Exploring the Performance of Multifactor Dimensionality Reduction in Large Scale SNP Studies and in the Presence of Genetic Heterogeneity among Epistatic Disease Models

Abstract

Background/Aims: In genetic studies of complex disease a consideration for the investigator is detection of joint effects. The Multifactor Dimensionality Reduction (MDR) algorithm searches for these effects with an exhaustive approach. Previously unknown aspects of MDR performance were the power to detect interactive effects given large numbers of non-model loci or varying degrees of heterogeneity among multiple epistatic disease models. Methods: To address the performance with many non-model loci, datasets of 500 cases and 500 controls with 100 to 10,000 SNPs were simulated for two-locus models, and one hundred 500-case/500-control datasets with 100 and 500 SNPs were simulated for three-locus models. Multiple levels of locus heterogeneity were simulated in several sample sizes. Results: These results show MDR is robust to locus heterogeneity when the definition of power is not as conservative as in previous simulation studies where all model loci were required to be found by the method. The results also indicate that MDR performance is related more strongly to broad-sense heritability than sample size and is not greatly affected by non-model loci. Conclusions: A study in which a population with high heritability estimates is sampled predisposes the MDR study to success more than a larger ascertainment in a population with smaller estimates.

Methods for Investigating Gene-Environment Interactions in Candidate Pathway and Genome-Wide Association Studies

“…Cordell (23) provides a good review of some of the more widely used methods in the context of G×G interactions, although most of these could also be applied to G×E. Currently the most popular is Multifactor Dimension Reduction (MDR) (30), which searches across all possible partitions of the cells of the multi-way contingency table for the best possible classifier of disease risk on multiple training sets and tests their predictions on the remaining data. Application to the 4-way table of colorectal cancer risks in relation to smoking, red meat, CYP1A2 , and NAT2 (Figure 2) confirms that the 4-way interaction model mentioned earlier yielded best classification in training sets but cross-validation shows that this predictor fares no better than chance, mainly due to the small samples size (12 cases and 2 controls) in the one high-risk category.…”

A screening methodology based on Random Forests to improve the detection of gene–gene interactions

“…The performance of MDR in large-scale studies is evaluated by calculating the proportion of simulated data sets in which MDR proposes the underlying epistasis model as the best model. 17 As no permutation tests are run, these percentages overestimate the power of MDR and cannot be compared with our results. Prescreening the data to narrow down the number of SNPs in the data set remains an appealing strategy in this context, as was shown in Table 2.…”

Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data

“…Namely, the power of SDR is influenced by the epistatic effect size, which is strictly related to the (cumulative) heritability of the model [38]. Moreover, as with MDR, the biological significance of SDR models may be difficult to interpret due to the non-linear distribution of high-risk and low-risk cells across the multidimensional space [39].…”