, namely the educational decision-making processes and especially whether applicants continue (or stop) applying regardless of initial rejection, it is essential to gain a deeper understanding of how social and genetic mechanisms interact and create barriers to educational opportunity 23 . Genetic endowment for educational attainment has been shown to be associated with various educational preferences and decisions. With each choice made throughout the school years, the gradient between education-linked genes and later life chances becomes stronger 9 , 12 , 41 – 43 . For instance, a study from New Zealand found that children with a higher polygenic score for educational attainment (hereafter, EA-PGS) developed reading skills earlier, and as high school students, those with higher scores also exhibited greater academic educational aspirations and were later selected into more competitive tracks. Harden and colleagues 42 found that students with a higher EA-PGS were more likely to enroll in advanced mathematics courses in the 9th grade, a critical juncture in math tracking within the US educational system, which requires students to follow a highly cumulative sequence of secondary school math courses. Students with a higher EA-PGS also tended to persist in the math tracks through the end of high school 42 . Further, also educational field choices appear to have a genetic component 44 , which is in line with findings suggesting that self-selection outweighs socialization effects during self-chosen life transitions 45 . Genetic endowment for educational attainment has also been found to be important at different educational transition points in Finland 12 , 46 . Accordingly, we expect that a higher EA-PGS is associated with the re-application behavior of individuals applying to university, especially when faced with rejection ( hypothesis 2 ). As social and genetic factors have previously been studied in isolation, we are further interested in whether controlling for the EA-PGS and mental health reduces the social origin effect on re-application behavior ( hypothesis 3 ). Family background has been found to moderate genetic influences on educational outcomes. These influences might be stronger in more affluent and educated families because nurturing can have multiplicative effects for genetic endowments associated with education, while offspring may also have more to lose if confronted with a rejection or similar disappointments 10 , 37 . Alternatively, well-off parents may leverage their resources to guarantee that their children attain a certain level of education and social standing, regardless of the children's innate genetic potential 34 , 47 . This implies that we see compensation of inherited genes 11 . Following this line of reasoning, the influence of genes might as well be stronger for socially disadvantaged individuals. On the one hand, poor resources and low genetic endowment could create a double jeopardy leading to negative feedback loops. On the other hand, traits associated with a high EA-PGS could potentially help overcome the experience of rejection, especially for socially disadvantaged individuals. Previous mixed results on the interaction effect of family socioeconomic background and education-linked genes could be explained if genetic endowment is more influential for socially disadvantaged individuals for less selective outcomes like upper-secondary school completion, and more crucial for individuals from well-off families for highly selective outcomes like the completion of postgraduate studies 37 . These empirical results and theories share the assumption that the influence of individuals’ education-linked genes on educational outcomes is influenced by their family socioeconomic background. We test if the association between genetic endowment and re-application behavior will be moderated by the available financial resources and cultural capital of the family, indicated by the income and educational level of the parents. It is expected that genetic endowment for high education is especially relevant for the re-application of individuals with less well-off parents who are more likely to stop applying otherwise ( hypothesis 4 ). The current study uses comprehensive register data from Finland linked to polygenic scores. Our study sample includes 14,079 individuals born 1987–1996 who had at least one genotyped parent. We have four principal research questions. First, is experiencing poor mental health associated with re-application behavior? Second, is the EA-PGS associated with the re-application behavior of individuals, especially when faced with rejection in university applications, irrespective of their social origin? Third, do education-linked genes, in part, explain the social disparity in re-application behavior found in previous research? Fourth, does social origin weaken or amplify the influence of education-linked genes on re-application behavior, i.e., is there evidence for gene-environment interactions? Institutional context The educational system in Finland—as in the Nordic countries in general—is relatively equal. This is pointed out in the international comparisons of socioeconomic and educational inheritance that have found the Nordic countries, including Finland, to be among the most egalitarian 48 – 50 . At the same time, relatively low income inequality and (child) poverty, a highly educated adult population, and comprehensive social safety net support the egalitarian objectives of the educational system. The Finnish higher education system is supported by governmental funding and does not require tuition fees. Almost all upper-secondary-level degrees provide eligibility for higher education. Higher education is divided into two parallel sectors: universities and universities of applied sciences (or polytechnics). Compared to universities that offer bachelor’s, master’s, and doctoral degrees, universities of applied sciences offer working-life oriented degrees mostly at the bachelor’s level. In Finland, there are 13 universities and 24 universities of applied sciences. The application procedure of the universities is centralized by the Finnish government, and it takes place annually in each spring semester. To apply for higher education in Finland, individuals must first obtain a degree from general upper-secondary school or vocational school. However, as Fig. 1 shows, in the cohorts we analyze only less than 1 percent of individuals with vocational secondary degrees enrolled to study in universities and 13 percent to universities of applied sciences. Of those who graduated from general upper-secondary school, by the age of 21, about 34 percent enrolled at a university and 39 percent at a university of applied sciences. A large majority of those who enrolled in university or polytechnics had a degree from a general upper-secondary school. Thus, it can also be expected that a large majority of the applicants have a matriculation examination (see Lehti et al., 2019). In the studied cohorts, individuals who applied to higher education had to participate in entry exams to be selected for a study program in the university or polytechnics. The selection of the students was based either on the entry exams and the results of the matriculation exams of the general upper-secondary school or only the entry exams, the quotas of these two selection groups depending on the study program. For individuals who applied to university and had vocational degrees, the intake was based on grades of the graduation diploma and entry exams. There were no age limits or fees in the entry exams. Although applicants may apply and be admitted to several study programs, they can only accept one study place within a year. Each study program has a certain amount of study places which limits the number of students who can be accepted. In Finland, most of the applicants (about 60 percent) are not accepted into university programs they applied to, which leads to a high number of re-applications and signifies a strong institutional barrier to obtaining a university degree 5 . It has been shown that major life events, such as becoming a parent, finding employment, or getting a study place in polytechnics, have a strong effect on individuals’ decision to stop applying to university 5 . Application behavior can also be influenced by institutional factors. If young individuals become unemployed, for instance, then the Finnish state requires them to apply for a study place to avoid losing unemployment benefits. For this reason, we take these life events into account. Table 1 Descriptive statistics comparing individuals who never applied to university to those who experienced rejection and successful applicants, a) relative frequencies (%) and b) averages (SD) Never applied Rejected Direct access Total a) Parental education X 2 (2) = 1100.0, p < 0.001 Less educated 7,298 2,994 833 11,125 88.6% 67.6% 59.2% 79.0% University/polytechnics 943 1,436 575 2,954 11.4% 32.4% 40.8% 21.0% Income 2001–2009 X 2 (4) = 673.6, p < 0.001 Low 1,924 600 171 2,695 23.8% 13.7% 12.3% 19.5% Medium 4,997 2,484 721 8,202 61.8% 56.8% 51.8% 59.2% High 1,166 1,293 500 2,959 14.4% 29.5% 35.9% 21.4% Sex X 2 (2) = 374.0, p < 0.001 Male 4,736 1,748 720 7,204 57.5% 39.5% 51.1% 51.2% Female 3,505 2,682 688 6,875 42.5% 60.5% 48.9% 48.8% Mental health diagnosis occurred before age 18 X 2 (2) = 173.9, p < 0.001 No 6,967 4,062 1,306 12,335 84.5% 91.7% 92.8% 87.6% Yes 1,274 368 102 1,744 15.5% 8.3% 7.2% 12.4% b) Educational attainment PGS of genotyped parent F (2, 14,076) 271.7, p < 0.001 Mean (SD) − .179 (.98) .152 (.97) .34 (.95) 14,079 Analytical approach We start by reporting descriptive statistics for individuals belonging to the following categories: 1) non-applicants, 2) applicants who experienced rejection, and 3) applicants with direct access. To conduct a fine-grained analysis of the mechanisms that might explain why some individuals are less persistent in their educational goals and stop applying to university, logistic discrete-time hazard models are used. The unit of analysis in these analyses is person-years in which the outcome event “stopping to apply to university” in time t is conditional on a rejection in time t-1 . Discrete-time hazard models are used to analyze the timing of events particularly when the events happen at specific points in time (see Heiskala and colleagues 5 , for details). These models are a type of survival analysis adapted to situations where time is measured in distinct intervals like once a year rather than continuously. Discrete-time hazard models can handle right-censored data like in our case when some individuals are still applying by the end of the observed period. We used logistic regression to model the hazard of stopping to apply as a function of time-invariant and time-variant predictors. These analyses allow us to model occurring life events and the use of psychotropic medication as time-variant. Log odds are computed and GxE interactions are plotted. First, we entered family background, namely parental education and income, and life events along with standard control variables (Model 