a genetic risk score (GRS). BMI was then regressed on the GRS, and they were able to explain roughly 2 % of the variance of BMI in their study. This is a very useful approach that uses existing genome-wide data sources without performing a GWAS, per se. These genetic risk factors can be included as covariates in traditional regression based models, and they can be included as interaction terms with hypothesized environmental moderators. Such information could also be used with propensity score matching models to compare individuals with comparable propensities to engage in a particular behavior.
