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A More Comprehensive Theory of Educational Attainment: An Empirical Analysis of the Determinants of Racial and Ethnic Inequality in the College Completion Process

The Civil Rights movement and the Great Society legislation of the 1960s and early 1970s resulted in numerous initiatives designed to bring race/ethnic minorities into mainstream American society. Many of these initiatives were implemented in the educational system, as educational attainment is an important determinant of social mobility. The numerous programs intended to integrate race/ethnic minorities into mainstream society via increased educational attainment have been somewhat successful, as the proportion of racial/ethnic minorities completing college has increased since the 1970s. However, the racial/ethnic gap in college completion has minimally changed, as the proportion of white students completing college has also increased. In an attempt to understand the determinants of educational inequality numerous theories of educational attainment have been developed. A handful of these perspectives, the Wisconsin Model, Oppositional Culture, Capital Deficiency, and Segmented Assimilation have gained prominence. However they can not consistently explain the race/ethnic achievement gap. To better understand racial/ethnic inequality this dissertation engages the leading theories of educational attainment. Initially, it uses the University of Washington Beyond High School Project data to independently examine each theory and assess whether it operates as hypothesized. After the independent assessment, a cumulative integrated theory of educational attainment is constructed, utilizing the key explanatory mechanisms from the leading theories of educational attainment, such as family context and encouragement from significant others. The integrated theory of attainment is advantageous as it best explains the racial/ethnic achievement gap and the educational attainment process. This dissertation also examines whether a cumulative integrated theory explains the racial/ethnic variation that exists across the educational transitions in the college completion process. Lastly, encouragement from significant others is examined as it is a central explanatory mechanism in the college completion process. The results illustrate that a less advantaged family context is the main obstacle for traditionally disadvantaged minority youth, while the advantage displayed youth from Asian ethnic groups is largely a function of their increased receipt of significant others college encouragement. Also, the results reveal that significant others encouragement, not only attenuates race/ethnic variation, it is also a key explanatory variable in the college completion process.

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Assessing educational inequality in high participation systems: the role of educational expansion and skills diffusion in comparative perspective

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• Published: 14 May 2024

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• Satoshi Araki   ORCID: orcid.org/0000-0003-0302-959X 1  

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A vast literature shows parental education significantly affects children’s chance of attaining higher education even in high participation systems (HPS). Comparative studies further argue that the strength of this intergenerational transmission of education varies across countries. However, the mechanisms behind this cross-national heterogeneity remain elusive. Extending recent arguments on the “EE-SD model” and using the OECD data for over 32,000 individuals in 26 countries, this study examines how the degree of educational inequality varies depending on the levels of educational expansion and skills diffusion. Country-specific analyses initially confirm the substantial link between parental and children’s educational attainment in all HPS. Nevertheless, multilevel regressions reveal that this unequal structure becomes weak in highly skilled societies net of quantity of higher education opportunities. Although further examination is necessary to establish causality, these results suggest that the accumulation of high skills in a society plays a role in mitigating intergenerational transmission of education. Potential mechanisms include (1) skills-based rewards allocation is fostered and (2) the comparative advantage of having educated parents in the human capital formation process diminishes due to the diffusion of high skills among the population across social strata. These findings also indicate that contradictory evidence on the persistence of educational inequality in relation to educational expansion may partially reflect the extent to which each study incorporates the skills dimension. Examining the roles of societal-level skills diffusion alongside higher education proliferation is essential to better understand social inequality and stratification mechanisms in HPS.

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Introduction

Access to higher education has markedly increased over the past decades, leading to the establishment of high participation systems (HPS) worldwide (Cantwell et al., 2018 ). Despite such an expansion of higher education opportunities, evidence shows that one’s educational attainment is unequally distributed based on socio-economic status (SES) (Marginson, 2016a , 2016b ; Pfeffer, 2008 ; Voss et al., 2022 ). This persistent association between SES and education is often explained through the lens of stratification theories, such as maximally maintained inequality (Raftery & Hout, 1993 ) and effectively maintained inequality (Lucas, 2001 ). Comparative studies also highlight the influence of societal conditions, suggesting that highly tracked education systems are likely to exacerbate educational inequalities despite facilitating smoother transitions from education to work (Bol et al., 2019 ; Burger, 2016 ; Österman, 2018 ; Reichelt et al., 2019 ; Traini, 2022 ).

Although researchers have extensively investigated the unequal structure of educational attainment from longitudinal and cross-national perspectives, the mechanisms behind the heterogeneous degrees of inequality across societies remain elusive. As mentioned, educational tracking has been seen as a key societal determinant. However, OECD ( 2018 ) reveals that the magnitude of SES significantly varies even among HPS with similar levels of tracking and higher education expansion. This suggests there are missing societal traits, not yet explored in prior studies, that explain cross-national variation in the extent of educational inequality. Identifying this “hidden” structure would also be valuable from a policy perspective in addressing unequal educational attainment in HPS.

In this regard, recent research has detected a unique social structure: (1) the expansion of higher education (i.e., educational expansion) is not identical to the accumulation of high skills (i.e., skills diffusion) and (2) these two societal dimensions play distinct roles in social stratification by influencing the amount and allocation of human capital and socio-economic rewards (Araki, 2020 ; Araki & Kariya, 2022 ; Hanushek & Woessmann, 2015 ). 1 Drawing on these findings, Araki ( 2023b ) proposed the “EE-SD model” to uncover the characteristics of higher education systems and relevant social problems. This framework, with close attention to skills alongside education per se, potentially offers a useful viewpoint to better understand educational stratification in HPS for two reasons.

First, given that high-SES families tend to transmit their high educational attainment via human capital development of offsprings in terms of high skills and aspirations (Davies et al., 2014 ), this relative advantage may weaken due to skills diffusion insofar as this societal shift occurs in a way that promotes the share of the population possessing high skills across social strata including the disadvantaged. This also means, so long as skills diffusion is realized exclusively among the advantaged without involving their low-SES counterparts, the degree of educational inequality may even intensify due to the exacerbated advantage of the high-SES group in human capital formation.

Second, in case skills diffusion promotes a more meritocratic rewards allocation process based on increased visibility of skills (Araki, 2020 ), it is plausible that the relative impact of family background on educational attainment diminishes while the importance of skills as such increases. Put differently, if HPS are formed merely as a consequence of expanding educational opportunities without skills diffusion, the advantaged may retain their prestigious positions on the education ladder regardless of their skills level. Furthermore, considering a possibility that the accumulation of high skills in a society does not make skills more visible, skills diffusion may not affect the structure of educational inequality due to the absence of skills-based meritocratic system. In either case, the association between intergenerational transmission of higher education and societal-level skills diffusion represents an essential knowledge gap in this vein.

From a comparative perspective, this paper thus examines how the linkage between SES and college completion varies across societies depending on the degree of skills diffusion, as well as higher education expansion and tracking. In what follows, the relevant literature is reviewed, followed by data/methods, analysis results, and discussion.

Inequality in educational attainment

In analyzing the association between individuals’ SES and educational attainment, scholars have long paid attention to how it shifts in tandem with educational expansion (Breen, 2010 ; Shavit et al., 1993 ). This agenda is particularly relevant in the contemporary world, many parts of which have achieved HPS with tertiary enrolment ratios exceeding 50% (Cantwell et al., 2018 ; Marginson, 2016a ).

One oft-cited concept in this line of research is maximally maintained inequality (MMI) proposed by Raftery and Hout ( 1993 ), although their primary focus was on secondary rather than tertiary education. Uncovering that the contribution of social origin to children’s education persisted despite increased educational opportunities in Ireland, they argued intergenerational inequality would only begin to decline once the advantaged reached a saturation point (e.g., 100% of enrolment). Subsequent studies have widely confirmed this MMI structure at the tertiary education level (Bar Haim & Shavit, 2013 ; Chesters & Watson, 2013 ; Czarnecki, 2018 ; Konstantinovskiy, 2017 ; Wakeling & Laurison, 2017 ).

Extending MMI, Lucas ( 2001 ) found that inequality had been effectively maintained, as advantaged individuals would secure valuable educational assets (e.g., prestigious institutions and fields of study) even when the disadvantaged caught up in terms of the level of educational qualifications. A vast literature has empirically supported the idea of effectively maintained inequality (EMI) in the higher education sector across the globe (Ayalon & Yogev, 2005 ; Boliver, 2011 ; Dias Lopes, 2020 ; Ding et al., 2021 ; Gerber & Cheung, 2008 ; Hällsten & Thaning, 2018 ; Kopycka, 2021 ; Reimer & Pollak, 2010 ; Seehuus, 2019 ; Torche, 2011 ; Triventi, 2013 ).

Meanwhile, introducing a standardized analytical model, Breen et al. ( 2009 ) argued that the link between origins and educational attainment weakened along with educational expansion in multiple countries. Much research reports a similar structure of nonpersistent inequality (Barone & Ruggera, 2018 ; Breen, 2010 ; Breen et al., 2010 ; Duru-Bellat & Kieffer, 2000 ; Pfeffer & Hertel, 2015 ). In line with these longitudinal findings, comparative work also detected the smaller social gap in education in societies with a larger share of highly educated populations, despite the observed MMI structure in each country (Liu et al., 2016 ).

In investigating the mechanisms behind cross-national variation in the SES effect on educational attainment, scholars have shed light on institutional stratification in education (Pfeffer, 2008 ). Evidence shows that (1) highly tracked systems make education-work transitions more effective (i.e., learners gain occupation-specific skills or at least educational credentials signifying those skills, thus obtaining occupations relevant to their fields of study) (Bol et al., 2019 ) but (2) strong tracking also intensifies educational inequality compared to more comprehensive education systems (Bol and van de Werfhorst, 2013 ; Burger, 2016 ; Chmielewski et al., 2013 ; Österman, 2018 ; Reichelt et al., 2019 ; Tieben & Wolbers, 2010 ; Van de Werfhorst, 2018 ; Van de Werfhorst and Mijs, 2010 ).

The link between SES and educational attainment has thus been uncovered in relation to societal-level educational expansion and tracking. Nonetheless, one puzzling fact is that the degree of inequality significantly varies even among HPS with similar social policies and education systems (OECD, 2018 ). It is plausible that some societal traits, which have been inadequately incorporated in prior studies, operate in forming educational inequality.

As argued, one potentially important process here is skills diffusion. Evidence shows (1) the degree of skills diffusion is positively correlated with that of educational expansion, making HPS more likely to advance skills diffusion (Araki, 2023b ) but (2) the effects of educational expansion and skills diffusion on socio-economic outcomes at the individual and societal levels are not identical (Araki, 2020 ; Hanushek & Woessmann, 2015 ). Given these arguments, one may assume that the accumulation of high skills in a society, apart from educational proliferation, plays a unique role in forming educational (in)equality especially via two possible mechanisms.

First, prior research argues that skills diffusion may accelerate the skills-based rewards distribution because of increased discernability of high skills, which allows the labor market to identify highly skilled human resources (Araki, 2020 ). Should this intensified meritocratic mechanism induced by skills diffusion be applicable to the schooling process, the impact of SES on educational attainment may diminish in contrast to the growing importance of high skills. Nonetheless, evidence is elusive concerning the extent to which skills diffusion actually increases skills’ visibility in such a way that educational assets are allocated based on skills instead of SES. Put differently, it is still possible that the structure of educational inequality is not affected, or rather exacerbated, by skills diffusion.

Second, the accumulation of high skills, especially among the disadvantaged, may mitigate the comparative advantage of high-SES groups in human capital formation. Considering that the advantaged are more likely to invest their resources to foster children’s skills, which are favored in the process of climbing the educational ladder, the effects of such interventions could be hindered by skills development among the disadvantaged group. One should note, despite this potential consequence of skills diffusion, SES may still exert its influence on education by exerting symbolic power in that children from advantaged families easily internalize legitimate culture and behaviors leading to better educational outcomes (Jæger & Karlson, 2018 ; Sieben & Lechner, 2019 ). In addition, in response to the catchup by low-SES groups, their high-SES counterparts may invest more to further enhance children’s skills both quantitatively and qualitatively (i.e., the level and type of skills, respectively). This perception is aligned with EMI theory on educational inequality.

