A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis
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The widespread use of high-throughput methods of single nucleotide polymorphism (SNP) genotyping has created a number of computational and statistical challenges. The problem of identifying SNP–SNP interactions in case–control studies has been studied extensively, and a number of new techniques have been developed. Little progress has been made, however, in the analysis of SNP–SNP interactions in relation to time-to-event data, such as patient survival time or time to cancer relapse. 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 survival analysis. The proposed Survival MDR (Surv-MDR) method handles survival data by modifying MDR’s constructive induction algorithm to use the log-rank test. Surv-MDR replaces balanced accuracy with log-rank test statistics as the score to determine the best models. We simulated datasets with a survival outcome related to two loci in the absence of any marginal effects. We compared Surv-MDR with Cox-regression for their ability to identify the true predictive loci in these simulated data. We also used this simulation to construct the empirical distribution of Surv-MDR’s testing score. We then applied Surv-MDR to genetic data from a population-based epidemiologic study to find prognostic markers of survival time following a bladder cancer diagnosis. We identified several two-loci SNP combinations that have strong associations with patients’ survival outcome. Surv-MDR is capable of detecting interaction models with weak main effects. These epistatic models tend to be dropped by traditional Cox regression approaches to evaluating interactions. With improved efficiency to handle genome wide datasets, Surv-MDR will play an important role in a research strategy that embraces the complexity of the genotype–phenotype mapping relationship since epistatic interactions are an important component of the genetic basis of disease.
KeywordsSingle Nucleotide Polymorphism Bladder Cancer Multifactor Dimensionality Reduction Genotype Combination Balance Accuracy
This work was funded by grant #IRG-82-003-22 from the American Cancer Society and NIH grants LM009012, LM010098, AI59694, CA078609, CA121382, CA102327, CA57494 and ES007373.
- He H, Oetting WS, Brott MJ, Basu S (2009) Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene interaction in a case-control study. BMC Med Genet 10:127Google Scholar
- Huang J, Lin A, Narasimhan B, Quertermous T, Hsiung CA, Ho LT, Grove JS, Olivier M, Ranade K, Risch NJ, Olshen RA (2004) Tree-structured supervised learning and the genetics of hypertension. PNAS 101:10529–10534Google Scholar
- Moore JH (2007) Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. In: Zhu X, Davidson I (eds) Knowledge discovery and data mining: challenges and realities with real world data. IGI Press, Hershey, pp 17–30Google Scholar
- Yan L, Verbel D, Saidi O (2004) Predicting prostate cancer recurrence via maximizing the concordance index. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, pp 479–485Google Scholar