Genomics of Pain in Sickle Cell Disease

Sickle cell disease (SCD) is a genetic blood disorder characterized by debilitating episodes of acute pain along with lifelong chronic pain. Pain is not only the major reason for high healthcare costs and poor quality of life but has also been reported as a predictor of mortality among SCD patients. Yet pain management in SCD remains a challenging task owing to the high inter-individual variability in the manifestation of pain. Research in the field of pain genetics, including some of our preliminary work, suggests that pain variability can be explained, to some extent, by genetic factors such as single nucleotide polymorphisms (SNPs). However, comprehensive candidate gene studies and polygenic modeling of the different SCD pain phenotypes are still largely missing. In this dissertation, I investigated the role of numerous SNPs in acute and chronic pain variability in SCD. First, I examined the effect of SNPs in four candidate genes: phenylethanolamine N-methyltransferase (PNMT), serotonin 1A receptor (HTR1A), dopamine beta-hydroxylase (DBH), and S100 calcium-binding protein B (S100B). DBH SNPs associated with both acute and chronic pain, PNMT and HTR1A SNPs associated with acute pain only, and S100B SNPs associated with chronic pain only. Several SNPs exhibited sex-specific associations. Functional analyses revealed that some SNPs may be altering tissue-specific gene expression, and/or potentially interfering with transcription factor binding. Pain being a complex phenotype, I also performed polygenic analyses by applying machine-learning and statistical methods to capture the collective effect of SNPs across several interacting genes on the chronic pain variability in SCD. Using a network-assisted analysis approach, I built a 12-SNP polygenic model for prediction of chronic pain severity among SCD patients. I also evaluated the contribution of SNPs in different nervous-system signaling pathways using pathway-based analysis. I found that SNPs in monoamine signaling genes accounted for ~ 30% of the variability in chronic pain. Findings from this research not only expand our understanding of the genetic factors involved in modulating pain in SCD, but also offer a platform for the development of polygenic pain prediction models for a more comprehensive and personalized approach towards pain diagnosis, classification, and management.