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

Sun, Jiangming LU orcid ; Wang, Yunpeng ; Folkersen, Lasse ; Borné, Yan LU ; Amlien, Inge ; Buil, Alfonso ; Orho-Melander, Marju LU ; Børglum, Anders D ; Hougaard, David M and Melander, Olle LU orcid , et al. (2021) In Nature Communications 12. p.5276-5276
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Genetic Predisposition to Disease, Machine Learning
in
Nature Communications
volume
12
pages
5276 - 5276
publisher
Nature Publishing Group
external identifiers
  • scopus:85114717685
  • pmid:34489429
ISSN
2041-1723
DOI
10.1038/s41467-021-25014-7
language
English
LU publication?
yes
id
57a7683a-e8e0-4228-8e35-bed47c93cdb9
date added to LUP
2021-09-14 10:22:56
date last changed
2024-04-20 11:18:58
@article{57a7683a-e8e0-4228-8e35-bed47c93cdb9,
  abstract     = {{<p>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>}},
  author       = {{Sun, Jiangming and Wang, Yunpeng and Folkersen, Lasse and Borné, Yan and Amlien, Inge and Buil, Alfonso and Orho-Melander, Marju and Børglum, Anders D and Hougaard, David M and Melander, Olle and Engström, Gunnar and Werge, Thomas and Lage, Kasper}},
  issn         = {{2041-1723}},
  keywords     = {{Genetic Predisposition to Disease; Machine Learning}},
  language     = {{eng}},
  month        = {{09}},
  pages        = {{5276--5276}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction}},
  url          = {{http://dx.doi.org/10.1038/s41467-021-25014-7}},
  doi          = {{10.1038/s41467-021-25014-7}},
  volume       = {{12}},
  year         = {{2021}},
}