Unfortunately, this form of research is still under development with most of the nonparametric tools being applied to cancer phenotypes. Given its nature, parametric logistic regression cannot be employed in GEM studies because large sample sizes would be required. Alternatively, researchers may opt to limit the 1 million markers on a DNA micro array to only those belonging to genes in candidate pathways (given prior knowledge to select these markers; Grady et al., 2011). The likely alternative approach is the application of nonparametric methods that are currently being adapted to GWAS, such as MDR and HotNet (Vandin et al., 2012, 2011). Many of these approaches provide flexibility in model specification providing a way to reduce the burden of genetic heterogeneity and multiple testing. For instance, MDR is a machine-learning alternative to logistic regression that assumes no particular genetic model while identifying combinations of SNPs that influence the likelihood of a disease state. As part of its method, MDR combines attribute selection and construction (i.e., the creation of a single attribute by pooling data across SNPs) with permutation testing. Like MDR,
