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

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 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. This improvement is enhanced in settings with low target sample sizes and in semi-simulated settings. Furthermore, MPP-PRS+ produces better or comparable PRS predictions than state-of-the-art methods across 12 out of 16 evaluated quantitative and binary traits in UK Biobank, with the exception being 4 lipid-related traits. This performance trend is promising and encourages application of MultiPopPred for reliable PRS estimation in low-resource populations with individual-level data for complex omnigenic traits.