Genetic and social causes of life chances: Improved parametrization of educational attainment and social mobility models through molecular genetic information in a longitudinal twin family study (TWINLIFE)

• Applicants: Professor Dr. Martin Diewald; Professor Dr. Peter Krawitz; Professor Dr. Markus M. Nöthen; Professor Dr. Rainer Riemann; Professor Dr. Frank M. Spinath
• Subject Area: Personality Psychology, Clinical and Medical Psychology, Methodology ; Empirical Social Research ; Human Genetics
• Term: from 2020 to 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 428902522

Genome-wide association-studies (GWAS) on a wide range of human traits have been successful in identifying a large number of genetic variants which show highly significant associations with phenotypical lfe course outcomes, and which replicate across studies. This indicates that molecular genetic data can be successfully utilized to explain highly relevant interindividual differences even in these normally distributed areas of complex behavior. This applies first of all to skill production and educational attainment, where outside Germany the inclusion of molecular genetic information in standard social science models has led to new insights in the genesis of social inequality. and here the dynamic interplay between genes and environmental conditions in the prediction of life chances, and the control for gene-environment correlation.In this proposal we add Germany a s a country with specific institutional design to this research agenda by applyingpolygenic score (PGS) analyses not only to educational attainment, but also socio-economic status, cognitive ability, personality, and mood disorders as other relevant outcomes. The integration into an ongoing extended twin-family study on social inequality is not only cost-efficient but can additionally promote our understanding in ways that are not viable in individual-based surveys. A standard GWAS utilizing unrelated individuals neglects intergenerational transmission of both genetics as well as culture and the environment. A recent study which used genetic data assessed in families showed that the prediction of children’s educational attainment based on the child’s PGS score for educational attainment can be improved by incorporating a PGS computed for the non-transmitted alleles between the child and his/her parents. The implication is that not only does our own genome influence our behaviour, but through shaping childhood and adolescent environment, so do our parental genotypes as well as sibling genotypes. These results highlight the importance of family DNA-data.Assessing DNA in a large sample of twins and their biological parents allows us to study, among others, the differential prediction of behavioral differences in dizygotic twins based on polygenic score differences, to establish the predictive power of PGS with relation to overall genetic effects in the same sample. We also aim to utilize PGS within mediation and moderation models including measured environmental conditions such as parental socio-economic status, family structure, and social support to test for gene-environmental interactions in the prediction. By controlling for PGSs, we can observe what happens to the “traditional” variables in social inequality and mobility research. With the family design of TwinLife we can compare and control for both parents’ genotypes in addition to the offspring genotype.

• DFG Programme: Research Grants
• Co-Investigators: Professor Dr. Andreas J. Forstner; Carlo Maj, Ph.D.