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Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia


Sickle cell anemia (SCA) is a paradigmatic single gene disorder caused by homozygosity with respect to a unique mutation at the β-globin locus. SCA is phenotypically complex, with different clinical courses ranging from early childhood mortality to a virtually unrecognized condition. Overt stroke is a severe complication affecting 6–8% of individuals with SCA. Modifier genes might interact to determine the susceptibility to stroke, but such genes have not yet been identified. Using Bayesian networks, we analyzed 108 SNPs in 39 candidate genes in 1,398 individuals with SCA. We found that 31 SNPs in 12 genes interact with fetal hemoglobin to modulate the risk of stroke. This network of interactions includes three genes in the TGF-β pathway and SELP, which is associated with stroke in the general population. We validated this model in a different population by predicting the occurrence of stroke in 114 individuals with 98.2% accuracy.

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Figure 1: Examples of Bayesian network structures.
Figure 2: The Bayesian network describing the joint association of 69 SNPs with stroke.
Figure 3: Box plot of the predictive probability of stroke (risk in 5 years) in an independent set of 7 individuals with stroke and 107 individuals without stroke.

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  1. Adams, R.J. et al. Stroke and conversion to high risk in children screened with transcranial Doppler ultrasound during the STOP study. Blood 103, 3689–3694 (2004).

    Article  CAS  Google Scholar 

  2. Steinberg, M.H., Forget, B.G., Higgs, D.R. & Nagel, R.L. Disorders of Hemoglobin: Genetics, Pathophysiology, and Clinical Management (Cambridge University Press, Cambridge, 2001).

    Google Scholar 

  3. Ware, R.E., Zimmerman, S.A. & Schultz, W.H. Hydroxyurea as an alternative to blood transfusions for the prevention of recurrent stroke in children with sickle cell disease. Blood 94, 3022–3026 (1999).

    CAS  PubMed  Google Scholar 

  4. Adams, R.J. et al. Prevention of a first stroke by transfusions in children with sickle cell anemia and abnormal results on transcranial Doppler ultrasonography. N. Engl. J. Med. 339, 5–11 (1998).

    Article  CAS  Google Scholar 

  5. Taylor, J.G.t. et al. Variants in the VCAM1 gene and risk for symptomatic stroke in sickle cell disease. Blood 100, 4303–4309 (2002).

    Article  CAS  Google Scholar 

  6. Hoppe, C. et al. Gene interactions and stroke risk in children with sickle cell anemia. Blood 103, 2391–2396 (2004).

    Article  CAS  Google Scholar 

  7. Adams, R.J. et al. Alpha thalassemia and stroke risk in sickle cell anemia. Am. J. Hematol. 45, 279–282 (1994).

    Article  CAS  Google Scholar 

  8. Platt, O.S. et al. Mortality in sickle cell disease. Life expectancy and risk factors for early death. N. Engl. J. Med. 330, 1639–1644 (1994).

    Article  CAS  Google Scholar 

  9. Gaston, M. et al. Recruitment in the Cooperative Study of Sickle Cell Disease (CSSCD). Control Clin. Trials 8, 131S–140S (1987).

    Article  CAS  Google Scholar 

  10. Gabriel, S.B. et al. Segregation at three loci explains familial and population risk in Hirschsprung disease. Nat. Genet. 31, 89–93 (2002).

    Article  CAS  Google Scholar 

  11. Collins, F.S., Green, E.D., Guttmacher, A.E. & Guyer, M.S. A vision for the future of genomics research. Nature 422, 835–847 (2003).

    Article  CAS  Google Scholar 

  12. Carlson, C.S., Eberle, M.A., Kruglyak, L. & Nickerson, D.A. Mapping complex disease loci in whole-genome association studies. Nature 429, 446–452 (2004).

    Article  CAS  Google Scholar 

  13. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004).

    Article  CAS  Google Scholar 

  14. Jansen, R. et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003).

    Article  CAS  Google Scholar 

  15. Lauritzen, S.L. & Sheehan, N.A. Graphical models for genetic analysis. Statist. Sci. 18, 489–514 (2004).

    Article  Google Scholar 

  16. Cowell, R.G., Dawid, A.P., Lauritzen, S.L. & Spiegelhalter, D.J. Probabilistic Networks and Expert Systems (Springer, New York, 1999).

    Google Scholar 

  17. Chakravarti, A. Population genetics–making sense out of sequence. Nat. Genet. 21, 56–60 (1999).

    Article  CAS  Google Scholar 

  18. Hoh, J. & Ott, J. Mathematical multi-locus approaches to localizing complex human trait genes. Nat. Rev. Genet. 4, 701–709 (2003).

    Article  CAS  Google Scholar 

  19. Hand, D.J., Mannila, H. & Smyth, P. Principles of Data Mining (MIT Press, Cambridge, Massachusetts, 2001).

    Google Scholar 

  20. Ling, Q. et al. Annexin II regulates fibrin homeostasis and neoangiogenesis in vivo. J. Clin. Invest. 113, 38–48 (2004).

    Article  CAS  Google Scholar 

  21. Angerio, A.D. & Lee, N.D. Sickle cell crisis and endothelin antagonists. Crit. Care Nurs. Q. 26, 225–229 (2003).

    Article  Google Scholar 

  22. Brown, C.B., Boyer, A.S., Runyan, R.B. & Barnett, J.V. Requirement of type III TGF-beta receptor for endocardial cell transformation in the heart. Science 283, 2080–2082 (1999).

    Article  CAS  Google Scholar 

  23. Zee, R.Y. et al. Polymorphism in the P-selectin and interleukin-4 genes as determinants of stroke: a population-based, prospective genetic analysis. Hum. Mol. Genet. 13, 389–396 (2004).

    Article  CAS  Google Scholar 

  24. Alexander, N., Higgs, D., Dover, G. & Serjeant, G.R. Are there clinical phenotypes of homozygous sickle cell disease? Br. J. Haematol. 126, 606–611 (2004).

    Article  Google Scholar 

  25. Steinberg, M.H. et al. Association of polymorphisms in genes of the transforming growth factor-beta pathway with sickle cell osteonecrosis. Blood 102, 262A–263A (2003).

    Article  Google Scholar 

  26. Botstein, D. & Risch, N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat. Genet. 33 Suppl: 228–237 (2003).

    Article  CAS  Google Scholar 

  27. Beaumont, M.A. & Rannala, B. The Bayesian revolution in genetics. Nat. Rev. Genet. 5, 251–261 (2004).

    Article  CAS  Google Scholar 

  28. Ohene-Frempong, K. et al. Cerebrovascular accidents in sickle cell disease: rates and risk factors. Blood 91, 288–294 (1998).

    CAS  PubMed  Google Scholar 

  29. Chiu, N.H. et al. Mass spectrometry of single-stranded restriction fragments captured by an undigested complementary sequence. Nucleic Acids Res. 28, E31 (2000).

    Article  CAS  Google Scholar 

  30. Cooper, G.F. & Herskovitz, G.F. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347 (1992).

    Google Scholar 

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We thank R. Adams, A. Anderson and R. Iyer for providing the blood samples of the individuals with stroke in the independent validation set. This work was supported by National Science Foundation and the National Heart, Lung and Blood Institute of the National Institutes of Health.

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Correspondence to Marco F Ramoni.

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Competing interests

P.S. and M.F.R. have financial interests in the company that produces one of the software programs used to analyze data reported in this paper.

Supplementary information

Supplementary Fig. 1

Box plot of the predictive probability of stroke (risk in 5 years) in an independent set of 7 stroke patients and 107 non-stroke patients obtained through logistic regression. (PDF 18 kb)

Supplementary Table 1

Correspondence table between SNPs in the network in Figure 2 and their RS number. (PDF 18 kb)

Supplementary Table 2

Results of the predictive validation. (PDF 29 kb)

Supplementary Table 3

Epidemiological statistics of the patient population. (PDF 10 kb)

Supplementary Table 4

Conditional probability distributions quantifying the network in Figure 2. (PDF 12 kb)

Supplementary Table 5

Summary statistics of the logistic regression model. (PDF 12 kb)

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Sebastiani, P., Ramoni, M., Nolan, V. et al. Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat Genet 37, 435–440 (2005).

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