Strong positive selection biases identity-by-descent-based inferences of recent demography and population structure in Plasmodium falciparum

Bing Guo, Victor Borda, Roland Laboulaye, Michele D. Spring, Mariusz Wojnarski, Brian A. Vesely, Joana C. Silva, Norman C. Waters, Timothy D. O’Connor () and Shannon Takala-Harrison ()
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Bing Guo: University of Maryland School of Medicine
Victor Borda: University of Maryland School of Medicine
Roland Laboulaye: University of Maryland School of Medicine
Michele D. Spring: Armed Forces Research Institute of Medical Sciences
Mariusz Wojnarski: Armed Forces Research Institute of Medical Sciences
Brian A. Vesely: Armed Forces Research Institute of Medical Sciences
Joana C. Silva: University of Maryland School of Medicine
Norman C. Waters: Armed Forces Research Institute of Medical Sciences
Timothy D. O’Connor: University of Maryland School of Medicine
Shannon Takala-Harrison: University of Maryland School of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Malaria genomic surveillance often estimates parasite genetic relatedness using metrics such as Identity-By-Decent (IBD), yet strong positive selection stemming from antimalarial drug resistance or other interventions may bias IBD-based estimates. In this study, we use simulations, a true IBD inference algorithm, and empirical data sets from different malaria transmission settings to investigate the extent of this bias and explore potential correction strategies. We analyze whole genome sequence data generated from 640 new and 3089 publicly available Plasmodium falciparum clinical isolates. We demonstrate that positive selection distorts IBD distributions, leading to underestimated effective population size and blurred population structure. Additionally, we discover that the removal of IBD peak regions partially restores the accuracy of IBD-based inferences, with this effect contingent on the population’s background genetic relatedness and extent of inbreeding. Consequently, we advocate for selection correction for parasite populations undergoing strong, recent positive selection, particularly in high malaria transmission settings.

Date: 2024
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DOI: 10.1038/s41467-024-46659-0

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