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Thromb Haemost 2017; 117(04): 758-768
DOI: 10.1160/TH16-08-0652
Stroke, Systemic or Venous Thromboembolism
Schattauer GmbH

Identification of unique venous thromboembolism-susceptibility variants in African-Americans

John A. Heit
1   Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
2   Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
,
Sebastian M. Armasu
3   Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
,
Bryan M. McCauley
3   Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
,
Iftikhar J. Kullo
1   Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
,
Hugues Sicotte
3   Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
,
Jyotishman Pathak
4   Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, New York, , USA
,
Christopher G. Chute
5   Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
,
Omri Gottesman
6   The Institute for Personalized Medicine, Mt. Sinai School of Medicine, New York, New York, USA
,
Erwin P. Bottinger
6   The Institute for Personalized Medicine, Mt. Sinai School of Medicine, New York, New York, USA
,
Joshua C. Denny
7   Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA
8   Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
,
Dan M. Roden
9   Department of Pharmacology, Vanderbilt University, Nashville, Tennessee, USA
,
Rongling Li
10   The Office of Population Genomics, National Human Genome Research Institute, Bethesda, Marlyland, USA
,
Marylyn D. Ritchie
11   Center for Systems Genomics, The Huck Institutes of the Life Sciences, Eberly College of Science, Pennsylvania State University, University Park, Pennsylvania, USA
,
Mariza de Andrade
3   Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
› Author Affiliations Financial support: This article was partially supported by grants from the National Institutes of Health, National Heart, Lung and Blood Institute (HL66216 and HL83141 to JAH), the National Human Genome Research Institute (HG04735 to JAH, HG06379 to IJK and CGC), and by Mayo Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Further Information

Publication History

Received: 19 August 2016

Accepted after minor revision: 12 January 2017

Publication Date:
28 November 2017 (online)

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Summary

To identify novel single nucleotide polymorphisms (SNPs) associated with venous thromboembolism (VTE) in African-Americans (AAs), we performed a genome-wide association study (GWAS) of VTE in AAs using the Electronic Medical Records and Genomics (eMERGE) Network, comprised of seven sites each with DNA biobanks (total ~39,200 unique DNA samples) with genome-wide SNP data (imputed to 1000 Genomes Project cosmopolitan reference panel) and linked to electronic health records (EHRs). Using a validated EHR-driven phenotype extraction algorithm, we identified VTE cases and controls and tested for an association between each SNP and VTE using unconditional logistic regression, adjusted for age, sex, stroke, site-platform combination and sickle cell risk genotype. Among 393 AA VTE cases and 4,941 AA controls, three intragenic SNPs reached genome-wide significance: LEMD3 rs138916004 (OR=3.2; p=1.3E-08), LY86 rs3804476 (OR=1.8; p=2E-08) and LOC100130298 rs142143628 (OR=4.5; p=4.4E-08); all three SNPs validated using internal cross-validation, parametric bootstrap and meta-analysis methods. LEMD3 rs138916004 and LOC100130298 rs142143628 are only present in Africans (1000G data). LEMD3 showed a significant differential expression in both NCBI Gene Expression Omnibus (GEO) and the Mayo Clinic gene expression data, LOC100130298 showed a significant differential expression only in the GEO expression data, and LY86 showed a significant differential expression only in the Mayo expression data. LEMD3 encodes for an antagonist of TGF-β-induced cell proliferation arrest. LY86 encodes for MD-1 which down-regulates the pro-inflammatory response to lipopolysaccharide; LY86 variation was previously associated with VTE in white women; LOC100130298 is a non-coding RNA gene with unknown regulatory activity in gene expression and epigenetics.

Supplementary Material to this article is available online at www.thrombosis-online.com.

Keywords

Genetic variation - venous thromboembolism - African Americans - association analyses

 

