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

Jiangming Sun, Yunpeng Wang, Lasse Folkersen, Yan Borné, Inge Amlien, Alfonso Buil, Marju Orho-Melander, Anders D Børglum, David M Hougaard, Regeneron Genetics Center, Olle Melander, Gunnar Engström, Thomas Werge, Kasper Lage

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)5276
JournalNature Communications
Volume12
DOIs
Publication statusPublished - 2021 Sep 6

Subject classification (UKÄ)

  • Medical Genetics

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