Gene-gene interaction analysis for the survival phenotype based on the Cox model

• Published in: Bioinformatics 2012-09, Vol.28 (18), p.i582-i588
• Main Authors: Lee, Seungyeoun, Kwon, Min-Seok, Oh, Jung Mi, Park, Taesung
• Format: Article
• Language: English
• Subjects: Algorithms; Bioinformatics; Classification; Computation; Computer simulation; Female; Frames; Gene Frequency; Genome-Wide Association Study; Genotype; Humans; Leukemia, Myeloid, Acute - genetics; Leukemia, Myeloid, Acute - mortality; Male; Models, Genetic; Multifactor Dimensionality Reduction - methods; Original Papers; Phenotype; Polymorphism, Single Nucleotide; Proportional Hazards Models; Reduction; Regression; Survival
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/bts415

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. 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. [e-mail]; [e-mail].