Global-scale population genetic analysis of Plasmodium falciparum identifies region-specific patterns of malaria parasite adaptation
Year: 2026
Type: article
Abstract: Malaria, caused by Plasmodium parasites, kills 610,000 people annually. The increasing availability of large-scale whole-genome sequencing (WGS) datasets enables investigation of parasite genetic diversity, population structure, and the spread of drug resistance. To investigate Plasmodium falciparum evolution, we analyse 17,565 isolates (17,489 previously published; 76 newly sequenced) from 39 countries, using identity-by-descent (IBD) networks, population structure inference, and selection scans. Analyses reveal distinct patterns of selection, transmission, and drug resistance. Selection analyses identify genomic regions across multiple metrics, encompassing established and emerging drug-resistance loci. These patterns highlight region-specific adaptive processes within Southeast Asia. IBD analyses reveal recent haplotype sharing that shows strong local selection. New WGS from Brazil show increased IBD around pfcrt, consistent with local drug-selection pressures. Similarly, isolates from Vietnam exhibit high relatedness and elevated IBD around pfKIC3, with all isolates carrying the pfkelch13 C580Y artemisinin resistance mutation. Elevated IBD in pfKIC7 and pfKIC9 in South America and the Horn of Africa suggests alternative pathways for future artemisinin resistance. Together, these findings highlight the genomic basis of P. falciparum adaptation and offer improved resolution for genomic surveillance of evolving parasite populations.
Source: Nature Communications
Language: English
Topic: Malaria Research and Control
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: G1108868498, G1131867873, AI assisted genomic profiling for the personalisation of treatment and control of infections, Using whole genome sequencing to reveal malaria parasite genetic diversity and drug resistance in Vietnam, G219660486, G2649494067, G295657101, G3251616133, G6529466185, Infection-AID: AI assisted genomic profiling to inform the Diagnosis, personalised treatment and control of infections, G7406135213, G7455513328, GenES_VBD network (Genomic Epidemiology tools for the Surveillance of Vector Borne Diseases: applied to tick species, reservoirs, and pathogens, G9000242701
OpenAlex ID: W7160830205