Nevertheless, the SES gap incurred by unequal human capital formation could still diminish in association with skills diffusion. For example, Huber, Gunderson, and Stephens ( 2020 ) found that the skills development mechanism played a role in reducing inequalities, although their focus was on educational spending and income inequality. Should this be the case for the distribution of higher education opportunities, it is logical to assume that the cross-national variation in the linkage between SES and educational attainment can be explained partially, if not completely, by the extent of skills diffusion in each society. Indeed, while showing the typological EE-SD framework, Araki ( 2023b ) argued that the structure of educational inequality would be an important agenda to be examined by incorporating both educational expansion and skills diffusion. Therefore, the current study investigates the heterogeneous SES effect on higher education attainment with particular attention to societal-level skills diffusion, educational expansion, and tracking.

Data and methods

Data and strategy.

The degree of skills diffusion has long been unmeasurable in a comparable way. However, the Organisation for Economic Co-operation and Development (OECD) has recently developed an international survey of adult skills (PIAAC), which permits a cross-national examination of cognitive skills. Although using this dataset merely covers OECD and partner countries, future research can use the framework and findings that follow as the foundation to investigate broader geographical areas.

PIAAC is composed of a standardized assessment of cognitive ability and background questionnaires including educational attainment and socio-demographic attributes. Participants are nationally representative individuals aged 16 to 65 and accordingly, one can infer the skills level of the population based on individual-level data (OECD, 2019 ). Because of its wide coverage of variables and high quality skills data, PIAAC has been used by the vast literature on educational inequality and socio-economic returns to education (Araki, 2020 , 2023a ; Hanushek et al., 2015 ; Heisig et al., 2020 ; Huber et al., 2020 ; Jerrim & Macmillan, 2015 ; Pensiero & Barone, 2024 ). One limitation of PIAAC is its scope: while it primarily assesses cognitive ability in terms of literacy and numeracy, 2 other types (e.g., noncognitive and occupation-specific skills) are not directly included. However, given that cognitive skills serve as the basis for other dimensions of skills and socio-economic outcomes (Krishnakumar & Nogales, 2020 ; OECD, 2016 ), it is sensible to use the PIAAC data to quantify the skills level.

Among PIAAC participants aged 16 to 65, this article focuses on respondents aged 25 to 34, considering that the association between SES and education could significantly vary across cohorts. This approach also reduces two risks: (1) as compared to younger groups, respondents are likely to have completed the highest level of education; and (2) unlike older groups, the influence of work experience and relevant attributes on educational attainment is assumed to be small. From the OECD public use database, 3 the current study extracts 32,549 respondents in 26 countries that provide valid data for all predictor and outcome variables as detailed below. See Table 1 for specific countries and the sample size with the gross tertiary enrolment ratio, which indicates that all countries are classifiable as HPS (i.e., over 50%).

One potential analytic approach here is to focus on how the contribution of SES to educational attainment has shifted over time in the process of educational expansion and skills diffusion in a given society. As reviewed, much research in this vein has employed a longitudinal approach to compare multiple cohorts within countries. Although this method gives detailed implications for each society, it does not completely address period effects (Glenn, 1976 ). In addition, because the strength of tracking is relatively stable (Brunello & Checchi, 2007 ), it is difficult to accurately detect the longitudinal change.

Two types of cross-country analyses thus become sound strategies. The first approach is to perform country-specific analyses using the individual-level data and to contrast the relative degree of educational inequality and societal-level degrees of educational expansion, skills diffusion, and tracking across cases. The second strategy is to employ hierarchical modeling with pooled data from all countries with both individual and country-level variables. While the first method provides evidence for each society, the second one shows the general tendency beyond the national boundary. Given the comparative advantage of these two options, both approaches are employed: (1) country-specific analyses using individual-level data in 26 countries and (2) multilevel regressions focused on the link between the strength of educational inequality and societal-level conditions.

The outcome variable is the possession of a bachelor’s degree or above, which is equivalent to the International Standard Classification of Education (ISCED) 2011 Level 6 and above. Although short-cycle tertiary education (ISCED 2011 Level 5) is sometimes included when assessing individuals’ educational attainment, the nature of this educational stage varies across countries (Di Stasio, 2017 ). To ensure international comparability, the current paper employs ISCED 2011 Level 6 and above as a measure for high educational attainment. Considering also (1) the potential bias incurred by using a categorical measure in hierarchical modeling and (2) the different nature between the possession of tertiary degrees and the length of educational experience, a continuous variable (i.e., years of schooling) is also adopted. Because one country does not provide data on this continuous measure, the nonlinear model (with the dummy for ISCED 2011 Level 6 and above) is primarily shown in the manuscript, while the linear model (with years of schooling as an outcome) is displayed as a robustness check (see the next section for more details).

As regards individual-level predictor variables, much research has used parental education, occupation, and economic status, as well as the number of books in the home (NBH). Among these, the PIAAC dataset includes parental education (i.e., father’s and mother’s highest levels of education) and NBH. Although NBH has been widely taken as a representative SES measure (Chmielewski, 2019 ; OECD, 2016 ; Sieben & Lechner, 2019 ), recent research points out its potential endogeneity problem especially when the respondents are children (Engzell, 2021 ). Considering also the meaning/value of NBH substantially varies across countries, the current study uses parental education to represent SES. This strategy focused on parental education is widely employed by the literature in this vein (Brand & Xie, 2010 ; Cheng et al., 2021 ; Oh & Kim, 2020 ; Pensiero & Barone, 2024 ; Torche, 2018 ). Following prior studies, three categories are constructed by combining paternal and maternal education (i.e., both parents, one parent, or neither parents are tertiary educated). Meanwhile, a robustness check is performed by replacing parental education with NBH, and the result is shown in the Appendix (Table 5 ). Note that the result of this supplementary analysis is consistent with the main findings that follow. Alongside parental education, individual-level predictors include gender (men dummy), age (30–34-year-old dummy), 4 and immigrant background (first-generation immigrant dummy) as these attributes are substantially associated with educational attainment (Breen & Jonsson, 2005 ).

Country-level variables cover the levels of educational expansion, skills diffusion, and tracking. To align with the outcome variable, the education measure is the percentage of the population who possess a bachelor’s degree and above. Considering the potential bias caused by using the simple means of individual-level education and skills in the PIAAC dataset for macro-level indicators, the societal-level variables here are not estimated based on PIAAC micro data but extracted from the national statistics in each country (OECD, 2014 ). It is noteworthy that the following results and implications are robust even when replacing this societal trait with the proportion of people with a degree including ISCED 2011 Level 5 (short-cycle tertiary) (see Table 6 in the Appendix).

For the skills indicator, following previous research (Araki, 2020 , 2023c ), the proportion of individuals whose mean score of literacy and numeracy in PIAAC is 326 and above (out of 500 points) is used. This is consistent with the OECD’s definition of high skills (OECD, 2019 ). As discussed, “skills” directly assessed by PIAAC refer to cognitive ability, and therefore, future research must incorporate other dimensions to advance this line of studies. These macro-level education and skills metrics are limited to the population aged 25 to 34 in line with individual-level variables. This way, the marginal distribution of educational opportunities and skills among the target age group is properly incorporated in the following analyses. Note that this measure reflects the skills level among the population ages 25–34, and hence, it is suitable to test one of the said two hypothetical mechanisms: a more meritocratic rewards allocation process is intensified by skills diffusion, leading to less educational inequality. Meanwhile, the percentage of people with high skills among those whose parents are not tertiary educated is also used to examine the second scenario: the larger share of highly skilled people among low-SES groups undermines the comparative advantage of high-SES groups in human capital formation. This skills indicator for the low parental education group is estimated by the OECD using all participants in each country and not limited to those aged 25 to 34. The analysis results of this second approach are thus shown in the Appendix (Table 6 ).

The degree of tracking is derived from Bol and van de Werfhorst ( 2013 ). Admittedly, other conditions could also affect the association between parental education and educational attainment against a background of skills diffusion and educational expansion. In particular, the macroeconomic structure may alter the social function of skills and higher education, while the overall degree of social inequality may influence educational inequality. Although the aforementioned three societal-level traits are primarily used given the potential bias incurred by a larger number of macro-level measures against the relatively small sample size for countries, GDP per capita and the Gini index are therefore added to the main analysis for a robustness check (see the Appendix, Table 6 ). Table 2 summarizes descriptive statistics.

Analytic models

For country-specific analyses, binary logistic regression is performed using individual-level data for 26 countries respectively as follows.

where i = individual, p i = the probability of holding a bachelor’s degree or above for individual i , b n = the coefficient of predictor variables, M i = the men dummy, A i = the age 30–34 dummy, I i = the first-generation immigrant dummy, BT i = the dummy for those whose parents are both tertiary educated, OT i = the dummy for those having one tertiary educated parent, and ε i = the residual for individual i . The primary focus here is on the parameters for parental education ( b 4 and b 5 ). Given that these coefficients do not provide straightforward implications (Breen et al., 2018 ; Mood, 2010 ), the average marginal effects (i.e., predicted probability for each parental education group) are also estimated and compared across countries to confirm the variation in the advantage of having tertiary educated parents (see Fig. 1 ). This serves as the basis for the following multilevel regressions.

In model 1 of hierarchical modeling, only individual-level predictors are employed with particular attention to b 4 , and b 5 in Eq. ( 2 ), where j = level two (i.e., country).

In models 2 to 4, three country-level variables and their interactions with two individual-level parental groups are added to model 1, respectively, to examine how the association between parental education and educational attainment varies in accordance with the societal conditions. Although there is a risk of biased estimation by including more than two level-two variables given the limited number of countries in the current model, model 5 concurrently incorporates the extent of educational expansion, skills diffusion, and tracking to confirm the robustness of models 2 to 4. GDP per capita and the Gini index are further added for robustness checks (see Table 6  in the Appendix). The basic concepts of these models are describable as follows.

where γ 00 = the average intercept, γ 0n = the coefficient of country-level predictor variables X , and u 0 j = the country ( j )-dependent deviation. Substituting Eq. ( 3 ) into Eq. ( 2 ) and denoting b n by γ n 0 while incorporating cross-level interaction terms, γ 4 n and γ 5 n in Eq. ( 4 ) below indicate the heterogeneous magnitudes of two parental education measures associated with three societal traits. Following the recent argument that random slopes on lower-level variables used in cross-level interactions should be incorporated (Heisig & Schaeffer, 2019 ), both random intercepts and slopes are estimated for parental education as follows.

where u nj = the country dependent deviation of the slopes for two parental education groups. Finally, as shown in Eq. ( 5 ) where Y ij is years of schooling for individual i in country j , model 6 employs a multilevel linear regression approach with the same predictors as model 5 for a robustness check.

Note that these cross-sectional models do not completely account for unobserved variables at the individual and societal levels. As the OECD has been administering the second cycle of PIAAC, future research must undertake longitudinal analyses to address this issue.

Table 3 shows the results of binary logistic regression of college completion for 26 countries. In all cases, parental education exhibits a positive sign for the chance of attaining tertiary education (i.e., b 4 and b 5 in Eq. 1 are positive and statistically significant). 5 In particular, the magnitude is notably large for the most advantaged group with both parents being tertiary educated (e.g., b 4 = 1.02, 95%CI 0.63 to 1.41; b 5 = 1.92, 95%CI 1.43 to 2.42 in Austria). Figure 1 indeed indicates that the predicted probability of obtaining a first degree substantially varies across three parental education groups, with the most disadvantaged tier (i.e., without tertiary educated parents) suffering from a limited chance of completing tertiary education in every country.

Predicted probability of completing tertiary education by parental education in 26 countries. The Y axis indicates the predicted probability of attaining tertiary education (ISCED 2011 level 6 or above) for three parental education groups as indicated at the top of the figure across 26 countries ( X axis). See also Table 1 for country abbreviations

Nonetheless, it is worth noting that despite the relative disadvantage within a country, the predicted probability of college completion among the low parental education group in some countries (e.g., 37.6%, 95%CI 33.9 to 41.3 in Finland) is higher than that of the second tier with one tertiary educated parent in others (e.g., 29.8%, 95%CI 22.7 to 36.9, in Belgium). In addition, as far as the extent of educational inequality is concerned, the gap in the chance of college completion between the most advantaged and disadvantaged groups differs across nations, ranging from 28.8 points in South Korea to 76.6 points in Turkey. The key question here is how this cross-national variation in intergenerational transmission of higher education is associated with societal-level conditions.

