A wide assortment of R tools are available for exploratory data analysis in high-dimensional settings and are easily applicable to data arising from population-based genetic association studies. In this chapter we illustrate the application of three such approaches, namely conditional inference trees, random forests, and logic regression. Through applications to simulated data, we explore the relative utility of each approach for uncovering underlying association between genetic polymorphisms and a quantitative trait.