  • References

  • 1 Heit JA, Cunningham JM, Petterson TM. et al. Genetic variation within the anticoagulant, procoagulant, fibrinolytic and innate immunity pathways as risk factors for venous thromboembolism. J Thromb Haemost 2011; 9: 1133-1142
    CrossrefPubMedGoogle Scholar
  • 2 Huang W, Goldberg RJ, Anderson FA. et al. Secular trends in occurrence of acute venous thromboembolism: the Worcester VTE study (1985 2009). Am J Med 2014; 127: 829-839 e5
    CrossrefPubMedGoogle Scholar
  • 3 Koton S, Schneider AL, Rosamond WD. et al. Stroke incidence and mortality trends in US communities, 1987 to 2011. J Am Med Assoc 2014; 312: 259-268
    CrossrefPubMedGoogle Scholar
  • 4 White RH, Keenan CR. Effects of race and ethnicity on the incidence of venous thromboembolism. Thromb Res 2009; 123 (04) Suppl S11-S17
    CrossrefPubMedGoogle Scholar
  • 5 Larsen TB, Sorensen HT, Skytthe A. et al. Major genetic susceptibility for venous thromboembolism in men: a study of Danish twins. Epidemiology 2003; 14: 328-332
    PubMedGoogle Scholar
  • 6 Heit JA, Phelps MA, Ward SA. et al. Familial segregation of venous thromboembolism. J Thromb Haemost 2004; 2: 731-736
    CrossrefPubMedGoogle Scholar
  • 7 Zöller B, Ohlsson H, Sundquist J. et al. Familial risk of venous thromboembolism in first-, second- and third-degree relatives: a nationwide family study in Sweden. Thromb Haemost 2013; 109: 458-463
    Article in Thieme ConnectPubMedGoogle Scholar
  • 8 Germain M, Chasman DI, de Haan H. et al. Meta-analysis of 65,734 individuals identifies TSPAN15 and SLC44A2 as two susceptibility loci for venous thromboembolism. Amer J Hum Gen 2015; 96: 532-542
    PubMedGoogle Scholar
  • 9 Dilley A, Austin H, Hooper WC. et al. Prevalence of the prothrombin 20210 G-to-A variant in blacks: infants, patients with venous thrombosis, patients with myocardial infarction, and control subjects. J Lab Clin Med 1998; 132: 452-455
    CrossrefPubMedGoogle Scholar
  • 10 Austin H, Key NS, Benson JM. et al. Sickle cell trait and the risk of venous thromboembolism among blacks. Blood 2007; 110: 908-912
    CrossrefPubMedGoogle Scholar
  • 11 Austin H, De Staercke C, Lally C. et al. New gene variants associated with venous thrombosis: a replication study in White and Black Americans. J Thromb Haemost 2011; 9: 489-495
    CrossrefPubMedGoogle Scholar
  • 12 Hernandez W, Gamazon ER, Smithberger E. et al. Novel genetic predictors of venous thromboembolism risk in African Americans. Blood 2016; 127: 1923-1929
    CrossrefPubMedGoogle Scholar
  • 13 Rees DC, Cox M, Clegg JB. World distribution of factor V Leiden. Lancet 1995; 346: 1133-1134
    CrossrefPubMedGoogle Scholar
  • 14 Dilley A, Austin H, Hooper WC. et al. Relation of three genetic traits to venous thrombosis in an African-American population. Am J Epidemiol 1998; 147: 30-35
    CrossrefPubMedGoogle Scholar
  • 15 Genome-Wide Studies in Biorepositories with Electronic Medical Record Data (U01) 2007 [cited; National Human Genome Research Institute RFA Announcement] Available from:. http: //grants.nih.gov/grants/guide/rfa-files/RFA-HG-07-005.html
    PubMedGoogle Scholar
  • 16 McCarty CA, Chisholm RL, Chute CG. et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics 2011; 4: 13
    CrossrefPubMedGoogle Scholar
  • 17 Gottesman O, Kuivaniemi H, Tromp G. et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med 2013; 15: 761-771
    CrossrefPubMedGoogle Scholar
  • 18 Pathak J, Wang J, Kashyap S. et al. Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience. J Am Med Inform Assoc 2011; 18: 376-386
    CrossrefPubMedGoogle Scholar
  • 19 McPeek Hinz ER, Bastarache L, Denny JC. A natural language processing algorithm to define a venous thromboembolism phenotype. Association of Moving Image Archivists Conference; 2013 November 2013; Richmond, VA. 2013: p. 975-983
    PubMedGoogle Scholar
  • 20 Turner S, Armstrong LL, Bradford Y. et al. Quality control procedures for genome-wide association studiesCurr Protoc Hum Genet 2011; Chapter 1: Unit1 19.
    PubMedGoogle Scholar
  • 21 Zuvich RL, Armstrong LL, Bielinski SJ. et al. Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality. Genetic Epidemiol 2011; 35: 887-898
    CrossrefPubMedGoogle Scholar
  • 22 Denny JC, Crawford DC, Ritchie MD. et al. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. Amer J Hum Gen 2011; 89: 529-542
    PubMedGoogle Scholar
  • 23 Ritchie MD, Denny JC, Crawford DC. et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Amer J Hum Gen 2010; 86: 560-572
    PubMedGoogle Scholar
  • 24 Heit JA. Epidemiology of venous thromboembolism. Nat Rev Cardiol 2015; 12: 464-474
