Research on neighborhood effects draws suggestive links between local spatial environments and a range of social, economic, and public health outcomes. Here, we consider the potential role of genetics in the geography of social stratification in the United States using genomic data from the National Longitudinal Study of Adolescent to Adult Health. We find that those with genotypes related to higher educational attainment sort into neighborhoods that are better educated and have higher population densities, both descriptively and using formal school and sibling fixed-effects models. We identify four mechanisms through which this geographic sorting on genetic endowment can magnify social stratification: assortative mating, social-genetic effects, gene-by-environment interactions, and gene–by–social-genetic interactions. We examine the presence of the latter three in our data, finding provisional yet suggestive evidence for social-genetic effects that putatively amount to about one-third of the influence of one’s own genomic profile. We find no evidence, however, for the presence of interactions between environments and individual genetic background. Collectively, these findings highlight the potential for geographic sorting on genotype to emerge both as a key methodological concern in population genetics and social science research and also a potentially overlooked dimension of social stratification worthy of future study.

Institute of Behavioral Science, University of Colorado BoulderE-mail: thla0691@colorado.edu

Justin Vinneau: Institute of Behavioral Science, University of Colorado Boulder

E-mail: justin.vinneau@colorado.edu

Jason D. Boardman: Institute of Behavioral Science, University of Colorado Boulder

E-mail: boardman@colorado.edu

Acknowledgements: This research uses data from The National Longitudinal Study of Adolescent Health (Add Health), a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). Laidley and Vinneau acknowledge generous support from the National Institutes of Health (NIH) grant 5T32DA017637. This work has also benefited from research, administrative, and computing support provided by the University of Colorado Population Center (CUPC Project 2P2CHD066613-06) funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. We thank the editors for their feedback and guidance in preparing the manuscript as well as our colleagues at the Institute of Behavioral Science (IBS) and Institute for Behavioral Genetics (IBG) at the University of Colorado Boulder, who provided early feedback. Any remaining errors are ours alone. The content is solely the responsibility of the authors and does not necessarily reflect the official views of the CUPC, NIH, IBS, IBG, or University of Colorado Boulder.