1). Second, we add mental health diagnosis during adolescence and medication use (Model 2). Third, education-linked genes were entered into the model (Model 3), Fourth, the moderation of genetic influences by family background (GxE interaction) was tested (Model 4). Results Table 1 displays descriptive group differences between individuals based on their application outcomes. Individuals who experienced rejection when applying to university, our main analysis sample, differ significantly from those who never applied to university and those who were granted direct access in terms of their parental socioeconomic status, prior mental health diagnoses, and genetic endowment for high educational attainment. Individuals that were followed in their re-application behavior (i.e., showed persistence in pursuing their educational goals or stopped applying to university) had on average somewhat less educated parents and lower family income, a lower EA PGS, and were more likely diagnosed with any mental illness compared to those who got into university directly (confirmed also in multivariate analyses presented in supplement Table S1). All study variables were statistically different between the two groups at p < .001 level. Women were less likely to be in the direct access category. This might appear counterintuitive as they apply more often to university, but it can be explained by sex differences in study field choices (e.g., fewer “walk in programs“). This group comparison, partially addressing our first two research questions, shows that both receiving a mental health diagnosis before reaching the age to apply to university and education-linked genes were important predictors of the studied application outcomes. We move on to investigate whether one discontinues applying after initial rejection. Therefore, only those having experienced a rejection are included in this analysis. Table 2 presents the results of a discrete-time hazard analysis of life events and psychotropic medication use linked to stopping to apply to university after experiencing a rejection during the previous year(s). Model 1 presents further evidence for social origin differences in re-application behavior in terms of parental education but not income. Model 2 indicates that, unlike for the life events, the coefficient for mental health — assessed both with having received a psychiatric diagnosis during adolescence and psychotropic medication use in a given application year — was not statistically significant. This points to a limited incremental role of mental health during this stage of the educational transition against our first hypothesis. We also observe that EA-PGS continues to play an imperative role in discontinuation even when life events and mental health were included in the model (Model 3). In other words, individuals with a higher polygenic score were more likely to reapply after rejection implying that persistent educational intentions and behaviors have indeed a genetic component. The effect of genetic endowments was observed independent of the parental socioeconomic background. This is noteworthy given that the other included variables explain a large part of the variance in persistence in pursuing educational goals (i.e., not stopping to apply to university). Concerning our third research question, we found that including EA-PGS along with mental health did not alter the coefficients for parental education and income and thus does not explain the observed social origin differences in the outcome. Finally, we did not find a GxE interaction between the EA-PGS and family background (Model 4). Figure 2 illustrates the non-significant interaction of the polygenic score for educational attainment on 20 percent intervals with parental education and income. Table 2 Predicting whether individuals stop applying to university in t conditional on rejection in t-1, logistic discrete-time hazard models (average marginal effects). Model 1: Family background & life events Model 2: +Mental health Model 3: +Genes Model 4: +GxE dy/dx (se) dy/dx (se) dy/dx (se) Stable predictors Female -0.028* -0.026 * -0.028 * -0.028 * (0.01) (0.01) (0.01) (0.01) Mental health diagnosis (age < 18) 0.010 0.011 0.010 (0.02) (0.02) (0.02) Predictors assessed in a given year Child born 0.263 *** 0.250 *** 0.252 *** 0.248 *** (0.07) (0.07) (0.07) (0.07) Employment (months) -0.024 *** -0.024 *** -0.025 *** -0.025 *** (0.00) (0.00) (0.00) (0.00) Started in polytechnics 0.320 *** 0.321 *** 0.321 *** 0.321 *** (0.02) (0.02) (0.02) (0.02) Unemployment (3 + months) -0.188 *** -0.188 *** -0.189 *** -0.190 *** (0.02) (0.02) (0.02) (0.02) Psychotropic medication use 0.024 0.023 0.022 (0.02) (0.02) (0.02) Family background Parents have university/polytechnic degree (Ref. less educated) -0.052 *** -0.053 *** -0.050 *** -0.055 *** (0.01) (0.01) (0.01) (0.01) Low parental income (Ref. medium) 0.011 0.011 0.012 -0.011 (0.02) (0.02) (0.02) (0.02) High parental income -0.007 -0.006 -0.004 -0.016 (0.02) (0.02) (0.02) (0.02) Genes EA-PGS -0.019 ** -0.030 *** (0.01) (0.01) Gene-environment (GxE) interactions High parental education 0.025 (0.01) Low parental income 0.010 (0.02) High parental income 0.005 (0.02) N (person-years) 6,020 6,020 6,020 6,020 Pseudo R 2 0.099 0.100 0.101 0.102 Note. *p < 0.05 **p < 0.01 ***p < 0.001. All models control for age, region of birth, year of upper-secondary education graduation, and source health survey and PGS. The 10 genetic principal components are entered together with the EA PGS. Discussion In this article, we investigated the influence of mental health and education-linked genes along with family background on re-application to universities in Finland. Our study was motivated by the importance of university enrollment for social stratification. We contribute to the debate on educational equality in three important ways. First, we focus on an educational transition that has received less attention in studies on education, namely application and re-application to university. Second, we contribute by examining the role of individuals’ mental health on educational decision-making and behavior. Third, we include information on parental background together with genetic endowment to attain higher education and study also their interplay. Our study expands the framework used by Heiskala and colleagues 5 who showed that social origins matter for re-application behavior reinforcing inequality in educational outcomes. We demonstrate that the combination of socially and biologically inherited family background seems to explain in part individuals’ behavior when applying for a study place in Finnish universities and specifically why some individuals are more persistent in pursuing their educational goals than others. Our findings underscore the importance of the EA-PGS and family background as predictors of university re-application behaviors. Individuals with a higher polygenic score were more likely to reapply after rejection, suggesting a genetic component in educational goal striving over and above social origin. We found an association between parents’ university or polytechnic degree and stopping to apply to university in line with prior evidence 5 . Parental income, however, was not associated with the outcome suggesting that educational expectations and cultural capital rather than material resources work as the mechanism of intergenerational transmission. Our findings could further imply that the EA-PGS captures grit and other characteristics that are needed to attain higher levels of education 51 and social status 52 . Notably, genetic influences persist even when accounting for alternative pathways like experiencing poor mental health and major life events such as the birth of a child. This also highlights the robustness of these associations. Being diagnosed with a mental illness during adolescence influenced whether a person applied (successfully) to university but poor mental health, measured both with prior diagnoses and psychotropic medication use, did not have an incremental effect on discontinuation. However, having received a mental illness diagnosis prior to the application period to university was more prevalent in individuals who never applied to university. This might partially explain why mental health does not matter for stop applying, as a diagnosis keeps you away from enrollment to university studies but affects the subsequent outcome persistence less. Our findings align with previous evidence that mental illness explains a large part of the population variation in educational outcomes — health selection —, yet is a weaker mechanism than expected to explain social origin differences 31 . To our knowledge, this is the first study to investigate the role of mental health in university application behavior. Future research is needed to further explore how mental illness influences educational decision-making and access to higher education across diverse institutional and cultural contexts. Genetic research has shown that genetic associations with educational and socioeconomic outcomes become more apparent under conditions of equal opportunity, as environmental dis-advantages and contextual barriers play a smaller role in shaping life chances 53 – 55 . In such context, outcomes are often interpreted as reflecting sustained effort, academic talent, and choice, seemingly free from the constraints of individuals’ family background or other matters of luck 56 . Yet the idea that behavioral choices are fully autonomous is challenged by genetic findings and theory 17 , 24 , 57 . The goal to minimize educational inequalities arising from differences in inherited genes seems to be a futile quest. However, societies can design institutions and educational systems that promote learning and skill development of all regardless of inherited genes 36 . Against our expectation that the EA-PGS is especially relevant for the re-application behavior of individuals with less well-off parents, we did not find any indication for the presence of GxE interactions. This could be related to our relatively small sample size. Based on these results we are not able to support any of the competing sociological theories on how the environment moderates the strength of the genetic influence on educational outcomes 37 , 47 , 58 , 59 . Trying to identify GxE interactions is vital for developing a non-deterministic understanding of how genetic influences evolve within our societal fabric 11 , 53 , 60 . Even though we did not find that the family background moderates genetic effects, our results are an example for how the influence of genetic endowment for attaining high education accumulates over educational environments via individual effort and choices. Earlier research has already found genetic associations between an EA-PGS and mathematics tracking and persistence in secondary school that appear to explain that the gradient between education-linked genes and socioeconomic outcomes becomes stronger over time rather than suggesting genetic determinism 42 . Our study has some limitations. That we had to rely on polygenic scores measured in one