Resting-state white blood cell (WBC) count is a marker of inflammation and immune system health. There is evidence that WBC count is not fixed over time and there is heterogeneity in WBC trajectory that is associated with morbidity and mortality. Latent class mixed modeling (LCMM) is a method that can identify unobserved heterogeneity in longitudinal data and attempts to classify individuals into groups based on a linear model of repeated measurements. We applied LCMM to repeated WBC count measures derived from electronic medical records of participants of the National Human Genetics Research Institute (NHRGI) electronic MEdical Record and GEnomics (eMERGE) network study, revealing two WBC count trajectory phenotypes. Advancing these phenotypes to GWAS, we found genetic associations between trajectory class membership and regions on chromosome 1p34.3 and chromosome 11q13.4. The chromosome 1 region contains CSF3R, which encodes the granulocyte colony-stimulating factor receptor. This protein is a major factor in neutrophil stimulation and proliferation. The association on chromosome 11 contain genes RNF169 and XRRA1; both involved in the regulation of double-strand break DNA repair.
Subscribe to Journal
Get full journal access for 1 year
only $14.88 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Shim WS, Kim HJ, Kang ES, Ahn CW, Lim SK, Lee HC, et al. The association of total and differential white blood cell count with metabolic syndrome in type 2 diabetic patients. Diabetes Res Clin Pract. 2006;73:284–91.
Chao T-T, Hsieh C-H, Lin J-D, Wu C-Z, Hsu C-H, Pei D, et al. Use of white blood cell counts to predict metabolic syndrome in the elderly: a 4 year longitudinal study. Aging Male. 2014;17:230–7.
Pei C, Chang J-B, Hsieh C-H, Lin J-D, Hsu C-H, Pei D, et al. Using white blood cell counts to predict metabolic syndrome in the elderly: A combined cross-sectional and longitudinal study. Eur J Intern Med. 2015;26:324–9.
Babio N, Ibarrola-Jurado N, Bulló M, Martínez-González MÁ, Wärnberg J, Salaverría I. et al. White blood cell counts as risk markers of developing metabolic syndrome and its components in the PREDIMED study. PLoS ONE. 2013;8:e58354
Huh JY, Ross GW, Chen R, Abbott RD, Bell C, Willcox B, et al. Total and differential white blood cell counts in late life predict 8-year incident stroke: the Honolulu Heart Program. J Am Geriatr Soc. 2015;63:439–46.
Loimaala A, Rontu R, Vuori I, Mercuri M, Lehtimäki T, Nenonen A, et al. Blood leukocyte count is a risk factor for intima-media thickening and subclinical carotid atherosclerosis in middle-aged men. Atherosclerosis. 2006;188:363–9.
Nilsson G, Hedberg P, Ohrvik J. White blood cell count in elderly is clinically useful in predicting long-term survival. J Aging Res. 2014;2014:475093.
Ruggiero C, Metter EJ, Cherubini A, Maggio M, Sen R, Najjar SS, et al. White blood cell count and mortality in the Baltimore Longitudinal Study of Aging. J Am Coll Cardiol. 2007;49:1841–50.
Chmielewski PP, Borysławski K, Chmielowiec K, Chmielowiec J, Strzelec B. The association between total leukocyte count and longevity: Evidence from longitudinal and cross-sectional data. Ann Anat. 2016;204:1–10.
Brown DW, Ford ES, Giles WH, Croft JB, Balluz LS, Mokdad AH. Associations between white blood cell count and risk for cerebrovascular disease mortality: NHANES II Mortality Study, 1976-92. Ann Epidemiol. 2004;14:425–30.
Ahmadi-Abhari S, Luben RN, Wareham NJ. Seventeen year risk of all-cause and cause-specific mortality associated with C-reactive protein, fibrinogen and leukocyte count in men and women: the EPIC-Norfolk…. Eur J Epidemiol. 2013. http://link.springer.com/article/10.1007/s10654-013-9819-6.
Coller BS. Leukocytosis and ischemic vascular disease morbidity and mortality: is it time to intervene? Arterioscler Thromb Vasc Biol. 2005;25:658–70.
Smith MR, Kinmonth A-L, Luben RN, Bingham S, Day NE, Wareham NJ, et al. Smoking status and differential white cell count in men and women in the EPIC-Norfolk population. Atherosclerosis. 2003;169:331–7.
Schwartz J, Weiss ST. Cigarette smoking and peripheral blood leukocyte differentials. Ann Epidemiol. 1994;4:236–42.
