Human Genetics

, Volume 129, Issue 1, pp 101–110 | Cite as

A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis

  • Jiang Gui
  • Jason H. Moore
  • Karl T. Kelsey
  • Carmen J. Marsit
  • Margaret R. Karagas
  • Angeline S. AndrewEmail author
Original Investigation


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.


Single Nucleotide Polymorphism Bladder Cancer Multifactor Dimensionality Reduction Genotype Combination Balance Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.

Supplementary material

439_2010_905_MOESM1_ESM.docx (18 kb)
Supplementary material 1 (DOCX 18 kb)


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Jiang Gui
    • 1
  • Jason H. Moore
    • 1
    • 2
    • 3
    • 4
    • 5
    • 8
  • Karl T. Kelsey
    • 6
  • Carmen J. Marsit
    • 7
  • Margaret R. Karagas
    • 1
  • Angeline S. Andrew
    • 1
    Email author
  1. 1.Department of Community and Family MedicineNorris-Cotton Cancer Center, Dartmouth Medical SchoolLebanonUSA
  2. 2.Department of Genetics, Computational Genetics LaboratoryDartmouth Medical SchoolLebanonUSA
  3. 3.Department of Computer ScienceUniversity of New HampshireDurhamUSA
  4. 4.Department of Computer ScienceUniversity of VermontBurlingtonUSA
  5. 5.Department of Psychiatry and Human BehaviorBrown UniversityProvidenceUSA
  6. 6.Department of Community HealthBrown UniversityProvidenceUSA
  7. 7.Department of Bio-Medical Pathology and Laboratory MedicineBrown UniversityProvidenceUSA
  8. 8.Translational Genomics Research InstitutePhoenixUSA

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