Polymorphisms (Snps) in Multiple Genes of the Tgf-ß/Bmp Pathway Are Associated with a Global Measure of Sickle Cell Disease Severity.

A global estimation of sickle cell disease severity has been difficult to establish making the integration of clinical and laboratory abnormalities of into a predictive model of disease complications and death, an unrealized goal. A useful model might help us to understand the interactions among common clinical and laboratory abnormalities and allow patients at risk of certain complications and early death to be identified early so that targeted treatment, if available, could be provided. To approach this problem we first integrated clinical and laboratory data from nearly 3500 individuals from the Cooperative Study of Sickle Cell Disease and applied to this data advanced statistical machine learning techniques to identify the significant associations between selected complications of disease and laboratory variables. We looked for predictors of the risk for early death in three separate age groups. The resulting model revealed that complex networks of interactions between clinical and laboratory variables underlie common disease complications and ultimately death. Predictors of risk in this model were the HBA1, HBA2 genotype, stroke, sepsis and acute chest syndrome. While this model can predict the risk for early death, given the presence of other disease complications and variations among common laboratory variables, it did not provide an understanding of the genetic basis for our observations. Accordingly, in over 1000 patients with sickle cell disease we genotyped SNPs in genes chosen because of their possible link to the pathophysiology of disease. We then used our estimate of global disease severity to find associations of genotypes with severity. In the initial screening studies we identified several genes in the TGF-ß/BMP pathway that were associated with selected disease subphenotypes (