Hsieh MM, Everhart JE, Byrd-Holt DD, Tisdale JF, Rodgers GP. Prevalence of neutropenia in the U.S. population: age, sex, smoking status, and ethnic differences. Ann Intern Med. 2007;146:486–92.
Dixon JB, O’Brien PE. Obesity and the white blood cell count: changes with sustained weight loss. Obes Surg. 2006;16:251–7.
Church TS, Finley CE, Earnest CP, Kampert JB, Gibbons LW, Blair SN. Relative associations of fitness and fatness to fibrinogen, white blood cell count, uric acid and metabolic syndrome. Int J Obes Relat Metab Disord. 2002;26:805–13.
Womack J, Tien PC, Feldman J, Shin JH, Fennie K, Anastos K, et al. Obesity and immune cell counts in women. Metabolism. 2007;56:998–1004.
Pilia G, Chen W-M, Scuteri A, Orrú M, Albai G, Dei M, et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2006;2:e132.
Haddy TB, Rana SR, Castro O. Benign ethnic neutropenia: what is a normal absolute neutrophil count? J Lab Clin Med. 1999;133:15–22.
Rana SR, Castro OL, Haddy TB. Leukocyte counts in 7,739 healthy black persons: effects of age and sex. Ann Clin Lab Sci. 1985;15:51–4.
Nalls MA, Wilson JG, Patterson NJ, Tandon A, Zmuda JM, Huntsman S, et al. Admixture mapping of white cell count: genetic locus responsible for lower white blood cell count in the Health ABC and Jackson Heart studies. Am J Hum Genet. 2008;82:81–7.
Reich D, Nalls MA, Kao WHL, Akylbekova EL, Tandon A, Patterson N, et al. Reduced neutrophil count in people of African descent is due to a regulatory variant in the Duffy antigen receptor for chemokines gene. PLoS Genet. 2009;5:e1000360.
Reiner AP, Lettre G, Nalls MA, Ganesh SK, Mathias R, Austin MA, 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. 2011;7:e1002108.
Li J, Glessner JT, Zhang H, Hou C, Wei Z, Bradfield JP, et al. GWAS of blood cell traits identifies novel associated loci and epistatic interactions in Caucasian and African-American children. Hum Mol Genet. 2013;22:1457–64.
Crosslin DR, McDavid A, Weston N, Nelson SC, Zheng X, Hart E, et al. Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network. Hum Genet. 2012;131:639–52.
Keller MF, Reiner AP, Okada Y, van Rooij FJA, Johnson AD, Chen M-H, et al. Trans-ethnic meta-analysis of white blood cell phenotypes. Hum Mol Genet. 2014;23:6944–60.
Telieps T, Köhler M, Treise I, Foertsch K, Adler T, Busch DH, et al. Longitudinal frequencies of blood leukocyte subpopulations differ between NOD and NOR mice but do not predict diabetes in NOD mice. J Diabetes Res. 2016;2016:4208156.
Manchia M, Cullis J, Turecki G, Rouleau GA, Uher R, Alda M. The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. PLoS ONE. 2013;8:e76295.
Tracy RP. Deep phenotyping’: characterizing populations in the era of genomics and systems biology. Curr Opin Lipidol. 2008;19:151–7.
Chiu Y-F, Justice AE, Melton PE. Longitudinal analytical approaches to genetic data. BMC Genet. 2016;17(Suppl 2):4.
Nagin DS. Group-based trajectory modeling: an overview. Ann Nutr Metab. 2014;65:205–10.
Strauss VY, Jones PW, Kadam UT, Jordan KP. Distinct trajectories of multimorbidity in primary care were identified using latent class growth analysis. J Clin Epidemiol. 2014;67:1163–71.
Gunzler DD, Morris N, Perzynski A, Ontaneda D, Briggs F, Miller D, et al. Heterogeneous depression trajectories in multiple sclerosis patients. Mult Scler Relat Disord. 2016;9:163–9.
Baker E, Iqbal E, Johnston C, Broadbent M, Shetty H, Stewart R, et al. Trajectories of dementia-related cognitive decline in a large mental health records derived patient cohort. PLoS ONE. 2017;12:e0178562.
Pugh SJ, Albert PS, Kim S, Grobman W, Hinkle SN, Newman RB, et al. Patterns of gestational weight gain and birthweight outcomes in the Eunice Kennedy Shriver National Institute of Child Health and Human Development Fetal Growth Studies-Singletons: a prospective study. Am J Obstet Gynecol. 2017. https://doi.org/10.1016/j.ajog.2017.05.013.
