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

Abstract

Polygenic risk scores (PRS) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across different populations. We propose ME-Bayes SL, a method for ancestry-specific polygenic prediction that borrows information in the summary statistics from genome-wide association studies (GWAS) across multiple ancestry groups. ME-Bayes SL conducts Bayesian hierarchical modeling under a multivariate spike-and-slab model for effect-size distribution and incorporates an ensemble learning step to combine information across different tuning parameter settings and ancestry groups. In our simulation studies and data analyses of 16 traits across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, ME-Bayes SL shows promising performance compared to alternatives. The method, for example, has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African Ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, underlying trait architecture, and the choice of reference samples for LD estimation, and thus ultimately, a combination of methods may be needed to generate the most robust PRS across diverse populations.

Ensembled best subset selection using summary statistics for polygenic risk prediction

“…While a large portion of GWAS data currently comes from individuals of European ancestry, it is well-known that polygenic risk scores do not transfer well between individuals of different ancestries, which can impact their utility for patients of non-European ancestry [64][65][66][67][68][69] . Many methods that utilize summary data from multiple populations have already been proposed and demonstrate improved prediction in under-represented populations 25,29,30,[70][71][72][73][74] . As genetics research moves towards greater diversity, ALL-Sum serves as a valuable foundation for extension to incorporating data from multiple ancestries, as well as admixed individuals [75][76][77] .…”

“…In high-dimensional problems such as PRS which involve a large number of SNPs, the best tuning parameters may be in the gaps or outside the bounds of the fixed grid being considered 23 . Recent works, in both theory and PRS applications, have demonstrated that ensembling multiple predictors can yield better prediction accuracy than choosing a single best predictor from grid search [24][25][26][27][28][29][30] .…”

JointPRS: A Data-Adaptive Framework for Multi-Population Genetic Risk Prediction Incorporating Genetic Correlation

“…In this study, we compared the prediction accuracy of JointPRS with five existing methods for cross-population predictions: PRS-CSx [17], MUSSEL [18], PROSPER [19], SD-PRX [20], and XPASS [21]. These methods can be classified into two groups: multiple population models (including JointPRS, PRS-CSx, MUSSEL, and PROSPER) and two population models (including SDPRX and XPASS).…”

“…To address this need, there has been an increase in the number of GWAS focused on non-European populations [8][9][10][11][12][13][14][15], complemented by the developments of various models tailored for multi-population PRS predictions. These models employ different strategies to leverage multiple GWAS results, including utilizing multiple populations, assuming sparse distributions for genetic risk variants across populations, and accounting for genetic correlations among populations [16][17][18][19][20][21]. However, to our knowledge, there is no method that integrates all these components under a coherent framework in the absence of individual-level validation data -a common situation in real data applications.…”

“…We illustrate the benefits of integrating genetic correlation structures and jointly using multiple populations over exclusive reliance on the target and the auxiliary European groups. In addition, we compare JointPRS with other methods, including PRS-CSx [17], MUSSEL [18], PROSPER [19], SDPRX [20], and XPASS [21], with and without a validation dataset. Our results illustrate the distinct contributions of joint modelling and genetic correlations to prediction accuracy, and suggest JointPRS as a promising method for predicting complex traits across populations with the auto-tuned JointPRS achieving comparable performances as top-performing alternative approaches that require heavy tuning on individual-level validation sets.…”