MultiPopPred: A Trans-Ethnic Disease Risk Prediction Method, and its Application to the South Asian Population

Abstract

Genome-wide association studies (GWAS) have guided significant contributions towards identifying disease associated Single Nucleotide Polymorphisms (SNPs) in Caucasian populations, albeit with limited focus on other understudied low-resource non-Caucasian populations. There have been active efforts over the years to understand and exploit the population specific versus shared aspects of the genotype-phenotype relation across different populations or ethnicities to bridge this gap. However, the efficacy of transfer learning models that are simpler than existing approaches and utilize individual-level data remains an open question. We propose MultiPopPred, a novel and simple trans-ethnic polygenic risk score (PRS) estimation method that taps into the shared genetic risk across populations and transfers information learned from multiple well-studied auxiliary populations to a less-studied target population. The default version of MultiPopPred (MPP-PRS+) harnesses individual-level data using a specially designed Nesterov-smoothed penalized shrinkage model and an L-BFGS optimization routine. Extensive comparative analyses performed on simulated genotype-phenotype data, assuming an infinitesimal/omnigenic model, reveal that MPP-PRS+ improves PRS prediction in the South Asian population by 38% on average across all simulation settings when compared to state-of-the-art trans-ethnic PRS estimation methods including SBayesRC-Multi and PROSPER. This improvement is enhanced in settings with low target sample sizes and in semi-simulated settings. These predictive advantages are further echoed in real-world evaluations against 16 UK Biobank quantitative and binary traits. For example, compared to SBayesRC-Multi, MPP-PRS+ achieves comparable performance (within 5% of SOTA performance) on 3 traits, superior performance on 7 other traits, and lags behind on 4 (lipid-related) traits. Neither method could meaningfully predict the remaining 2 traits. MPP-PRS+ maintains a similarly competitive, albeit distinct, performance trend compared to PROSPER. These performance trends are promising and encourage application of MultiPopPred for reliable PRS estimation in low-resource populations with individual-level data for complex omnigenic traits.