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

Regeneron Genetics Center


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
Issue number1
Pages (from-to)5276
Publication statusPublished - 6 Sep 2021


  • Age Factors
  • Biological Specimen Banks
  • Breast Neoplasms/genetics
  • Coronary Artery Disease/genetics
  • Diabetes Mellitus, Type 2/genetics
  • Female
  • Genetic Predisposition to Disease/genetics
  • Humans
  • Inflammatory Bowel Diseases/genetics
  • Machine Learning
  • Male
  • Multifactorial Inheritance/genetics
  • Reproducibility of Results
  • Schizophrenia/genetics
  • Sweden
  • United Kingdom


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