Genetic Modifiers of Fetal Haemoglobin in Sickle Cell Disease.
Stephan MenzelSwee Lay Lay TheinPublished in: Molecular diagnosis & therapy (2019)
Fetal haemoglobin (HbF) levels have a clinically beneficial effect on sickle cell disease (SCD). Patients with SCD demonstrate extreme variability in HbF levels (1-30%), a large part of which is likely genetically determined. The main genetic modifier loci for HbF persistence, HBS1L-MYB, BCL11A and the β-globin gene cluster in adults also act in SCD patients. Their effects are, however, modified significantly by a disease pathology that includes a drastically shortened erythrocyte lifespan with an enhanced survival of those red blood cells that carry HbF (F cells). We propose a model of how HbF modifier genes and disease pathology interact to shape HbF levels measured in patients. We review current knowledge on the action of these loci in SCD, their genetic architecture, and their putative functional components. At each locus, one strong candidate for a causative, functional DNA change has been proposed: Xmn1-HBG2 at the β-globin cluster, rs1427407 at BCL11A and the 3 bp deletion rs66650371 at HBS1L-MYB. These, however, explain only part of the impact of these loci and additional variants are yet to be identified. Further progress in understanding the genetic control of HbF levels requires that confounding factors inherent in SCD, such as ethnic complexity, the role of F cells and the influence of drugs, are suitably addressed. This will depend on international collaboration and on large, well-characterised patient cohorts with genome-wide single-nucleotide polymorphism or sequence data.
Keyphrases
- genome wide
- sickle cell disease
- dna methylation
- copy number
- end stage renal disease
- ejection fraction
- induced apoptosis
- newly diagnosed
- chronic kidney disease
- transcription factor
- peritoneal dialysis
- gene expression
- genome wide association study
- signaling pathway
- red blood cell
- patient reported outcomes
- endoplasmic reticulum stress
- circulating tumor
- cell death
- data analysis