Novel and known variants in GJA3 and LIM2 in congenital cataract families from North India.

BACKGROUND: To identify the underlying genetic defects in autosomal dominant (ADCC) and autosomal recessive (ARCC) congenital cataract families from North India. METHODS: Detailed family histories were collected, pedigrees drawn followed by slit-lamp examination and lens photography. Mutation screening was performed using Sanger sequencing in the known candidate genes for crystallins, connexins, and membrane proteins. The pathogenicity of identified variants was assessed bioinformatically. RESULTS: In two ADCC families (CC-281 and CC-3015) with posterior lenticonus cataract, a novel change c.263C > T (p.P88L) in GJA3 in CC-281 family and a previously reported substitution c.388C > T (p.R130C) in LIM2 in CC-3015 family was observed. In an ARCC family (CC-3005) having central pulverulent cataract, a novel frameshift deletion (c.764delT;p.L255R46fs) in GJA3 was detected. The observed variants segregated completely with phenotypes in the affected members and were neither present in unaffected family members nor in the ethnically matched 150 controls (tested for two novel variants), hence excluding these as polymorphisms. CONCLUSIONS: Present study identified two novel mutations i.e., c.263C > T;p.P88L and c.764delT;p.L255R46fs in GJA3 in an ADCC and an ARCC family having posterior lenticonus and central pulverulent cataract, respectively. In another ADCC family with posterior lenticonus cataract, a previously reported mutation c.388C > T;p.R130C in LIM2 was observed. R130 may be a mutation hotspot as previously ADCC families from different ethnicities (UK/Czechia, China, Spain, Japan) also harbored the same substitution, however, with different phenotypes i.e., nuclear pulverulent, membranous, nuclear, lamellar, and sutural/lamellar. Findings in present study thus expand the mutation spectrum and phenotypic heterogeneity linked with GJA3 and LIM2.

Polygenic Risk Score Assessment for Coronary Artery Disease in Asian Indians.

We evaluated the performance of various polygenic risk score (PRS) models derived from European (EU), South Asian (SA), and Punjabi Asian Indians (AI) studies on 13,974 subjects from AI ancestry. While all models successfully predicted Coronary artery disease (CAD) risk, the AI, SA, and EU + AI were superior predictors and more transportable than the EU model; the predictive performance in training and test sets was 18% and 22% higher in AI and EU + AI models, respectively than in EU. Comparing individuals with extreme PRS quartiles, the AI and EU + AI captured individuals with high CAD risk showed 2.6 to 4.6 times higher efficiency than the EU. Interestingly, including the clinical risk score did not significantly change the performance of any genetic model. The enrichment of diversity variants in EU PRS improves risk prediction and transportability. Establishing population-specific normative and risk factors and inclusion into genetic models would refine the risk stratification and improve the clinical utility of CAD PRS.

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) (PRSAI) and Europeans (PRSEU) 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 (PRSAI and PRSEU) successfully predicted the T2D risk. However, the PRSAI 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), PRSAI 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 PRSEU. Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRSAI and 0.72 to 0.75 in PRSEU. 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.