Table 4 summarizes the results of multilevel regressions. In model 1 with only individual-level variables, all predictors show a significant sign for educational attainment at the 0.1% level. That is, even when accounting for gender, age, and immigrant background, as well as cross-national differences in the intercept and slopes, the strong association between parental education and educational attainment is confirmed. As observed earlier in the country-specific analyses, the magnitude of having two tertiary educated parents is larger than that of only one highly educated parent ( γ 40 = 1.91, 95%CI 1.73 to 2.08; γ 50 = 1.08, 95%CI 0.95 to 1.21). This substantial linkage between parental education, especially having two tertiary educated parents, and the chance of college completion holds regardless of models in the following analyses.

Model 2 adds one country-level variable (i.e., the proportion of tertiary educated people) and its cross-level interactions with two measures for parental education. Apart from the significant coefficients of individual-level predictors, the interaction terms between parental education and the degree of educational expansion shows negative and statistically significant signs ( γ 41 = −0.02, 95%CI −0.04 to 0.00, P = 0.022; γ 51 = −0.02, 95%CI −0.04 to −0.01, P = 0.006). This suggests, as argued by some prior studies, the advantage of having tertiary educated parents is likely to be smaller in societies where the aggregate education level is relatively high. Put differently, although further longitudinal investigations are necessary to establish causality, the accumulation of educational opportunities in a society might operate as an equalizer in mitigating the influence of parental education.

An identical structure is observed in model 3, where the degree of educational expansion is replaced with that of skills diffusion (i.e., the proportion of highly skilled people). In addition to the significant links between individual-level predictors and the probability of obtaining a first degree, the coefficient of the interaction terms between parental education and the societal-level skills indicator is negative ( γ 42 = −0.03, 95%CI −0.05 to −0.01, P = 0.003; γ 52 = −0.03, 95%CI −0.05 to −0.02, P < 0.001). As with the extent of educational expansion, this result indicates a possibility that the role of parental education in children’s educational attainment could decline in societies with a higher degree of skills diffusion. In Model 4, the strength of tracking is included instead of societal-level education and skills measures. As reported by previous research in this vein, the positive signs of interaction terms between tracking and parental education are confirmed ( γ 43 = 0.15, 95%CI −0.02 to 0.31, P = 0.088; γ 53 = 0.15, 95%CI 0.04 to 0.27, P = 0.007). That is, the extent of intergenerational educational inequality is likely to be stronger in more tracked systems.

Model 5 incorporates all three country-level predictors and their interaction terms. One important result here is that the interaction between parental education and the degree of educational expansion is almost nullified ( γ 41 = −0.00, 95%CI −0.03 to 0.02, P = 0.773; γ 51 = 0.01, 95%CI −0.01 to 0.02, P = 0.326). In contrast, the one between parental education and the skills diffusion measure holds its negative sign ( γ 42 = −0.03, 95%CI −0.05 to −0.00, P = 0.030; γ 52 = −0.03, 95%CI −0.05 to −0.02, P < 0.001). The same structure is observed when (1) conducting multilevel linear regression with years of schooling as the outcome in Model 6 ( γ 41 = −0.04, P = 0.133; γ 51 = −0.01, P = 0.453; γ 42 = −0.04, P = 0.072; γ 52 = −0.05, P = 0.001), (2) incorporating the proportion of people with short-cycle tertiary education for the educational expansion metric in Model A2 ( γ 42 = −0.02, 95%CI −0.04 to 0.00, P = 0.034; γ 52 = −0.03, 95%CI −0.04 to −0.02, P < 0.001), and (3) adjusting for GDP per capita and the Gini index as additional societal-level conditions in Model A3 ( γ 42 = −0.03, 95%CI −0.06 to 0.00, P = 0.059; γ 52 = −0.03, 95%CI −0.04 to −0.01, P < 0.001). Figure 2 indeed depicts the diminishing effect of parental education in tandem with higher proportions of the population with high skills.

Marginal effects of parental education by the degree of skill diffusion. The Y axis is the marginal effects of parental education (i.e., one parent is tertiary educated; both parents are tertiary educated) across the degree of skills diffusion (i.e., the percentage of population with high skills) ranging from 0 to 50 ( X axis) based on the multilevel binary logistic regression (model 5). The dashed lines indicate the 95% confidence intervals

The sole exception is the final model using the share of highly skilled people among the low parental education group as the societal-level skills measure (Model A4 in the Appendix, Table 6 ). While its interaction with the second tier (i.e., only one tertiary educated parent) shows a substantially negative coefficient ( γ 52 = −0.02, 95%CI −0.04 to −0.01, P = 0.001), the one with the top tier (i.e., both parents are tertiary educated) is not statistically significant despite its negative sign ( γ 42 = −0.02, 95%CI −0.04 to −0.00, P = 0.106). This suggests that the second hypothetical scenario (i.e., the advantage of high-SES groups in human capital formation diminishes along with skills diffusion among the disadvantaged) is partially supported in that the second layer in parental education with one tertiary educated parent encounters their diminishing advantage in educational attainment. However, the most advantaged group seems to retain their relative position even when the disadvantaged advances their cognitive skills. In the next section, after summarizing the key findings, some implications are discussed.

Discussion and conclusion

This study investigates cross-national variation in intergenerational transmission of education among high participation systems (HPS). Drawing on the EE-SD framework (Araki, 2023b ), particular attention is paid to societal-level higher education expansion, tracking, and skills diffusion. Using the OECD PIAAC data for 32,549 adults in 26 countries, individual-level analyses first corroborate the literature in that parental education is significantly associated with the likelihood of college completion. However, multilevel regressions show that educational inequality is not persistent; rather, the advantage of having tertiary educated parents becomes smaller in societies with a higher proportion of tertiary graduates. This result supports prior evidence indicating the equalizing function of educational expansion.

Nevertheless, once incorporating the degrees of skills diffusion and tracking as societal-level predictors, the interaction between the extent of educational expansion and parental education loses its significant sign. Instead, the skills indicator holds a negative interaction effect with parental education. Further research is necessary to claim causation, given that (1) unobserved societal traits may be significantly affecting the link between parental education and the societal-level education/skills indicators and (2) the degree of skills diffusion may mediate the effect of educational expansion as detailed below. Yet, based on these results, it is plausible that the accumulation of high skills in a society plays a role in altering the power of parental education over children’s chance of attaining higher education.

Assuming that the observed results reflect a causal relationship to a certain extent, they are interpretable as representing distinct functions of educational expansion and skills diffusion. When focusing solely on the proliferation of educational opportunities (as in model 2), growth in higher education appears to operate as an equalizer and mitigates intergenerational transmission of higher education. However, because educational expansion and skills diffusion often advance hand in hand (Araki, 2023b ), the seemingly equalizing effect of educational expansion might actually be attributed to the contribution of skills diffusion. Consequently, the observed effect of educational proliferation can be cancelled out once the skills dimension is taken into account. Importantly, this does not necessarily mean that educational expansion is irrelevant to the heterogeneous link between parental education and children’s educational attainment, as skills diffusion may mediate the contribution of increased higher education opportunities. That is, educational expansion may indirectly reduce educational inequality via fostering high skills. Nonetheless, regardless of the extent to which skills diffusion incorporates the influence of educational proliferation, it is noteworthy that the accumulation of high skills itself is substantially associated with the degree of educational inequality.

As regards the potential mechanism whereby skills diffusion may curb intergenerational transmission of education, one must recognize the qualitative difference between two societal conditions. While educational expansion means the larger number/share of the population with a tertiary degree as such regardless of their actual skills, skills diffusion represents the accumulation of individuals with high skills in a society. Accordingly, the level of skills diffusion can signify the quantity of human resources possessing adequate abilities to make rational choices and contribute to a more meritocratic society, where educational and other assets may be allocated based on individuals’ merit rather than SES. This perception aligns with prior studies that have demonstrated the distinct roles of skills in the rewards allocation process (Araki & Kariya, 2022 ; Hanushek et al., 2015 ). Importantly, this diminishing inequality cannot be observed solely through educational expansion, as it does not guarantee a sufficient number of individuals with high skills who can effectively promote the establishment of a meritocratic mechanism. 6

Should this be the case, the extent of intergenerational transmission of education is likely to diminish especially when skills diffusion occurs among low-SES groups. This is because the accumulation of high skills among the disadvantaged undermines the relative advantage of high-SES groups (e.g., those with tertiary educated parents in the current analysis) in the human capital formation process, which in turn affects their chance of college completion. Notwithstanding, the empirical findings (model A4 in the Appendix, Table 6 ) only partially support this hypothesis. In tandem with the higher proportion of high skills among the disadvantaged whose parents are not tertiary educated, the advantage of having one tertiary educated parent declines. However, the advantageous position of having both a tertiary educated mother and father is not significantly devalued despite the negative sign. This suggests that the catching-up effect of advancing cognitive skills among the disadvantaged is more likely to be observable against the second tier of parental education strata, whereas the most advantaged group may maintain their higher chance of college completion, at least in the initial stage. These trends, which demonstrate persistent and flexible advantages among the socially privileged, are consistent with MMI (Raftery & Hout, 1993 ) and EMI (Lucas, 2001 ). If in fact this partial equalizing effect of skills diffusion exists, it raises an important question: will further advancement of skills development among the disadvantaged eventually mitigate the advantageous position of the top tier? In particular, from the EMI perspective, it is worth exploring whether the advantaged group will maintain their superiority by targeting certain fields of study favored in the labor market, particular higher education institutions with high prestige, and/or further advanced degrees (e.g., master’s and doctoral levels). Longitudinal studies are essential to answer these questions.

As such, one can better understand cross-national variation in educational inequality by shedding light on the diffusion of high skills, as well as higher education. This also suggests that the contradictory views on the effect of educational expansion in the literature (i.e., persistent versus nonpersistent inequality) may partially reflect the extent to which each study incorporates the influence of skills diffusion. When analyzing the consequences of increased educational opportunities, research may find a declining contribution of SES to educational attainment so long as educational proliferation in the target case is accompanied by skills diffusion (i.e., when trends in aggregate levels of education and skills are aligned). In contrast, if one focuses on societies (or periods/cohorts) where these two societal traits are significantly decoupled and educational opportunities increase without corresponding skills diffusion, the unequal structure is likely to persist because the equalizing role of skills diffusion is absent.

Nevertheless, the current paper adopts a cross-country approach unlike much research in this vein. Therefore, further examination is imperative to conclude whether, to what extent, and how skills diffusion actually operates as a fundamental societal trait. First, country-specific longitudinal analyses are pivotal to detect causal relationships across SES, higher education attainment, skills diffusion, and other factors. Second, in addition to the retrospective approach used in this paper, prospective analyses are essential lest we overrate the degree of intergenerational inequality (Breen & Ermisch, 2017 ; Lawrence & Breen, 2016 ; Song & Mare, 2015 ). Third, variables should be extended for family background (e.g., parental skills, occupations, and income), educational outcomes (e.g., aspiration, completion, fields of study, and prestige of institutions), skills (e.g., noncognitive and occupation-specific skills), and societal conditions (e.g., labor and welfare policy). Because the values of specific types of educational credentials and skills may differ in accordance with macroeconomic and socio-cultural settings, interactional effects of these variables need to be carefully examined. Likewise, given that the metrics for individual-level education and skills and societal-level educational expansion and skills diffusion are closely linked in the current study (and thus estimation could be somewhat biased), future research must incorporate different types of education and skills measures at the individual and societal levels. Fourth, as an extension of this line of studies, one should investigate how the trend of educational expansion and skills diffusion affect not only educational attainment but also (in)equalities in socio-economic outcomes beyond the schooling stage. This is particularly important as recent research shows that educational equalization does not necessarily result in the weakening linkage between parental education and children’s earnings over time, except Scandinavian countries (Pensiero & Barone, 2024 ). Finally, in addressing these tasks, analyses of non-OECD and non-HPS cases would be useful to obtain insights from a comparative perspective.

With this potential for further development, the present study contributes to advancing our knowledge on educational inequality among HPS. It is particularly noteworthy that the accumulation of high skills, along with the expansion of higher education opportunities, may collectively operate as an equalizer and mitigates intergenerational inequality in educational attainment.

Terms “educational expansion” and “skills diffusion” imply longitudinal changes in a given society, that is, an increase in the number/share of individuals with higher levels of educational attainment and skills, respectively. Meanwhile, macro-level data in the following empirical analyses are collected at one point in time. Therefore, the “levels of education/skills” are used in some of the empirical part, but the terms “educational expansion” and “skills diffusion” are also employed unless this strategy violates the accuracy of arguments.

