Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits

Abstract

Hematologic measures such as hematocrit and white blood cell (WBC) count are heritable and clinically relevant. We analyzed erythrocyte and WBC phenotypes in 52,531 individuals (37,775 of European ancestry, 11,589 African Americans, and 3,167 Hispanic Americans) from 16 population-based cohorts with Illumina HumanExome BeadChip genotypes. We then performed replication analyses of new discoveries in 18,018 European-American women and 5,261 Han Chinese. We identified and replicated four new erythrocyte trait–locus associations (CEP89, SHROOM3, FADS2, and APOE) and six new WBC loci for neutrophil count (S1PR4), monocyte count (BTBD8, NLRP12, and IL17RA), eosinophil count (IRF1), and total WBC count (MYB). The association of a rare missense variant in S1PR4 supports the role of sphingosine-1-phosphate signaling in leukocyte trafficking and circulating neutrophil counts. Loss-of-function experiments for S1pr4 in mouse and s1pr4 in zebrafish demonstrated phenotypes consistent with the association observed in humans and altered kinetics of neutrophil recruitment and resolution in response to tissue injury.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Forest plots of the S1PR4 variant encoding p.Arg365Leu for total WBC count.
Figure 2
Figure 3: Blood neutrophils in S1pr4−/− mice.
Figure 4: Reduction in neutrophil counts in zebrafish embryos with decreased s1pr4 expression by morpholino-mediated knockdown with two independent morpholino oligonucleotides.
Figure 5: Neutrophil migration in response to injury is altered in zebrafish embryos with low s1pr4 gene expression.

Similar content being viewed by others

References

  1. Whitfield, J.B. & Martin, N.G. Genetic and environmental influences on the size and number of cells in the blood. Genet. Epidemiol. 2, 133–144 (1985).

    CAS  PubMed  Google Scholar 

  2. Evans, D.M., Frazer, I.H. & Martin, N.G. Genetic and environmental causes of variation in basal levels of blood cells. Twin Res. 2, 250–257 (1999).

    CAS  PubMed  Google Scholar 

  3. Lin, J.P. et al. Evidence for linkage of red blood cell size and count: genome-wide scans in the Framingham Heart Study. Am. J. Hematol. 82, 605–610 (2007).

    CAS  PubMed  Google Scholar 

  4. Pilia, G. et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2, e132 (2006).

    PubMed  PubMed Central  Google Scholar 

  5. Zakai, N.A. et al. A prospective study of anemia status, hemoglobin concentration, and mortality in an elderly cohort: the Cardiovascular Health Study. Arch. Intern. Med. 165, 2214–2220 (2005).

    PubMed  Google Scholar 

  6. Brennan, M.L. et al. Comprehensive peroxidase-based hematologic profiling for the prediction of 1-year myocardial infarction and death. Circulation 122, 70–79 (2010).

    PubMed  PubMed Central  Google Scholar 

  7. Elwood, P.C., Waters, W.E., Benjamin, I.T. & Sweetnam, P.M. Mortality and anaemia in women. Lancet 1, 891–894 (1974).

    CAS  PubMed  Google Scholar 

  8. Reiner, A.P. et al. Genome-wide association study of white blood cell count in 16,388 African Americans: the continental origins and genetic epidemiology network (COGENT). PLoS Genet. 7, e1002108 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ganesh, S.K. et al. Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium. Nat. Genet. 41, 1191–1198 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. van der Harst, P. et al. Seventy-five genetic loci influencing the human red blood cell. Nature 492, 369–375 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Kamatani, Y. et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat. Genet. 42, 210–215 (2010).

