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

Abstract

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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Acknowledgements

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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Authors

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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). https://doi.org/10.1038/ng1533

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  • DOI: https://doi.org/10.1038/ng1533

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