Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity

Abstract

The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions with other genes and environmental exposures. We previously introduced multifactor dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus genotype information to improve the identification of polymorphism combinations associated with disease risk. The MDR approach is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., assumes no particular inheritance model), and is directly applicable to case-control and discordant sib-pair study designs. Both empirical and theoretical studies suggest that MDR has excellent power for identifying high-order gene-gene interactions. However, the power of MDR for identifying gene-gene interactions in the presence of common sources of noise is not currently known. The goal of this study was to evaluate the power of MDR for identifying gene-gene interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic or locus heterogeneity. Using simulated data, we show that MDR has high power to identify gene-gene interactions in the presence of 5% genotyping error, 5% missing data, or a combination of both. However, MDR has reduced power for some models in the presence of 50% phenocopy, and very limited power in the presence of 50% genetic heterogeneity. Extending MDR to address genetic heterogeneity should be a priority for the continued methodological development of this new approach.

An ensemble learning approach jointly modeling main and interaction effects in genetic association studies

“…The first four models, Ep1-Ep4, modified from the models used by Becker et al [2005], are two-locus epistatic models that exhibit interaction effects as well as main effects (marginal effects). The last four models, P1-P4 as described by Ritchie et al [2003] and Sha et al [2006], are four pure interaction models that exhibit interaction effects in the absence of any main effects when the Hardy-Weinberg equilibrium is assumed.…”

“…Forming haplotypes over multiple neighboring loci in one gene can increase the power of gene mapping studies [Zhao et al, 2000;Fallin et al, 2001;Schaid et al, 2002;Zhang et al, 2003], but these methods only work locally in a given genomic region. Although various authors have postulated the need for methods that investigate multiple interacting genes jointly [Tiwari and Elston, 1998;Cox et al, 1999;Templeton, 2000;Wilson, 2001;Cordell et al, 2001;Cordell, 2002;Culverhouse et al, 2002;Moore and Williams, 2002;Moore, 2003], only a few viable approaches in this direction exist [Hoh et al, 2001;Xiong et al, 2002;Potter, 2006;Dudbridge et al, 2006;Ritchie et al, 2001Ritchie et al, , 2003Moore, 2004;Nelson et al, 2001;Culverhouse et al, 2004;Sha et al, 2006;Millstein et al, 2006].…”

“…This group includes the multifactor dimensionality reduction (MDR) method proposed by Ritchie et al [2001Ritchie et al [ , 2003 and reviewed recently by Moore [2004], the combinatorial partitioning method proposed by Nelson et al [2001], the restrict partitioning method proposed by Culverhouse et al [2004], the Combinatorial searching method (CSM) proposed by Sha et al [2006], and the focused interaction testing framework (FITF) proposed by Millstein et al [2006] among others. The methods in this group can search for a set of interacting loci that jointly have significant effects on the disease.…”

Multifactor dimensionality reduction reveals gene–gene interactions associated with multiple sclerosis susceptibility in African Americans

“…Multifactor dimensionality reduction (MDR) is a model-free, non-parametric data reduction method for detecting multilocus genotype combinations that predict disease risk for common, complex disease. Empirical studies with simulated data indicate that MDR has good power to identify high-order gene-gene interactions, 17 and has been proven to be maximally efficient at discriminating between clinical end points using multilocus genotype data. 18 Studies with simulated data (with multiple models of different allele frequencies and heritability) have also shown that MDR has high power to identify interactions in the presence of many types of noise commonly found in real data sets -including missing data and genotyping error.…”

“…18 Studies with simulated data (with multiple models of different allele frequencies and heritability) have also shown that MDR has high power to identify interactions in the presence of many types of noise commonly found in real data sets -including missing data and genotyping error. 17 Additionally, MDR has been shown to have better power to detect interactions than classification and regression trees and stepwise logistic regression (unpublished results).…”