Non-parametric methods (e.g., data mining, machine learning, and neural network modeling) have been proposed as a discovery tool for exploring these highly dimensional spaces without the need of a priori hypotheses. Of particular interest are non-parametric data-mining methods, such as multifactor dimensionality reduction (MDR; Chen et al., 2011b; Hahn et al., 2003; Ritchie et al., 2003a), because they are easier to interpret than neural network models (Lucek and Ott, 1997; Motsinger-Reif et al., 2008; Ritchie et al., 2003b). Notably, despite the utility of data-mining approaches, the possibility of obtaining false-positive results that can result from chance patterns in the data still exists. MDR has been applied to several complex phenotypes, such as multiple sclerosis, coronary artery disease, and cancer (Agirbasli et al., 2011; Brassat et al., 2006; Gui et al., 2011), however, applications to addiction phenotypes are lacking.
