Replication and Characterization of Association between ABO SNPs and Red Blood Cell Traits by Meta-Analysis in Europeans.

Abstract

Red blood cell (RBC) traits are routinely measured in clinical practice as important markers of health. Deviations from the physiological ranges are usually a sign of disease, although variation between healthy individuals also occurs, at least partly due to genetic factors. Recent large scale genetic studies identified loci associated with one or more of these traits; further characterization of known loci and identification of new loci is necessary to better understand their role in health and disease and to identify potential molecular mechanisms. We performed meta-analysis of Metabochip association results for six RBC traits-hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV) and red blood cell count (RCC)-in 11 093 Europeans from seven studies of the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium. We identified 394 non-overlapping SNPs in five loci at genome-wide significance: 6p22.1-6p21.33 (with HFE among others), 6q23.2 (with HBS1L among others), 6q23.3 (contains no genes), 9q34.3 (only ABO gene) and 22q13.1 (with TMPRSS6 among others), replicating previous findings of association with RBC traits at these loci and extending them by imputation to 1000 Genomes. We further characterized associations between ABO SNPs and three traits: hemoglobin, hematocrit and red blood cell count, replicating them in an independent cohort. Conditional analyses indicated the independent association of each of these traits with ABO SNPs and a role for blood group O in mediating the association. The 15 most significant RBC-associated ABO SNPs were also associated with five cardiometabolic traits, with discordance in the direction of effect between groups of traits, suggesting that ABO may act through more than one mechanism to influence cardiometabolic risk.British Heart Foundation (Grant ID: RG/10/12/28456, RG/08/013/25942, RG/13/16/30528, RG/98002, RG/07/008/23674); Medical Research Council (Grant ID: G0000934, G0500877, MC_UU_12019/1, K013351); Wellcome Trust (Grant ID: 068545/Z/02, 097451/Z/11/Z); European Commission Framework Programme 6 (Grant ID: 018996); French Ministry of Research; Department of Health Policy Research Programme (England); Chief Scientist Office of Scotland (Grant ID: CZB/4/672, CZQ/1/38); National Institute on Ageing (NIA) (Grant ID: AG1764406S1, 5RO1AG13196); Pfizer plc (Unrestricted Investigator Led Grant); Diabetes UK (Clinical Research Fellowship 10/0003985); Stroke Association; National Heart Lung and Blood Institute (5RO1HL036310); Agency for Health Care Policy Research (HS06516); John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socio-economic Status and Health; Swiss National Science Foundation (33CSCO-122661); GlaxoSmithKline. Faculty of Biology and Medicine of Lausanne,Switzerland.This is the final version of the article. It first appeared from Public Library of Science (PLOS) via http://dx.doi.org/10.1371/journal.pone.015691