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SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics
Motivation: Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training data sets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods fordoi:10.1101/359463 fatcat:fxvig4p6t5hqrlcfincm4qzxca