Although PIAAC also assesses ICT skills, this article focuses on literacy and numeracy because some countries do not provide available data on ICT skills.

See the OECD website for the PIAAC data ( https://webfs.oecd.org/piaac/puf-data/ ) [Accessed: August 1, 2023]. Some countries listed in this webpage are not included in the following analyses due to the absence of comparable data for tracking.

This dummy variable is employed instead of the continuous age measure because an ample number of respondents have only age group information in the PIAAC public-use dataset.

The analysis also reveals the nuanced function of other predictors including gender, age, and immigrant background. Although the primary focus of this article is on parental education, future research will benefit from investigating how and why the association between educational attainment and these attributes varies across societies.

Another supposition is that the accumulation of high skills merely enhances the importance of other types of family background, such as parental occupations and economic class. Should this be the case, skills diffusion does not necessarily promote a meritocratic system even though the effect of parental education per se diminishes. This is an important agenda for future research.

Data availability

Data are available from the OECD website: https://webfs.oecd.org/piaac/puf-data/

Code availability

Available upon request.

Araki, S. (2020). Educational expansion, skills diffusion, and the economic value of credentials and skills. American Sociological Review, 85 (1), 128–175. https://doi.org/10.1177/0003122419897873

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Acknowledgements

I would like to thank Takehiko Kariya, Richard Breen, Jan O. Jonsson, Herman van de Werfhorst, Cláudia Sarrico, and anonymous reviewers for their invaluable comments and suggestions.

This research is supported by the Social Sciences Internal Seed Grant Scheme, the University of Hong Kong (000250635).

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• To investigate how virtual reality influences the educational assessment of students to improvet their learning experience and knowledge.

Topic 4: An evaluation of the impact of the rising cost of academic education on students of lower-income backgrounds in the UK.

Research Aim: The aim of the research is to evaluate the impact of the rising cost of academic education on students of lower-income backgrounds in the UK.

• To analyse the factors impacting the affordability of higher education in the UK.
• To understand the challenges of lower-income background students in the UK.
• To investigate the impact of the rising cost of academic education on the lower-income background students in the UK and how the meritorious students can be supported.

Topic 5: An investigation into the impact of interactions among students of multiple ethnicities on the cross-cultural communication and behaviour of the pupils.

Research Aim: The aim of the research is to investigate the impact of interactions among students of multiple ethnicities on cross-cultural communication and pupil behaviourt .

• To analyse the impact of student interactions among different ethnicities.
• To determine the importance of cross-cultural communication and tolerance of the students.
• To examine the impact of interactions among students of multiple ethnicities on the cross-cultural communication and behaviour of the pupils.

Dissertation Topics in Education Dynamics

Topic 1: the need to use information and communication technology to study in public institutions in any country of your choice. a reflection on the impact of covid-19 on the education sector in the chosen country..

Research Aim: This research will focus on the lack of good information and communication technology equipment in public institutions of study and the need to find the education sector to meet the new standard of learning in work. It will also analyse the pandemic’s impact on students in public institutions at home throughout the pandemic without any academic activities.

Topic 2: The fear of maintaining social distancing in schools

Research Aim: This research aims to evaluate the fear of maintaining social distancing in schools. It will also suggest possible solutions to minimise the fear of parents, educators, and students.

Topic 3: Online Education- Increased screen time or quality education

Research Aim: This research aims to identify whether online education exposes students to increased screen time or quality education.

Topic 4: The emergence of coding courses for young children and their cognitive development and age. A comparative study.

Research Aim: This research aims to identify how far coding education is beneficial for children. What sort of positive and negative consequences are concerned with the future of young children with their access to such kind of advanced technology?

Topic 5: Data science and growing opportunities for data scientists

Research Aim: This research will focus on identifying the emergence of degrees and courses in data science, their importance, and growing opportunities for data scientists. Who can become a data scientist? What is its career scope?

Covid-19 Education Topics 

Impacts of coronavirus on education.

Research Aim: This study aims to review the impacts of Coronavirus on education.

Online educational programs to educate students during COVID-19

Research Aim: The widespread Coronavirus pandemic and the lockdown have disrupted the education of many students, including school, college, and university levels. This study will identify the online programs offered through various platforms, schools, colleges, and universities. It will discuss how students can have access to these courses and how it will benefit them.

Impact of COVID-19 on educational institutes

Research Aim: This study will focus on identifying the impacts of COVID-19 on educational institutes. What steps can be taken to ensure a safe environment for the students and teaching staff?

Role of teachers and professors during the Coronavirus pandemic.

Research Aim: This study will focus on the contribution of teachers, professors, and institutions in providing education. What challenges are they facing? What would be the possible ways to improve the current education system?

The future of education post Coronavirus pandemic.

Research Aim: This study will collect information about current education methods during the pandemic and predict the future of education after the coronavirus pandemic.

Topic C1: How the Coronavirus pandemic is reshaping education?

Research Aim: Coronavirus has offered a stark reminder of the very human nature of schools. Students have leapt into online learning but cannot wait to get back into her building. Being online, I don’t think you really get a true sense of whether a student is really engaged and has a proper understanding. This study will aim to understand the extent to which the coronavirus pandemic is reshaping education.

Topic C2: How Coronavirus could affect the well-being of people with intellectual disabilities

Research Aim: We are all feeling more anxious than usual. We may be worried about accessing food and services, going to work, enduring self-isolation, or catching COVID-19. While some anxiety is normal, some of us may be more resilient to changes in our routines and the general uncertainty the world is experiencing. But for the 1.5 million people in the UK with an intellectual disability, these effects may be much greater. This research will aim to establish how Coronavirus could affect the wellbeing of people with intellectual disabilities.

Topic C3: The impact of the 2019–20 Coronavirus pandemic on education

Research Aim: The 2019–20 Coronavirus pandemic has affected educational systems worldwide, leading to the widespread closures of schools and universities. As of 28 March 2020, over 1.7 billion learners were out of school due to school closures in response to COVID-19. According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting nearly 90% of the world’s student population. This research will explore the impact of the 2019–20 Coronavirus pandemic on education.

Topic C4: What actions are being taken by universities in response to Coronavirus?

Research Aim: We have seen a growing number of decisions by universities to start implementing social distancing strategies, such as moving to more online teaching delivery and increasing homework by staff. At present, there is no government advice to universities about this. Therefore any operational decision by individual universities must be based on their local circumstances, which vary for various reasons. This research will examine some of the measures we see across the universities in response to the pandemic.

Topic C5: The impact of Coronavirus on international students and the response from universities

Research Aim: This research will explore the impact of Coronavirus on international students and the response from universities.

The Best Education Dissertation Topics

Topic 1: a comparison of wonderlic tests and standardised tests as means to assess academic performance..

Research Aim: Although there are many techniques and methods for assessing academic performance, this research will focus on the comparative analysis of Wonderlic and standardised tests. In the end, the research will conclude which approach would be better in different academic situations.

Topic 2: The theory and practice of educational games as a means to promote better learning.

Research Aim: In recent times, many pieces of research have focused on identifying different learning approaches to provide quality education. This research will analyse the concept of educational games for young children to promote and improve the learning mechanisms.

Topic 3: The impact of learning ability of a child: A case study of kindergarten students

Research Aim: With the emergence of technological advancements, many organisations, including education institutes, have started embracing innovative technologies. The main purpose of these advancements is to improvise the different ways of education. This research will focus on how the use of smart technology has improved the learning ability of kindergarten students.

Topic 4: Comparing and analysing the teaching approaches and mechanisms of privately owned schools and public school: Case of developing countries

Research Aim: Due to the rise of capitalist economies, many institutions have developed unique mechanisms to improve business operations and sales. The same is the case with educational institutes. However, the teaching mechanism and approach for private schools have been more effective than public schools. Therefore, this research will critically analyse the teaching approaches and mechanisms of privately owned schools and public schools and compare and analyse their teaching approaches.

Topic 5: Analysing the current curricular development of K12 students and how it can be linked with current economic issues

Research Aim: It has been found that the curriculum of the majority of educational institutes has become obsolete and monotonous. In other words, students are not being taught current affairs and the latest knowledge with respect to technology, etc. Therefore, the main aim of this research will be to analyse the current curricular development of K12 students and how it can be moulded to reflect the true economic conditions and issues of society.

Early Childhood Education Dissertation Topics

Early childhood education in the UK and many other countries refers to any form of education that children between 2 and 6 years obtain. Some early childhood education dissertation topics are listed below:

Topic 6: The effectiveness and implementation of early childhood education curriculum interventions

Research Aim: This research will discuss how effective curriculum interventions have been in early childhood education and how they can be effectively implemented.

Topic 7: Linking theory to practice and back again: The use of collaborative enquiry and video documentation to facilitate critical thinking in preservice teacher education

Research Aim: This research will provide theoretical and practical evidence to establish how collaboration inquiry and video documentation effectively affect critical thinking in preservice education.

Topic 8: Improving early childhood literacy development and English education through the use of multiple media tools

Research Aim: This research will analyse how effective medical tools are in early childhood education.

Topic 9: Supporting emergent literacy at the preschool level through the use of technology.

Research Aim: This research will present how supportive technology can be for emergent literacy at the preschool level.

Topic 10: Merging multimodality, technology, and literacy in the era of kindergarten digital storytelling

Research Aim: This research will explore the effectiveness of multimodality, literacy, and technology in today’s era of kindergarten storytelling.

Topic 11: Computer-based reading program with at-risk pre-kindergarten students

Research Aim: This research will discuss how computer-based reading programs are at risk with pre-kindergarten students.

Topic 12: Pre-school educational settings and the nature of children’s leadership

Research Aim: This research will analyse how educational settings in preschool help develop leadership skills in children.

Topic 13: How urban students’ academic accomplishments can be influenced by a school district’s preschool education policies

Research Aim: This research will discuss the academic accomplishments of children and how educational policies influence them.

Topic 14: Investigating the relationship between kindergarten achievement and classroom quality

Research Aim: This study will investigate the relationship between kindergarten students’ achievement and education quality.

Topic 15: Creating efficient learning environments to facilitate the process of early childhood education

Research Aim: This research will understand the learning environments that facilitate the process of childhood education.

Elementary Education Dissertation Topics

In the United Kingdom and other developed regions of the world, elementary education is the first level of compulsory education that children between the ages of 6 and 13 years obtain.

Primary or elementary education helps establish history, geography, mathematics, science, and other social sciences. In some countries, basic sexual education is also part of the elementary education curriculum. Some important primary education issues to explore are listed below:

Topic 16: Establishing the factors inhibiting and enhancing elementary school children’s creativity

Research Aim: This research will discuss the factors that help enhance children’s creativity in elementary school.

Topic 17: Developing life skills in elementary school pupils in the United Kingdom (or any other country of your choice)

Research Aim: This research will explore how life skills are developed in elementary school in the UK. (Country can be changed according to your choosing)

Topic 18: Monitoring and evaluating instruction in private elementary schools in China from the perspective of headteachers and teachers.

Topic 19: including children with special educational needs in a mainstream elementary educational institute – a case study of any elementary school.

Research Aim: This research will discuss inclusive education, i.e. including special needs students in elementary school. You are free to choose the school of your choice.

Topic 20: Implementing inclusive education in elementary/primary schools in Australia – The challenges and opportunities

Research Aim: This research will discuss the challenges and opportunities of implementing inclusive education in Australian elementary schools.

Topic 21: Classroom evaluation in England – Teachers’ practices and perceptions in Maths

Research Aim: This research will evaluate a typical classroom in England. It will assess the practices adopted by math teachers in elementary school.

Topic 22: Integrating outdoor learning activities with elementary curriculum in the United Kingdom

Research Aim: This research will analyse the results of integrating outdoor learning activities with curriculum activities.

Topic 23: Investigating the use of technology in elementary school physical education

Research Aim: This research will investigate and analyse the use of technology in elementary school physical education.

Topic 24: Investigating the impact of the age of information on current courses taught in primary school

Research Aim: This research will investigate the impact of information on courses taught in primary school.

Topic 25: Should primary schools allow their students to study more independently to achieve improved performance?

Research Aim: This research will assess whether elementary school students should be allowed to study independently and its impacts.

