Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction.

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

SUBMITTER: Sun J 

PROVIDER: S-EPMC8421428 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature