Lessons in adjusting for genetic confounding in population research on education and health

Social scientists often fit regression models with a range of covariates to infer causal effects in observational, population-based data, but the resulting estimates may be biased by unknown, unmeasured, and poorly measured confounders. Adjusting for genetic confounding using polygenic indices (PGIs) has been forwarded as one way to reduce this bias. However, whether and how relationships of interest to social scientists change when adjusting for PGIs or genetic confounding more broadly remains poorly understood. The current study sheds light on this issue by evaluating associations between years of schooling and self-rated health, body mass index, and depressive symptoms before and after adjusting for genetic confounding using data from the 2006–2012 waves of the Health and Retirement Study (n = 11,614), a nationally representative study of older U.S. adults. We adjust for genetic confounding in two ways: first by controlling for PGIs, and second by using PolygENic Genetic confoUnding INference (PENGUIN), a method based on variance component estimation. We find that controlling for PGIs modestly attenuates associations between education and each measure of health, and PENGUIN attenuates estimates further. However, a significant protective relationship between education and health remains when adjusting for genetic confounding with either method. Adjusting for genetic confounding using available methods thus does not call into question the robust relationship between education and health, underscoring the fundamental role of social and behavioral factors in shaping educational health disparities. Our findings also illustrate the limitations of adjusting for genetic confounding with PGIs specifically. In an era where PGIs are now broadly available to social scientists in population-based datasets, we urge caution when using them as controls for genetic confounding.