Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Abstract

Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.23-fold (95% CI: [1.18; 1.29]; P-value < 2 x 10-16) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 3.94 x 10-6, and PRSmix+ improved the prediction accuracy by 1.71-fold (95% CI: [1.48; 1.94]; P-value = 9.98 x 10-10 and 1.41-fold (95% CI: [1.24; 1.58]; P-value = 2.51 x 10-6) in European and South Asian ancestries, respectively. Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Polygenic prediction and gene regulation networks

“…This pattern closely mirrors what is observed in complex human traits [17]. For instance, the prediction accuracy of height, anatomical features being those with best predictions, is around R 2 ∼ 0.2 [30]. This would suggest that genomes are in a regime of low (statistical) prediction (Figure 2).…”

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups

“…Two recent studies, both using simple weighting methods, have shown significant potential for cross-trait borrowing to improve PRS performance for individual traits. 30 , 31 There is, however, likely to be scope for additional improvement by developing formal Bayesian methods that can utilize flexible models for effect-size distribution simultaneously across ancestries and traits.…”

MUSSEL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups

“…PRS-CSx, which allows a heavy-tail Strawderman-Berger prior, while theoretically expected to be advantageous for handling such large-effect SNPs, does not show much advantage either. In the future, other heavy-tail type priors such as the Bayesian Lasso (i.e., Laplacian) 32 , Horseshoe 33 , and Bayesian Bridge 34 , are worth investigating. Another potential limitation of the method originates in the SL step: when the tuning sample is small (e.g., <1000), the prediction algorithms utilized in SL may be overfit in the presence of a large number of tuning parameters, ultimately leading to low predictive power in an independent sample.…”

“…ME-Bayes SL can also be modified to enhance performance of PRS by borrowing information simultaneously across traits and genetically correlated traits. Two recent studies, both using simple weighting methods, have shown significant potential for cross-trait borrowing to improve PRS performance for individual traits 32,33 . There is, however, likely to be scope for additional improvement by developing formal Bayesian methods that can utilize flexible models for effect-size distribution simultaneously across ancestries and traits.…”