Justice AE, Howard AG, Chittoor G, Fernandez-Rhodes L, Graff M, Voruganti VS, et al. Genome-wide association of trajectories of systolic blood pressure change. BMC Proc. 2016;10:321–7.
Dick DM, Cho SB, Latendresse SJ, Aliev F, Nurnberger JI Jr, et al. Genetic influences on alcohol use across stages of development: GABRA2 and longitudinal trajectories of drunkenness from adolescence to young adulthood. Addict Biol. 2014;19:1055–64.
Lessov-Schlaggar CN, Kristjansson SD, Bucholz KK, Heath AC, Madden PAF. Genetic influences on developmental smoking trajectories. Addiction. 2012;107:1696–704.
Riglin L, Collishaw S, Thapar AK, Dalsgaard S, Langley K, Davey Smith G. et al. Association of genetic risk variants to attention-deficit hyperactivity disorder trajectories in the general population. JAMA Psychiatr. 2016;73:1285–92.
Holliday EG, McLean DE, Nyholt DR, Mowry BJ. Susceptibility locus on chromosome 1q23-25 for a schizophrenia subtype resembling deficit schizophrenia identified by latent class analysis. Arch Gen Psychiatry. 2009;66:1058–67.
Chen WJ. Taiwan Schizophrenia Linkage Study: lessons learned from endophenotype-based genome-wide linkage scans and perspective. Am J Med Genet B Neuropsychiatr Genet. 2013;162B:636–47.
Bureau A, Croteau J, Tayeb A, Mérette C, Labbe A. Latent class model with familial dependence to address heterogeneity in complex diseases: adapting the approach to family-based association studies. Genet Epidemiol. 2011;35:182–9.
Wickrama KKAS, O’Neal CW, Lee TK. Early community context, genes, and youth body mass index trajectories: an investigation of gene-community interplay over early life course. J Adolesc Health. 2013;53:328–34.
Proust-Lima C, Philipps V, Liquet B. Estimation of extended mixed models using latent classes and latent processes: The R package lcmm. J Stat Softw, Artic. 2017;78:1–56.
Lionel AC, Tammimies K, Vaags AK, Rosenfeld JA, Ahn JW, Merico D, et al. Disruption of the ASTN2/TRIM32 locus at 9q33.1 is a risk factor in males for autism spectrum disorders, ADHD and other neurodevelopmental phenotypes. Hum Mol Genet. 2014;23:2752–68.
Wang K-S, Tonarelli S, Luo X, Wang L, Su B, Zuo L, et al. Polymorphisms within ASTN2 gene are associated with age at onset of Alzheimer’s disease. J Neural Transm. 2015;122:701–8.
Vrijenhoek T, Buizer-Voskamp JE, van der Stelt I, Strengman E, Genetic Risk and Outcome in Psychosis (GROUP) Consortium, Sabatti C, et al. Recurrent CNVs disrupt three candidate genes in schizophrenia patients. Am J Hum Genet. 2008;83:504–10.
Poulsen M, Lukas C, Lukas J, Bekker-Jensen S, Mailand N. Human RNF169 is a negative regulator of the ubiquitin-dependent response to DNA double-strand breaks. J Cell Biol. 2012;197:189–99.
Oren A, Toporik A, Biton S, Almogy N, Eshel D, Bernstein J, et al. hCHL2, a novel chordin-related gene, displays differential expression and complex alternative splicing in human tissues and during myoblast and osteoblast maturation. Gene. 2004;331:17–31.
Hammond SM. An overview of microRNAs. Adv Drug Deliv Rev. 2015;87:3–14.
Mesak FM, Osada N, Hashimoto K, Liu QY, Ng CE. Molecular cloning, genomic characterization and over-expression of a novel gene, XRRA1, identified from human colorectal cancer cell HCT116Clone2_XRR and macaque testis. BMC Genom. 2003;4:32.
Kalies KU, Hartmann E. Membrane topology of the 12- and the 25-kDa subunits of the mammalian signal peptidase complex. J Biol Chem. 1996;271:3925–9.
Pan X, De Aragão CDBP, Velasco-Martin JP, Priestman DA, Wu HY, Takahashi K, et al. Neuraminidases 3 and 4 regulate neuronal function by catabolizing brain gangliosides. FASEB J. 2017;31:3467–83.
Jedidi K, Ramaswamy V, Desarbo WS. A maximum likelihood method for latent class regression involving a censored dependent variable. Psychometrika. 1993;58:375–94.
Gardner L, Patterson AM, Ashton BA, Stone MA, Middleton J. The human Duffy antigen binds selected inflammatory but not homeostatic chemokines. Biochem Biophys Res Commun. 2004;321:306–12.
Lindner C, Thiagarajah S, Wilkinson JM, Panoutsopoulou K, Day-Williams AG, arcOGEN Consortium. et al. Investigation of association between hip osteoarthritis susceptibility loci and radiographic proximal femur shape. Arthritis Rheumatol. 2015;67:2076–84.
Ohno R. Granulocyte colony-stimulating factor, granulocyte-macrophage colony-stimulating factor and macrophage colony-stimulating factor in the treatment of acute myeloid leukemia and acute lymphoblastic leukemia. Leuk Res. 1998;22:1143–54.
Zeidler C, Welte K. Kostmann syndrome and severe congenital neutropenia. Semin Hematol. 2002;39:82–8.
Chen J, Feng W, Jiang J, Deng Y, Huen MSY. Ring finger protein RNF169 antagonizes the ubiquitin-dependent signaling cascade at sites of DNA damage. J Biol Chem. 2012;287:27715–22.
Fishilevich S, Zimmerman S, Kohn A, Iny Stein T, Olender T, Kolker E, et al. Genic insights from integrated human proteomics in GeneCards. Database 2016; https://doi.org/10.1093/database/baw030.
Weber GM, Adams WG, Bernstam EV, Bickel JP, Fox KP, Marsolo K, et al. Biases introduced by filtering electronic health records for patients with ‘complete data’. J Am Med Inform Assoc. 2017;24:1134–41.
McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genom. 2011;4:13.
Roden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR, et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther. 2008;84:362–9.
CRAN-Package lcmm. https://cran.r-project.org/web/packages/lcmm/index.html (accessed 29 Jun 2017).
Chassin L, Fora DB, King KM. Trajectories of alcohol and drug use and dependence from adolescence to adulthood: the effects of familial alcoholism and personality. J Abnorm Psychol. 2004;113:483–98.
Andruff H, Carraro N, Thompson A, Gaudreau P. Latent class growth modelling: A tutorial. Tutor Quant Methods Psychol. 2009;5:11–24.
van de Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Struct Equ Model. 2017;24:451–67.
R Core Team. R: A Language and Environment for Statistical Computing. 2017. https://www.R-project.org/.
Zuvich RL, Armstrong LL, Bielinski SJ, Bradford Y, Carlson CS, Crawford DC, et al. Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality. Genet Epidemiol. 2011;35:887–98.
McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.
Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.
Loh P-R, Danecek P, Palamara PF, Fuchsberger C, A Reshef Y, K Finucane H, et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet. 2016;48:1443–8.
Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, et al. The eMERGE Genotype Set of 83,717 Subjects Imputed to ~40 Million Variants Genome Wide and Association with the Herpes Zoster Medical Record Phenotype. Genet Epidemiol. 2018; e-pub ahead of print 8 Oct 2018: https://doi.org/10.1002/gepi.22167.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.
Gogarten SM, Bhangale T, Conomos MP, Laurie CA, McHugh CP, Painter I, et al. GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics. 2012;28:3329–31.
Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7.
The eMERGE Network was initiated and funded by NHGRI through the following grants:
Phase III: U01HG8657 (Kaiser Permanente Washington, formerly Group Health Cooperative/University of Washington, Seattle); U01HG8685 (Brigham and Women’s Hospital); U01HG8672 (Vanderbilt University Medical Center); U01HG8666 (Cincinnati Children’s Hospital Medical Center); U01HG6379 (Mayo Clinic); U01HG8679 (Geisinger Clinic); U01HG8680 (Columbia University Health Sciences); U01HG8684 (Children’s Hospital of Philadelphia); U01HG8673 (Northwestern University); U01HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG8676 (Partners Healthcare/Broad Institute); and U01HG8664 (Baylor College of Medicine).
Phase II: U01HG006828 (Cincinnati Children’s Hospital Medical Center/Boston Children’s Hospital); U01HG006830 (Children’s Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center), U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers, and U01HG004438 (CIDR) serving as a Sequencing Center.
Phase I: U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and Vanderbilt University Medical Center); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-04603 (Vanderbilt University Medical Center, also serving as the Administrative Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers.
Conflict of interest
The authors declare that they have no conflict of interest.
Electronic supplementary material
About this article
Cite this article
Hall, T.O., Stanaway, I.B., Carrell, D.S. et al. Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling. Genes Immun 20, 555–565 (2019). https://doi.org/10.1038/s41435-018-0051-y