medRxivGenetic & Genomic MedicinePreprint
Random glucose GWAS in 493,036 individuals provides insights into diabetes pathophysiology, complications and treatment stratification
Vasiliki LagouiD, Longda JiangiD, Anna UlrichiD, Liudmila ZudinaiD, Karla Sofia Gutiérrez GonzáleziD, Zhanna BalkhiyarovaiD, Alessia FaggianiD, Shiqian CheniD, Petar V. TodoroviD, Sodbo SharapoviD, Alessia DavidiD, Letizia Marullo, Reedik MägiiD, Roxana‐Maria RujaniD, Emma AhlqvistiD, Guðmar ÞorleifssoniD, He GaoiD, Εvangelos ΕvangelouiD, Beben BenyaminiD, Robert A. ScottiD, Aaron J. IsaacsiD, Jing Hua ZhaoiD, Sara M. WillemsiD, Toby JohnsoniD, Christian GiegeriD, , Inga ProkopenkoiD
Abstract
Homeostatic control of blood glucose requires different physiological responses in the fasting and post-prandial states. We reasoned that glucose measurements under non-standardised conditions (random glucose; RG) may capture diverse glucoregulatory processes more effectively than previous genome-wide association studies (GWAS) of fasting glycaemia or after standardised glucose loads. Through GWAS meta-analysis of RG in 493,036 individuals without diabetes of diverse ethnicities we identified 128 associated loci represented by 162 distinct signals, including 14 with sex-dimorphic effects, 9 discovered through trans-ethnic analysis, and 70 novel signals for glycaemic traits. Novel RG loci were particularly enriched in expression in the ileum and colon, indicating a prominent role for the gastrointestinal tract in the control of blood glucose. Functional studies and molecular dynamics simulations of coding variants of GLP1R, a well-established type 2 diabetes treatment target, provided a genetic framework for optimal selection of GLP-1R agonist therapy. We also provided new evidence from Mendelian randomisation that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Thus, our approach based on RG GWAS provided wide-ranging insights into the biology of glucose regulation, diabetes complications and the potential for treatment stratification.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 10
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