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Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network

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Abstract

White blood cell count (WBC) is unique among identified inflammatory predictors of chronic disease in that it is routinely measured in asymptomatic patients in the course of routine patient care. We led a genome-wide association analysis to identify variants associated with WBC levels in 13,923 subjects in the electronic Medical Records and Genomics (eMERGE) Network. We identified two regions of interest that were each unique to subjects of genetically determined ancestry to the African continent (AA) or to the European continent (EA). WBC varies among different ancestry groups. Despite being ancestry specific, these regions were identifiable in the combined analysis. In AA subjects, the region surrounding the Duffy antigen/chemokine receptor gene (DARC) on 1q21 exhibited significant association (p value = 6.71e−55). These results validate the previously reported association between WBC and of the regulatory variant rs2814778 in the promoter region, which causes the Duffy negative phenotype (Fy−/−). A second missense variant (rs12075) is responsible for the two principal antigens, Fya and Fyb of the Duffy blood group system. The two variants, consisting of four alleles, act in concert to produce five antigens and subsequent phenotypes. We were able to identify the marginal and novel interaction effects of these two variants on WBC. In the EA subjects, we identified significantly associated SNPs tagging three separate genes in the 17q21 region: (1) GSDMA, (2) MED24, and (3) PSMD3. Variants in this region have been reported to be associated with WBC, neutrophil count, and inflammatory diseases including asthma and Crohn’s disease.

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Acknowledgments

The authors are grateful to all the participants in the eMERGE study. They also acknowledge Xiuwen Zheng and the fast PCA program to make principal component analysis on this many subjects achievable. This study was supported by the following U01 grants from the National Human Genome Research Institute (NHGRI), a component of the National Institutes of Health (NIH), Bethesda, MD, USA: (1) HG004610, AG06781 (Group Health Cooperative), (2) HG04599 (Mayo Clinic), (3) HG004608 (Marshfield Clinic), (4) HG004609 (Northwestern University), (5) HG004438 (CIDR), (6) HG004424 (BROAD), and (7) HG004603 (Vanderbilt University). Additional support was provided by the University of Washington’s Northwest Institute of Genetic Medicine from Washington State Life Sciences Discovery funds (Grant 265508).

Conflict of interest

None of the authors have a financial interest related to this work.

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Authors and Affiliations

  1. Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, 98195, USA

    David R. Crosslin & Gail P. Jarvik

  2. Group Health Research Institute, Seattle, WA, 98124, USA

    Noah Weston, Eugene Hart, Katherine Newton & Eric B. Larson

  3. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA

    Andrew McDavid & Chris S. Carlson

  4. Department of Biostatistics, University of Washington, Floor 15, UW Tower, Campus Mail Box 359461, Seattle, WA, 98195, USA

    David R. Crosslin, Sarah C. Nelson & Xiuwen Zheng

  5. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA

    Mariza de Andrade

  6. Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, 55905, USA

    Iftikhar J. Kullo

  7. Essentia Institute of Rural Health, Duluth, MN, 55805, USA

    Catherine A. McCarty

  8. Center for Inherited Disease Research, Johns Hopkins University, Baltimore, MD, 21224, USA

    Kimberly F. Doheny & Elizabeth Pugh

  9. Divisions of General Internal Medicine and Health and Biomedical Informatics, Northwestern University, Chicago, IL, 60611, USA

    Abel Kho

  10. Division of Endocrinology, Metabolism, and Molecular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA

    M. Geoffrey Hayes

  11. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA

    Stephanie Pretel

  12. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA

    Dana C. Crawford

  13. Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University, Nashville, TN, 37232, USA

    Alexander Saip

  14. Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA

    Marylyn D. Ritchie

  15. Center for Human Genetics Research, Vanderbilt University, Nashville, TN, 37232, USA

    Dana C. Crawford

  16. Department of Medicine, Division of General Internal Medicine, University of Washington, Seattle, WA, 98195, USA

    Paul K. Crane

  17. Office of Population Genomics, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA

    Rongling Li

  18. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA

    Daniel B. Mirel & Andrew Crenshaw

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  1. David R. Crosslin
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  2. Andrew McDavid
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  3. Noah Weston
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  4. Sarah C. Nelson
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  6. Eugene Hart
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  13. M. Geoffrey Hayes
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  19. Katherine Newton
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  20. Rongling Li
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  24. Chris S. Carlson
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The electronic Medical Records and Genomics (eMERGE) Network

Corresponding author

Correspondence to David R. Crosslin.

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Supplemental Figure 1: Plots of eigenvectors 1 and 2 using PCA. Principal components analysis of 17, 150 eMERGE subjects from all field centers, anchored by HapMap controls. Color-coding is according to self-identified or self-observed race.

Supplemental Figure 2: Box-percentile plots of median age, median BMI, median leukocyte count and median differentials. The bottom right plot is an annotated example showing the layout of the box-percentile plots. p values listed are from the Kruskal–Wallis test as listed in Table 2.

Supplemental Figure 3: Summary of effects of predictors on WBC in the a AA, b EE and c pooled models. Continuous variables are compared using default ranges (interquartile).

Supplemental Figure 4: Box-percentile plots of median leukocyte count and median differentials by Duffy phenotype (Fya+b+, Fya+b−, Fya−b+, Fya−b−) in the subjects of African ancestry. The bottom right plot in Fig. 1 is an annotated example showing the layout of the box-percentile plots. p values listed are from the Kruskal–Wallis test.

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Crosslin, D.R., McDavid, A., Weston, N. et al. Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network. Hum Genet 131, 639–652 (2012). https://doi.org/10.1007/s00439-011-1103-9

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