Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus

Y. M. Cho, M. D. Ritchie, J. H. Moore, J. Y. Park, K. U. Lee, H. D. Shin, H. K. Lee, K. S. Park

Research output: Contribution to journalArticlepeer-review

168 Scopus citations


Aims/hypothesis. Type 2 diabetes mellitus is a complex genetic disease, which results from interactions between multiple genes and environmental factors without any single factor having strong independent effects. This study was done to identify gene to gene interactions which could be associated with the risk of Type 2 diabetes. Methods. We genotyped 23 different loci in the 15 candidate genes of Type 2 diabetes in 504 unrelated Type 2 diabetic patients and 133 non-diabetic control subjects. We analysed gene to gene interactions among 23 polymorphic loci using the multifactor-dimensionality reduction (MDR) method, which has been shown to be effective for detecting and characterising gene to gene interactions in case-control studies with relatively small samples. Results. The MDR analysis showed a significant gene to gene interaction between the Ala55Val polymorphism in the uncoupling protein 2 gene (UCP2) and the 161C>T polymorphism in the exon 6 of peroxisome proliferator-activated receptor γ (PPARγ) gene. This interaction showed the maximum consistency and minimum prediction error among all gene to gene interaction models evaluated. Moreover, the combination of the UCP2 55 Ala/Val heterozygote and the PPARγ 161 C/C homozygote was associated with a reduced risk of Type 2 diabetes (odds ratio: 0.51, 95% CI: 0.34 to 0.77, p=0.0016). Conclusions/interpretation. Using the MDR method, we showed a two-locus interaction between the UCP2 and PPARγ genes among 23 loci in the candidate genes of Type 2 diabetes. The determination of such genotype combinations contributing to Type 2 diabetes mellitus could provide a new tool for identifying high-risk individuals.

Original languageEnglish (US)
Pages (from-to)549-554
Number of pages6
Issue number3
StatePublished - Mar 2004

All Science Journal Classification (ASJC) codes

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism


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