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Updated: Nov 20, 2025

Detection of Residual Donor Erythroid Progenitor Cells after Hematopoietic Stem Cell Transplantation for Patients with Hemoglobinopathies
Published on: September 6, 2017
Genetic determinants of ferritin, haemoglobin levels and haemoglobin trajectories: results from Donor InSight
Tiffany Timmer1,2,3, Michael Tanck4, Christopher Penkett5,6
1Department of Donor Medicine Research - Donor Studies, Sanquin Research, Amsterdam, The Netherlands.
Background And Objectives:
Blood donors might develop iron deficiency as approximately 250 mg of iron is lost with every donation. Susceptibility to iron deficiency and low haemoglobin levels differs between individuals, which might be due to genetic variation. Therefore, the aim of this study was to investigate associations between single nucleotide polymorphisms (SNPs) and haemoglobin trajectories, haemoglobin levels and ferritin levels in blood donors.
Materials And Methods:
In 2655 donors participating in the observational cohort study Donor InSight-III (2015-2017), haemoglobin and ferritin levels were measured in venous EDTA whole blood and plasma samples, respectively. Haemoglobin trajectories (stable/declining) were determined by fitting growth-mixture models on repeated pre-donation capillary haemoglobin measurements. Genotyping was done using the UK Biobank - version 2 Axiom Array. Single SNP analyses adopting an additive genetic model on imputed genetic variants were performed for haemoglobin trajectories, haemoglobin levels and ferritin levels. Conditional analyses identified independent SNPs.
Results:
Twelve, twenty and twenty-four independent SNPs were associated with haemoglobin trajectories, haemoglobin levels and ferritin levels respectively (P < 1 x 10-5 ). Rs112016443 reached genome-wide significance for ferritin levels, which influences WDSUB1 expression.
Conclusion:
Rs112016443 was genome-wide significantly associated with ferritin levels in Dutch donors. Further validation studies are needed, as well as studies towards underlying mechanisms and predicting iron deficiency using SNPs.
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