Daniel Barnes, Benjamin Voight, Amelie Bonnefond, Ruth Loos, Eco De Geus, Alexander Teumer, Goncalo Abecasis, Nicole Soranzo, Serena Sanna, Ozren Polasek, Juliane Winkelmann, Augustine Kong, ROBERTO ELOSUA, Michael Stumvoll, David Altshuler, Udo Seedorf, Ines Barroso, Felicity Payne, Nabila Bouatia-Naji, Christian Gieger, Robert Sladek, Sophie Gallina, Mariano Dei, Claudia Langenberg, Josee Dupuis, Man Li, James Pankow, Sally Ricketts, Mark Fleming, James F Wilson, Eleanor Wheeler, Peter Kovacs, Alexandre Stewart, Inga Prokopenko, Philippe Froguel, Panos Deloukas, Nita Forouhi, Jeanette Erdmann, Matthew Heeney, Reedik Mägi, Patrik Magnusson, Marcus Kleber, Thomas Meitinger, Massimo Mangino, Caroline Hayward, MANUELA UDA, Christa Meisinger, Rona Strawbridge, Manuel Serrano Ríos, Ida Surakka, Anuj Goel, Gonneke Willemsen, Konrad Oexle, Mark McCarthy, Olle Melander, Muredach Reilly, Igor Rudan, Alistair Hall, Hugh Watkins, Biological Psychology, EMGO+ - Lifestyle, Overweight and Diabetes, Department of Chemical Engineering [Stanford], Stanford University, École nationale supérieure d'architecture de Nantes (ENSA Nantes), Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 (Evo-Eco-Paléo), Université de Lille-Centre National de la Recherche Scientifique (CNRS), and Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 (Evo-Eco-Paléo (EEP))
OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c.