Read More About   Elementary Education / Primary Education

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Secondary Education Dissertation Topics

Secondary school education primarily covers formal education obtained by pupils between 13 and 18 years. Secondary education is compulsory in most countries, including the United Kingdom, and it may be taught in the form of Ordinary Levels, Advanced Levels, and SSC and HSC exams.

There is a wide array of research areas to be explored in this field of study, and any of the following research topics could be selected for your education dissertation.

Topic 26: Investigating the impact of teacher education on secondary education in the European Union

Research Aim: This research will investigate how secondary education in the EU is impacted by teacher education.

Topic 27: Investigating the impact of secondary school dual enrolment course participation on pupils’ academic accomplishments

Research Aim: This research will investigate the impact of dual course enrolment and its impact on academic accomplishments in secondary school.

Topic 28: The role of sustainability in learning and teaching in secondary schools to transform the soul of education

Research Aim: This study will explore the role of sustainable learning and teaching in secondary school, and it helps transform the soul of education.

Topic 29: Investigating secondary school teachers’ thinking in a professional development project

Research Aim: This research will analyse the teachers’ thinking with the help of a professional development project.

Topic 30: Betraying the college dream: How student aspirations are undermined by the disconnected post-secondary and K-12 education systems

Research Aim: The post-secondary schooling and K-12 education systems are misaligned. Thus, this research will investigate how this adversely impacts students, and as a result, more and more students are giving up on their college dreams.

Topic 31: Analysing supply & demand in light of the rising cost of secondary education

Research Aim: This research will assess and analyse the rising cost of education and its impact on the supply and demand for education.

Topic 32: To study the use of instructional and information technologies in teacher training in secondary schools and colleges in the United Kingdom

Research Aim: This research will study the use of technologies in teacher training and how they impact secondary education in the UK.

Topic 33: Should secondary school teachers emphasise today’s demanding issues such as energy conservation, sustainability, and environmental protection?

Research Aim: This research will study whether or not teachers should emphasise current issues like energy preservation, sustainability, and environmental protection.

Topic 34: How can religious and racial tolerance increase among pupils by reintroducing religious education in the secondary schooling system?

Research Aim: This research will explore whether introducing religious education in secondary education helps decrease religious and racial intolerance.

Topic 35: To investigate the benefits of teaching business management and entrepreneurship-related courses to secondary school students over social science courses.

Research Aim:  This research will study the uses and benefits of teaching business management and entrepreneurship at the secondary schooling level.  Read More About   Secondary Education

Higher Education Dissertation Topics

Higher education or college/university education covers the formal education available to college, undergraduate and postgraduate students. Some interesting higher education dissertation topics are listed below.

Topic 36: International mobility of graduate and undergraduate students of mathematics, engineering, technology and science; Push and Pull Factors

Research Aim: This research will study and analyse the push and pull factors that impact the graduate and undergraduate students’ choice of university.

Topic 37: International graduate students and their decisions to stay or leave the US; The decisive factors

Research Aim: This study will explore the factors that lead students to decide whether they should stay or leave their universities in the US.

Topic 38: Aligning higher education to labour market requirements in the UK

Research Aim: This research will assess whether higher education in the UK should be aligned with the labour  market requirements or not.

Topic 39: Internationalisation drivers, obstacles and rationales: A case study of any higher education institute in the UK

Research Aim: This research will analyse the internationalisation drivers, obstacles, and rationales of higher education institutes in the UK.

Topic 40: An investigation into the governance systems of academic planning in both private and public sector higher education institutes

Research Aim: This research will investigate the governance systems of academic planning in both, private and public higher education institutes.

Topic 41: Higher education system: Should all universities follow the same education pattern?

Research Aim: This research will explore whether every university should follow the same educational pattern.

Topic 42: Evaluating teaching quality in higher education schools from students’ perspective

Research Aim: This research will evaluate the performance of teachers based on students’ perspectives. Suggestions will be provided as to how it should be improved.

Topic 43: Identify the factors affecting student mobility in Europe – The quality aspect

Research Aim: This research will explore the factors that impact student mobility in Europe.

Topic 44: Assessing and Evaluating the Impact of Hiring, Firing and Retiring Professors in Higher Education System on Students

Research Aim: This research will evaluate the various impacts of hiring, firing and retiring professors in the higher education system on students.

Topic 45: Do university graduates perform better than those who do not obtain formal education but have practical work experience?

Research Aim: This research will evaluate and assess the performance of two sets of students. First, university graduates, second, will be those who have no formal education but have practical work experience.  Also Read:   Fashion and Culture Dissertation Topics

Teaching Method Dissertation Topics

The role of primary, secondary or higher education teachers is highly important, particularly considering the ever-increasing need to provide a growth-oriented academic environment to students. Some interesting teaching method dissertation research topics are listed below.

Topic 46: The influence of teaching methods on students’ academic success and achievements

Research Aim: This research will explore the influence of teaching methods on students’ academic success and achievements.

Topic 47: A Transactional Method to Learning and Teaching in an English Language Arts Methodologies Conference

Research Aim: This study will explore the transactional learning and teaching method in an English language arts methodologies conference.

Topic 48: How the effect of constructivist teaching methods can largely influence the algebraic understanding of primary and secondary school students

Research Aim: This research will understand how constructivist teaching methods affect primary and secondary school students.

Topic 49: Student learning of DNA and the effect of teaching methods

Research Aim: This research will study the impact of various teaching methods on students.

Topic 50: Teaching English through conventional and direct approaches – A qualitative study

Research Aim: This will be a qualitative study that will help assess the teaching of English as a subject through direct and conventional approaches.

Topic 51: Investigating the relationships of teachers’ pedagogical beliefs, knowledge and efficiency: A multimethod approach

Research Aim: This research will investigate the relationship between teachers’ pedagogical beliefs, efficiency, and knowledge.

Topic 52: Exploring the benefits of employing the Socratic methodology as an approach to learning

Research Aim: This research will investigate the benefits of the Socratic method of learning approach.

Topic 53: The benefits of introducing mathematics software to higher education mathematics teachers

Research Aim: This study will explore the benefits of introducing mathematics software to higher education math teachers.

Topic 54: The increasing importance of teachers training taking into consideration various threats to students such as weapons and drugs

Research Aim: This research will understand the importance of teachers’ training with respect to threats such as drugs and weapons.

Topic 55: Are the teachers more prone to violence at the hands of their students as compared to the past: How the profession of teaching has evolved over the last twenty years

Research Aim: Purpose: This research will compare the teaching profession in the past twenty years and will conclude on how it has changed.  Read More About:   Teaching Methods in the UK

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Education Leadership and Policy Studies Dissertation Topics

The role of leadership, politics, and policies cannot be overlooked. Education leadership and policy studies have two main roots, including organisational theory and political science. This may be an interesting area of research for your educational dissertation.

Topic 56: Implementing educational change in failing middle schools through examination of one’s fundamental leadership procedures and processes

Research Aim: This research will explore the impact of implementing educational change in failing middle schools by examining leadership procedures at the institutes.

Topic 57: Historical background and development of co-curricular transcripts

Research Aim: This research will date back to history and explore how co-curricular transcripts were developed, and will compare them to today’s processes.

Topic 58: Evaluating the self-efficiency of high-performing first-generation university students

Research Aim: This research will evaluate the self-efficiency of high performing university students.

Topic 59: The influence of spiritual growth on leadership development of college and university students

Research Aim: This research will explore the influence of spiritual growth on the leadership skills development of university students.

Topic 60: The peril and power of globalisation: The Higher education, the World Bank, and the Global Knowledge Economy

Research Aim: This research will focus on the globalisation aspect of education and will talk about how it has been impacted.

Topic 61: The self-reported impact of instructional coaching on middle school teachers’ practices

Research Aim: This research will investigate the impact of instructional coaching on middle school teachers’ practices.

Topic 62: An investigation into the policies designed to address bullying in schools of the UK: The beliefs, opinions and perceptions of teachers and principals

Research Aim: This research will explore in-depth the policies that are designed to address and eliminate bullying in UK schools.

Topic 63: Investigating the causes of under-representation of black students in advanced placement courses in the USA

Research Aim: This research will explore the racial issues in the educational system of the US, i.e. underrepresentation of black students.

Topic 64: Lecture note-taking skills of adolescents with and without learning disabilities

Research Aim: This research will understand the note-taking skills of adolescents and how they differ with respect to learning.

Topic 65: A qualitative study to evaluate the educational policies in the UK

Research Aim:  This study will assess and evaluate the various educational policies in the UK.  Read More About:  Courses About International Education Leadership And Policy

Adult Education Dissertation Topics

Vocation-based or professional adult education has gained tremendous popularity in the academic world over the last couple of decades. Here is a wide range of research topics within this field of study to base your dissertation on.

Topic 66: Investing social and personal benefits and costs of basic adult education from students’ perspective

Research Aim: This research will investigate the social and personal benefits and costs of basic adult education.

Topic 67: The perception of adult learners regarding their satisfaction with their educational experiences

Research Aim: This research will explore the perception of adult learners regarding their educational experiences.

Topic 68: Use of bounded agency approach to promoting participation in adult education programmes

Research Aim: This research will discuss the bounded agency approach to promote participation in adult education programs.

Topic 69: A psychoanalytic investigation to explore adult teaching and learning theory

Research Aim: This research will conduct a psychoanalytic investigation in order to explore adult teaching and learning.

Topic 70: Comparing the perception of adult learners in face-to-face and online courses

Research Aim: This research will compare the perception of adult learners in online and face to face courses.

Topic 71: Use of Hatcher-Assagioli Synthesis to analyse practices, principles, and goals for community-based adult education

Research Aim: This research will utilise Hatcher Assagioli Synthesis to analyse community-based education practices, principles, and goals.

Topic 72: A review of the UK government spending on adult education over the last two decades

Research Aim: This research will review the UK government’s spending on adult education for two decades.

Topic 73: The relationship between unemployment and government funding for adult education – A quantitative analysis

Research Aim: This research will explore the relationship between government funding and unemployment for adult education.

Topic 74: The impact of entrepreneurship, wealth building and personal finance-related courses in adult education

Research Aim: This study will study the impact of courses like entrepreneurship, personal finance et. For adult education.

Topic 75: Frequent career changes over working life and the increasing importance of adult education in today’s world

Research Aim: This research will assess the importance of adult education and how it influences students to change their career choices frequently.

Private School Education Dissertation Topics

Private schools have become a large profit-making industry in both the developed and developing world. More and more parents want to send their children to private schools even though the expenses associated with private education are constantly on the rise. Following are some suggestions for your education dissertation research:

Topic 76: Evaluating the effectiveness of management in private schools in the UAE

Research Aim: This research will evaluate the effectiveness of private school management in the UAE.

Topic 77: To study the level of cooperation between home schools, public schools and private schools in the United Kingdom

Research Aim: This research will explore the cooperation level in home schools, and private and public schools in the UK.

Topic 78: A qualitative analysis to determine the causes of why parents choose to send their children to private schools in South Asian countries

Research Aim: This research will conduct qualitative analysis to determine why parents send their children to private schools in Asia.

Topic 79: Investigating the policies concerning the fee structure of private schools in Shanghai

Research Aim: This research will investigate the various policies that concern the fee structure of private Shanghai schools.

Topic 80: An empirical analysis of the impacts of the universal primary education policies on educational performances in South Asia

Research Aim: This research will conduct an empirical analysis to understand the impact of universal primary education policies on educational performance in the South Asian region.

Topic 81: Use of information technology and teaching tools in private schools in the UK

Research Aim: This research will explore the use of technology in private schools and assess its effectiveness.

Topic 82: Schooling for money – The Impact of the Profit motive on Swiss Educational Reform

Research Aim: This research will study the Swiss educational reform and its impact on the system.

Topic 83: Challenges and experiences of children with disabilities in private schools in India

Research Aim: This research will evaluate the challenges and experiences of children with disabilities in Indian schools.

Topic 84: Why are private school students considered to have the edge over public schools – A qualitative study

Research Aim: This will be a qualitative study on why private school students are preferred over public school students.

Topic 85: Emphasis on personality formation and character in private schools – Are private school students more competitive than public school students?

Research Aim:  This research will study the personality formation and character building of private school students.  Read More About   Private School Fee Increase for First Time.

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Public School Education Dissertation Topics

Most schools in the developed world are publicly funded, offering elementary, secondary, and higher education. There is a wide array of topics of research under this field of study that can be explored. Some of them are suggested below:

Exploring the Funding of Public Schools – How they can be improved. This research will explore the ways through which public schools are funded and will study what can be done to improve them.

