Evaluation of phenotyping errors on polygenic risk score predictions

Accurate disease risk prediction is essential in healthcare to provide personalized disease prevention and treatment strategies not only to the patients, but also to the general population. In addition to demographic and environmental factors, advancements in genomic research have revealed that genetics play an important role in determining the susceptibility of diseases. However, for most complex diseases, individual genetic variants are only weakly to moderately associated with the diseases. Thus, they are not clinically informative in determining disease risks. Nevertheless, recent findings suggest that the combined effects from multiple disease-associated variants, or polygenic risk score (PRS), can stratify disease risk similar to that of rare monogenic mutations. The development of polygenic risk score provides a promising tool to evaluate the genetic contribution of disease risk; however, the quality of the risk prediction depends on many contributing factors including the precision of the target phenotypes. In this study, we evaluated the impact of phenotyping errors on the accuracies of PRS risk prediction. We utilized electronic Medical Records and Genomics Network (eMERGE) data to simulate various types of disease phenotypes. For each phenotype, we quantified the impact of phenotyping errors generated from the differential and non-differential mechanism by comparing the prediction accuracies of PRS on the independent testing data. In addition, our results showed that the rate of accuracy degradation depended on both the phenotype and the mechanism of phenotyping error.