of the parents, even though innovative, made it impossible to use other methods designed to limit environmental confounding when assessing the relative contributions of genetic and social factors 61 . In Finnish data, the EA-PGS usually explains somewhat less of the population variation than found in the original GWAS 12 , 62 , therefore, our results might underestimate genetic influences on re-application behavior. Parental education and income are not exogenous predictors of children’s educational outcomes, which makes studying GxE interactions difficult 35 . The use of the information of the non-genotyped parent to assess the family background further had its shortcomings due to the presence of assortative mating and the possibility of confounding genetic nurture effects 63 . While starting with a relatively large, genotyped sample, the sample size available to us for more specific research questions such as an individual’s persistence in applying to university when confronted with rejection was smaller. Detecting significant interaction, furthermore, requires a lot of statistical power. The results should be replicated and extended in an independent sample. However, we were able to rely on otherwise rich and comprehensive data. One of the main strengths of the study is the use of objective measures of an individual's educational choices, namely their re-application behavior, at an important transition point. The longitudinal dimension of the Finnish register data allowed us to include not only information about parents’ socioeconomic status but also detailed information about health status and life events that can influence whether an individual stops applying to university. Even though the study was not pre-registered, we restricted the researcher’s degree of freedom by closely following the variable coding and analysis plan of Heiskala and colleagues 5 . However, we deviated from it by not controlling for entrance exam grades because they are themselves directly influenced by the EA-PGS. Further, we went beyond their analyses by using information on mental health and genes to explain their main result that re-application is strongly influenced by the family background. In conclusion, experiencing poor mental health plays an important role in whether an individual applies to a university but appears to be less influential for deciding whether to reapply after initial rejection. Individuals with a higher polygenic score were more likely to re-apply to university, suggesting a genetic component in persistent educational intentions beyond family background even after experiencing rejection. Yet, neither experiencing poor mental health nor education-linked genes measured in parents could explain the social origin gap in university enrollment noteworthy. Future research of social stratification should also consider the effect of genes to uncover how socially and biologically inherited family background are interweaved in the inequality of educational opportunities. Methods Research data We utilized administrative records of Statistics Finland with annual information on income and education-related variables, such as successful and unsuccessful university applications, registers of the Finnish Institute for Health and Welfare (THL) including information on health care visits and diagnoses, and registers of the Finnish Social Insurance Institution (Kela) on reimbursed drugs. These anonymously merged registers were linked to genetic data collected by the THL as part of three population-representative health surveys (1992–2020). The total sample size of the genetically-informed register data is 39,570. We focus on individuals born between 1987 and 1996 for whom comprehensive data on their own educational history and parental background were available. As only a few of this cohort were genotyped, we circumvented the problem by utilizing the PGSs of one of their parents instead (55.1% mothers; see 63 – 65 . With this relatively established approach, the ranking of individuals/families remains approximately the same but the variance that can be explained by the EA-PGS of the parents is cut in half compared to a situation in which we would have had the genotype of the offspring available. Priority was given to mothers when polygenic scores for both parents were available. This resulted in a study sample of 14,079 individuals. 5,838 individuals applied to university, of which 1,408 were granted access on their first attempt, and 8,241 did not apply to university. The observation period was 2006–2022, but the re-application behavior after initial rejection was followed up for a maximum of 4 years. Measures We categorized the outcomes of the application to Finnish universities into three categories: 1) Never applied, 2) Experienced a rejection (Rejected), and 3) Applied with success (Direct access). For practical reasons, the direct access category includes a few cases who later applied for another study place in university and were accepted. To measure persistence in pursuing educational goals, our study’s main outcome is re-application behavior after rejection 5 . We were especially interested in individuals who unsuccessfully applied at least once between the years 2006–2021 and whether they re-applied after the initial rejection. About two-thirds of all university applicants in Finland apply without being accepted. In our study sample, this results in 4,430 rejected applicants with genetic information available. This resulted in a total 6,020 person-years of 3,813 individuals in the analyses of stop applying. In the realm of schooling, the most relevant polygenic score measures genetic endowments for achieving high educational attainment. The EA-PGS was informed by summary statistics of a well-powered genome-wide association study (Okbay et al., 2022). It was produced with the Bayesian PRS-CS method that infers posterior SNP effect sizes under continuous shrinkage priors using an external linkage disequilibrium reference panel, accounting of all identified SNPs without a p-value threshold 66 . The EA-PGS of one’s parent predicted, without any controls, 3.5 percent of the variance in whether an individual ever applied to a university during the observation period. For graphical illustrations, the PGS was recoded into 20 percent intervals. Available resources of the family were measured with the combined taxable income between 2001–2009. Income was deflated and averaged over the years. Individuals were then categorized into three groups based on parental income, contrasting 20% highest, medium, and 20% lowest income. Parental level of education was assessed in 2009 with whether parents had a university or polytechnic degree. Information on the family background was obtained from the administrative records for the non-genotyped parent to minimize confounding of genetic and social predictors. The EA-PGS of the genotyped parent correlated significantly with the non-genotyped parent’s university or polytechnic degree, r = .15, and income, r = .10, (p < .001), suggesting the presence of assortative mating based on these traits. To account for the role of poor mental health, we recorded if an individual has got a psychiatric diagnosis in public specialized health care before age 18 using the International Classification of Diseases (ICD-10) classification (e.g., schizophrenia, depression, & anxiety disorder). Psychotropic medication use was identified according to the Anatomical Therapeutic Chemical (ATC) classification system (e.g., antipsychotics, anxiolytics, antidepressants, and psychostimulants) in a given year (< age 25). Psychotropic medication as a measure has the advantage that is covers also private healthcare and allows for more accurate timing of mental health episodes than diagnoses. Studied life events, namely having a child, starting studies in polytechnics, number of months in employment (based on income), and unemployment periods of more than 3 months, were measured from 2006 onwards up to age 24. In our discrete-time hazard model focusing on life events, psychotropic medication use, and stopping to apply to university, these major life events were measured the same year as the event (stop applying to university). Standard covariates include age, gender, place of birth at NUTS2 level, year of matriculation qualification, top ten genetic principal components to account for population stratification, and the source health survey and the genetic information. Declarations Author Contribution HD, HL, OS, and MV jointly contributed to the formulation of the research hypotheses, development of the data analysis plans, and interpretation of the results. HD drafted the manuscript. OS, HD, and HL merged and prepared the register data. All authors critically revised the manuscript and approved the final version for submission. Acknowledgement The study was supported by the Academy of Finland (Research Council of Finland) grant 342605 (MEDIG), grant 355009, and the Flagship Programme (decision number: 345547). Data Availability The authors do not have permission to share data. The Stata code written to prepare and analyze the anonymously merged registers is available at https://github.com/sohedo/Reapplication. References Breen, R. & Jonsson, J. O. Analyzing Educational Careers: A Multinomial Transition Model. Am. Sociol. Rev. 65, 754–772 (2000). Lievore, I. & Triventi, M. Social background and school track choice: An analysis informed by the rational choice framework. Acta Sociol. 65, 111–129 (2022). Lucas, S. R. Effectively Maintained Inequality: Education Transitions, Track Mobility, and Social Background Effects. Am. J. Sociol. 106, 1642–1690 (2001). Härkänen, T. et al. Systematic handling of missing data in complex study designs – experiences from the Health 2000 and 2011 Surveys. J. Appl. Stat. 43, 2772–2790 (2016). Heiskala, L., Kilpi-Jakonen, E., Sirniö, O. & Erola, J. Persistent university intentions: Social origin differences in stopping applying to university after educational rejection(s). Res. Soc. Stratif. Mobil. 85, 100801 (2023). Tervonen, L. Essays in the Economics of Education. (University of Helsinki, Helsinki, 2023). Dobewall, H. et al. Health and educational aspirations in adolescence: a longitudinal study in Finland. BMC Public Health 19, 1447 (2019). Ringbom, I. et al. 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Early Adolescent Health Problems, School Performance, and Upper Secondary Educational Pathways: A Counterfactual-Based Mediation Analysis. Soc. Forces 99, 1146–1175 (2021). Loe, I. M. & Feldman, H. M. Academic and Educational Outcomes of Children With ADHD. J. Pediatr. Psychol. 32, 643–654 (2007). Dobewall, H., Sirniö, O. & Vaalavuo, M. Does social disadvantage persist over generations due to unevenly distributed mental health problems? A longitudinal investigation of Finnish register data. Soc. Sci. Med. 330, 116037 (2023). Hakulinen, C. et al. Mental disorders and long-term labour market outcomes: nationwide cohort study of 2 055 720 individuals. Acta Psychiatr. Scand. 