Gene-environment correlation, or rGE, is known to cause confounding in conventional genetic discovery studies. One way that rGE can come about is through self-selection into environments. In this study, we leverage a large sample of genotyped Swedish dizygotic twin pairs, coupled with population-wide register data, to investigate genetically driven self-selection into residential neighborhoods. We use grids as small as 250x250 meters to construct highly fine-grained neighborhood characteristics. The high level of geographic granularity makes it possible to distinguish not just regions or districts, but e.g. higher or lower SES neighborhoods in the same area. Furthermore, access to high-quality population-wide data for a large selection of variables means that we can construct neighborhood characteristics not just based on education or income, but on things like occupational status, political participation and ethnic diversity as well as anthropometric traits like height or BMI. Using these fine-grained neighborhood characteristics, we can investigate how within-pair differences in polygenic indices for a range of traits are related to self-selection into residential neighborhoods during different periods of the life cycle. This allows us to look at not just the existence of, but also the specific character of, self-selection as a driver of gene-environment correlation. Preliminary results show that the twin with e.g. a higher polygenic index for educational attainment is causally more likely to self-select into a high-SES neighborhood when moving out of their childhood home, and that this social stratification persists both over the life-cycle and across generations.
Rafael Ahlskog, Uppsala University
Sven Oskarsson, Uppsala University