Associations between the GRS and obesity-related traits (BMI, weight, waist circumference, obesity) were tested with linear and logistic regression models. These and subsequent models were adjusted for demographic and geographic control variables: age was specified as a linear and a quadratic term; a product term was included for the interaction between age and sex to account for sex differences in BMI and obesity distributions at different ages; the 4 ARIC Study Centers where participants were enrolled in the study were entered as a series of dummy variables (this collection of variables is referred to hereafter and elsewhere in the manuscript as demographics and geography). Predictiveness of the GRS was evaluated using 3 metrics that are established tools for evaluating risk markers in general 33 as well as for the specific case of genetic risk scores 34: 1) R2, the proportion of variation explained in BMI. R2 was estimated using demographics and geography-adjusted linear regression models. 2) AUC, the area under the receiver operating characteristic curve for obesity, also known as the discrimination index. The AUC corresponds to the probability that
