Abstract
Motivation: For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP–SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene–gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) method to the survival phenotype by using the log-rank test for defining a binary attribute. However, the Surv-MDR method has some drawbacks in the sense that it needs more intensive computations and does not allow for a covariate adjustment. In this article, we propose a new approach, called Cox-MDR, which is an extension of the generalized multifactor dimensionality reduction (GMDR) to the survival phenotype by using a martingale residual as a score to classify multi-level genotypes as high- and low-risk groups. The advantages of Cox-MDR over Surv-MDR are to allow for the effects of discrete and quantitative covariates in the frame of Cox regression model and to require less computation than Surv-MDR.Results: Through simulation studies, we compared the power of Cox-MDR with those of Surv-MDR and Cox regression model for various heritability and minor allele frequency combinations without and with adjusting for covariate. We found that Cox-MDR and Cox regression model perform better than Surv-MDR for low minor allele frequency of 0.2, but Surv-MDR has high power for minor allele frequency of 0.4. However, when the effect of covariate is adjusted for, Cox-MDR and Cox regression model perform much better than Surv-MDR. We also compared the performance of Cox-MDR and Surv-MDR for a real data of leukemia patients to detect the gene–gene interactions with the survival time.Contact: leesy@sejong.ac.kr; tspark@snu.ac.kr
Highlights
Massive amounts of information for single-nucleotide polymorphisms (SNPs) across the whole genome have become available from high-throughput technology, which allows genomewide association studies (GWAS) to be performed
We propose a new approach, called the Cox-multifactor dimensionality reduction (MDR) method, which is an extension of generalized multifactor dimensionality reduction (GMDR) to the survival time using the martingale residual as a score obtained from a Cox model
We compare the power of Cox-MDR with those of Surv-MDR and Cox regression model without and with adjusting for covariates
Summary
Massive amounts of information for single-nucleotide polymorphisms (SNPs) across the whole genome have become available from high-throughput technology, which allows genomewide association studies (GWAS) to be performed. As recently reviewed by Manolio (2010), nearly 600 genome-wide association studies covering 150 distinct diseases and traits have been reported, with nearly 800 SNP-trait associations reported as significant under P < 5 × 10−8. In early GWAS, statistical methods for identifying susceptibility have considered a single SNP at a time and have selected a subset of the top few SNPs from a ranked list of SNPs. replication studies have been implemented to determine whether these associations held for other samples. Some of the replication studies, show that significant associations are not found from the top ranked list This single-locus approach has been moved into a multiple-loci approach because most complex diseases are associated with multiple genes and their interactions. The traditional parametric approach, such as the logistic regression model, has limited power in detecting non-linear patterns of interaction and needs a large amount of study samples when multiple SNPs and gene–gene interactions are considered
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