Background Multifactor Dimensionality Reduction (MDR) is a novel method developed to detect gene-gene interactions in case-control association analysis by exhaustively searching multi-locus combinations. permutation testing is used, where one permutation distribution is generated for all models. An alternative is n-locus permutation testing, where a separate distribution is created for each n-level of interaction tested. Findings In this study, we show that the false positive rate for the MDR method is at or below a selected alpha level, and demonstrate the conservative nature of omnibus testing. We compare the power and false positive rates of both permutation approaches and find omnibus permutation testing optimal for preserving power while protecting against false positives. Conclusion Omnibus permutation testing should be used with the MDR method. Background One of the main goals of genetic epidemiology is the identification and characterization of polymorphisms that present an increased risk of disease. It is increasingly assumed that complex buy 442-52-4 diseases are the result of a myriad of genetic and environmental risk factors [1,2]. This complex etiology limits the utility of traditional, parametric statistical approaches in genetic association studies [3,4]. The ubiquitous nature of gene-gene and gene-environment interactions [1,5,6] has inspired the development the novel statistical approaches designed to detect epistasis [7-9]. Multifactor Dimensionality Reduction (MDR) is one such method . MDR was designed to detect interactions in categorical independent variables and a dichotomous dependent variable (i.e. case/control status or drug treatment response/non-response). MDR performs an exhaustive search of all possible single-locus through n-locus interactions (as computationally feasible) to evaluate all possible high/low risk models of disease. MDR selects a single model as optimal for each n-locus interaction as a result of these evaluations. Permutation testing (PT) is used to determine the significance of these models. MDR is nonparametric and model-free, so no hypotheses concerning buy 442-52-4 the value of any statistical parameter nor any genetic inheritance model are made . MDR has successfully identified interactive effects in simulated data as well as real data applications in diseases such as hypertension [3,11,12], cancer [10,13,14], and atrial fibrillation [15,16]. The Mouse monoclonal to CD14.4AW4 reacts with CD14, a 53-55 kDa molecule. CD14 is a human high affinity cell-surface receptor for complexes of lipopolysaccharide (LPS-endotoxin) and serum LPS-binding protein (LPB). CD14 antigen has a strong presence on the surface of monocytes/macrophages, is weakly expressed on granulocytes, but not expressed by myeloid progenitor cells. CD14 functions as a receptor for endotoxin; when the monocytes become activated they release cytokines such as TNF, and up-regulate cell surface molecules including adhesion molecules.This clone is cross reactive with non-human primate end-goal of an MDR analysis is ultimately hypothesis generation (or refinement within candidate gene strategies) . Hypothesis testing is used within the MDR analysis framework to determine whether resulting models are significantly different than expected by chance. Significance of a model is intended to indicate an interesting model that should be followed up in replication cohorts or functional studies. In recent work, there has been more emphasis on selecting all statistically significant models  in order to avoid missing a true signal (false negatives) in exchange for risking the selection of a few false positives. This generation of multiple hypotheses opens up questions about the PT procedure used to ascribe significance to this end set of models. PT is a commonly used nonparametric statistical procedure that involves re-sampling the data without replacement to actually construct the distribution of the test statistic under the buy 442-52-4 null hypothesis rather than make specific distributional assumptions. If the value of the test statistic based on the original samples is extreme relative to this distribution (i.e. if it falls far into the tail of the distribution), then the null hypothesis is rejected . Validity of PT relies only on the property of exchangeability under the null hypothesis C that the joint distribution of the data samples must remain invariant to permutations of the data subscripts. Thus, permutation tests maintain a wide applicability under a much broader range of data and research conditions than most parametric tests . In addition, PT requires minimal assumptions about the data being examined, yet often has power equal to, or even greater than, parametric counterparts that require stronger, and sometimes untenable data assumptions . Unlike many parametric and other nonparametric tests, the results of permutation tests (the p-values) are unbiased . The chief drawback of this method is that it is computationally expensive, but the easy availability of fast computing has made this a practical approach even for large datasets..