However, analysis of the crude and GAPDH adjusted PGC-1a data yielded similar results providing evidence that the effects noted in this manuscript were the result of the exercise intervention on PGC-1a specifically and not secondary changes in GAPDH. The primary strengths are a well-controlled exercise training study with high adherence rates and a wide range of changes in the dependent and independent variables. In summary, exercise in individuals with type 2 diabetes should be initiated soon after diagnosis and include training programs aimed at improving plasma substrate availability, endocrine function, and skeletal muscle factors shown to improve glycemic outcomes. The view that the genetic basis of common human diseases can be explained by sequence variation in a few genetic loci has been recently replaced by a new appreciation for the complexity of biological networks and the interplay between proteins that jointly influence phenotypes. The recent advances in high-throughput genotyping techniques have made large quantities of genotype data commonplace in genetic epidemiology studies and therefore have enabled researchers to interrogate the entire genome. Researchers have extensively analyzed single SNP effects for a wide variety of diseases/abundant cbx1 proteins required formation sahf cell phenotpyes with variable results, but in most cases with a large proportion of the genetic component left unexplained. It has been proposed that these limitations are due to the analytical strategy that limits analyses to only single SNPs, and it is therefore becoming more commonplace to assess the challenge of identifying SNP-SNP interactions. The problem of identifying interactive SNP effects in a casecontrol study, which can be formulated as predicting binary outcomes, has been studied extensively and has demonstrated great promise in recent years. Multifactor Dimensionality Reduction MDR was developed as a nonparametric and modelfree data mining method for detecting, characterizing, and interpreting epistasis in the absence of significant main effects in genetic and epidemiologic studies of complex traits such as disease susceptibility. The goal of MDR is to change the representation of the data using a constructive induction algorithm to make non-additive interactions easier to detect using a classification method such as naive Bayes or logistic regression. Comparative studies that use extensive simulations show that MDR has the best performance when the true multi-SNP effects are nonadditive. Despite the fact that MDR has been extended to various settings, there have been few attempts to develop methods that systematically identify SNP-SNP interactions in relation to quantitative outcomes such as body mass index, tumor size and survival time. Because in many cases, analyzing phenotypes as continuous rather than binary outcomes can be more powerful due to large variability in the outcome distribution it is important to develop methods that permit the analyses of continuous traits.