Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix.

Observational studies are commonly used in psychology and epidemiology to identify risk factors correlated with health outcomes. However, these studies are vulnerable to confounding when shared genetic variation influences both the putative risk factor and outcome. Researchers have often controlled for this type of genetic confounding using polygenic scores, but these scores are noisy and biased estimators of a trait's genetic component. While some newer methods offer significant improvements over polygenic scores, they still rely on genome-wide association studies (GWAS) summary statistics, which may be untenable for certain datasets. Here, we develop an analogous method that leverages a genetic relatedness matrix to control genetic confounding when testing for nongenetic risk factors. In simulations, we find that our method outperforms existing approaches, particularly at sample sizes that are large by the standards of much human research but smaller than datasets often used in human genetics. We also demonstrate that existing methods are susceptible to poor GWAS portability, whereas our method is inherently robust to such concerns, conditional on the availability of individual genotype data. Finally, we apply our method to the UK Biobank to reanalyze social risk factors for health outcomes in previously understudied cohorts.