| Title: | Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction |
| Journal: | Nature Communications |
| Published: | 6 Sep 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34489429/ |
| DOI: | https://doi.org/10.1038/s41467-021-25014-7 |
| URL: | https://www.nature.com/articles/s41467-021-25014-7.pdf |
| Citations: | 18 (10 in last 2 years) as of 8 Aug 2024 |
Publication 10044
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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.</p>
13 Authors
- Jiangming Sun
- Yunpeng Wang
- Lasse Folkersen
- Yan Borné
- Inge Amlien
- Alfonso Buil
- Marju Orho-Melander
- Anders D. Børglum
- David M. Hougaard
- Olle Melander
- Gunnar Engström
- Thomas Werge
- Kasper Lage
1 Application
| Application ID | Title |
|---|---|
| 32048 | Lifespan Changes in Brain and Cognition- Identifying risk and protective factors. |
Enabling scientific discoveries that improve human health