number of included loci. Testing for association between disease status and polygenic score was performed in the target sample using a logistic regression approach and the following two models: a) using disease status as a response variable, and the polygenic score and the first two principal components from the PCA as predictors; and b) the same model but with exclusion of the polygenic score from the predictor list. These two models were then compared to determine whether inclusion of the polygenic score in the model significantly improved fit. In a second step, the score based analysis was recalculated by exchanging the target and discovery samples in order to cross-validate the results. Since the size of the discovery sample is essential for obtaining an accurate prediction (Purcell et al. 2009), the total German sample was used as a discovery sample for the subsequent score based analyses of the COGA, SAGE, and OZ-ALC samples.