140, 371–381 (2019). Kessler, R. C. et al. Individual and Societal Effects of Mental Disorders on Earnings in the United States: Results From the National Comorbidity Survey Replication. Am. J. Psychiatry 165, 703–711 (2008). Erola, J. & Kilpi-Jakonen, E. Compensation and other forms of accumulation in intergenerational social inequality. in Social Inequality Across the Generations 3–24 (Edward Elgar Publishing, 2017). Biroli, P. et al. The Economics and Econometrics of Gene-Environment Interplay. Preprint at https://doi.org/10.48550/arXiv.2203.00729 (2022). Cheesman, R. et al. A population-wide gene-environment interaction study on how genes, schools, and residential areas shape achievement. Npj Sci. Learn. 7, 3748 (2022). Ghirardi, G. & Bernardi, F. Compensating or boosting genetic propensities? Gene-family socioeconomic status interactions by educational outcome selectivity. Soc. Sci. Res. 129, 103174 (2025). Erikson, R. Is it enough to be bright? Parental background, cognitive ability and educational attainment. Eur. Soc. 18, 117–135 (2016). Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018). Tan, T. et al. Family-GWAS reveals effects of environment and mating on genetic associations. 2024.10.01.24314703 Preprint at https://doi.org/10.1101/2024.10.01.24314703 (2024). Belsky, D. W. et al. Genetic analysis of social-class mobility in five longitudinal studies. Proc. Natl. Acad. Sci. 115, E7275–E7284 (2018). Harden, K. P. et al. Genetic associations with mathematics tracking and persistence in secondary school. Npj Sci. Learn. 5, 1–8 (2020). Sewell, W. H., Hauser, R. M., Springer, K. W. & Hauser, T. S. As we age: A review of the WISCONSIN LONGITUDINAL STUDY, 1957–2001. Res. Soc. Stratif. Mobil. 20, 3–111 (2003). Cheesman, R. et al. Genetic associations with educational fields in > 460,000 individuals. Preprint at https://doi.org/10.31234/osf.io/epura (2024). Bardi, A., Buchanan, K. E., Goodwin, R., Slabu, L. & Robinson, M. Value stability and change during self-chosen life transitions: Self-selection versus socialization effects. J. Pers. Soc. Psychol. 106, 131–147 (2014). Lahtinen, H., Korhonen, K., Martikainen, P. & Morris, T. Polygenic Prediction of Education and Its Role in the Intergenerational Transmission of Education: Cohort Changes Among Finnish Men and Women Born in 1925–1989. Demography 60, 1523–1547 (2023). de Zeeuw, E. L. et al. The moderating role of SES on genetic differences in educational achievement in the Netherlands. Npj Sci. Learn. 4, 1–8 (2019). Björklund, A., Eriksson, T., Jäntti, M., Raaum, O. & Österbacka, E. Brother correlations in earnings in Denmark, Finland, Norway and Sweden compared to the United States. J. Popul. Econ. 15, 757–772 (2002). Erola, J. Social mobility and education of Finnish cohorts born 1936-75: Succeeding while failing in equality of opportunity? Acta Sociol. 52, 307–327 (2009). Sirniö, O., Lehti, H., Grätz, M., Barclay, K. & Erola, J. The pattern of educational inequality - The contribution of family background on levels of education over time and across four countries. Preprint at https://doi.org/10.31235/osf.io/nupfs (2020). Kevenaar, S. T., van Bergen, E., Oldehinkel, A. J., Boomsma, D. I. & Dolan, C. V. The relationship of school performance with self-control and grit is strongly genetic and weakly causal. Npj Sci. Learn. 8, 1–11 (2023). Abdellaoui, A. et al. Socio-economic status is a social construct with heritable components and genetic consequences. Nat. Hum. Behav. 1–13 (2025) doi: 10.1038/s41562-025-02150-4 . Herd, P. et al. Genes, Gender Inequality, and Educational Attainment. Am. Sociol. Rev. 84, 1069–1098 (2019). Plomin, R. Blueprint: How DNA Makes Us Who We Are . (The MIT Press, Cambridge, Massachusetts London, England, 2018). Rimfeld, K. et al. Genetic influence on social outcomes during and after the Soviet era in Estonia. Nat. Hum. Behav. 2, 269–275 (2018). Rawls, J. A Theory of Justice: Original Edition . (Harvard University Press, 1971). doi: 10.2307/j.ctvjf9z6v . Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015). Baier, T. et al. Genetic Influences on Educational Achievement in Cross-National Perspective. Eur. Sociol. Rev. 38, 959–974 (2022). Scarr-Salapatek, S. Race, Social Class, and IQ: Population differences in heritability of IQ scores were found for racial and social class groups. Science 174, 1285–1295 (1971). Barcellos, S. H., Carvalho, L. & Turley, P. The Effect of Education on the Relationship between Genetics, Early-Life Disadvantages, and Later-Life SES. Working Paper at https://doi.org/10.3386/w28750 (2021). Trejo, S. & Kanopka, K. Using the phenotype differences model to identify genetic effects in samples of partially genotyped sibling pairs. Proc. Natl. Acad. Sci. U. S. A. 121, e2405725121 (2024). Dobewall, H. et al. Nature’s Curriculum: Genes Linked to Educational Attainment and Adult Socioeconomic Status in a Nordic Welfare State. Preprint at https://doi.org/10.31235/osf.io/g453y (2024). Kong, A. et al. The nature of nurture: Effects of parental genotypes. Science 359, 424–428 (2018). Balbona, J. V., Kim, Y. & Keller, M. C. Estimation of Parental Effects Using Polygenic Scores. Behav. Genet. 51, 264–278 (2021). Deelen, J. et al. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nat. Commun. 10, 3669 (2019). Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019). Supplement: Mental health, genes linked to education, and re-application to university Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6717200","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469098971,"identity":"7d46094e-b179-4e6c-adeb-72d9e72fc853","order_by":0,"name":"Henrik Dobewall","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACAxCRcICBsUECxCqwkGOQYG5gqDAgWouBhDGPBGMDwxlCWhiQtCT2gLXgcZg5e++xDw/OMMj2Szcfk/hhIJG+X7qxgeFAAW4tlj3nkmck3GAwnjnnWJpkj4FEbo/MQaAWfA67kWPMkPCBIXHDjRyzGzwgLRKJDcwf8Gm5/wamJf/bzT9Ah/EAtRCwhQeo5QbYFrbbQFsSCGs5A3LYGQmQX8x/yxhIGPbcSGw4gFfL8TPGjD+O2YBC7LHhmwobefYZyQcfHPiDWwsUSKByDxDUMApGwSgYBaMALwAAIbNXI/l9FxgAAAAASUVORK5CYII=","orcid":"","institution":"Finnish Institute for Health and Welfare","correspondingAuthor":true,"prefix":"","firstName":"Henrik","middleName":"","lastName":"Dobewall","suffix":""},{"id":469098972,"identity":"67288efe-2007-4354-aaeb-a4cf1ee51afc","order_by":1,"name":"Hannu Lehti","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Hannu","middleName":"","lastName":"Lehti","suffix":""},{"id":469098973,"identity":"e9c82a6e-fe86-426c-9bf6-bec49151a0e5","order_by":2,"name":"Outi Sirniö","email":"","orcid":"","institution":"Finnish Institute for Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Outi","middleName":"","lastName":"Sirniö","suffix":""},{"id":469098974,"identity":"23f8af36-e397-4bcb-9486-844e737241b3","order_by":3,"name":"Maria Vaalavuo","email":"","orcid":"","institution":"Finnish Institute for Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Vaalavuo","suffix":""}],"badges":[],"createdAt":"2025-05-21 13:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6717200/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6717200/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84690823,"identity":"33e4e6fb-abc2-4fb7-ac92-fc1e1aa178c6","added_by":"auto","created_at":"2025-06-16 09:39:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51126,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the education system in Finland. Adapted with permission Lehti et al., 2019\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6717200/v1/fbaa3e85f4152bb619fee600.png"},{"id":84690822,"identity":"0c49974a-3f33-4c31-9034-2220202926ad","added_by":"auto","created_at":"2025-06-16 09:39:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147632,"visible":true,"origin":"","legend":"\u003cp\u003eNon-significant interaction of the EA-PGS and a) education and b) income. Stop applying to university after being rejected in t-1. Average marginal effects with 95% CIs presented (based on the results reported in Table 2)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6717200/v1/45431f5fa9292ae18e947d2a.png"},{"id":92565504,"identity":"d9392f29-6c65-4289-a577-75b7d997c5e7","added_by":"auto","created_at":"2025-10-01 06:17:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1047044,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6717200/v1/c89518fa-86d3-4d04-a5fd-45c31c004c39.pdf"},{"id":84690817,"identity":"54e77b12-af95-4168-94d1-7a9902ceb54a","added_by":"auto","created_at":"2025-06-16 09:39:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17681,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6717200/v1/8cdc208c67f2eed39da4c9cb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Re-application to university after rejection – The role of mental health and education-linked genes in predicting persistence in pursuing educational goals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEducational pathways consist of several transitions that young people make as they progress through the education system. These pathways reflect both the opportunities and constraints shaped by individuals\u0026rsquo; socioeconomic background and the characteristics of the prevailing educational system. Educational transitions, e.g., which school to attend, which track to choose, and whether to continue education after each level is accomplished, have been shown to be partially dependent on individuals\u0026rsquo; family socioeconomic background \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, even in egalitarian welfare states like Finland \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Each transition consists of two processes, namely application to an institution and the institution\u0026rsquo;s decision about the application, and social origin differences in the former which are driven by educational expectations and aspirations.\u003c/p\u003e \u003cp\u003eTo better understand the mechanisms underlying educational inequalities, the focus should be placed on individuals\u0026rsquo; educational decision-making processes and behaviors that stem from their socially and genetically inherited background. Two recent Finnish studies have identified that the social origin gap in university enrollment is partly driven by applicants with less educated parents being less likely to continue applying for study places and enroll at university after an initial rejection compared to individuals with more highly educated parents \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. We build on this work and its findings but consider the potential contribution of mental health and genetic endowments in individuals\u0026rsquo; persistence in pursuing educational goals and ask whether family socioeconomic background moderates the influence of genes during this educational transition.\u003c/p\u003e \u003cp\u003eOur first focus is on how experiencing poor mental health, in addition to family background and major life events, interplay with genetic endowments in social stratification via university re-application behavior. Despite mental illness having a significant impact on individuals\u0026rsquo; educational aspirations and decisions \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, to our knowledge, the association between poor mental health and university application behavior has not been studied before.\u003c/p\u003e \u003cp\u003eThe role of genetic endowment in re-application behavior is currently unexplored even though the link between genes and intelligence, educational attainment, and educational choices is widely acknowledged \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Persistence, understood as a motivational personality trait, is a genetically influenced tendency to continue working toward goals despite obstacles or initial failure and reflects an individual's ability to maintain effort and commitment over time \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Coined as grit in the sociological literature \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, it is strongly associated with educational outcomes even after accounting for individual differences in intelligence \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The possibility to measure the genetic component of complex traits introduces new perspectives on equality of (educational) opportunities. While the concept has long guided normative theories of justice, its meaning had become contested in light of recent findings in the field of sociogenomics \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In genetic terminology, educational choices can be understood as active gene\u0026ndash;environment correlations, which occur when individuals select or create environments that align with their genetically influenced traits \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, also application behavior should be at least indirectly influenced by genetic endowments. However, the returns for status attainment of educational goal striving appear to be more pronounced among the socioeconomically advantaged \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It is therefore possible that accounting for education-linked genes alters the effect of family background on discontinuation.\u003c/p\u003e \u003cp\u003eHealth selection effects in educational outcomes are widely recognized, while individuals from lower socioeconomic backgrounds are disproportionately diagnosed with a mental illness \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Register-based studies conducted in Nordic welfare states have found that being diagnosed with a mental illness and the use of psychotropic medication in adolescence predict lower academic performance and educational attainment \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Young individuals with attention-deficit/hyperactivity disorder, for instance, are more likely to experience academic underachievement, educational difficulties, and increased grade retention due to impairments in body functions, activity limitations, and restrictions in social participation \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Poor mental health is further inversely associated not only with educational attainment but also with labor market outcomes in adulthood \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. We, therefore, add to the literature by investigating the role of experiencing poor mental health during the transition from secondary level to university. We expect mental illness to be negatively associated with university application outcomes in general, but potentially even more strongly associated with discontinuation once rejected (\u003cem\u003ehypothesis 1\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eUntil recently, it has been considered that the link between family background and educational choices is due to various socio-cultural and economic resources that parents provide their children\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, while the exploitation of genetic data has directed our gaze to the influence of an individual\u0026rsquo;s genotype and its potential interaction with the environment \u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Indeed, a recent study by Okbay \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e found that a polygenic score summing up the combined effect of many genetic variants to estimate the likelihood of achieving high educational attainment explains up to 16 percent of the variance in educational attainment in between-family genome-wide association studies (GWAS). This is comparable to the variance accounted for by parental background measured jointly with education, income, and unemployment \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The genetic effects in educational attainment identifiable by within-family GWAS, at the moment, remain comparatively smaller \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo better understand the mechanisms that drive educational attainment, namely the educational decision-making processes and especially whether applicants continue (or stop) applying regardless of initial rejection, it is essential to gain a deeper understanding of how social and genetic mechanisms interact and create barriers to educational opportunity \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Genetic endowment for educational attainment has been shown to be associated with various educational preferences and decisions. With each choice made throughout the school years, the gradient between education-linked genes and later life chances becomes stronger \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor instance, a study from New Zealand found that children with a higher polygenic score for educational attainment (hereafter, EA-PGS) developed reading skills earlier, and as high school students, those with higher scores also exhibited greater academic educational aspirations and were later selected into more competitive tracks. Harden and colleagues \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e found that students with a higher EA-PGS were more likely to enroll in advanced mathematics courses in the 9th grade, a critical juncture in math tracking within the US educational system, which requires students to follow a highly cumulative sequence of secondary school math courses. Students with a higher EA-PGS also tended to persist in the math tracks through the end of high school \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Further, also educational field choices appear to have a genetic component \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, which is in line with findings suggesting that self-selection outweighs socialization effects during self-chosen life transitions \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Genetic endowment for educational attainment has also been found to be important at different educational transition points in Finland \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Accordingly, we expect that a higher EA-PGS is associated with the re-application behavior of individuals applying to university, especially when faced with rejection (\u003cem\u003ehypothesis 2\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eAs social and genetic factors have previously been studied in isolation, we are further interested in whether controlling for the EA-PGS and mental health reduces the social origin effect on re-application behavior (\u003cem\u003ehypothesis 3\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eFamily background has been found to moderate genetic influences on educational outcomes. These influences might be stronger in more affluent and educated families because nurturing can have multiplicative effects for genetic endowments associated with education, while offspring may also have more to lose if confronted with a rejection or similar disappointments \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Alternatively, well-off parents may leverage their resources to guarantee that their children attain a certain level of education and social standing, regardless of the children's innate genetic potential \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This implies that we see compensation of inherited genes \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Following this line of reasoning, the influence of genes might as well be stronger for socially disadvantaged individuals. On the one hand, poor resources and low genetic endowment could create a double jeopardy leading to negative feedback loops. On the other hand, traits associated with a high EA-PGS could potentially help overcome the experience of rejection, especially for socially disadvantaged individuals. Previous mixed results on the interaction effect of family socioeconomic background and education-linked genes could be explained if genetic endowment is more influential for socially disadvantaged individuals for less selective outcomes like upper-secondary school completion, and more crucial for individuals from well-off families for highly selective outcomes like the completion of postgraduate studies \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These empirical results and theories share the assumption that the influence of individuals\u0026rsquo; education-linked genes on educational outcomes is influenced by their family socioeconomic background. We test if the association between genetic endowment and re-application behavior will be moderated by the available financial resources and cultural capital of the family, indicated by the income and educational level of the parents. It is expected that genetic endowment for high education is especially relevant for the re-application of individuals with less well-off parents who are more likely to stop applying otherwise (\u003cem\u003ehypothesis 4\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe current study uses comprehensive register data from Finland linked to polygenic scores. Our study sample includes 14,079 individuals born 1987\u0026ndash;1996 who had at least one genotyped parent. We have four principal research questions. First, is experiencing poor mental health associated with re-application behavior? Second, is the EA-PGS associated with the re-application behavior of individuals, especially when faced with rejection in university applications, irrespective of their social origin? Third, do education-linked genes, in part, explain the social disparity in re-application behavior found in previous research? Fourth, does social origin weaken or amplify the influence of education-linked genes on re-application behavior, i.e., is there evidence for gene-environment interactions?\u003c/p\u003e\n\u003ch3\u003eInstitutional context\u003c/h3\u003e\n\u003cp\u003eThe educational system in Finland\u0026mdash;as in the Nordic countries in general\u0026mdash;is relatively equal. This is pointed out in the international comparisons of socioeconomic and educational inheritance that have found the Nordic countries, including Finland, to be among the most egalitarian \u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. At the same time, relatively low income inequality and (child) poverty, a highly educated adult population, and comprehensive social safety net support the egalitarian objectives of the educational system.