White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3′ UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.
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We thank all participants and study coordinating centers of the participating studies and cohorts. P.L.A. was supported by NHLBI R21 HL121422-02. G.L. was supported by the Canada Research Chair program and the Canadian Institute of Health Research MOP#123382. This work was supported in part by the National Institute on Aging, NIH Intramural Research Program. Data analyses utilized the computational resources of the NIH HPC Biowulf cluster at the NIH. The Framingham Heart Study (FHS) acknowledges the Shared Computing Cluster at Boston University. Infrastructure and data analysis for the ARIC study was partly supported by Grant Number UL1RR025005, a component of the NIH Roadmap for Medical Research, and grant R01 HL086694 from the NHLBI. Airwave study thanks Louisa Cavaliero who assisted in data collection and management, Peter McFarlane and the Glasgow CARE, Patricia Munroe at Queen Mary University of London, and Joanna Sarnecka and Ania Zawodniak at Northwick Park for their contributions to the study. SOLID-TIMI-52 and STABILITY thank Liling Warren for contributions to the genetic analysis of the study datasets. Estonian Genome Center, University of Tartu (EGCUT) thanks Mr. V. Soo, Mr. S. Smith, and Dr. L. Milani for their contribution. The Rotterdam Study (RS) thanks Ms. Mila Jhamai, Ms. Sarah Higgins, and Mr. Marijn Verkerk for their help in creating the Exomechip database, and Ms. Carolina Medina-Gomez, Mr. Lennard Karsten, and Dr. Linda Broer. Additional acknowledgments and funding information are provided in the Supplemental Data .
© 2016 American Society of Human Genetics