Human Genetics

, Volume 133, Issue 1, pp 95–109 | Cite as

A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects

  • Khader Shameer
  • Joshua C. Denny
  • Keyue Ding
  • Hayan Jouni
  • David R. Crosslin
  • Mariza de Andrade
  • Christopher G. Chute
  • Peggy Peissig
  • Jennifer A. Pacheco
  • Rongling Li
  • Lisa Bastarache
  • Abel N. Kho
  • Marylyn D. Ritchie
  • Daniel R. Masys
  • Rex L. Chisholm
  • Eric B. Larson
  • Catherine A. McCarty
  • Dan M. Roden
  • Gail P. Jarvik
  • Iftikhar J. KulloEmail author
Original Investigation


Platelets are enucleated cell fragments derived from megakaryocytes that play key roles in hemostasis and in the pathogenesis of atherothrombosis and cancer. Platelet traits are highly heritable and identification of genetic variants associated with platelet traits and assessing their pleiotropic effects may help to understand the role of underlying biological pathways. We conducted an electronic medical record (EMR)-based study to identify common variants that influence inter-individual variation in the number of circulating platelets (PLT) and mean platelet volume (MPV), by performing a genome-wide association study (GWAS). We characterized genetic variants associated with MPV and PLT using functional, pathway and disease enrichment analyses; we assessed pleiotropic effects of such variants by performing a phenome-wide association study (PheWAS) with a wide range of EMR-derived phenotypes. A total of 13,582 participants in the electronic MEdical Records and GEnomic network had data for PLT and 6,291 participants had data for MPV. We identified five chromosomal regions associated with PLT and eight associated with MPV at genome-wide significance (P < 5E−8). In addition, we replicated 20 SNPs [out of 56 SNPs (α: 0.05/56 = 9E−4)] influencing PLT and 22 SNPs [out of 29 SNPs (α: 0.05/29 = 2E−3)] influencing MPV in a published meta-analysis of GWAS of PLT and MPV. While our GWAS did not find any new associations, our functional analyses revealed that genes in these regions influence thrombopoiesis and encode kinases, membrane proteins, proteins involved in cellular trafficking, transcription factors, proteasome complex subunits, proteins of signal transduction pathways, proteins involved in megakaryocyte development, and platelet production and hemostasis. PheWAS using a single-SNP Bonferroni correction for 1,368 diagnoses (0.05/1368 = 3.6E−5) revealed that several variants in these genes have pleiotropic associations with myocardial infarction, autoimmune, and hematologic disorders. We conclude that multiple genetic loci influence interindividual variation in platelet traits and also have significant pleiotropic effects; the related genes are in multiple functional pathways including those relevant to thrombopoiesis.


Enrichment Analysis Mean Platelet Volume European Ancestry Disease Ontology Term Enrichment Analysis 
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.



The eMERGE network was initiated and funded by the National Human Genome Research Institute (NHGR1), with additional funding from National Institute of General Medical Sciences (NIGMS) through the following grants: U01-HG-04599 (Mayo Clinic); U01-HG-004610 and UO1-AG-06781 (Group Health Cooperative); U01-HG-004608 (Marshfield Clinic); U01HG004609 (Northwestern University); and U01-HG-04603 (Vanderbilt University, also serving as the Administrative Coordinating Center). We also acknowledge the genotyping centers U01-HG-004424 (Broad Institute) and U01-HG-004438 (Johns Hopkins University, Center for Inherited Disease Research). Additional genotyping support was provided by a Washington State Life Sciences Discovery Fund award to the Northwest Institute of Genetic Medicine (G.P.J). Additional support for PheWAS was provided through R01-LM-010685.

Conflict of interest

The authors declare no competing financial interests.

Supplementary material

439_2013_1355_MOESM1_ESM.pdf (967 kb)
Supplementary material 1 (PDF 967 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Khader Shameer
    • 1
  • Joshua C. Denny
    • 2
  • Keyue Ding
    • 1
  • Hayan Jouni
    • 1
  • David R. Crosslin
    • 3
  • Mariza de Andrade
    • 4
  • Christopher G. Chute
    • 4
  • Peggy Peissig
    • 5
  • Jennifer A. Pacheco
    • 6
  • Rongling Li
    • 7
  • Lisa Bastarache
    • 2
  • Abel N. Kho
    • 8
  • Marylyn D. Ritchie
    • 9
  • Daniel R. Masys
    • 10
  • Rex L. Chisholm
    • 6
  • Eric B. Larson
    • 11
  • Catherine A. McCarty
    • 12
  • Dan M. Roden
    • 13
  • Gail P. Jarvik
    • 14
  • Iftikhar J. Kullo
    • 1
    Email author
  1. 1.Division of Cardiovascular DiseasesMayo ClinicRochesterUSA
  2. 2.Departments of Medicine and Biomedical InformaticsVanderbilt UniversityNashvilleUSA
  3. 3.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  4. 4.Division of Biomedical Statistics and InformaticsMayo ClinicRochesterUSA
  5. 5.Biomedical Informatics Research CenterMarshfield ClinicMarshfieldUSA
  6. 6.Feinberg School of MedicineNorthwestern UniversityChicagoUSA
  7. 7.Office of Population GenomicsNational Human Genome Research InstituteBethesdaUSA
  8. 8.Department of MedicineNorthwestern UniversityChicagoUSA
  9. 9.Center for Systems Genomics, The Huck Institutes of the Life Sciences, Eberly College of SciencePennsylvania State UniversityUniversity ParkUSA
  10. 10.Department of Biomedical InformaticsVanderbilt University School of MedicineNashvilleUSA
  11. 11.Group Health Research InstituteSeattleUSA
  12. 12.Essentia Institute of Rural HealthDuluthUSA
  13. 13.Department of PharmacologyVanderbilt University School of MedicineNashvilleUSA
  14. 14.Department of Genome SciencesUniversity of WashingtonSeattleUSA

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