\u003c/p\u003e \u003cp\u003eThe Finnish higher education system is supported by governmental funding and does not require tuition fees. Almost all upper-secondary-level degrees provide eligibility for higher education. Higher education is divided into two parallel sectors: universities and universities of applied sciences (or polytechnics). Compared to universities that offer bachelor\u0026rsquo;s, master\u0026rsquo;s, and doctoral degrees, universities of applied sciences offer working-life oriented degrees mostly at the bachelor\u0026rsquo;s level. In Finland, there are 13 universities and 24 universities of applied sciences. The application procedure of the universities is centralized by the Finnish government, and it takes place annually in each spring semester.\u003c/p\u003e \u003cp\u003eTo apply for higher education in Finland, individuals must first obtain a degree from general upper-secondary school or vocational school. However, as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows, in the cohorts we analyze only less than 1 percent of individuals with vocational secondary degrees enrolled to study in universities and 13 percent to universities of applied sciences. Of those who graduated from general upper-secondary school, by the age of 21, about 34 percent enrolled at a university and 39 percent at a university of applied sciences. A large majority of those who enrolled in university or polytechnics had a degree from a general upper-secondary school. Thus, it can also be expected that a large majority of the applicants have a matriculation examination (see Lehti et al., 2019).\u003c/p\u003e \u003cp\u003eIn the studied cohorts, individuals who applied to higher education had to participate in entry exams to be selected for a study program in the university or polytechnics. The selection of the students was based either on the entry exams and the results of the matriculation exams of the general upper-secondary school or only the entry exams, the quotas of these two selection groups depending on the study program. For individuals who applied to university and had vocational degrees, the intake was based on grades of the graduation diploma and entry exams. There were no age limits or fees in the entry exams. Although applicants may apply and be admitted to several study programs, they can only accept one study place within a year. Each study program has a certain amount of study places which limits the number of students who can be accepted. In Finland, most of the applicants (about 60 percent) are not accepted into university programs they applied to, which leads to a high number of re-applications and signifies a strong institutional barrier to obtaining a university degree \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. It has been shown that major life events, such as becoming a parent, finding employment, or getting a study place in polytechnics, have a strong effect on individuals\u0026rsquo; decision to stop applying to university \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Application behavior can also be influenced by institutional factors. If young individuals become unemployed, for instance, then the Finnish state requires them to apply for a study place to avoid losing unemployment benefits. For this reason, we take these life events into account.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics comparing individuals who never applied to university to those who experienced rejection and successful applicants, a) relative frequencies (%) and b) averages (SD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever applied\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirect access\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ea)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (2)\u0026thinsp;=\u0026thinsp;1100.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess educated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity/polytechnics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome 2001\u0026ndash;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (4)\u0026thinsp;=\u0026thinsp;673.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (2)\u0026thinsp;=\u0026thinsp;374.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health diagnosis occurred before age 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (2)\u0026thinsp;=\u0026thinsp;173.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eb)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment PGS of genotyped parent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (2, 14,076) 271.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.179 (.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.152 (.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.34 (.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical approach\u003c/h2\u003e \u003cp\u003eWe start by reporting descriptive statistics for individuals belonging to the following categories: 1) non-applicants, 2) applicants who experienced rejection, and 3) applicants with direct access. To conduct a fine-grained analysis of the mechanisms that might explain why some individuals are less persistent in their educational goals and stop applying to university, logistic discrete-time hazard models are used. The unit of analysis in these analyses is person-years in which the outcome event \u0026ldquo;stopping to apply to university\u0026rdquo; in time \u003cem\u003et\u003c/em\u003e is conditional on a rejection in time \u003cem\u003et-1\u003c/em\u003e. Discrete-time hazard models are used to analyze the timing of events particularly when the events happen at specific points in time (see Heiskala and colleagues \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, for details). These models are a type of survival analysis adapted to situations where time is measured in distinct intervals like once a year rather than continuously. Discrete-time hazard models can handle right-censored data like in our case when some individuals are still applying by the end of the observed period. We used logistic regression to model the hazard of stopping to apply as a function of time-invariant and time-variant predictors. These analyses allow us to model occurring life events and the use of psychotropic medication as time-variant. Log odds are computed and GxE interactions are plotted. First, we entered family background, namely parental education and income, and life events along with standard control variables (Model 1). Second, we add mental health diagnosis during adolescence and medication use (Model 2). Third, education-linked genes were entered into the model (Model 3), Fourth, the moderation of genetic influences by family background (GxE interaction) was tested (Model 4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays descriptive group differences between individuals based on their application outcomes. Individuals who experienced rejection when applying to university, our main analysis sample, differ significantly from those who never applied to university and those who were granted direct access in terms of their parental socioeconomic status, prior mental health diagnoses, and genetic endowment for high educational attainment. Individuals that were followed in their re-application behavior (i.e., showed persistence in pursuing their educational goals or stopped applying to university) had on average somewhat less educated parents and lower family income, a lower EA PGS, and were more likely diagnosed with any mental illness compared to those who got into university directly (confirmed also in multivariate analyses presented in supplement Table S1). All study variables were statistically different between the two groups at p\u0026thinsp;\u0026lt;\u0026thinsp;.001 level. Women were less likely to be in the direct access category. This might appear counterintuitive as they apply more often to university, but it can be explained by sex differences in study field choices (e.g., fewer \u0026ldquo;walk in programs\u0026ldquo;). This group comparison, partially addressing our first two research questions, shows that both receiving a mental health diagnosis before reaching the age to apply to university and education-linked genes were important predictors of the studied application outcomes.\u003c/p\u003e\n\u003cp\u003eWe move on to investigate whether one discontinues applying after initial rejection. Therefore, only those having experienced a rejection are included in this analysis. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of a discrete-time hazard analysis of life events and psychotropic medication use linked to stopping to apply to university after experiencing a rejection during the previous year(s). Model 1 presents further evidence for social origin differences in re-application behavior in terms of parental education but not income. Model 2 indicates that, unlike for the life events, the coefficient for mental health \u0026mdash; assessed both with having received a psychiatric diagnosis during adolescence and psychotropic medication use in a given application year \u0026mdash; was not statistically significant. This points to a limited incremental role of mental health during this stage of the educational transition against our first hypothesis.\u003c/p\u003e\n\u003cp\u003eWe also observe that EA-PGS continues to play an imperative role in discontinuation even when life events and mental health were included in the model (Model 3). In other words, individuals with a higher polygenic score were more likely to reapply after rejection implying that persistent educational intentions and behaviors have indeed a genetic component. The effect of genetic endowments was observed independent of the parental socioeconomic background. This is noteworthy given that the other included variables explain a large part of the variance in persistence in pursuing educational goals (i.e., not stopping to apply to university). Concerning our third research question, we found that including EA-PGS along with mental health did not alter the coefficients for parental education and income and thus does not explain the observed social origin differences in the outcome.\u003c/p\u003e\n\u003cp\u003eFinally, we did not find a GxE interaction between the EA-PGS and family background (Model 4). Figure\u0026nbsp;2 illustrates the non-significant interaction of the polygenic score for educational attainment on 20 percent intervals with parental education and income.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePredicting whether individuals stop applying to university in t conditional on rejection in t-1, logistic discrete-time hazard models (average marginal effects).