    CAS  PubMed  Google Scholar 

  12. Pistis, G. et al. Genome wide association analysis of a founder population identified TAF3 as a gene for MCHC in humans. PLoS One 8, e69206 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Nalls, M.A. et al. Multiple loci are associated with white blood cell phenotypes. PLoS Genet. 7, e1002113 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Okada, Y. et al. Identification of nine novel loci associated with white blood cell subtypes in a Japanese population. PLoS Genet. 7, e1002067 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Auer, P.L. et al. Imputation of exome sequence variants into population- based samples and blood-cell-trait-associated loci in African Americans: NHLBI GO Exome Sequencing Project. Am. J. Hum. Genet. 91, 794–808 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Auer, P.L. et al. Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits. Nat. Genet. 46, 629–634 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Okada, Y. et al. Meta-analysis identifies multiple loci associated with kidney function–related traits in East Asian populations. Nat. Genet. 44, 904–909 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Meyer, T.E. et al. Genome-wide association studies of serum magnesium, potassium, and sodium concentrations identify six loci influencing serum magnesium levels. PLoS Genet. 6, e1001045 (2010).

    PubMed  PubMed Central  Google Scholar 

  19. Chambers, J.C. et al. Genetic loci influencing kidney function and chronic kidney disease. Nat. Genet. 42, 373–375 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Benyamin, B. et al. Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis. Nat. Commun. 5, 4926 (2014).

    CAS  PubMed  Google Scholar 

  21. Lemaitre, R.N. et al. Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. PLoS Genet. 7, e1002193 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Wu, Y. et al. Genetic association with lipids in Filipinos: waist circumference modifies an APOA5 effect on triglyceride levels. J. Lipid Res. 54, 3198–3205 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Rasmussen-Torvik, L.J. et al. High density GWAS for LDL cholesterol in African Americans using electronic medical records reveals a strong protective variant in APOE. Clin. Transl. Sci. 5, 394–399 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Chasman, D.I. et al. Genetic determinants of statin-induced low-density lipoprotein cholesterol reduction: the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial. Circ Cardiovasc Genet 5, 257–264 (2012).

    CAS  PubMed  Google Scholar 

  25. Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet. 44, 269–276 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Corder, E.H. et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet. 7, 180–184 (1994).

    CAS  PubMed  Google Scholar 

  27. Talbot, C. et al. Protection against Alzheimer's disease with apoE ɛ2. Lancet 343, 1432–1433 (1994).

    CAS  PubMed  Google Scholar 

  28. Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Moffatt, M.F. et al. A large-scale, consortium-based genomewide association study of asthma. N. Engl. J. Med. 363, 1211–1221 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Barrett, J.C. et al. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat. Genet. 40, 955–962 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. McGovern, D.P. et al. Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn's disease. Hum. Mol. Genet. 19, 3468–3476 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Sabater-Lleal, M. et al. Multiethnic meta-analysis of genome-wide association studies in >100 000 subjects identifies 23 fibrinogen-associated loci but no strong evidence of a causal association between circulating fibrinogen and cardiovascular disease. Circulation 128, 1310–1324 (2013).

    CAS  PubMed  Google Scholar 

  34. Zhang, X. et al. Synthesis of 53 tissue and cell line expression QTL datasets reveals master eQTLs. BMC Genomics 15, 532 (2014).

    PubMed  PubMed Central  Google Scholar 

  35. Allende, M.L. et al. Sphingosine-1-phosphate lyase deficiency produces a pro-inflammatory response while impairing neutrophil trafficking. J. Biol. Chem. 286, 7348–7358 (2011).

    CAS  PubMed  Google Scholar 

  36. Allende, M.L. et al. S1P1 receptor directs the release of immature B cells from bone marrow into blood. J. Exp. Med. 207, 1113–1124 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Amatruda, J.F. & Zon, L.I. Dissecting hematopoiesis and disease using the zebrafish. Dev. Biol. 216, 1–15 (1999).

    CAS  PubMed  Google Scholar 

  38. Rivera, J., Proia, R.L. & Olivera, A. The alliance of sphingosine-1-phosphate and its receptors in immunity. Nat. Rev. Immunol. 8, 753–763 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Allende, M.L., Dreier, J.L., Mandala, S. & Proia, R.L. Expression of the sphingosine 1-phosphate receptor, S1P1, on T-cells controls thymic emigration. J. Biol. Chem. 279, 15396–15401 (2004).

