Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations

Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRS AI ) and Europeans (PRS EU ) using 13,974 AI individuals. Methods: Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS). Results: Both genetic models (PRS AI and PRS EU ) successfully predicted the T2D risk. However, the PRS AI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63–1.97; p = 1.6 × 10 −152 ] and 12.2% OR 1.38 (95% CI 1.30–1.46; p = 7.1 × 10 −237 ) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRS AI showed about two-fold OR 20.73 (95% CI 10.27–41.83; p = 2.7 × 10 −17 ) and 1.4-fold OR 3.19 (95% CI 2.51–4.06; p = 4.8 × 10 −21 ) higher predictability to identify subgroups with higher genetic risk than the PRS EU . Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRS AI and 0.72 to 0.75 in PRS EU . Conclusion: Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations