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Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification
Yi Ding1, Kangcheng Hou2, Kathryn S Burch3
1Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA. yiding920@ucla.edu.
Nature Genetics
|December 21, 2021
Summary
Estimating polygenic risk scores (PRSs) for individuals has significant uncertainty. Bayesian methods quantify this PRS variance, revealing that most credible intervals span multiple risk deciles, impacting genetic stratification accuracy.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Polygenic risk scores (PRSs) are widely used to estimate individual genetic predisposition.
- While cohort-level accuracy of PRSs is established, the uncertainty of individual-level PRS estimates is less understood.
- This underexplored uncertainty can affect the interpretation and application of PRSs in personalized medicine and genetic studies.
Purpose of the Study:
- To investigate and quantify the uncertainty associated with individual-level polygenic risk score (PRS) estimates.
- To evaluate the utility of Bayesian PRS methods in estimating PRS variance and generating credible intervals.
- To assess the impact of PRS uncertainty on the stratification of individuals based on genetic risk.
Main Methods:
- Utilized Bayesian PRS methods to estimate the variance of individual PRS.
- Employed posterior sampling to generate well-calibrated credible intervals for PRS.
- Analyzed 13 real traits in a large UK Biobank cohort (n=291,273) of White British ancestry.
- Developed an analytical estimator for expected individual PRS variance based on SNP heritability, number of causal SNPs, and sample size.
Main Results:
- Observed substantial variances in individual PRS estimates across 13 traits, impacting PRS-based stratification.
- On average, only 0.8% of individuals with PRS point estimates in the top decile had 95% credible intervals fully contained within that decile.
- Demonstrated that Bayesian PRS methods effectively estimate PRS variance and provide calibrated credible intervals.
Conclusions:
- Individual-level PRS estimates possess significant uncertainty that must be considered.
- Bayesian approaches offer a robust framework for quantifying PRS uncertainty via variance estimation and credible intervals.
- Incorporating PRS uncertainty is crucial for accurate interpretation and application in downstream genetic analyses and risk prediction.
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