Topic 86: Investigating the impact of teacher leadership in public schools in the UK

Research Aim: This research will study the impact of teacher leadership in UK public schools.

Topic 87: Is it true that public schools are better able to prepare their students to face the challenges of the real world as compared to private schools

Research Aim: This research will explore a common misconception that public school students are better prepared to face real-world challenges than private school students.

Topic 88: Can publicly-funded religious schools help to counter radicalisation and terrorism?

Research Aim: This study will explore an important topic, i.e. can terrorism and radicalisation be countered with public funding of schools.

Topic 89: Encouraging values and morals in the younger generation by reintroducing religious education in public schools in the UK

Research Aim: This research will study how reintroducing religious education help encourage values and morals in the younger generation in the UK.

Topic 90: Healthy eating habits and the role of public schools offering courses such as meal choice, cooking and home economics

Research Aim: This research will explore the benefits of cooking and home economic courses and promote healthy eating.

Topic 91: How public school students can be encouraged to participate in sporting activities?

Research Aim: This research will assess how public school students can be encouraged to participate in sporting activities.

Topic 92: The perception of special education administrators on the use of paraprofessionals in the education of students with disabilities

Topic 93: the perception of special education administrators on the use of paraprofessionals in the education of students with disabilities.

Research Aim: This research will discuss special education administrators and how they help offer quality education to disabled students.

Topic 94: Educating students with disabilities and the beliefs of public school principals

Research Aim: This research will explore the beliefs of public school principals and how these beliefs help offer quality education to disabled students.

Topic 95: Advanced teaching tools in public school classrooms – How they Help Improve Quality Education

Research Aim:  This research will explore how quality education is provided by implementing innovative technology in classrooms.

Read More About   Public Schools in the UK

Home Schooling Dissertation Topics

Homeschooling is a highly growing educational phenomenon in developed countries. Any form of education that children obtain within their home setting under the supervision of their parents/adults is classified as homeschooling. Some interesting home school dissertation topics are suggested below:

Topic 96: A qualitative study to understand the significance of the role of information technology in homeschooling

Research Aim: This research will assess the importance of information technology for homeschooling through qualitative research.

Topic 97: The advantages and disadvantages of home schooling – Do home children perform with the top private and public school students?

Research Aim: This research will assess the pros and cons of home-schooling. It will also assess the performance of home schooled-students as compared to private and public school students.

Topic 98: A qualitative analysis of socialisation and academic accomplishments among home schooled university students

Research Aim: This research will be a qualitative analysis with respect to socialisation and academic accomplishments with respect to home schooled university students.

Topic 99: Factors motivating students to choose home schooling over conventional schooling systems

Research Aim: This study will explore the factors that motivate students to opt for homeschooling over the conventional schooling system.

Topic 100: A qualitative study to understand the parental motivation for home-schooling

Research Aim: This will be a qualitative study to assess the parental motivation to home-school their children.

Topic 101: Are partnerships available in homeschooling? Exploring their Effectiveness

Research Aim: This research will explore whether partnerships are available in homeschooling or not and how beneficial they prove to be.

Topic 102: A qualitative analysis to understand the educational beliefs of home schooled pupils and their parents

Research Aim: This research will conduct a qualitative analysis to assess the educational beliefs of homeschooled students and their parents.

Topic 103: A qualitative analysis of the relationship between financial literacy and homeschooling

Research Aim: This research will assess whether there is a relationship between homeschooling and financial literacy or not.

Topic 104: The duties and responsibilities of parents concerning the homeschooling of their children

Research Aim: This research will explore the duties and responsibilities of parents with respect to the homeschooling of their children.

Topic 105: Do Homeschool Children Develop Personalities and Characters Like Private and Public Schooled Children?

Research Aim: This research will explore whether homeschooled children build strong character and confident personalities just like private and public schooled students or not.

Latest Education Dissertation Topics

• A Comparative Study on the Impact of Technology Integration on Student Learning Outcomes
• The Role of Teacher Feedback in Student Academic Achievement
• Efficacy of Differentiated Instruction in Diverse Classroom Settings
• Influence of Socioeconomic Factors on Educational Attainment
• Investigating the Effectiveness of Inquiry-Based Learning in Science Education
• Promoting Critical Thinking Skills Through Literature-Based Instruction
• Examining the Relationship Between Parental Involvement and Student Success
• Implementing Culturally Responsive Pedagogy in Urban Schools
• The Impact of Early Childhood Education Programs on Long-Term Academic Success
• The Effects of Gender Bias in Classroom Interactions on Student Achievement
• Impact of School Climate on Student Well-being and Academic Performance
• The Role of Emotional Intelligence in Teacher Effectiveness
• The Influence of Motivation on Student Engagement and Achievement
• The Impact of Peer Tutoring on Academic Achievement in Mathematics
• Exploring the Relationship Between Student-Teacher Rapport and Classroom Behaviour

Simple Ordering Process

A system that works for everyone, important notes:.

As a student of education looking to get good grades, it is essential to develop new ideas and experiment with existing education theories – i.e., to add value and interest to your research topic.

The field of education is vast and interrelated with so many other academic disciplines. That is why creating an education dissertation topic that is particular, sound, and actually solves a practical problem that may be rampant in the field is imperative.

We can’t stress how important it is to develop a logical research topic; it is the basis of your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, and there is a possibility that the study is not viable.

This impacts your time and efforts in  writing your dissertation , as you may end up in a cycle of rejection at the very initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

While developing a research topic, keeping our advice in mind will allow you to pick one of the best education dissertation topics that fulfils your requirement of writing a research paper and add to the body of knowledge.

Therefore, it is recommended that when finalizing your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and be practically implemented. Take a look at some of our sample education dissertation topics to get an idea for your own dissertation.

How to Structure Your Education Dissertation

A well-structured   dissertation can help students   to achieve a high overall academic grade.

• A Title Page
• Acknowledgements
• Declaration
• Abstract: A summary of the research completed
• Table of Contents
• Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems. An outline of the structure of a dissertation can also be added to this chapter.
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Home > ETD > ETD_BACHELORS > 11865

Bachelor's Theses

A study on the relationship of educational attainment and job performance of workers among the six sectors of the business process outsourcing industry in metro manila.

Kristine O. Gan Angela Marie K. Miravite Sandy Mu Angelica Keith L. Tan

Date of Publication

Document type.

Bachelor's Thesis

Degree Name

Bachelor of Science in Business Management

Ramon V. Del Rosario College of Business

Department/Unit

Business Management

Thesis Adviser

Delfin Mauricio

Defense Panel Chair

Raymond D. Paderna

Defense Panel Member

Raymund L. Dimaranan

Abstract/Summary

Business Processing Outsourcing as one of the fastest growing industries in the Philippines, was considered one of the competitive mechanisms for economic improvement. For employees to land good jobs, one of the basic requirements is to have at least a college degree. But in the Philippines, there were actually BPO companies who hire high school graduates and college drop outs causing other people to doubt their effectiveness as an organization. Because of this, the relationship between educational attainment and job performance of the workers at six BPO sectors – animation, back office, call center, engineering, information technology and transcription – in Metro Manila was studied to see whether or not educational attainment plays a significant role in assessing job performance.

Though there were pas studies conducted about job performance, most research focused on how job satisfaction and motivation affected it. This thesis sought a different approach and used educational attainment (independent variable) measured as from first year high school to post-graduate studies, while job performance (dependent variable) was determined by job knowledge, communication and attitude.

The researchers were able gather a total sample of 236 employees using non-probability purposive sampling with each sector named as strata. A correlational research designed was used. Moreover, the survey questionnaire utilized a like Scale to measure the job performance of the respondents. After analyzing the data through Pearson-Product-Moment-Correlation test, results showed that the educational attainment and job performance had indirect but weak and statistically significant relationship, therefore educational attainment does have a significant relationship to job performance among the six sectors of BPO. However, when the statistical test was implemented per sector, it resulted otherwise. These results illustrate that each sector does not necessarily require formal education to be able to attain good performance.

Since higher educational attainment does not guarantee better performance, future researchers should utilized other variables such as age, marital status, shift, years of experience, salary and benefits to identify other factors which can improve BPO workers job performance. This study can also aid in the hiring process of BPO companies, especially today that there exists underemployment or job mismatch in the BPO industry. Furthermore, educational attainment and job performance of other industries may also be tapped in order to compare the BPO industry's standards.

Abstract Format

Accession number, shelf location.

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

196 leaves ; 28 cm.

Recommended Citation

Gan, K. O., Miravite, A. K., Mu, S., & Tan, A. L. (2012). A Study on the relationship of educational attainment and job performance of workers among the six sectors of the business process outsourcing industry in Metro Manila. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11865

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Integration of multiple-omics data to reveal the shared genetic architecture of educational attainment, intelligence, cognitive performance, and Alzheimer’s disease

1 Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China

Haoyan Wang

2 Beidahuang Industry Group General Hospital, Harbin, China

3 Aier Eye Hospital, Harbin, China

Shizheng Qiu

Tianyi zang.

Yadu Gautam , Cincinnati Children’s Hospital Medical Center, United States

Associated Data

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

Growing evidence suggests the effect of educational attainment (EA) on Alzheimer’s disease (AD), but less is known about the shared genetic architecture between them. Here, leveraging genome-wide association studies (GWAS) for AD (N = 21,982/41,944), EA (N = 1,131,881), cognitive performance (N = 257,828), and intelligence (N = 78,308), we investigated their causal association with the linkage disequilibrium score (LDSC) and Mendelian randomization and their shared loci with the conjunctional false discovery rate (conjFDR), transcriptome-wide association studies (TWAS), and colocalization. We observed significant genetic correlations of EA (r g = −0.22, p = 5.07E-05), cognitive performance (r g = −0.27, p = 2.44E-05), and intelligence (r g = −0.30, p = 3.00E-04) with AD, and a causal relationship between EA and AD (OR = 0.74, 95% CI: 0.58–0.94, p = 0.013). We identified 13 shared loci at conjFDR <0.01, of which five were novel, and prioritized three causal genes. These findings inform early prevention strategies for AD.

Introduction

Alzheimer’s disease (AD) remains the most common neurodegenerative disease among the elderly, which affects more than 44 million people worldwide ( Scheltens et al., 2016 ; Van Cauwenberghe et al., 2016 ). Known drugs or treatments may hardly completely reverse the progression of AD, so understanding the modifiable risk factors of AD remains the first choice for prevention.

Educational attainment (EA) and cognitive performance are known modifiable factors for dementia ( Larsson et al., 2017 ; Alzheimer’s Association, 2020 ). Years of continuous formal education and cognitive training make brains form a cognitive reserve ( Stern, 2012 ). Brains of individuals with higher education would continue to perform cognitive tasks even if the excessive accumulation of amyloid-beta (Aβ) and tau protein exists ( Stern et al., 1994 ; Dekhtyar et al., 2019 ). Amieva et al. (2014 ) followed up 171 less-educated people and 271 highly educated people for 20 years and found that cognitive performance of highly educated people decreased 15–16 years before reaching the threshold of AD, while the less-educated people developed AD in only 7 years. In other words, EA greatly delays the progression of AD by maintaining cognitive performance.

Significantly, EA has been found to have associations with both intelligence and cognitive performance. Intelligence can continuously improve through learning, and individuals with higher intelligence or childhood intelligence tend to have longer years of schooling, as evidenced by previous studies ( Anderson et al., 2020 ; Lovden et al., 2020 ). Moreover, epidemiological research has indicated that lower IQ in children can be a predictor of poor cognitive function and an increased risk of developing AD later in life ( Snowdon et al., 1996 ; Vinueza-Veloz et al., 2020 ). Although there is currently no direct evidence establishing a link between early intelligence and the degree or type of neuropathological features of dementia in older adults, it is possible that the phenotypic association between the two is influenced by shared genetic variants ( Yeo et al., 2011 ). Consequently, the correlation between these cognition-related phenotypes (EA, cognitive performance, intelligence, and childhood intelligence) may introduce some complexity when discerning their causal relationship with AD. Furthermore, the shared genetic architecture and causal genes underlying the cognition-related phenotypes and AD remain unidentified.

