title: Development of a predictive model for type 2 diabetes mellitus using genetic and clinical data.
abstract: OBJECTIVE
  Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.
  
  
  METHODS
  We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.
  
  
  RESULTS
  We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2-52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.
  
  
  CONCLUSIONS
  The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our "multistage adjustment" model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.
authors.affiliation: Division of Structural and Functional Genomics, Korea National Institute, Osong, Korea.
authors.forename: Juyoung
authors.initials: J
authors.lastname: Lee
authors.forename: Bhumsuk
authors.initials: B
authors.lastname: Keam
authors.forename: Eun Jung
authors.initials: EJ
authors.lastname: Jang
authors.forename: Mi Sun
authors.initials: MS
authors.lastname: Park
authors.forename: Ji Young
authors.initials: JY
authors.lastname: Lee
authors.forename: Dan Bi
authors.initials: DB
authors.lastname: Kim
authors.forename: Chang-Hoon
authors.initials: CH
authors.lastname: Lee
authors.forename: Tak
authors.initials: T
authors.lastname: Kim
authors.forename: Bermseok
authors.initials: B
authors.lastname: Oh
authors.forename: Heon Jin
authors.initials: HJ
authors.lastname: Park
authors.forename: Kyu-Bum
authors.initials: KB
authors.lastname: Kwack
authors.forename: Chaeshin
authors.initials: C
authors.lastname: Chu
authors.forename: Hyung-Lae
authors.initials: HL
authors.lastname: Kim
keywords: classification; early predictive model; single nucleotide polymorphism (SNP); type 2 diabetes mellitus (T2DM)