title: Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies.
abstract: BACKGROUND
  There is increasing interest in investigating genetic risk models in empirical studies, but such studies are premature when the expected predictive ability of the risk model is low. We assessed how accurately the predictive ability of genetic risk models can be estimated in simulated data that are created based on the odds ratios (ORs) and frequencies of single-nucleotide polymorphisms (SNPs) obtained from genome-wide association studies (GWASs).
  
  
  METHODS
  We aimed to replicate published prediction studies that reported the area under the receiver operating characteristic curve (AUC) as a measure of predictive ability. We searched GWAS articles for all SNPs included in these models and extracted ORs and risk allele frequencies to construct genotypes and disease status for a hypothetical population. Using these hypothetical data, we reconstructed the published genetic risk models and compared their AUC values to those reported in the original articles.
  
  
  RESULTS
  The accuracy of the AUC values varied with the method used for the construction of the risk models. When logistic regression analysis was used to construct the genetic risk model, AUC values estimated by the simulation method were similar to the published values with a median absolute difference of 0.02 [range: 0.00, 0.04]. This difference was 0.03 [range: 0.01, 0.06] and 0.05 [range: 0.01, 0.08] for unweighted and weighted risk scores.
  
  
  CONCLUSIONS
  The predictive ability of genetic risk models can be estimated using simulated data based on results from GWASs. Simulation methods can be useful to estimate the predictive ability in the absence of empirical data and to decide whether empirical investigation of genetic risk models is warranted.
authors: Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands.
authors: Suman
authors: S
authors: Kundu
authors: Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands.
authors: Raluca
authors: R
authors: Mihaescu
authors: Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands.
authors: Catherina M C
authors: CM
authors: Meijer
authors: Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands.
authors: Rachel
authors: R
authors: Bakker
authors: Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands ; Department of Epidemiology, Rollins School of Public Health, Emory University Atlanta, GA, USA.
authors: A Cecile J W
authors: AC
authors: Janssens
keywords: AUC; GWAS; genetic; modeling; predictive ability; risk prediction