Department of Economics and Business Economics

Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification

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  • Yi Ding, University of California at Los Angeles
  • ,
  • Kangcheng Hou, University of California at Los Angeles
  • ,
  • Kathryn S. Burch, University of California at Los Angeles
  • ,
  • Sandra Lapinska, University of California at Los Angeles
  • ,
  • Florian Privé
  • Bjarni Vilhjálmsson
  • Sriram Sankararaman, University of California at Los Angeles
  • ,
  • Bogdan Pasaniuc, University of California at Los Angeles

Although the cohort-level accuracy of polygenic risk scores (PRSs)-estimates of genetic value at the individual level-has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of an individual's PRS and can yield well-calibrated credible intervals via posterior sampling. For 13 real traits in the UK Biobank (n = 291,273 unrelated 'white British'), we observe large variances in individual PRS estimates which impact interpretation of PRS-based stratification; averaging across traits, only 0.8% (s.d. = 1.6%) of individuals with PRS point estimates in the top decile have corresponding 95% credible intervals fully contained in the top decile. We provide an analytical estimator for the expectation of individual PRS variance as a function of SNP heritability, number of causal SNPs and sample size. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.

Original languageEnglish
JournalNature Genetics
Volume54
Issue1
Pages (from-to)30–39
Number of pages10
ISSN1061-4036
DOIs
Publication statusPublished - Jan 2022

    Research areas

  • ACCURACY, COMPLEX TRAITS, DISEASE, ERROR, MODELS, PREDICTION, REGRESSION, TRAJECTORIES

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