A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

Jiang Gui,Pim Van Der Harst,Gerjan Navis,Folkert W Asselbergs,Scott M Williams,Jason H Moore,Hans L Hillege,Diane Gilbert-Diamond,Peter Andrews,Wiek H Van Gilst,Momiao Xiong

doi:10.1371/journal.pone.0066545

Abstract

We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR’s constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDR’s testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study.

Highlights

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 [1]
We present an extension of the multifactor dimensionality reduction (MDR) algorithm to detect and characterize epistatic interactions in the context of a quantitative outcome (QMDR)
We present the results of Quantitative MDR (QMDR) applied to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study

Summary

Introduction

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 [1]. The recent advances in high-throughput genotyping techniques have made large quantities of genotype data commonplace in genetic epidemiology studies and have enabled researchers to interrogate the entire genome. Researchers have extensively analyzed single SNP effects for a wide variety of diseases/phenotpyes with variable results, but in most cases with a large proportion of the genetic component (heritability) left unexplained. 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 [3,4,5]. 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 [3]. Comparative studies [6,7] that use extensive simulations show that MDR has the best performance when the true multi-SNP effects are nonadditive [2,3,7,8,9,10,11,12]

Methods

Results

Conclusion