139-OR: Large-Scale Sex-Stratified Additive and Recessive GWAS Identifies Novel Large-Effect Variants and Improves Polygenic Prediction for Type 2 Diabetes

Introduction: Most genome-wide association studies (GWAS) of type 2 diabetes (T2D) assume an additive mode of inheritance and equal effects in men and women. These assumptions can preclude the discovery of variants with effects that are non-additive and/or sex-specific. Focused exploration of these effects may reveal novel genetic variation associated with T2D and improve the predictive performance of polygenic risk scores (PRS) . Methods: We performed a sex-stratified GWAS, using additive and recessive models for T2D, in individuals of European ancestry in the UK Biobank (UKBB) and Genetic Epidemiology Research on Aging cohort. We also generated sex-specific and non-sex-specific T2D PRSs, using PRS-CS, and assessed their performance in Mass General Brigham Biobank (MGBB) . Results: As the largest sex-stratified additive and recessive GWAS of T2D performed to date, this study included 30,625 cases and 223,442 controls. In the recessive analysis, we identified 7 novel variants, of which 1 was female-specific and 1 was male-specific. Among these variants, 4 were associated with over 10-fold increase in risk for T2D. The male-specific variant, rs35725476 (OR = 1.49, p = 3×10- 9) , is associated with higher expression of a long non-coding RNA in pancreatic islets (p = 3×10- 14) . In the additive analysis, we identified a novel female-specific protective variant, rs12109272 (OR = 0.91, p = 3×10-8) , which is associated with lower PCSK1 expression, lower risk of gestational diabetes (p = 1×10- 6) and lower fasting glucose (p = 6×10- 32) in independent cohorts. Finally, the sex-specific PRS outperformed the non-sex-specific PRS at predicting T2D in MGBB (AUC of 0.653 versus 0.643, p [of difference] = 2×10-4 in males; AUC of 0.674 versus 0.656, p [of difference] = 3×10-4 in females) . Conclusions: These findings demonstrate the value of non-additive and sex-stratified analyses for both variant discovery and improving polygenic prediction for T2D.