    CrossrefPubMedGoogle Scholar
  • 25 Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3 (Bethesda) 2011; 1: 457-470
    PubMedGoogle Scholar
  • 26 Delaneau O, Zagury JF, Marchini J. Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods 2013; 10: 5-6
    CrossrefPubMedGoogle Scholar
  • 27 Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009; 5: e1000529
    CrossrefPubMedGoogle Scholar
  • 28 Purcell S, Neale B, Todd-Brown K. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Amer J Hum Gen 2007; 81: 559-575
    PubMedGoogle Scholar
  • 29 Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet 2006; 2: e190
    CrossrefPubMedGoogle Scholar
  • 30 Price AL, Patterson NJ, Plenge RM. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904-909
    CrossrefPubMedGoogle Scholar
  • 31 Panagiotou OA, Ioannidis JP. What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Intern J Epidemiol 2012; 41: 273-286
    CrossrefPubMedGoogle Scholar
  • 32 Buzkova, Lumley, Rice. Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions. Ann Hum Gen. 2011; 75: 36-45
    PubMedGoogle Scholar
  • 33 R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013
    PubMedGoogle Scholar
  • 34 Lewis DA, Suchindran S, Beckman MG. et al. Whole blood gene expression profiles distinguish clinical phenotypes of venous thromboembolism. Thromb Res 2015; 135: 659-665
    CrossrefPubMedGoogle Scholar
  • 35 Cunningham JM, Oberg AL, Borralho PM. et al. Evaluation of a new high-dimensional miRNA profiling platform. BMC Med Genomics 2009; 2: 57
    CrossrefPubMedGoogle Scholar
  • 36 Mahoney DW, Therneau TM, Anderson SK. et al. Quality assessment metrics for whole genome gene expression profiling of paraffin embedded samples. BMC Res Notes 2013; 6: 33
    CrossrefPubMedGoogle Scholar
  • 37 Gagnon-Bartsch JA, Speed TP. Using control genes to correct for unwanted variation in microarray data. Biostatistics 2012; 13: 539-552
    CrossrefPubMedGoogle Scholar
  • 38 Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3: Article3
    PubMedGoogle Scholar
  • 39 Lin F, Blake DL, Callebaut I. et al. MAN1, an inner nuclear membrane protein that shares the LEM domain with lamina-associated polypeptide 2 and emerin. J Biol Chem 2000; 275: 4840-4847
    CrossrefPubMedGoogle Scholar
  • 40 Lin F, Morrison JM, Wu W. et al. MAN1, an integral protein of the inner nuclear membrane, binds Smad2 and Smad3 and antagonizes transforming growth factor-beta signaling. Human Mol Gen 2005; 14: 437-445
    CrossrefPubMedGoogle Scholar
  • 41 Bourgeois B, Gilquin B, Tellier-Lebegue C. et al. Inhibition of TGF-beta signaling at the nuclear envelope: characterisation of interactions between MAN1, Smad2 and Smad3, and PPM1A. Sci Signal 2013; 6: ra49.
    PubMedGoogle Scholar
  • 42 Horbelt D, Denkis A, Knaus P. A portrait of Transforming Growth Factor beta superfamily signalling: Background matters. Int J Biochem Cell Biol 2012; 44: 469-474
    CrossrefPubMedGoogle Scholar
  • 43 Ashfaque M, Sundquist K, Wang X. et al. Transforming growth factor (TGF)-levels and unprovoked recurrent venous thromboembolism. J Thromb Thrombolysis 2014; 38: 348-354
    CrossrefPubMedGoogle Scholar
  • 44 Tracz MJ, Juncos JP, Grande JP. et al. Induction of heme oxygenase-1 is a beneficial response in a murine model of venous thrombosis. Am J Pathol 2008; 173: 1882-1890
    CrossrefPubMedGoogle Scholar
  • 45 Mustafa S, Weltermann A, Fritsche R. et al. Genetic variation in heme oxygenase 1 (HMOX1) and the risk of recurrent venous thromboembolism. J Vasc Surg 2008; 47: 566-570
    CrossrefPubMedGoogle Scholar
  • 46 Kim HJ, Kim PK, Bae SM. et al. Transforming growth factor-beta-induced protein (TGFBIp/beta ig-h3) activates platelets and promotes thrombogenesis. Blood 2009; 114: 5206-5215
    CrossrefPubMedGoogle Scholar
  • 47 Smeeth L, Cook C, Thomas S. et al. Risk of deep vein thrombosis and pulmonary embolism after acute infection in a community setting. Lancet 2006; 367: 1075-1079
    CrossrefPubMedGoogle Scholar
  • 48 Reitsma P, Rosendaal F. Activation of innate immunity in patients with venous thrombosis: the Leiden Thrombophilia Study. J Thromb Haemost 2004; 2: 619-622
    CrossrefPubMedGoogle Scholar
  • 49 Heit JA, Armasu SM, Asmann YW. et al. A genome-wide association study of venous thromboembolism identifies risk variants in chromosomes 1q24.2 and 9q. J Thromb Haemost 2012; 10: 1521-1531
    CrossrefPubMedGoogle Scholar
  • 50 Heller R, Bogomolov M, Benjamini Y. Deciding whether follow-up studies have replicated findings in a preliminary large-scale omics study. Proc Nat Acad Sci USA 2014; 111: 16262-16267
    CrossrefPubMedGoogle Scholar