A newly developed modern analytic approach, Multivariate Adaptive Regression Splines (MARS), was used to identify both genetic and non-genetic factors involved in the etiology of a common disease. We tested this method on the simulated data provided by the Genetic Analysis Workshop (GAW) 12 in problem 2 for the isolated population. MARS simultaneously analyzes all inputs, in this case DNA sequence variants and non-genetic data, and selectively prunes away variables contributing insignificantly to fit by internal cross-validation to arrive at a generalizable predictive model of the response. The relevant factors identified, by means of an importance value computed by MARS, were assumed to be associated with risk to the disease. The application of a series of subsequent models identified the quantitative traits and a single major gene contributing directly to risk liability using five sets of 7,000 individuals.