    CAS  PubMed  Google Scholar 

  40. Matloubian, M. et al. Lymphocyte egress from thymus and peripheral lymphoid organs is dependent on S1P receptor 1. Nature 427, 355–360 (2004).

    CAS  PubMed  Google Scholar 

  41. Schwab, S.R. & Cyster, J.G. Finding a way out: lymphocyte egress from lymphoid organs. Nat. Immunol. 8, 1295–1301 (2007).

    CAS  PubMed  Google Scholar 

  42. Golfier, S. et al. Shaping of terminal megakaryocyte differentiation and proplatelet development by sphingosine-1-phosphate receptor S1P4 . FASEB J. 24, 4701–4710 (2010).

    CAS  PubMed  Google Scholar 

  43. Schulze, T. et al. Sphingosine-1-phospate receptor 4 (S1P) deficiency profoundly affects dendritic cell function and TH17-cell differentiation in a murine model. FASEB J. 25, 4024–4036 (2011).

    CAS  PubMed  Google Scholar 

  44. Dillmann, C., Mora, J., Olesch, C., Brüne, B. & Weigert, A. S1PR4 is required for plasmacytoid dendritic cell differentiation. Biol. Chem. 396, 775–782 (2015).

    CAS  PubMed  Google Scholar 

  45. Olivera, A. et al. Sphingosine kinase 1 and sphingosine-1-phosphate receptor 2 are vital to recovery from anaphylactic shock in mice. J. Clin. Invest. 120, 1429–1440 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Eash, K.J., Greenbaum, A.M., Gopalan, P.K. & Link, D.C. CXCR2 and CXCR4 antagonistically regulate neutrophil trafficking from murine bone marrow. J. Clin. Invest. 120, 2423–2431 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Beck, T.C., Gomes, A.C., Cyster, J.G. & Pereira, J.P. CXCR4 and a cell-extrinsic mechanism control immature B lymphocyte egress from bone marrow. J. Exp. Med. 211, 2567–2581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. McEver, R.P., Moore, K.L. & Cummings, R.D. Leukocyte trafficking mediated by selectin–carbohydrate interactions. J. Biol. Chem. 270, 11025–11028 (1995).

    CAS  PubMed  Google Scholar 

  49. Ye, Z. et al. ATP binding by monarch-1/NLRP12 is critical for its inhibitory function. Mol. Cell. Biol. 28, 1841–1850 (2008).

    CAS  PubMed  Google Scholar 

  50. Borghini, S. et al. Clinical presentation and pathogenesis of cold-induced autoinflammatory disease in a family with recurrence of an NLRP12 mutation. Arthritis Rheum. 63, 830–839 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Landrum, M.J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).

    CAS  PubMed  Google Scholar 

  52. Arthur, J.C. et al. Cutting edge: NLRP12 controls dendritic and myeloid cell migration to affect contact hypersensitivity. J. Immunol. 185, 4515–4519 (2010).

    CAS  PubMed  Google Scholar 

  53. Gaffen, S.L. An overview of IL-17 function and signaling. Cytokine 43, 402–407 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Butcher, M.J., Gjurich, B.N., Phillips, T. & Galkina, E.V. The IL-17A/IL-17RA axis plays a proatherogenic role via the regulation of aortic myeloid cell recruitment. Circ. Res. 110, 675–687 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Puel, A. et al. Chronic mucocutaneous candidiasis in humans with inborn errors of interleukin-17 immunity. Science 332, 65–68 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Hashiguchi, M. et al. IL-33 activates eosinophils of visceral adipose tissue both directly and via innate lymphoid cells. Eur. J. Immunol. 45, 876–885 (2015).

    CAS  PubMed  Google Scholar 

  57. Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Kouro, T. & Takatsu, K. IL-5– and eosinophil-mediated inflammation: from discovery to therapy. Int. Immunol. 21, 1303–1309 (2009).

