The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort.

ABSTRACT: Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating chara

SUBMITTER: Shigemizu D 

PROVIDER: S-EPMC3961382 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature