Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction
Jiangming Sun, Yunpeng Wang, Lasse Folkersen, Yan Borné, Inge K. Amlien, Alfonso Buil, Marju Orho‐Melander, Anders D. Børglum, David M. Hougaard, Luca A. Lotta, Marcus B. Jones, Aris Baras, Olle Melander, Gunnar Engström, Thomas Werge, Kasper Lage
Abstract
A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual's disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.