Kamal, R.; Narayanan, M.

Genome-wide association studies (GWAS) aimed at estimating the disease risk of genetic factors have long been focusing on homogeneous Caucasian populations, at the expense of other understudied non-Caucasian populations. Therefore, active efforts are underway to understand the differences and commonalities in exhibited disease risk across different populations or ethnicities. There is, consequently, a pressing need for computational methods that efficiently exploit these population specific vs. shared aspects of the genotype-phenotype relation. We propose MultiPopPred, a novel 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. MultiPopPred employs a specially designed Nesterov-smoothed penalized shrinkage model and a L-BFGS optimization routine. We present three variants of MultiPopPred based on the availability of individual-level vs. summary-level data and the weightage of each auxiliary population. Extensive comparative analyses performed on simulated genotype-phenotype data reveal that MultiPopPred improves PRS prediction in the South Asian population by 65% on settings with low target sample sizes and by 21% overall across all simulation settings, when compared to state-of the-art trans-ethnic PRS estimation methods. This performance trend is promising and encourages application and further assessment of MultiPopPred under other simulation and real-world settings.