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

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  • Jiangming Sun, Mental Health Center Sct. Hans, H. Lundbeck A/S, Lund University Diabetes Centre
  • ,
  • Yunpeng Wang, University of Oslo
  • ,
  • Lasse Folkersen, Mental Health Center Sct. Hans, H. Lundbeck A/S
  • ,
  • Yan Borné, Lund University Diabetes Centre
  • ,
  • Inge Amlien, University of Oslo
  • ,
  • Alfonso Buil, Mental Health Center Sct. Hans, H. Lundbeck A/S
  • ,
  • Marju Orho-Melander, Lund University Diabetes Centre
  • ,
  • Anders D. Børglum
  • David M. Hougaard, H. Lundbeck A/S, Statens Serum Institut
  • ,
  • Luca Andrea Lotta, Regeneron Pharmaceuticals, Inc.
  • ,
  • Marcus Jones, Regeneron Pharmaceuticals, Inc.
  • ,
  • Aris Baras, Regeneron Pharmaceuticals, Inc.
  • ,
  • Olle Melander, Lund University Diabetes Centre
  • ,
  • Gunnar Engström, Lund University Diabetes Centre
  • ,
  • Thomas Werge, Mental Health Center Sct. Hans, H. Lundbeck A/S, University of Copenhagen
  • ,
  • Kasper Lage, Mental Health Center Sct. Hans, Broad Institute, Harvard University
  • ,
  • Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc.

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
Article number5276
JournalNature Communications
Volume12
Issue1
ISSN2041-1723
DOIs
Publication statusPublished - Dec 2021

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