Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

Abstract

BackgroundGenome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs) is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS) however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed.ResultsWe adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS) for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units) technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium) data.ConclusionsBased on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

Genome-wide interaction analysis reveals replicated epistatic effects on brain structure

“…The EPISIS software (Ueki and Tamiya, 2012) is an implementation of the machine-learning algorithm called sure independence screening (SIS) developed by Fan and Lv (2008). The SIS algorithm is a correlation learning method that can be applied to ultra-high dimensional datasets where the number of predictors p is much greater than the number of observations n .…”

“…EPISIS utilizes the massively parallel processing available in GPGPU (General-purpose computing on graphics processing units) framework to test p ( p -1)/2 SNP-SNP interactions in the ADNI dataset in a feasible timeframe. We used the SIS algorithm with cell-wise dummy coding (CDC; Ueki and Tamiya, 2012) to reduce the full predictor space into a subset d of n/ln(n) interaction terms (Fan and Lv, 2008). In our dataset n = 737 so in this case d = 111 SNP-SNP pairs.…”

“…The subset of predictors are chosen based on the strength of the correlation of a SNP pair with the outcome measure. The SNP pairs are coded into a contingency table using cell-wise dummy coding (see Ueki and Tamiya 2012) so that the relationship of a SNP pair with the outcome measure can be estimated in a single correlation test. By selecting the set of predictors with the greatest correlation with the phenotype, by definition that set maximizes the variance explained by a subset of SNPs of size d. After screening the full set of possible two-way SNP-SNP interactions, we applied ridge regression (Hoerl, 1962; Kohannim et al, 2011) to the subset of interaction terms (the multiplicative loading of each SNP-SNP pair) and selected significant SNP-SNP interaction terms using the extended Bayesian Information Criterion (EBIC; Chen and Chen, 2008) with γ = 0.5.…”

“…The extended BIC does not provide P-values and classical significance measures, but instead provides a cut off that controls the Type 1 error rate and selects the most parsimonious model based on the number of predictors in a model and also the total error variance (see Chen and Chen, 2008). The choice of the parameter γ was chosen based on simulations to control the false positive rate in SNP-SNP interaction testing across the genome (Ueki and Tamiya, 2012). A single exhaustive search of the genome-wide, SNP-SNP interactome with EPISIS was completed in 7 hours (using one NVIDIA Tesla C2050 GPU card).…”

Genome-wide gene–gene interaction analysis for next-generation sequencing

“…1 Interaction analysis provides a complementary strategy to the genome-wide association studies (GWAS). 2,3 Many statistical methods including logistic regression and linkage disequilibrium (LD)-based methods have been developed to detect interaction. 2,[4][5][6][7][8] However, these methods were originally designed to detect interaction for common variants and are difficult to apply to rare variants because of their high type 1 error rates and low power to detect interaction between rare variants.…”