bioRxivMicrobiology
Associations between the Plasmodium falciparum genome and sickle haemoglobin identified in mild malaria cases from Ghana
W. L. Hamilton, A. J. Forster, Y. Aniweh, V. Asoala, E. Drury, K. A. Rockett, G. A. Awandare, D. P. Kwiatkowski, G. Band, L. N. Amenga-Etego
Abstract
BackgroundHeterozygosity for sickle haemoglobin (HbS) confers protection against severe malaria caused by the parasite Plasmodium falciparum. Recent work has suggested that this protective effect can depend on the parasite genotype: P. falciparum sickle-associated (Pfsa) variants were found disproportionately in individuals with severe malaria carrying HbS alleles in The Gambia and Kenya. Interactions between the P. falciparum genome and HbS have not previously been investigated in mild malaria cases or in Ghana. MethodsWe performed a genome-wide association analysis of P. falciparum against human {beta}-globin genotypes in a sample of 1,368 people with mild malaria in northern Ghana. ResultsWe replicated the previously identified associations with HbS at two parasite loci (Pfsa1 and Pfsa3). Pfsa2 was absent from this population. A candidate newly identified locus within the serine/ threonine kinase FIKK4.2, which we putatively term Pfsa4, was also associated with HbS; this finding replicated in a published sample from Mali. The Pfsa1-4 mutations vary widely in frequencies across Africa, are absent or very low frequency in Asia, and are highly correlated with each-other across multiple populations. We found no strong associations with haemoglobin C. ConclusionsThis study replicates previously reported sickle-associated loci in the P. falciparum genome and has produced new evidence of a potential association with sickle haemoglobin at a fourth parasite locus. Further research is needed to validate the tentative fourth locus. These findings add new complexity to the emerging picture of association between human and co-evolving malaria parasite genomes, suggesting new avenues for functional exploration.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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