SickleGen Project Summary

Sickle cell anemia is a devastating disease that in the United States affects primarily African Americans. The goals of SickleGen are to capture the genetic diversity that is likely to underlie the notoriously heterogeneous clinical course of this disease, and with this information, develop predictive network models and genetic signatures that will allow us to foretell the likelihood of its severe vasculopathic complications and which patients might be most likely to have early mortality.

Consistent with the importance of vasculopathic complications in the pathogenesis of sickle cell disease, candidate gene association studies and preliminary analysis of genome-wide association studies have identified genetic polymorphisms in canonical pathways regulating proliferative vascular responses to injury, such as the TGF-beta/BMP pathway.

To accomplish our goals our consortium of established investigators has developed a large contemporary patient database and biological sample repository, and assembled the necessary laboratory and analytical capabilities.

With our established resources, we will directly examine genotype-phenotype relationships focusing on five major sub-phenotypes. The results have the potential to transform not only the sickle cell field, but also provide unique and generalizable insights into the fundamental vascular responses to inflammatory, oxidative and hemolytic stress.

We propose that sickle cell disease represents a “crucible” of vascular stress that sharply identifies genetic variants that may broadly regulate:

  • proliferative vascular responses in the systemic and pulmonary vasculature
  • vascular responses to aging
  • the intrinsic propensity of red cells to hemolyze
  • the control of HbF production

Genotyping new samples will enable us to validate our prior genotype-phenotype associations in sickle cell anemia. Resequencing promising candidate genes will allow discovery of additional polymorphisms, perhaps revealing functional variants. However, finding genetic variation alone is insufficient; as we capture the genetic heterogeneity associated with selected sub-phenotypes of this disease, we will develop predictive network models that will be prognostically useful.