    CAS  PubMed  Google Scholar 

  59. Tin, A. et al. Using multiple measures for quantitative trait association analyses: application to estimated glomerular filtration rate. J. Hum. Genet. 58, 461–466 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Li, J. et al. Piezo1 integration of vascular architecture with physiological force. Nature 515, 279–282 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Ranade, S.S. et al. Piezo1, a mechanically activated ion channel, is required for vascular development in mice. Proc. Natl. Acad. Sci. USA 111, 10347–10352 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Peyronnet, R. et al. Piezo1-dependent stretch-activated channels are inhibited by Polycystin-2 in renal tubular epithelial cells. EMBO Rep. 14, 1143–1148 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Miyamoto, T. et al. Functional role for Piezo1 in stretch-evoked Ca2+ influx and ATP release in urothelial cell cultures. J. Biol. Chem. 289, 16565–16575 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Brohawn, S.G., Su, Z. & MacKinnon, R. Mechanosensitivity is mediated directly by the lipid membrane in TRAAK and TREK1 K+ channels. Proc. Natl. Acad. Sci. USA 111, 3614–3619 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Sandberg, M.B., Nybo, M., Birgens, H. & Frederiksen, H. Hereditary xerocytosis and familial haemolysis due to mutation in the PIEZO1 gene: a simple diagnostic approach. Int. J. Lab. Hematol. 36, e62–e65 (2014).

    CAS  PubMed  Google Scholar 

  66. Yeo, N.C. et al. Shroom3 contributes to the maintenance of the glomerular filtration barrier integrity. Genome Res. 25, 57–65 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Feliubadaló, L. et al. Non–type I cystinuria caused by mutations in SLC7A9, encoding a subunit (bo,+AT) of rBAT. Nat. Genet. 23, 52–57 (1999).

    PubMed  Google Scholar 

  68. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. van den Berg, J.J. et al. Increased n-3 polyunsaturated fatty acid content of red blood cells from fish oil–fed rabbits increases in vitro lipid peroxidation, but decreases hemolysis. Free Radic. Biol. Med. 11, 393–399 (1991).

    CAS  PubMed  Google Scholar 

  70. Waldron, T. et al. c-Myb and its target Bmi1 are required for p190BCR/ABL leukemogenesis in mouse and human cells. Leukemia 26, 644–653 (2012).

    CAS  PubMed  Google Scholar 

  71. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Stahl, E.A. et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat. Genet. 42, 508–514 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Levy, D. et al. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677–687 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Shameer, K. et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum. Genet. 133, 95–109 (2014).

    PubMed  Google Scholar 

  75. Plagnol, V. et al. Genome-wide association analysis of autoantibody positivity in type 1 diabetes cases. PLoS Genet. 7, e1002216 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Dichgans, M. et al. Shared genetic susceptibility to ischemic stroke and coronary artery disease: a genome-wide analysis of common variants. Stroke 45, 24–36 (2014).

    CAS  PubMed  Google Scholar 

  77. Psaty, B.M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet 2, 73–80 (2009).

    PubMed  PubMed Central  Google Scholar 

  78. Liu, X., Jian, X. & Boerwinkle, E. dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum. Mutat. 32, 894–899 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Liu, X., Jian, X. & Boerwinkle, E. dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum. Mutat. 34, E2393–E2402 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Grove, M.L. et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS One 8, e68095 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Li, B. & Leal, S.M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Wu, M.C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Johnson, A.D. et al. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24, 2938–2939 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Bain, B.J. & England, J.M. Normal haematological values: sex difference in neutrophil count. BMJ 1, 306–309 (1975).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Bain, B.J. & England, J.M. Variations in leucocyte count during menstrual cycle. BMJ 2, 473–475 (1975).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the staff and participants of all studies for their important contributions. A complete list of acknowledgments for each study is available in the Supplementary Note.

