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, Olle Melander, Gunnar Engström, Thomas Werge and Kasper Lage ()
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Jiangming Sun: Mental Health Center Sct. Hans, Mental Health Services Copenhagen
Yunpeng Wang: University of Oslo
Lasse Folkersen: Mental Health Center Sct. Hans, Mental Health Services Copenhagen
Yan Borné: Malmö, Lund University
Inge Amlien: University of Oslo
Alfonso Buil: Mental Health Center Sct. Hans, Mental Health Services Copenhagen
Marju Orho-Melander: Malmö, Lund University
Anders D. Børglum: The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH)
David M. Hougaard: The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH)
Olle Melander: Malmö, Lund University
Gunnar Engström: Malmö, Lund University
Thomas Werge: Mental Health Center Sct. Hans, Mental Health Services Copenhagen
Kasper Lage: Mental Health Center Sct. Hans, Mental Health Services Copenhagen

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25014-7

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DOI: 10.1038/s41467-021-25014-7

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