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Defining malaria parasite population subdivisions, transmission dynamics and infection origins using SNP barcodes

Harrison, G. L. A.; Mehra, S.; Razook, Z.; Tessier, N.; Lee, S.; Hetzel, M. W.; Tavul, L.; Laman, M.; Amato, R.; Miotto, O.; Makita, L.; Burke, N.; Jensen, A.; Kwiatkowski, D.; Betuela, I.; Siba, P. M.; Bahlo, M.; Mueller, I.; Barry, A. E.

2023-09-04 infectious diseases
10.1101/2023.09.04.23294444 medRxiv
Show abstract

Pathogen genomic surveillance demands rapid, low-cost genotyping solutions for tracking infections. Here we use single nucleotide polymorphism (SNP) barcodes to generate practical information for malaria surveillance and control. Using 91 Plasmodium falciparum genomes from three provinces of Papua New Guinea (PNG), we assessed SNP panels with different allele frequency characteristics. A 191 local SNP barcode captured similar patterns of population structure evident with 5786 whole genome SNPs. Geographically informative SNPs (iSNPs, FST>0.05) show increased population clustering whilst randomly selected SNPs (rSNPs) and SNPs with similar allele frequencies (FST<0.05) amongst different countries (universal, uSNPs) or local PNG populations (balanced, bSNPs) indicated little clustering. SNP panels must be validated in local settings to ensure they capture the diversity and population structure of the target population. Applied to 727 P. falciparum isolates from 16 provinces of PNG, the full barcode identified variable transmission dynamics, and eight major sub-populations, as well as the source of a malaria outbreak in a low transmission setting.

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