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1: Family background \u0026amp; life events\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2:\u003c/p\u003e\n\u003cp\u003e+Mental health\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3:\u003c/p\u003e\n\u003cp\u003e+Genes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 4:\u003c/p\u003e\n\u003cp\u003e+GxE\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edy/dx (se)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edy/dx (se)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edy/dx (se)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStable predictors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.028*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.028\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.028\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMental health diagnosis (age\u0026thinsp;\u0026lt;\u0026thinsp;18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors assessed in a given year\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChild born\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.263\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.252\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.248\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmployment (months)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.024\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.024\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.025\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.025\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStarted in polytechnics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.320\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.321\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.321\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.321\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnemployment (3\u0026thinsp;+\u0026thinsp;months)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.188\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.188\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.189\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.190\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePsychotropic medication use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFamily background\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParents have university/polytechnic degree (Ref. less educated)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow parental income (Ref. medium)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh parental income\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEA-PGS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.019\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGene-environment (GxE) interactions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh parental education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow parental income\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh parental income\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN (person-years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePseudo R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. All models control for age, region of birth, year of upper-secondary education graduation, and source health survey and PGS. The 10 genetic principal components are entered together with the EA PGS.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this article, we investigated the influence of mental health and education-linked genes along with family background on re-application to universities in Finland. Our study was motivated by the importance of university enrollment for social stratification. We contribute to the debate on educational equality in three important ways. First, we focus on an educational transition that has received less attention in studies on education, namely application and re-application to university. Second, we contribute by examining the role of individuals’ mental health on educational decision-making and behavior. Third, we include information on parental background together with genetic endowment to attain higher education and study also their interplay. Our study expands the framework used by Heiskala and colleagues \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e who showed that social origins matter for re-application behavior reinforcing inequality in educational outcomes.\u003c/p\u003e \u003cp\u003eWe demonstrate that the combination of socially and biologically inherited family background seems to explain in part individuals’ behavior when applying for a study place in Finnish universities and specifically why some individuals are more persistent in pursuing their educational goals than others. Our findings underscore the importance of the EA-PGS and family background as predictors of university re-application behaviors. Individuals with a higher polygenic score were more likely to reapply after rejection, suggesting a genetic component in educational goal striving over and above social origin. We found an association between parents’ university or polytechnic degree and stopping to apply to university in line with prior evidence \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Parental income, however, was not associated with the outcome suggesting that educational expectations and cultural capital rather than material resources work as the mechanism of intergenerational transmission. Our findings could further imply that the EA-PGS captures grit and other characteristics that are needed to attain higher levels of education \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and social status \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, genetic influences persist even when accounting for alternative pathways like experiencing poor mental health and major life events such as the birth of a child. This also highlights the robustness of these associations. Being diagnosed with a mental illness during adolescence influenced whether a person applied (successfully) to university but poor mental health, measured both with prior diagnoses and psychotropic medication use, did not have an incremental effect on discontinuation. However, having received a mental illness diagnosis prior to the application period to university was more prevalent in individuals who never applied to university. This might partially explain why mental health does not matter for stop applying, as a diagnosis keeps you away from enrollment to university studies but affects the subsequent outcome persistence less. Our findings align with previous evidence that mental illness explains a large part of the population variation in educational outcomes — health selection —, yet is a weaker mechanism than expected to explain social origin differences \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. To our knowledge, this is the first study to investigate the role of mental health in university application behavior. Future research is needed to further explore how mental illness influences educational decision-making and access to higher education across diverse institutional and cultural contexts.\u003c/p\u003e \u003cp\u003eGenetic research has shown that genetic associations with educational and socioeconomic outcomes become more apparent under conditions of equal opportunity, as environmental dis-advantages and contextual barriers play a smaller role in shaping life chances \u003csup\u003e\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e–\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In such context, outcomes are often interpreted as reflecting sustained effort, academic talent, and choice, seemingly free from the constraints of individuals’ family background or other matters of luck \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Yet the idea that behavioral choices are fully autonomous is challenged by genetic findings and theory \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The goal to minimize educational inequalities arising from differences in inherited genes seems to be a futile quest. However, societies can design institutions and educational systems that promote learning and skill development of all regardless of inherited genes \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAgainst our expectation that the EA-PGS is especially relevant for the re-application behavior of individuals with less well-off parents, we did not find any indication for the presence of GxE interactions. This could be related to our relatively small sample size. Based on these results we are not able to support any of the competing sociological theories on how the environment moderates the strength of the genetic influence on educational outcomes \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Trying to identify GxE interactions is vital for developing a non-deterministic understanding of how genetic influences evolve within our societal fabric \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Even though we did not find that the family background moderates genetic effects, our results are an example for how the influence of genetic endowment for attaining high education accumulates over educational environments via individual effort and choices. Earlier research has already found genetic associations between an EA-PGS and mathematics tracking and persistence in secondary school that appear to explain that the gradient between education-linked genes and socioeconomic outcomes becomes stronger over time rather than suggesting genetic determinism \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has some limitations. That we had to rely on polygenic scores measured in one of the parents, even though innovative, made it impossible to use other methods designed to limit environmental confounding when assessing the relative contributions of genetic and social factors \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. In Finnish data, the EA-PGS usually explains somewhat less of the population variation than found in the original GWAS \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, therefore, our results might underestimate genetic influences on re-application behavior. Parental education and income are not exogenous predictors of children’s educational outcomes, which makes studying GxE interactions difficult \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The use of the information of the non-genotyped parent to assess the family background further had its shortcomings due to the presence of assortative mating and the possibility of confounding genetic nurture effects \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. While starting with a relatively large, genotyped sample, the sample size available to us for more specific research questions such as an individual’s persistence in applying to university when confronted with rejection was smaller. Detecting significant interaction, furthermore, requires a lot of statistical power. The results should be replicated and extended in an independent sample.\u003c/p\u003e \u003cp\u003eHowever, we were able to rely on otherwise rich and comprehensive data. One of the main strengths of the study is the use of objective measures of an individual's educational choices, namely their re-application behavior, at an important transition point. The longitudinal dimension of the Finnish register data allowed us to include not only information about parents’ socioeconomic status but also detailed information about health status and life events that can influence whether an individual stops applying to university. Even though the study was not pre-registered, we restricted the researcher’s degree of freedom by closely following the variable coding and analysis plan of Heiskala and colleagues \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, we deviated from it by not controlling for entrance exam grades because they are themselves directly influenced by the EA-PGS. Further, we went beyond their analyses by using information on mental health and genes to explain their main result that re-application is strongly influenced by the family background.