Enabling Personal Genomics With an Explicit Test of Epistasis

Abstract

One goal of personal genomics is to use information about genomic variation to predict who is at risk for various common diseases. Technological advances in genotyping have spawned several personal genetic testing services that market genotyping services directly to the consumer. An important goal of consumer genetic testing is to provide health information along with the genotyping results. This has the potential to integrate detailed personal genetic and genomic information into healthcare decision making. Despite the potential importance of these advances, there are some important limitations. One concern is that much of the literature that is used to formulate personal genetics reports is based on genetic association studies that consider each genetic variant independently of the others. It is our working hypothesis that the true value of personal genomics will only be realized when the complexity of the genotype-to-phenotype mapping relationship is embraced, rather than ignored. We focus here on complexity in genetic architecture due to epistasis or nonlinear gene-gene interaction. We have previously developed a multifactor dimensionality reduction (MDR) algorithm and software package for detecting nonlinear interactions in genetic association studies. In most prior MDR analyses, the permutation testing strategy used to assess statistical significance was unable to differentiate MDR models that captured only interaction effects from those that also detected independent main effects. Statistical interpretation of MDR models required post-hoc analysis using entropy-based measures of interaction information. We introduce here a novel permutation test that allows the effects of nonlinear interactions between multiple genetic variants to be specifically tested in a manner that is not confounded by linear additive effects. We show using data simulated across 35 different NIH Public Access NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript epistasis models with varying effect sizes (heritabilities = 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4) and sample sizes (n = 400, 800, 1600) that the power to detect interactions using the explicit test of epistasis is no different than a standard permutation test. We also show that the test has the appropriate size or type I error rate of approximately 0.05. We then apply MDR with the new explicit test of epistasis to a large genetic study of bladder cancer (n=914) and show that a previously reported nonlinear interaction between two XPD gene polymorphisms is indeed significant (P = 0.005), even after considering the strong additive effect of smoking in the model. Finally, we evaluated the power of the explicit test of epistasis to detect the nonlinear interaction between two XPD gene polymorphisms by simulating data from the MDR model of bladder cancer susceptibility. We show that the power to detect the interaction alone was 1.00 while the power to detect the independent effect of smoking alone was 0.06 which is close to the expected type I error ...

Genes in the insulin and insulin-like growth factor pathway and odds of metachronous colorectal neoplasia

“…Using the filtered data containing all 1,439 participants, we considered the best 1-, 2-, and 3-locus models. Permutation testing (1,000-fold) was done using MDRpt (version 1.0) to obtain a P value from an explicit test of interaction (Greene et al 2010), which evaluates the significance of each interaction independent of any main effects. Finally, the ''high risk'' versus ''low risk'' groups identified from each MDR model were analyzed using a logistic regression model that also contained age, sex, follow-up time, and advanced adenoma at baseline, which are strong predictors of metachronous neoplasia, to determine if the genetic combinations added additional information.…”

A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis

“…Availability of the methods as part of the open-source MDR software package will make this possible. Second, it will be interesting to compare the covariate adjustment methods to the new explicit test of epistasis that holds marginal effects constant during permutation testing for MDR [Greene et al, 2010]. Can covariate adjustment methods be used in conjunction with these special permutation test methods?…”

Confronting the missing epistasis problem: on the reproducibility of gene–gene interactions

“…multifactordimensionalityreduction.org) (Greene et al 2010; Moore and Andrews 2015; Ritchie et al 2001). The comparative analysis was aimed at conforming to the originally reported form of the interaction as closely as possible, using a logistic regression framework.…”

“…MDR analyses were performed individually for each interaction (considering no fewer and no more SNPs than the set of SNPs particular to a given interaction), using the collapsed dataset and adjusting for sub-study. The significance of each analysis was evaluated using both explicit (Greene et al 2010) and non-explicit permutation tests, with 10,000 permutations each. The nonexplicit permutation tests were not used to declare the significance of any interaction; instead, they were performed to assess the possible presence of non-interacting effects, and to allow comparability with interactions originally analyzed in that manner.…”

“…We were able to evaluate 164 (85.9 %) of the reported interactions, after excluding those involving duplicate sets of polymorphisms, or polymorphisms that could not be imputed, had low imputation quality, or exhibited high LD. Each candidate interaction was analyzed using three different methods: (a) a comparative logistic regression analysis, which evaluated the same functional form of the interaction as was used in the original study; (b) a naïve logistic regression analysis, which tested interactions using a best-fitting combination of possible inheritance models of the SNPs, and (c) MDR using explicit permutation testing (Greene et al 2010). We also attempted to identify lower-order epistatic effects nested within interactions of order three or more using information gain analysis in MDR.…”