Observational epidemiological studies can mitigate genetic confounding with the 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 historically controlled for this type of genetic confounding using polygenic scores, but these scores are often noisy and biased estimators of a trait's genetic component. Here, we develop a method that leverages solutions to a similar problem in the field of phylogenetics. Motivated by inference of causal effects in phylogenetics, we show that the genetic relationship matrix (GRM) can be used to control genetic confounding when testing for non-genetic risk factors. In simulations, we find that our method out-performs existing approaches, particularly in the sample sizes characteristic of datasets in psychology and epidemiology. We also demonstrate that while existing methods are susceptible to poor GWAS portability, our method is inherently robust to such concerns. Finally, we apply our method to the UK Biobank to re-analyze social risk factors for health outcomes in previously understudied cohorts.