\u003c/p\u003e \u003cp\u003eIn conclusion, experiencing poor mental health plays an important role in whether an individual applies to a university but appears to be less influential for deciding whether to reapply after initial rejection. Individuals with a higher polygenic score were more likely to re-apply to university, suggesting a genetic component in persistent educational intentions beyond family background even after experiencing rejection. Yet, neither experiencing poor mental health nor education-linked genes measured in parents could explain the social origin gap in university enrollment noteworthy. Future research of social stratification should also consider the effect of genes to uncover how socially and biologically inherited family background are interweaved in the inequality of educational opportunities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methods","content":"\u003ch2\u003eResearch data\u003c/h2\u003e\u003cp\u003eWe utilized administrative records of Statistics Finland with annual information on income and education-related variables, such as successful and unsuccessful university applications, registers of the Finnish Institute for Health and Welfare (THL) including information on health care visits and diagnoses, and registers of the Finnish Social Insurance Institution (Kela) on reimbursed drugs. These anonymously merged registers were linked to genetic data collected by the THL as part of three population-representative health surveys (1992–2020). The total sample size of the genetically-informed register data is 39,570. We focus on individuals born between 1987 and 1996 for whom comprehensive data on their own educational history and parental background were available. As only a few of this cohort were genotyped, we circumvented the problem by utilizing the PGSs of one of their parents instead (55.1% mothers; see \u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e–\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. With this relatively established approach, the ranking of individuals/families remains approximately the same but the variance that can be explained by the EA-PGS of the parents is cut in half compared to a situation in which we would have had the genotype of the offspring available. Priority was given to mothers when polygenic scores for both parents were available.\u003c/p\u003e\u003cp\u003eThis resulted in a study sample of 14,079 individuals. 5,838 individuals applied to university, of which 1,408 were granted access on their first attempt, and 8,241 did not apply to university. The observation period was 2006–2022, but the re-application behavior after initial rejection was followed up for a maximum of 4 years.\u003c/p\u003e\u003ch3\u003eMeasures\u003c/h3\u003e\u003cp\u003eWe categorized the outcomes of the application to Finnish universities into three categories: 1) Never applied, 2) Experienced a rejection (Rejected), and 3) Applied with success (Direct access). For practical reasons, the direct access category includes a few cases who later applied for another study place in university and were accepted.\u003c/p\u003e\u003cp\u003eTo measure persistence in pursuing educational goals, our study’s main outcome is re-application behavior after rejection \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. We were especially interested in individuals who unsuccessfully applied at least once between the years 2006–2021 and whether they re-applied after the initial rejection. About two-thirds of all university applicants in Finland apply without being accepted. In our study sample, this results in 4,430 rejected applicants with genetic information available. This resulted in a total 6,020 person-years of 3,813 individuals in the analyses of stop applying.\u003c/p\u003e\u003cp\u003eIn the realm of schooling, the most relevant polygenic score measures genetic endowments for achieving high educational attainment. The EA-PGS was informed by summary statistics of a well-powered genome-wide association study (Okbay et al., 2022). It was produced with the Bayesian PRS-CS method that infers posterior SNP effect sizes under continuous shrinkage priors using an external linkage disequilibrium reference panel, accounting of all identified SNPs without a p-value threshold \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The EA-PGS of one’s parent predicted, without any controls, 3.5 percent of the variance in whether an individual ever applied to a university during the observation period. For graphical illustrations, the PGS was recoded into 20 percent intervals.\u003c/p\u003e\u003cp\u003eAvailable resources of the family were measured with the combined taxable income between 2001–2009. Income was deflated and averaged over the years. Individuals were then categorized into three groups based on parental income, contrasting 20% highest, medium, and 20% lowest income. Parental level of education was assessed in 2009 with whether parents had a university or polytechnic degree. Information on the family background was obtained from the administrative records for the non-genotyped parent to minimize confounding of genetic and social predictors. The EA-PGS of the genotyped parent correlated significantly with the non-genotyped parent’s university or polytechnic degree, \u003cem\u003er\u003c/em\u003e = .15, and income, \u003cem\u003er\u003c/em\u003e = .10, (p \u0026lt; .001), suggesting the presence of assortative mating based on these traits.\u003c/p\u003e\u003cp\u003eTo account for the role of poor mental health, we recorded if an individual has got a psychiatric diagnosis in public specialized health care before age 18 using the International Classification of Diseases (ICD-10) classification (e.g., schizophrenia, depression, \u0026amp; anxiety disorder). Psychotropic medication use was identified according to the Anatomical Therapeutic Chemical (ATC) classification system (e.g., antipsychotics, anxiolytics, antidepressants, and psychostimulants) in a given year (\u0026lt; age 25). Psychotropic medication as a measure has the advantage that is covers also private healthcare and allows for more accurate timing of mental health episodes than diagnoses.\u003c/p\u003e\u003cp\u003eStudied life events, namely having a child, starting studies in polytechnics, number of months in employment (based on income), and unemployment periods of more than 3 months, were measured from 2006 onwards up to age 24. In our discrete-time hazard model focusing on life events, psychotropic medication use, and stopping to apply to university, these major life events were measured the same year as the event (stop applying to university).\u003c/p\u003e\u003cp\u003eStandard covariates include age, gender, place of birth at NUTS2 level, year of matriculation qualification, top ten genetic principal components to account for population stratification, and the source health survey and the genetic information.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHD, HL, OS, and MV jointly contributed to the formulation of the research hypotheses, development of the data analysis plans, and interpretation of the results. HD drafted the manuscript. OS, HD, and HL merged and prepared the register data. All authors critically revised the manuscript and approved the final version for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe study was supported by the Academy of Finland (Research Council of Finland) grant 342605 (MEDIG), grant 355009, and the Flagship Programme (decision number: 345547).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors do not have permission to share data. The Stata code written to prepare and analyze the anonymously merged registers is available at https://github.com/sohedo/Reapplication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBreen, R. \u0026amp; Jonsson, J. O. Analyzing Educational Careers: A Multinomial Transition Model. \u003cem\u003eAm. Sociol. Rev.\u003c/em\u003e 65, 754\u0026ndash;772 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLievore, I. \u0026amp; Triventi, M. 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Commun.\u003c/em\u003e 10, 1776 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupplement: Mental health, genes linked to education, and re-application to university\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6717200/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6717200/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocioeconomic disparities in university enrollment may stem from lower re-application rates after rejection among individuals from less educated families. We investigate the role of mental health and education-linked genes alongside family background in predicting re-application to university.\u003c/p\u003e \u003cp\u003eThe study uses longitudinal register data from 14,079 individuals in Finland, data on psychiatric diagnoses and medication, and parental polygenic scores to assess the genetic endowment to high educational attainment (EA-PGS) combined with application records (4,430 rejected). Discrete-time hazard models are employed.\u003c/p\u003e \u003cp\u003ePoor mental health predicted whether a person applied to university and experienced rejection but not discontinuation. Individuals with a higher EA-PGS were more likely to re-apply after rejection, suggesting a genetic component in persistence. However, neither mental health nor genes explained the social origin gap in re-application rates. No gene-environment interactions were found.\u003c/p\u003e \u003cp\u003eThe study informs debates on educational equality and shows how genes express themselves in smaller or bigger decisions.\u003c/p\u003e","manuscriptTitle":"Re-application to university after rejection – The role of mental health and education-linked genes in predicting persistence in pursuing educational goals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 09:38:51","doi":"10.21203/rs.3.rs-6717200/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47d1b380-18bc-49eb-bc03-139b911bc3f8","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49803843,"name":"Scientific community and society/Social sciences/Sociology"},{"id":49803844,"name":"Biological sciences/Psychology/Human behaviour"},{"id":49803845,"name":"Scientific community and society/Social sciences/Education"}],"tags":[],"updatedAt":"2025-10-01T06:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 09:38:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6717200","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6717200","identity":"rs-6717200","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}