An important goal of human genetics is the identification of DNA sequence variations that are predictive of who is at risk for various common diseases. The focus of the present study is on the challenge of detecting and characterizing nonlinear attribute interactions or dependencies in the context of a genome-wide genetic study. The first question we address is whether the ReliefF algorithm is suitable for attribute selection in this domain. The second question we address is whether we can improve ReliefF for selecting important genetic attributes. Using simulated genetic datasets, we show that ReliefF is significantly better than a naïve chi-square test of independence for selecting two interacting attributes out of 10³ candidates. In addition, we show that ReliefF can be improved in this domain by systematically removing the worst attributes and re-estimating ReliefF weights. Our simulation studies demonstrate that this new Tuned ReliefF (TuRF) algorithm is significantly better than ReliefF.