Although observational studies are difficult to adjust for these complex covariates, Mendelian randomization (MR) studies based on genome-wide association studies (GWAS) may have the power to provide an independent effect of each exposure on AD. In this study, we aimed to investigate the shared genetic architecture between AD and four cognition-related phenotypes, including genetic correlations, local genetic correlations, independent causal relationship, polygenic overlap, and shared causal genes ( Figure 1 ). We explored the underlying potential biological mechanisms and provided an important contribution to prevent the occurrence of AD.

Overall study design. We first retrieved five GWAS data sources. For cognition-related phenotypes that showed significant genetic correlation with AD, we conducted further genome-wide cross-trait analysis to investigate genetic overlap between them by integrating multiple-omics data.

Participant samples

We obtained GWAS summary statistics for AD from a meta-analysis of 46 AD GWAS datasets (21,982 cases and 41,944 controls) by the International Genomics of Alzheimer’s Project (IGAP) ( Lambert et al., 2013 ; Kunkle et al., 2019 ). A total of 9,456,058 common variants and 2,024,574 rare variants passed the quality control ( Kunkle et al., 2019 ). We obtained GWAS summary statistics for EA and cognitive performance from a meta-analysis of 71 cohort-level results by the Social Science Genetic Association Consortium (SSGAC), containing 1,131,881 and 257,828 individuals, respectively ( Lee et al., 2018 ). GWAS summary statistics for intelligence were obtained from the Center for Neurogenomics and Cognitive Research (CNCR) CTGlab ( Sniekers et al., 2017 ). Sniekers et al. (2017 ) performed a meta-analysis of several intelligence GWAS, including UK Biobank web-based measure (N = 17,862), UK Biobank touchscreen measure (N = 36,257), Childhood Intelligence Consortium (CHIC) (N = 12,441), and five additional cohorts (N = 11,748). We obtained childhood intelligence GWAS from CHIC ( Benyamin et al., 2014 ). CHIC assessed the intelligence of 17,989 children of European origin aged 6–18 years and performed the single-nucleotide polymorphism (SNP) classification of GWAS, including six discovery (N = 12,441) and three replication (N = 5,548) cohorts ( Benyamin et al., 2014 ). Details of the participants were shown in the original studies ( Lambert et al., 2013 ; Sniekers et al., 2017 ; Lee et al., 2018 ; Kunkle et al., 2019 ).

We removed the rare variants (MAF <0.01) and performed the exchange of the reference genome (hg18/hg19) required in part of the study. We only focused on autosomal chromosomes and excluded the HLA region in this study. All participants were of European descent, and informed consent was obtained from all the participants in each study ( Sniekers et al., 2017 ; Lee et al., 2018 ; Kunkle et al., 2019 ). The study protocols were approved by the corresponding institutional review boards.

Genome-wide genetic correlation analysis

We performed a cross-trait linkage disequilibrium score (LDSC) regression to evaluate the genetic correlations of AD with the four cognition-related phenotypes ( Bulik-Sullivan B. et al., 2015 ; Qiu et al., 2023 ). We used precomputed LD scores derived from HapMap3 reference panels, which contained more than one million European participants from UK Biobank ( Bulik-Sullivan B. et al., 2015 ; Bulik-Sullivan B. K. et al., 2015 ). LDSC regression first calculated the average LD between SNPs across the genome and then regression of the GWAS summary statistics based on baseline LD scores ( Bulik-Sullivan B. et al., 2015 ). The slope of the regression line represented an estimate of heritability for a trait or disease. Notably, LDSC also corrected for confounding factors such as sample overlap and population stratification, which might affect heritability estimates. More details about the LDSC algorithm have been reported in previous studies ( Bulik-Sullivan B. et al., 2015 ). The statistically significant association after multiple testing is defined to be p < 0.05/4 = 0.0125.

Local genetic correlation analysis

We divided the gene components into pre-specified LD-independent segments (1,703 segments) and calculated the local genetic correlation of each segment separately. Herein, we performed two powerful computing tools: Heritability Estimation from Summary Statistics (HESS) and pairwise analysis of GWAS (GWAS-PW) ( Pickrell et al., 2016 ; Shi et al., 2017 ). HESS was used for estimating and visualizing the local SNP-heritability and genetic correlations and calculating genetic covariance to measure the similarity between a pair of traits driven by genetic variants ( Shi et al., 2017 ). GWAS-PW was used for evaluating the local correlations and associated SNPs of each segment, under a Bayesian colocalization framework ( Pickrell et al., 2016 ; Shi et al., 2017 ). The statistically significant association for HESS is defined to be p < 0.05/1703 = 2.94E-05 after correcting for multiple testing. The statistically significant association for GWAS-PW is defined to be posterior probability 3 (PPA_3) > 0.9.

Univariable MR

MR study is an effective method for the analysis of causal inference in epidemiology ( Hemani et al., 2018 ). MR analysis uses independent genome-wide significant SNPs ( P < 5E-08, r 2 < 0.3) as instrumental variables (IVs) to estimate the causal estimates of exposure on the outcome ( Hemani et al., 2018 ; Qiu et al., 2021 ; Hu et al., 2022 ; Qiu et al., 2022 ). The genotypes of SNP instruments are established at birth and may not be altered by confounding factors. Here, we selected 262, 116, and 146 independent genome-wide significant SNPs from GWAS summary statistics for EA, cognitive performance, and intelligence, respectively, as instruments to perform two-sample MR ( Hemani et al., 2018 ). We implemented MR-Egger and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) as sensitivity analyses ( Bowden et al., 2016 ; Hemani et al., 2018 ; Verbanck et al., 2018 ). The TwoSampleMR (version 0.5.6) and MR-PRESSO (version 1.0) R packages were used for MR analyses. p < 0.05/3 = 0.0167 was considered significant enrichment after multiple testing.

mtCOJO method and GSMR analysis

The association across EA, cognitive performance, childhood intelligence, and intelligence may interfere with the effect of one of these cognition-related phenotypes on AD. Here, we performed multi-trait-based conditional and joint (mtCOJO) analysis to adjust for pleiotropic SNPs among the cognition-related phenotypes and then implemented generalized summary-data-based Mendelian randomization (GSMR) analysis using conditional GWAS data ( Zhu et al., 2018 ).

The mtCOJO method is performed as described in Zhu et al. (2018) . If we adjust for the three covariates (cognitive performance, childhood intelligence, and intelligence) when estimating the influence of an SNP on EA, it is to use the causal estimates of cognitive performance, childhood intelligence, and intelligence on EA calculated by GSMR analysis as the condition in a GWAS conditional analysis ( Zhu et al., 2018 ). In other words, in exploring the causal relationship between EA and AD, we removed the effects of three covariates from exposure. As a result, we obtained conditional GWAS data for each phenotype after adjusting for pleiotropy.

Leveraging conditional GWAS data, we investigated the causality between cognition-related phenotypes and AD using GSMR analysis ( Hemani et al., 2018 ). Based on the MR framework, GSMR performs summary-based Mendelian randomization (SMR) analysis on each SNP instrument separately, considers the sampling variance of each SNP and LD between SNPs, and integrates the causal estimation of all SNP instruments through the generalized least squares method ( Zhu et al., 2016 ; Zhu et al., 2018 ). Thus, GSMR excluded the estimation biases that might arise from pleiotropy ( Zhu et al., 2018 ). We then carried out an instrument selector called heterogeneity in dependent instruments (HEIDI)-outlier to distinguish causality from pleiotropy ( Zhu et al., 2018 ). Finally, we used 1000 Genomes Phase III as LD reference panels to clump the SNPs and selected independent genome-wide significant SNPs of conditional GWAS (LD: r < 0.1, p < 5E-08).

Conditional Q–Q plots

We performed genomic controls to adjust for the expansion and deflation of the empirical null distribution in GWAS due to population stratification and overcorrection of test statistics for polygenic traits ( Yang et al., 2011 ; Andreassen et al., 2013 ). To assess the pleiotropic enrichment and shared risk loci of cognition-related phenotypes associated with AD, we generated conditional quantile–quantile (Q–Q) plots and computed the conditional false discovery rate (condFDR) statistics. Conditional Q–Q plots were generated using the log 10 ( p ) value of all SNP loci for the main trait (e.g., AD) and the log 10 ( p ) values of SNP loci across several different thresholds for the conditional trait (e.g., EA) ( Andreassen et al., 2013 ; Andreassen et al., 2014 ; Desikan et al., 2015 ). The thresholds of condFDR included p < 1, p < 0.1, p < 0.01, p < 0.001, and p < 0.0001. Details of these statistical methods have been described in previous publications ( Andreassen et al., 2013 ; Andreassen et al., 2014 ; Desikan et al., 2015 ).

Conditional false discovery rate

The unconditional FDR (uFDR) refers to the probability that an SNP locus is associated with a trait as a false positive and is expressed as the ratio of the observed p -value to the observed quantile under the null hypothesis ( Andreassen et al., 2013 ; Andreassen et al., 2014 ; Desikan et al., 2015 ). The condFDR is an extension of uFDR defined as the probability that an SNP locus is not associated with a main trait i (e.g., AD) if the p -value in both traits is less than a preset significance threshold and vice versa ( Andreassen et al., 2013 ; Andreassen et al., 2014 ; Desikan et al., 2015 ). The conjFDR minimizes the effect of a single trait driving the shared association signal ( Andreassen et al., 2013 ; Andreassen et al., 2014 ; Desikan et al., 2015 ). When the conjFDR value of this SNP is less than 0.01, it is generally considered to be significantly associated with both traits. The conjFDR method is implemented using the R package “GWAScFDR.”

eQTL analysis and functional enrichment

To further assess whether the shared risk loci of AD and cognition-related phenotypes could regulate gene expression, we performed the expression quantitative trait loci (eQTL) analysis in whole blood and 13 brain tissues (brain amygdala, anterior cingulate cortex, caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, hippocampus, hypothalamus, frontal cortex, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord cervical, and substantia nigra) from the Genotype-Tissue Expression (GTEx) ( GTEx Consortium et al., 2017 ). 1E-06 was used as the threshold for FDR <0.05. To explore the functional locations and pathways of shared risk loci, we performed enrichment analysis in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) using the clusterProfiler package ( Yu et al., 2012 ). p < 0.05 was considered significant enrichment after multiple testing.

TWAS and colocalization analysis

GWAS identifies susceptibility loci for complex traits, but whether these loci influence the phenotypes through gene expression remains unknown. TWAS were used as an association test to identify the expression of putative risk genes in complex traits ( Gusev et al., 2016 ). Here, we selected dorsolateral prefrontal cortex (DLPFC) RNA-seq datasets from the CommonMind Consortium (CMC) as the expression reference weights and imputed expression into GWAS summary statistics using Fusion software ( Fromer et al., 2016 ; Gusev et al., 2016 ; Senthil et al., 2017 ). For genes showing significant association in the TWAS test ( p < 0.05/No. of genes), we further performed colocalization analysis to scan for shared causal genes ( Giambartolomei et al., 2014 ; Wallace, 2020 ). Colocalization used the Bayesian statistical test to calculate the posterior probability of five hypotheses (H0–H5), and the posterior probability PPH4 > 0.75 was interpreted as colocalization generally. The common significance of TWAS and colocalization analysis ensured the accuracy of the association test ( Gusev et al., 2016 ).

Fine-mapping of causal gene sets

In order to confirm the credibility of the causal genes identified by TWAS/colocalization analysis, we carried out the fine-mapping of causal gene sets (FOCUS) ( Mancuso et al., 2019 ). FOCUS assigned the probability of associated signals to the risk genes identified by TWAS, which could be used for gene prioritization in functional analysis ( Mancuso et al., 2019 ). The overlapping signals of TWAS, colocalization, and fine-mapping might be credible causal genes. We prioritized the mapping in brain tissues and showed the predicted expression correlation of each gene within the risk region.

Shared genomic architectures

We performed a cross-trait LDSC regression and observed negative genetic correlations of AD with EA (r g = −0.22, p = 5.07E-05), cognitive performance (r g = −0.27, p = 2.44E-05), and intelligence (r g = −0.30, p = 3.00E-04) ( Table 1 ). Nevertheless, we found non-significant genetic correlations of childhood intelligence and AD (r g = −0.02, p = 0.60) ( Table 1 ).

Genetic correlations between AD and cognition-related phenotypes.

r g : genetic correlation, r g _se : standard error of genetic correlation, r g _z : Z-score of genetic correlation.

p : the statistically significant association is defined to be p < 0.05/4 = 0.125.

