5613165 investigating variants associated with quantitative red blood cell phenotypes from topmed predictions in african sickle cell disease populations.

Background: Sickle cell disease (SCD) is caused by a single point mutation (c.20A>T, p.Glu6Val), which results in the formation of mutant haemoglobin S (HbS) instead of haemoglobin A (HbA). It is a life-long inherited recessive disorder that predominantly affects people of African, Indian, and Mediterranean descent. The polymerisation of HbS within red blood cells (RBCs) is the primary pathophysiological alteration in SCD, which results in the sickle-shape of RBCs. Sickle-shaped RBCs are more prone to haemolysis and blocking blood flow, resulting in anaemia and painful episodes. Haematological indices including haematocrit (HCT), haemoglobin concentration (Hb), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red blood cell width (RDW) and RBC count indicate the size, development, and haemoglobin content of RBCs. Haematological indices are routinely measured as a means of monitoring SCD. A study recently found associations between 14 novel variants and haematological indices when using whole genome sequencing (WGS) data. The association between these variants and haematological indices have not yet been investigated in African SCD patients. This study aims to determine the associations between these variants and haematological indices in SCD patients from Cameroon and Senegal compared to non-SCD individuals. Aim: Investigating the association of 13 novel variants previously found in the Trans-Omics for Precision Medicine (TOPMed) project to haematological indices in African SCD patients. Methods: The 14 variants from the TOPMed study will be genotyped using a TaqMan Assay platform. Sanger sequencing will then be performed on 10% of the cohort to validate the TaqMan Assay result. The application R will be used for the descriptive statistics using sociodemographic, clinical, and haematological indices to evaluate the cases and controls. The Hardy-Weinberg Equilibrium will be used to ensure quality control. A linear regression analysis will be performed in R to determine the association between the variants and RBC phenotypes. Expected results: We are expecting to find an association between the SNPs and haematologocial indices. Conclusion: The variants of focus in this study were prioritized using WGS data, therefore capturing variants that are usually not included in GWAS arrays or imputation reference panels. The associations of the 13 novel variants with haematological indices have not yet been tested in African SCD populations. The results of this investigation could positively impact future studies into predictive modelling using haematological indices. References 1. Cardenes, N. et al., Blood, 2014; 123(18) 2. Chen, M.-H.et al., Cell, 2020; 182(5) 3. Hu, Y. et al., The American Journal of Human Genetics, 2021; 108(5) 4. Xu, M. et al., PLOS Computational Biology, 2014; 13(10)