This work was supported by the following grants and contracts: US National Institutes of Health contracts (N01AG12100, HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C, HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC25195, N02HL64278, N01AG62101, N01AG62103, N01AG62106, HHSN268200782096C, HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C, N01HC95159, N01HC95160, N01HC95161, N01HC95162, N01HC95163, N01HC95164, N01HC95165, N01HC95166, N01HC95167, N01HC95168, N01HC95169, RR024156, N02HL64278, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C, RC2HL102924, and CA137088); US National Institutes of Health grants (5RC2HL102419, HL080295, HL087652, HL103612, HL105756, HL120393, AG023629, DK063491, R01DK089256, R01HL087700, R01HL088215, R01HL117078, 1R01AG032098-01A1, U01-HG005152, R25CA094880, R01HL122684, R01HL04880, R01HL32262, R01DK49216, R01HL10001, R01DK092760, and R01OD017870); a Clinical and Translational Science Institute grant (UL1TR000124); a Danish Heart Foundation grant (07-10-R61-A1754-B838-22392F); a Biobanking and BioMolecular resources Research Infrastructure–The Netherlands (BBMRI-NL) grant (NWO 184.021.007); a Health Insurance Foundation grant (2012B233); and Academy of Finland grants (134309, 126925, 121584, 124282, 129378, 117787, and 41071).

This work was supported in part by the NIDDK Division of Intramural Research.

The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the US Department of Health and Human Services.

This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute.

Author information

Authors and Affiliations

Consortia

Contributions

N.P., Y. Zhou, Y. Zhang, E.P.B., I.J.D., O.H.F., M.E.G., V.G., T.H., T.B.H., A.H., L.J.L., A.L., O.P., J.M.S., A.D., Y.H., C.J.O'D., A.P.R., and S.K.G. designed the study. Y. Zhang, I.B.B., E.P.B., M.C., I.J.D., L.D., M.F.F., M.E.G., V.G., T.B.H., A.H., R.D.J., J.J., M.K., T.L., A.L., M.E.N., B.M.P., O.T.R., S.S.R., J.M.S., B.H.T., R.P.T., Jiansong Wang, and C.J.O'D. recruited and assessed participants. P.L.A., J.B.-J., N.G., L.-P.L., Y. Zhang, F.W.A., E.B., I.B.B., E.P.B., P.I.W.d.B., M.F.F., M.L.G., T.L., D.C.L., Y. Liu, S.S.R., F.R., J.I.R., K.D.T., and A.G.U. generated genotyping data. Y. Zhou, M.L.A., V.C., E.J.H., B.H., K.H., X.Z., V.M.N., A.M.R.D.S., R.L.P., and L.I.Z. performed functional experiments. N.P., U.M.S., T.S.A., M.L.A., P.L.A., J.B.-J., N.G., B.H., Y. Lu, M.A.N., R.P., A.V.S., Y. Zhang, J.S.F., N.F., M.L.G., R.J.F.L., B.M.P., A.D., L.A.C., J.G.W., R.L.P., L.I.Z., C.J.O'D., A.P.R., and S.K.G. analyzed and interpreted data. N.P., U.M.S., W.Z., T.S.A., J.B.-J., J.A.B., M.-H.C., J.D.E., N.G., A.D.J., M.L., Y. Lu, L.-P.L., A.M., R.E.M., M.A.N., R.P., A.V.S., F.J.A.v.R., M.-L.Y., Judy Wang, and A.P.R. performed statistical analysis. N.P., U.M.S., Y. Zhou, A.P.R., and S.K.G. wrote the manuscript. All authors were given the opportunity to comment and provide revisions to the manuscript text.

Corresponding authors

Correspondence to Nathan Pankratz or Santhi K Ganesh.

Ethics declarations

Competing interests

The author declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Note. (PDF 2138 kb)

Supplementary Tables 1–16

Supplementary Tables 1–16. (XLSX 619 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

the CHARGE Consortium Hematology Working Group. Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits. Nat Genet 48, 867–876 (2016). https://doi.org/10.1038/ng.3607

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3607

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing