Advertisement

Mammalian Genome

, Volume 24, Issue 3–4, pp 105–118 | Cite as

Genome-wide association mapping of blood cell traits in mice

  • Richard C. Davis
  • Atila van Nas
  • Brian Bennett
  • Luz Orozco
  • Calvin Pan
  • Christoph D. Rau
  • Eleazar Eskin
  • Aldons J. LusisEmail author
Article

Abstract

Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75 Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice.

Keywords

Quantitative Trait Locus Mean Corpuscular Volume Linkage Disequilibrium Block Mean Corpuscular Hemoglobin Concentration Recombinant Inbred Strain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by NIH grants HL030568, HL028841, DK094311.

Disclosures

The authors have no conflicts of interest to disclose.

Supplementary material

335_2013_9448_MOESM1_ESM.pdf (62 kb)
Supplementary material 1 (PDF 62 kb)
335_2013_9448_MOESM2_ESM.pdf (166 kb)
Supplementary material 2 (PDF 166 kb)
335_2013_9448_MOESM3_ESM.pdf (99 kb)
Supplementary material 3 (PDF 98 kb)
335_2013_9448_MOESM4_ESM.pdf (64 kb)
Supplementary material 4 (PDF 63 kb)
335_2013_9448_MOESM5_ESM.pdf (58 kb)
Supplementary material 5 (PDF 57 kb)
335_2013_9448_MOESM6_ESM.pdf (41 kb)
Supplementary material 6 (PDF 41 kb)

References

  1. Bennett BJ, Farber CR, Orozco L et al (2010) A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res 20:281–290PubMedCrossRefGoogle Scholar
  2. Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890PubMedCrossRefGoogle Scholar
  3. Davis RC, van Nas A, Castellani LW et al (2012) Systems genetics of susceptibility to obesity-induced diabetes in mice. Physiol Genomics 44:1–13PubMedCrossRefGoogle Scholar
  4. Evans DM, Frazer IH, Martin NG (1999) Genetic and environmental causes of variation in basal levels of blood cells. Twin Res 2:250–257PubMedGoogle Scholar
  5. Farber CR, Bennett BJ, Orozco L et al (2011) Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis. PLoS Genet 7:e1002038PubMedCrossRefGoogle Scholar
  6. Flint J, Eskin E (2012) Genome-wide association studies in mice. Nat Rev Genet 13:807–817PubMedCrossRefGoogle Scholar
  7. Flint J, Valdar W, Shifman S, Mott R (2005) Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet 6:271–286PubMedCrossRefGoogle Scholar
  8. Ganesh SK, Zakai NA, van Rooij FJ et al (2009) Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium. Nat Genet 41:1191–1198PubMedCrossRefGoogle Scholar
  9. Garner C, Tatu T, Reittie JE et al (2000) Genetic influences on F cells and other hematologic variables: a twin heritability study. Blood 95:342–346PubMedGoogle Scholar
  10. Gillum RF, Mussolino ME, Madans JH (2005) Counts of neutrophils, lymphocytes, and monocytes, cause-specific mortality and coronary heart disease: the NHANES-I epidemiologic follow-up study. Ann Epidemiol 15:266–271PubMedCrossRefGoogle Scholar
  11. Hedrick CC, Castellani LW, Warden CH, Puppione DL, Lusis AJ (1993) Influence of mouse apolipoprotein A-II on plasma lipoproteins in transgenic mice. J Biol Chem 268:20676–20682PubMedGoogle Scholar
  12. Horvat S, Bunger L (1999) Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay for the mouse leptin receptor (Lepr(db)) mutation. Lab Anim 33:380–384PubMedCrossRefGoogle Scholar
  13. Kang HM, Zaitlen NA, Wade CM et al (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723PubMedCrossRefGoogle Scholar
  14. Kelada SN, Aylor DL, Peck BC et al (2012) Genetic analysis of hematological parameters in incipient lines of the collaborative cross. G3 (Bethesda) 2:157–165CrossRefGoogle Scholar
  15. Laurie CC, Nickerson DA, Anderson AD et al (2007) Linkage disequilibrium in wild mice. PLoS Genet 3:e144PubMedCrossRefGoogle Scholar
  16. Lloyd-Jones DM, Camargo CA, Allen LA, Giugliano RP, O’Donnell CJ (2003) Predictors of long-term mortality after hospitalization for primary unstable angina pectoris and non-ST-elevation myocardial infarction. Am J Cardiol 92:1155–1159PubMedCrossRefGoogle Scholar
  17. Nalls MA, Couper DJ, Tanaka T et al (2011) Multiple loci are associated with white blood cell phenotypes. PLoS Genet 7:e1002113PubMedCrossRefGoogle Scholar
  18. Orozco LD, Bennett BJ, Farber CR et al (2012) Unraveling inflammatory responses using systems genetics and gene-environment interactions in macrophages. Cell 151:658–670PubMedCrossRefGoogle Scholar
  19. Payseur BA, Place M, Weber JL (2008) Linkage disequilibrium between STRPs and SNPs across the human genome. Am J Hum Genet 82:1039–1050PubMedCrossRefGoogle Scholar
  20. Peters LL, Shavit JA, Lambert AJ et al (2010) Sequence variation at multiple loci influences red cell hemoglobin concentration. Blood 116:e139–e149PubMedCrossRefGoogle Scholar
  21. Puppione DL, Charugundla S (1994) A microprecipitation technique suitable for measuring alpha-lipoprotein cholesterol. Lipids 29:595–597PubMedCrossRefGoogle Scholar
  22. Reiner AP, Lettre G, Nalls MA et al (2011) 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:e1002108PubMedCrossRefGoogle Scholar
  23. Shankar A, Wang JJ, Rochtchina E, Yu MC, Kefford R, Mitchell P (2006) Association between circulating white blood cell count and cancer mortality: a population-based cohort study. Arch Intern Med 166:188–194PubMedCrossRefGoogle Scholar
  24. Soranzo N, Spector TD, Mangino M et al (2009) A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet 41:1182–1190PubMedCrossRefGoogle Scholar
  25. van der Harst P (2013) Seventy-five genetic loci influencing the human red blood cell. Nature 492:369–375CrossRefGoogle Scholar
  26. van Nas A, Ingram-Drake L, Sinsheimer JS et al (2010) Expression quantitative trait loci: replication, tissue- and sex-specificity in mice. Genetics 185:1059–1068PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Richard C. Davis
    • 2
  • Atila van Nas
    • 1
    • 2
  • Brian Bennett
    • 2
  • Luz Orozco
    • 1
    • 2
  • Calvin Pan
    • 1
  • Christoph D. Rau
    • 3
  • Eleazar Eskin
    • 1
    • 5
  • Aldons J. Lusis
    • 1
    • 2
    • 3
    • 4
    • 6
    Email author
  1. 1.Department of Human Genetics, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of Medicine/Division of Cardiology, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA
  3. 3.Department of Microbiology, Immunology & Molecular Genetics, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA
  4. 4.Molecular Biology Institute, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA
  5. 5.Department of Computer ScienceUniversity of CaliforniaLos AngelesUSA
  6. 6.Med-Cardio/MicrobioUniversity of CaliforniaLos AngelesUSA

Personalised recommendations