The Construction of Risk Prediction Models Using GWAS Data and Its Application to a Type 2 Diabetes Prospective Cohort

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 characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves ([Image: see text]). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.