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 beta-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-beta 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.

Figure 1

Examples of Bayesian network structures. (a) A simple Bayesian network with two nodes representing a SNP (G) and a phenotype (P). The probability distribution of G represents the genotype distribution in the population, and the conditional probability distribution of P describes the distribution of the phenotype given each genotype. (b) The association between G and P can be reversed using Bayes theorem. (c) A Bayesian network linking four SNPs (G1–G4) to a phenotype P. The phenotype is independent of the other SNPs, once we know the SNPs G3 and G4. The joint probability distribution of the network is fully specified by the five distributions representing the distribution of G1 (two parameters), of G2 given G1 (six parameters), of G3 given G2 (six parameters), of G4 given G2 (six parameters) and of P given G3 and G4 (nine parameters). The full probability distribution requires 81 × 2 − 1 = 161 parameters; this network requires only 29.

Figure 2

The Bayesian network describing the joint association of 69 SNPs with stroke. Nodes represent SNPs or clinical factors; the numbers after each gene distinguish different SNPs on the same gene. SNP are shown as blue nodes; their rs numbers are given in Supplementary Table 4 online. Clinical variables (HbF.G, fetal hemoglobin (g dL⁻¹); HbF.P, fetal hemoglobin (%); HbG, total hemoglobin concentration; Thalassemia, heterozygosity or homozygosity with respect to a 3.7-kb α-thalassemia deletion) are shown as pink nodes. Twenty-five SNPs in ADCY9, ANXA2, BMP6, CCL2, CSF2, ECE1, ERG, MET, SELP, TEK and TGFBR3 are directly associated with the phenotype and have the largest independent effect on the risk of stroke. Note the association of stroke with several SNPs in ADCY9, BMP6, MET, SELP and TGFBR3, which usually reduces the possibility of false positives.

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. The plot shows a split of the predictive probabilities between these two outcomes: the predictive probabilities of stroke in the 7 individuals with stroke are >0.6, whereas the predictive probabilities of stroke in the 107 individuals without stroke are close to 0 (for only 2 individuals is the probability >0.5). The predictive probabilities are given in Supplementary Table 5 online.