Summary
Many important indicators of social status (such as one’s level of education, occupation, and income) have been shown to be moderately heritable, meaning that a part of the variation in social status can be explained by genetic differences across population members. I have been able to identify for the first time specific genetic variants that are robustly associated with such an indicator, namely educational attainment (Rietveld et al., 2013, Science). By methodologically advancing the estimation of the interaction between genes and environments, this proposal will settle two long lasting debates in social science genetics.
First, I will show how heritability studies –despite earlier firm rejections of this position– can be informative for policies aiming to reduce social inequalities (Objective 1). Second, I will assess the critique that social science genetics attributes effects to genes which should be attributed to the environments through which these genes operate (Objective 2). In doing so, I will extend existing methodology to quantify the incremental explanatory power of genes over environmental factors such as the social status of parents (Objective 2A), and I will develop a solution for the endogenous inclusion of environmental factors in genetic summary indices (i.e., polygenic risk scores) which currently impacts the validity of gene-by-environment studies (Objective 2B).
All newly developed methods will be tested extensively using simulations, and made available for others by means of free software code. Empirically, I will interact genes and various natural experiments (policy changes) to identify interventions that can ameliorate social inequalities in terms of education, occupational status, and income. For this purpose, I will draw on a unique combination of data sources including UK Biobank (~500,000 genotyped individuals) and administrative data from Statistics Netherlands which I will link to data from the Dutch Twin Registry for this project.
First, I will show how heritability studies –despite earlier firm rejections of this position– can be informative for policies aiming to reduce social inequalities (Objective 1). Second, I will assess the critique that social science genetics attributes effects to genes which should be attributed to the environments through which these genes operate (Objective 2). In doing so, I will extend existing methodology to quantify the incremental explanatory power of genes over environmental factors such as the social status of parents (Objective 2A), and I will develop a solution for the endogenous inclusion of environmental factors in genetic summary indices (i.e., polygenic risk scores) which currently impacts the validity of gene-by-environment studies (Objective 2B).
All newly developed methods will be tested extensively using simulations, and made available for others by means of free software code. Empirically, I will interact genes and various natural experiments (policy changes) to identify interventions that can ameliorate social inequalities in terms of education, occupational status, and income. For this purpose, I will draw on a unique combination of data sources including UK Biobank (~500,000 genotyped individuals) and administrative data from Statistics Netherlands which I will link to data from the Dutch Twin Registry for this project.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/946647 |
| Start date: | 01-01-2021 |
| End date: | 31-12-2026 |
| Total budget - Public funding: | 1 499 923,00 Euro - 1 499 923,00 Euro |
Cordis data
Original description
Many important indicators of social status (such as one’s level of education, occupation, and income) have been shown to be moderately heritable, meaning that a part of the variation in social status can be explained by genetic differences across population members. I have been able to identify for the first time specific genetic variants that are robustly associated with such an indicator, namely educational attainment (Rietveld et al., 2013, Science). By methodologically advancing the estimation of the interaction between genes and environments, this proposal will settle two long lasting debates in social science genetics.First, I will show how heritability studies –despite earlier firm rejections of this position– can be informative for policies aiming to reduce social inequalities (Objective 1). Second, I will assess the critique that social science genetics attributes effects to genes which should be attributed to the environments through which these genes operate (Objective 2). In doing so, I will extend existing methodology to quantify the incremental explanatory power of genes over environmental factors such as the social status of parents (Objective 2A), and I will develop a solution for the endogenous inclusion of environmental factors in genetic summary indices (i.e., polygenic risk scores) which currently impacts the validity of gene-by-environment studies (Objective 2B).
All newly developed methods will be tested extensively using simulations, and made available for others by means of free software code. Empirically, I will interact genes and various natural experiments (policy changes) to identify interventions that can ameliorate social inequalities in terms of education, occupational status, and income. For this purpose, I will draw on a unique combination of data sources including UK Biobank (~500,000 genotyped individuals) and administrative data from Statistics Netherlands which I will link to data from the Dutch Twin Registry for this project.
Status
SIGNEDCall topic
ERC-2020-STGUpdate Date
27-04-2024
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