In order to observe the similarity between two traits driven by genetic variants in specific regions of the genome, we calculated the local genetic covariance of AD with EA, cognitive performance, and intelligence in 1,703 regions with independent LD. As a result, both EA ( P AD&EA = 1.62E-09) and intelligence ( P AD&intelligence = 4.33E-06) were associated with AD in the chromosome 19: 44.7M–46.1M region ( Figure 2 , Supplementary Tables S1–3 ). The local genetic overlap of AD and EA was also shown in the chromosome 14: 9.12M–9.31M region using GWAS-PW analysis ( Supplementary Tables S4–6 ).

HESS analysis of AD with EA, cognitive performance, and intelligence. For each sub-figure, top and middle parts represent local genetic correlation and covariance, respectively, and colored bars represent loci that have significant local genetic correlation and covariance. Bottom part represents local SNP-heritability for individual traits, and colored bars represent loci that have significant local SNP-heritability. AD , Alzheimer’s disease; EA , educational attainment; CP , cognitive performance; INT , intelligence. (A) The local genetic correlation of AD and EA. (B) The local genetic correlation of AD and cognitive performance. (C) The local genetic correlation of AD and intelligence.

Independent causal relationship

Univariable MR analysis showed causal relationships between the genetically predicted cognition-related phenotypes and AD ( Figure 3 ). Every one standard deviation (SD) increase in EA, cognitive performance, and intelligence was associated with 30%, 26%, and 27% lower risk of AD (EA: odds ratio (OR), 0.70; 95% confidence interval (CI), 0.60 to 0.81, p = 2.28E-06; cognitive performance: OR, 0.74; 95% CI, 0.63 to 0.87, p = 0.00023; and intelligence: OR, 0.73; 95% CI, 0.62 to 0.87, p = 0.00028), respectively. MR-PRESSO and MR-Egger found no evidence of directional pleiotropy.

Univariable MR results for the causal relationship between cognition-related phenotypes and AD.

Owing to the potential causal relationship between EA and AD and the possible limitations of multivariable MR, we further performed mtCOJO analysis to adjust for pleiotropic SNPs among the cognition-related phenotypes and then implemented GSMR analysis using conditional GWAS ( Zhu et al., 2018 ). After conditioning, genetically predicted EA was significantly associated with AD risk (OR = 0.74, 95% CI: 0.58–0.94, p = 0.013) ( Supplementary Table S7 ). However, there was a non-significant causal relationship between cognitive performance or intelligence and AD. The number of SNP instruments for childhood intelligence was not sufficient to perform MR.

Pleiotropic enrichment and polygenic overlap

To describe the pleiotropic enrichment between cognition-related phenotypes and AD, we generated conditional Q–Q plots for conditioning the cognition-related phenotypes on AD ( Andreassen et al., 2013 ; Andreassen et al., 2014 ; Desikan et al., 2015 ). A significant upward deflection of the conditional Q–Q plot was observed for EA, cognitive performance, and intelligence as conditional traits, suggesting a significant pleiotropic enrichment of AD with the cognition-related phenotypes ( Figure 4 ).

Conditional quantile–quantile plots. Dotted lines indicate the expected line under the null hypothesis, and leftward deflection demonstrates the degree of pleiotropic enrichment. (A) Conditional Q-Q plots for AD and EA. (B) Conditional Q-Q plots for AD and cognitive performance. (C) Conditional Q-Q plots for AD and intelligence.

In order to identify the overlapping loci of cognition-related phenotypes and AD, we applied a conjFDR statistical framework based on GWAS. At conjFDR <0.01, we identified 4, 5, and 4 shared risk loci for AD and EA, cognitive performance, and intelligence, respectively ( Figure 5 ; Table 2 ). Among these shared loci, APOE , PICALM , and HBEGF were susceptibility loci reported in previous GWAS for AD ( Naj et al., 2014 ; Rosenberg et al., 2016 ; Jun et al., 2017 ). VAC14 , EFL1 , CKM , SKA2 , and NECTIN2 were novel risk loci. HBEGF was identified in the shared risk loci for both AD and all cognition-related phenotypes. HBEGF encodes a growth factor called heparin-binding epidermal growth factor-like growth factor (HB-EGF), which binds to APP, the transmembrane glycoprotein central to AD, and acts synergistically with EGF to promote ERK signaling and neuritogenesis ( da Rocha et al., 2021 ).

Conditional Manhattan plot. The shared risk loci between AD and cognition-related phenotypes were marked. The statistically significant causality is defined to be conjFDR <0.05. (A) AD and EA. (B) AD and cognitive performance. (C) AD and intelligence.

Shared risk loci of AD and cognition-related phenotypes using conjFDR.

conjFDR, conjunctional false discovery rate.

We further evaluated the cis-expression of these loci in GTEx and found that they regulated the expression of WDR55 , RP11-394B2.1 , AP3B2 , SAXO2 , ADAMTS7P1 , UBE2Q2P2 , GOLGA2P10 , GOLGA6L9 , MARK4 , KLC3 , TRIM37 , RAD51C , TEX14 , {"type":"entrez-nucleotide","attrs":{"text":"AC099850.1","term_id":"17047215","term_text":"AC099850.1"}} AC099850.1 , SKA2 , PRR11 , and NECTIN2 in whole blood and various brain tissues ( Supplementary Table S8 ). In addition, we enriched these risk loci in GO and KEGG. The shared risk loci of EA and AD were mainly enriched in the formation and regulation of Aβ and the binding of tau protein or lipoprotein receptor ( Supplementary Figure S1 ). The shared risk loci of cognitive performance and AD were mainly enriched in keratinocyte migration and glycoprotein biosynthetic processes ( Supplementary Figure S2 ).

Credible causal genes

Shared loci identified by conjFDR (closest to risk SNPs at physical distance) may still be affected by limitations of GWAS ( Zhu et al., 2016 ). In order to identify the credible causal genes of these phenotypes, we conducted TWAS, colocalization, and fine-mapping. We selected brain tissue RNA-seq datasets as expression reference weights and performed the expression imputation to GWAS on each chromosome in turn ( Gusev et al., 2016 ). As a result, a total of 18 AD genes, 439 EA genes, 292 cognitive performance genes, and 276 intelligence genes showed strong associations with the brain ( Supplementary Tables S9–12 ). TSPAN14 , FAM180B , GOLGA6L9 , and MTCH2 showed significant correlation signals in both AD and cognitive performance. TSPAN14 , FAM180B , and GOLGA6L9 showed significant correlation signals in both AD and intelligence. Almost all causal genes identified by TWAS were replicated in colocalization ( Supplementary Tables S9–12 ). Fine-mapping prioritized three credible causal genes ( TSPAN14 , FAM180B , and GOLGA6L9 ), which were selected in the credible set the most times in AD and cognition-related phenotypes ( Supplementary Table S13 ).

In this study, we observed substantial polygenic overlap between cognition-related phenotypes and AD. We found high genetic correlations of AD with EA, cognitive performance, and intelligence but without childhood intelligence. Similarly, previous studies showed that genetic variants that mediated the biological effects of AD were unlikely to operate early in life, and no evidence showed that the genetic burden of AD was linked to early cognition ( Korologou-Linden et al., 2019 ; Lamballais et al., 2020 ). As years of education increased, intelligence might increase and become a protective factor for AD. Further local genetic correlation analysis identified chromosome 19: 44.7M–46.1M as a significantly associated region for EA, intelligence, and AD. Most of the genes in this region were associated with AD risk, such as APOE , TOMM40 , PVRL2 , APOC1 , and MARK4 ( Zhou et al., 2019 ; Serrano-Pozo et al., 2021 ).

Given the interference of covariates among the four cognition-related phenotypes, we used two approaches to investigate the independent causal relationship: multivariable MR and mtCOJO analysis. The results suggested that higher EA might causally reduce the risk of AD, independently of cognitive performance and intelligence. Similarly, Korologou-Linden et al. (2019 ) found that when SNPs related to education were removed, the association between low learning performance of adolescents and AD was weakened. Most of these behavioral cognition/education-related phenotypes had ambiguous bidirectional causality. Considering the results of our study alone, we speculated that intelligence would change significantly with the increase in age, years of education, and cognitive training and that the acquired effect was the real reason for AD ( Polderman et al., 2015 ).

Using conditional Q–Q plots and conjFDR statistical framework, we found significant pleiotropic overlap between AD and cognition-related phenotypes and identified 11 shared risk loci: APOE , CCDC83 , PICALM , HBEGF , PICALM , EED , VAC14 , EFL1 , CKM , SKA2 , and NECTIN2 . Although VAC14 , EFL1 , CKM , SKA2 , and NECTIN2 were novel risk loci, most of them were reported to be associated with AD, the risk of cognitive decline, or other brain diseases. Given the limitations of traditional GWAS, we further integrated GWAS and eQTL of brain tissues to identify potential causal genes that affected traits by regulating gene expression ( Zhu et al., 2016 ; Tam et al., 2019 ). Although methods to identify causal genes are still lacking, we assessed the most credible risk genes using three approaches (TWAS, colocalization, and fine-mapping) that approximate causal genes. In this way, we extended the risk loci of GWAS to the transcriptome level and prioritized TSPAN14 , FAM180B , and GOLGA6L9 as the credible causal genes. Among these genes, TSPAN14 regulates maturation and trafficking of the transmembrane metalloprotease ADAM10 , and ADAM10 is involved in reducing the generation of Aβ peptides ( Kunkle et al., 2019 ; Schwartzentruber et al., 2021 ). FAM180B is also a potential susceptibility gene of AD and appears in the protein–protein interaction network associated with APOE ( Han et al., 2017 ). MTCH2 induces the production of solute carriers, which is reported as a risk gene for AD in multiple brain tissue transcripts ( Ruggiero et al., 2017 ). Although GOLGA6L9 is a novel locus never reported previously, variant rs2665103, the shared risk loci of AD and cognition-related phenotypes identified by conjFDR statistics, upregulates GOLGA6L9 expression in cerebellum, suggesting its potential biological role ( β = 0.56, p = 7.4E-08).

Based on large-scale GWAS summary statistics, our study has several strengths. First of all, GWAS for EA (1,131,881 individuals), cognitive performance (257,828 individuals), intelligence (78,308 individuals), and childhood intelligence (17,989 individuals) contain huge sample sizes, which greatly improves statistical power. Second, we adjust the covariates and provide unbiased causal estimation in the study of causality. Third, since it is difficult to determine the true causal genes in the current research, we perform TWAS/colocalization to test the association between risk gene regions and expression and verify the credible causal genes in fine-mapping. However, we acknowledge certain limitations in our study. The 13 cohorts of intelligence GWAS consists of eight children (<18 years; N = 19,509) and five adult cohorts (18–78 years; N = 58,799) ( Sniekers et al., 2017 ). If the childhood intelligence covariates are removed from the intelligence GWAS, the number of remaining SNPs may be inadequate for instrumental variable analysis and genetic association analysis. Finally, the different analyses performed in the study are interconnected and built upon each other to provide a comprehensive understanding of the genetic relationship between AD and cognition-related traits. These findings suggest that implementing early prevention strategies that focus on education and cognitive training could potentially reduce the risk of developing AD. The identification of shared risk loci and credible causal genes provides potential targets and personalized medicine approaches for future therapeutic interventions.

In conclusion, this genome-wide cross-trait analysis strengthened the view that genetically predicted EA, cognitive performance, and intelligence were statistically related to AD risk. We identified 11 pleiotropic risk loci of AD and cognition-related phenotypes, of which five were novel. Our research provided new insights into the shared genetic basis of AD and cognition-related phenotypes from multiple levels and opened a new way for the early prevention of AD.

Acknowledgments

The authors would like to thank the International Genomics of Alzheimer’s Project (IGAP), Social Science Genetic Association Consortium (SSGAC), Center for Neurogenomics and Cognitive Research (CNCR) CTGlab, and GWAS Catalog for AD GWAS summary statistics. The authors thank the CommonMind Consortium (CMC) and Genotype-Tissue Expression (GTEx) for eQTL summary statistics. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families.

Funding Statement

This research was supported by the National Natural Science Foundation of China (62076082) and National Key Research and Development Project (2016YFC0901605).

Data availability statement

Author contributions.

FW, HW, YY, and BH have contributed equally to this work. FW, HW, YY, and SQ designed the study and analyzed the data. YH and TZ supervised the study. BH revised the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1243879/full#supplementary-material

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