title: Gene-gene interaction analysis for the survival phenotype based on the Cox model.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  leesy@sejong.ac.kr; tspark@snu.ac.kr.
authors.affiliation: Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Korea. leesy@sejong.ac.kr
authors.forename: Seungyeoun
authors.initials: S
authors.lastname: Lee
authors.forename: Min-Seok
authors.initials: MS
authors.lastname: Kwon
authors.forename: Jung Mi
authors.initials: JM
authors.lastname: Oh
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
mesh_terms: D000465:Algorithms; D005260:Female; D005787:Gene Frequency; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics / Q000401:mortality; D008297:Male; D008957:Models, Genetic; D058011:Multifactor Dimensionality Reduction / Q000379:methods*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D016016:Proportional Hazards Models