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Gene-Gene Interaction Analysis for the Survival Phenotype Based on the Kaplan-Meier Median Estimate.

In this study, we propose a simple and computationally efficient method based on the multifactor dimensional reduction algorithm to identify gene-gene interactions associated with the survival phenotype. The proposed method, referred to as KM-MDR, uses the Kaplan-Meier median survival time as a clas...

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Published in:BioMed Research International pp. 1 - 11
Main Authors: Park, Mira, Lee, Jung Wun, Park, Taesung, Lee, SeungYeoun
Format: equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 5/9/2020
Online Access:View this record in EBSCOhost
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        23146133
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      jtl: BioMed Research International
      issn: 23146133
      maglogo: N
    pubinfo:
      dt: 5/9/2020
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        143137497
        143137497
        143137497
        10.1155/2020/5282345
        143137497
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      tig:
        atl: Gene-Gene Interaction Analysis for the Survival Phenotype Based on the Kaplan-Meier Median Estimate.
      aug:
        au:
          Park, Mira
          Lee, Jung Wun
          Park, Taesung
          Lee, SeungYeoun
        affil: Department of Preventive Medicine, Eulji University, Daejeon, Republic of Korea
      sug:
        subj:
          Gene Expression Profiling
          Survival Analysis
          Phenotype Evaluation
          Kaplan-Meier Estimator
          Computer Simulation
          Models, Statistical
          Human
          Algorithms
          Genotype Evaluation
          Risk Assessment
          Log-Rank Test
          Ovarian Neoplasms
          Cancer Patients
      ab: In this study, we propose a simple and computationally efficient method based on the multifactor dimensional reduction algorithm to identify gene-gene interactions associated with the survival phenotype. The proposed method, referred to as KM-MDR, uses the Kaplan-Meier median survival time as a classifier. The KM-MDR method classifies multilocus genotypes into a binary attribute for high- or low-risk groups using median survival time and replaces balanced accuracy with log-rank test statistics as a score to determine the best model. Through intensive simulation studies, we compared the power of KM-MDR with that of Surv-MDR, Cox-MDR, and AFT-MDR. It was found that KM-MDR has a similar power to that of Surv-MDR, with less computing time, and has comparable power to that of Cox-MDR and AFT-MDR, even when there is a covariate effect. Furthermore, we apply KM-MDR to a real dataset of ovarian cancer patients from The Cancer Genome Atlas (TCGA).
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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