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The molecular genetic architecture of educational attainment and its significance for cognitive health

Periodic Reporting for period 4 - EdGe (The molecular genetic architecture of educational attainment and its significance for cognitive health)

Okres sprawozdawczy: 2020-03-01 do 2020-08-31

Since many social and economic outcomes are moderately heritable, it is possible to discover genetic variants associated with them. Such discoveries can yield new insights into the causal pathways underlying human behaviour, the complex interplay of environmental and genetic factors, and the relationship between socio-economic traits and health.
This projects builds on a genome-wide association study on educational attainment (EA) led by the applicant (Rietveld et al. 2013, Science), which identified for the first time specific genetic variants robustly associated with a socio-economic outcome. The project leverages the unique resources of the Social Science Genetic Association Consortium (SSGAC), which is co-led by the applicant.
The research will extend existing knowledge by: 1) discovering additional genetic variants and causal pathways associated with EA; 2) developing methods to use the available genetic association results in novel, more efficient ways; 3) shedding new light on characteristics related to EA such as economic preferences, cognitive function, and cognitive health; 4) showing how policies promoting EA interact with genetic predisposition; 5) using genetic information to better understand the causal effects of educational policy interventions, 6) developing better tools to identify individuals at risk for cognition-related diseases before the onset of symptoms; and 7) identifying causal pathways of genetic influence on cognitive health via neurobiological measures. The project aims to elucidate the complex causal pathways connecting genes, environment, individual characteristics, and health-related outcomes; make methodological contributions applicable in genetic epidemiology and the social sciences; and contribute towards designing more effective public policy, which could improve public health and lower health costs.
Our work so far has yielded the following main outputs:

1) GWAS on educational attainment in a sample of N~300,000, identifying 74 genetic loci (Nature 2016)
2) GWAS on educational attainment in a sample of N~1,100,000, identifying 1,271 genetic loci (Nature Genetics 2018)
3) Development of a new method to identify causal effects in non-experimental data using polygenic scores (PNAS 2018)
4) Development of multivariate statistical techniques for genetic discovery and to improve the predictive accuracy of polygenic scores (MTAG and Genomic SEM, Nature Genetics 2016 and Nature Human Behaviour 2019)
5) Development of a tool to calculate the expected predictive accuracy of polygenic scores and the number of GWAS findings under generalized conditions that allow for various genetic architectures and imperfect genetic correlations across samples (MetaGap calculator, Plos Genetics 2017)
6) Large scale GWAS on several health-relevant behaviours and proxies of health, including diet, risk-taking, subjective well-being, reproductive behaviour, and cognitive performance (2 x Nature Genetics 2016, Nature Genetics 2017, Nature Genetics 2019, Molecular Psychiatry 2020)
7) A large-scale epigenome-wide association study of educational attainment (Molecular Psychiatry 2017)
8) Several reviews of recent developments in statistical genetics and social science genetics (Science 2018, International Journal of Epidemiology 2019, Nature Human Behaviour 2020)
9) A paper that combines brain scans and genome-wide data in a large population sample that investigate the relationship between total brain volume and cognitive performance (Psychological Science 2019)
10) A paper that uses GWAS results for educational attainment to discover genetic heterogeneity in clinical diagnoses for schizophrenia (Nature Communications 2018)

Overall, there are already 19 key publications (and numerous follow-up studies) that originated from the ERC grant until now. These 19 key publications have already yielded 2955 citations (13 April 2020, Google Scholar), demonstrating the fast and high impact of our research.
Our work is pushing the boundaries in complex trait genetics and social science genetics thanks to unparalleled GWAS sample size, new statistical methods, and a careful communication of the results. Our GWAS on EA and related traits are by far the largest and most successful genetic association studies on social-scientific outcomes to date. Furthermore, our work is building a new, inter-disciplinary research field that combines the social sciences, molecular genetics, and the medical sciences. Our work and the emergence of the new field of social science genetics has also been discussed at length in several books by other scholars (e.g. Dalton Conley and Jason Flechter’s “The Genome Factor”, 2017, and David Reich’s “Who We Are and How We Got Here”, 2018), reflecting the impact of our research.

We expect that by the end of the project in 2020, our work will have led to substantial new insights into mental health outcomes (e.g. schizophrenia, depression, general cognitive performance), and new insights into the origins and consequences of educational attainment.
Manhattan plot of a genome-wide association study on educational attainment in ~1.1 mio individuals