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Photo: KG Pressfoto

Marju Orho-Melander

Professor

Photo: KG Pressfoto

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

Author

  • 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

Summary, in English

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.

Department/s

  • Cardiovascular Research - Translational Studies
  • EXODIAB: Excellence of Diabetes Research in Sweden
  • Diabetes - Molecular Metabolism
  • Nutrition Epidemiology
  • EpiHealth: Epidemiology for Health
  • Diabetes - Cardiovascular Disease
  • Cardiovascular Research - Hypertension
  • Cardiovascular Research - Epidemiology

Publishing year

2021-09-06

Language

English

Pages

5276-5276

Publication/Series

Nature Communications

Volume

12

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Medical Genetics

Status

Published

Research group

  • Cardiovascular Research - Translational Studies
  • Diabetes - Molecular Metabolism
  • Nutrition Epidemiology
  • Diabetes - Cardiovascular Disease
  • Cardiovascular Research - Hypertension
  • Cardiovascular Research - Epidemiology

ISBN/ISSN/Other

  • ISSN: 2041-1723