Human genetics is an evolving discipline that is being driven by rapid advances in technologies that make it possible to measure enormous quantities of genetic information. An important goal of human genetics is to understand the mapping relationship between interindividual variation in DNA sequences (i.e., the genome) and variability in disease susceptibility (i.e., the phenotype). The focus of the present study is the detection and characterization of nonlinear interactions among DNA sequence variations in human populations using data mining and machine learning methods. We first review the concept difficulty and then review a multifactor dimensionality reduction (MDR) approach that was developed specifically for this domain. We then present some ideas about how to scale the MDR approach to datasets with thousands of attributes (i.e., genome-wide analysis). Finally, we end with some ideas about how nonlinear genetic models might be statistically interpreted to facilitate making biological inferences.