Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study

OBJECTIVE—Prediction of type 2 diabetes based on genetic testing might improve identification of high-risk subjects. Genome-wide association (GWA) studies identified multiple new genetic variants that associate with type 2 diabetes. The predictive value of genetic testing for prediction of type 2 diabetes in the general population is unclear.RESEARCH DESIGN AND METHODS—We investigated 18 polymorphisms from recent GWA studies on type 2 diabetes in the Rotterdam Study, a prospective, population-based study among homogeneous Caucasian individuals of 55 years and older (genotyped subjects, n = 6,544; prevalent cases, n = 686; incident cases during follow-up, n = 601; mean follow-up 10.6 years). The predictive value of these polymorphisms was examined alone and in addition to clinical characteristics using logistic and Cox regression analyses. The discriminative accuracy of the prediction models was assessed by the area under the receiver operating characteristic curves (AUCs).RESULTS—Of the 18 polymorphisms, the ADAMTS9, CDKAL1, CDKN2A/B-rs1412829, FTO, IGF2BP2, JAZF1, SLC30A8, TCF7L2, and WFS1 variants were associated with type 2 diabetes risk in our population. The AUC was 0.60 (95% CI 0.57–0.63) for prediction based on the genetic polymorphisms; 0.66 (0.63–0.68) for age, sex, and BMI; and 0.68 (0.66–0.71) for the genetic polymorphisms and clinical characteristics combined.CONCLUSIONS—We showed that 9 of 18 well-established genetic risk variants were associated with type 2 diabetes in a population-based study. Combining genetic variants has low predictive value for future type 2 diabetes at a population-based level. The genetic polymorphisms only marginally improved the prediction of type 2 diabetes beyond clinical characteristics.Type 2 diabetes is a multifactorial disease caused by a complex interplay of multiple genetic variants and many environmental factors. With the recent genome-wide association (GWA) studies, the number of replicated common genetic variants associated with type 2 diabetes has rapidly increased (1–7). A total of 18 polymorphisms have been firmly replicated (1–7). It is unclear whether and how the currently known genetic variants can be used in practice, because the combined effect of these variants has not been investigated in a population-based study. Particularly, because most GWA studies were enriched for patients with a positive family history and early onset of the disease, association of these variants to type 2 diabetes risk in the general population, including elderly individuals, remains to be determined.Because complex diseases are caused by multiple genetic variants, predictive testing based on a single genetic marker will be of limited value (8,9). Simulation studies suggest that the predictive value could be improved by combining multiple common low-risk variants (10–13). Several empirical studies on the predictive value of genetic polymorphisms have been conducted before the recent GWA data were available (14–16). In a case-control study, Weedon et al. (16) showed that combining the information of three polymorphisms improved disease prediction, albeit to a limited extent. Vaxillaire et al. (15) investigated 19 polymorphisms and found that the predictive value was low compared with clinical characteristics.Genetic variants associated with risk of type 2 diabetes could potentially be useful for the prediction, prevention, and early treatment of the disease. We investigated whether combining the currently known and well-replicated genetic variants predicts type 2 diabetes in the Rotterdam Study, a prospective population-based follow-up study. We investigated whether these genetic variants improve prediction beyond clinical characteristics.