Transferability of European-derived cardiometabolic polygenic risk scores in the South Asians and their interplay with family history

Background & Aims We aimed to investigate the effect of polygenic risk scores (PRSs) derived from individuals of European (EUR) ancestry on common diseases among individuals of South Asian (SAS) ancestry in the UK Biobank (UKB). Additionally, we studied the interaction between PRS and family history (FH) in the same population. Methods To calculate the PRS, we used a previously published panel of SNPs derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. We applied the PRS using summary statistics from genome-wide association studies (GWAS) for cardiometabolic and lifestyle diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D). Each PRS was adjusted according to an individual’s predicted genetic ancestry to derive an adjusted PRS (aPRS). We calculated the percentiles based on aPRS and divided them according to the percentiles into three categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Results The risk of developing severe obesity for individuals of SAS ancestry was almost threefold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 3.67 (95% CI = 2.47-5.48, P < 0.01). While the risk of severe obesity was lower in the low-aPRS group (OR = 0.19, CI = 0.05–0.52, P < 0.01). Comparable results were found in the EUR data, where the low-PRS group had an OR of 0.26 (95% CI= 0.24-0.3, P < 0.01) and the high-PRS group had an OR of 3.2 (95% CI = 3.1-3.3, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS exhibit further higher risk to these diseases, thereby implying a greater genetic predisposition to these conditions. Conclusion Our findings suggest that using CAD, obesity, and T2D GWAS summary statistics predominantly from the EUR population have sufficient power to identify SAS individuals with higher genetic risk. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, we believe that the predictive power of PRS would improve.