Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction
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- Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction
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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.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 5276 |
| Tidsskrift | Nature Communications |
| Vol/bind | 12 |
| ISSN | 2041-1723 |
| DOI | |
| Status | Udgivet - 2021 |
Bibliografisk note
Funding Information:
This research has been conducted using the UK Biobank Resource with application number 32048. We thank Dr. Nadine Fornelos Martins (Broad Institute) for her valuable comments and support during the elaboration of this manuscript. J.S. was supported by a grant from Lundbeck foundation (no. 2016-721). Y.W. was supported by the mobility grant from the Research Council of Norway (no. 251134), Young Talented Research grant (no.302854), and UiO:Life Science: Convergence Environment (4MENT), University of Oslo, Norway. K.L. was supported by grants from the Stanley Center for Psychiatric Research, the National Institute of Mental Health (R01 MH109903 and U01 MH121499), the Simons Foundation Autism Research Initiative (awards 515064 and 735604), the Lundbeck Foundation (R223-2016-721 and R350-2020-963), the National Institute of Diabetes and Digestive and Kidney Diseases (U01 DK078616), and a Broad Next10 grant. The genotyping of the iPSYCH samples was supported by grants from the Lundbeck Foundation, the Stanley Foundation, the Simons Foundation (SFARI 311789), and NIMH (5U01MH094432-02). Part of the computation was performed on the Norwegian high-performance computation resources, sigma2, through project no. NN9767K.
Publisher Copyright:
© 2021, The Author(s).
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