Journal Article
Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes
Kara A. Moser; Rashid A. Madebe; Ozkan Aydemir; Mercy G. Chiduo; Celine I. Mandara; Susan F. Rumisha; Frank Chaky; Madeline Denton; Patrick W. Marsh; Robert Verity; Oliver J. Watson; Billy Ngasala; Sigsbert Mkude; Fabrizio Molteni; Ritha Njau; Marian Warsame; Renata Mandike; Abdunoor M. Kabanywanyi; Muhidin K. Mahende; Erasmus Kamugisha; Maimuna Ahmed; Reginald A. Kavishe; George Greer; Chonge A. Kitojo; Erik J. Reaves; Linda Mlunde; Dunstan Bishanga; Ally Mohamed; Jonathan J. Juliano; Deus S. Ishengoma; Jeffrey A. Bailey
Molecular Ecology · Vol. 30, Issue 1 · pp. 100-113 · 2021
Abstract
High‐throughput Plasmodium genomic data is increasingly useful in assessing prevalence of clinically important mutations and malaria transmission patterns. Understanding parasite diversity is important for identification of specific human or parasite populations that can be targeted by control programmes, and to monitor the spread of mutations associated with drug resistance. An up‐to‐date understanding of regional parasite population dynamics is also critical to monitor the impact of control efforts. However, this data is largely absent from high‐burden nations in Africa, and to date, no such analysis has been conducted for malaria parasites in Tanzania countrywide. To this end, over 1,000 P. falciparum clinical isolates were collected in 2017 from 13 sites in seven administrative regions across Tanzania, and parasites were genotyped at 1,800 variable positions genome‐wide using molecular inversion probes. Population structure was detectable among Tanzanian P. falciparum parasites, approximately separating parasites from the northern and southern districts and identifying genetically admixed populations in the north. Isolates from nearby districts were more likely to be genetically related compared to parasites sampled from more distant districts. Known drug resistance mutations were seen at increased frequency in northern districts (including two infections carrying pfk13 ‐R561H), and additional variants with undetermined significance for antimalarial resistance also varied by geography. Malaria Indicator Survey (2017) data corresponded with genetic findings, including average region‐level complexity‐of‐infection and malaria prevalence estimates. The parasite populations identified here provide important information on extant spatial patterns of genetic diversity of Tanzanian parasites, to which future surveys of genetic relatedness can be compared.