Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes

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.

Keywords: Plasmodium falciparum; Tanzania; drug resistance; isolation-by-distance; malaria; molecular inversion probes; population structure.

Figure 1:

Region-level malaria prevalence in Tanzania. Malaria prevalence in children under five years of age at the regional level, as measured by rapid diagnostic tests (RDTs), was calculated using data from the 2017 Malaria Indicator Survey (MIS). Districts in which sampling occurred for the current study are labeled.

Figure 2:

Separation of Tanzanian P. falciparum samples by geographic region using discriminatory analysis of principal components (DAPC). DAPC analysis using the first 112 components of a principal component analysis (PCA) is shown (explaining 46% of the variation in the original PCA). Each dot is a sample colored by its geographic origin based on the 12 districts included in the analysis (inset map). A minimum spanning tree, constructed from genetic distances between groups, is overlaid on the plot (black dashed line).

Figure 3:

Tanzanian parasite population structure and associated variants. A. Admixture analysis using 737 samples and 1,614 genome-wide SNPs revealed two populations, K1 (purple) and K2 (turquoise) across the 12 districts included in the analysis. B. Frequency of key SNPs that were associated with population structure by random forest (Figure S8) and PCA (Figures S9), stratified by K subpopulation. Area of the circle represents the frequency of the alternate allele at that position within each subpopulation, and districts labels colored as in Figure 2 (from north, to northwest, to south, to east).

Figure 4:

Genetic relatedness by geographic distance. A. Relatedness (as measured by the inbreeding coefficient F) binned by geographic distance (greater circle distance). An average-of-averages approach (average of all district-level average F) was used to avoid district comparisons with more samples contributing more to a bin. Bars represent 95% confidence intervals. The number of district-level pairwise comparisons in each bin is shown at the top of the plot (for example, there are 12 districts included in this analysis, so the number of same-district comparisons in n=12 for bin size 0 km). B. Heatmap of averaged F between districts. Districts are arranged by geographic location (north and northwest, to south and southeast, to east).

Figure 5:

Patterns of antimalarial use and malaria transmission with genetic measures. Data from the 2017 Malaria Indicator Survey (MIS) was assessed for correlation with genetic measures from this study. A. District level prevalences of pfcrt-K76T are presented on region-level estimates of chloroquine use among children under five years of age. Mutation frequencies for A. and B. can be found in Supplemental Dataset 1. B. District level prevalences of pfdhps-A581G are presented on region-level estimates of SP/Fansidar use among children under five years of age. C. The relationship between P. falciparum prevalence, as measured by rapid diagnostic tests (RDTs) in school-age children (Abt Associates, 2017), and average COI estimate, at the district level. Each point is a district, and the size of the district indicates how many samples were in each district.