Extensive genetic diversity in *Plasmodium vivax* from Sudan and its genetic
relationships with other geographical isolates

Musab M. Ali Albsheer<sup>a,b</sup>, Alfred Hubbard<sup>c</sup>, Cheikh Cambel Dieng<sup>d,*</sup>,
Eyoab Iyasu Gebremeskel<sup>e</sup>, Safaa Ahmed<sup>a</sup>, Virginie Rougeron<sup>f</sup>, Muntaser E. Ibrahim<sup>a</sup>,
Eugenia Lo<sup>d,*</sup>, Muzamil M. Abdel Hamid<sup>a,**</sup>

<figure><img src="image_1.png" /></figure>

ARTICLE INFO

Keywords:

Plasmodium vivax

Genetic diversity

Sudan

Microsatellite

Haplotype

Malaria

A B S T R A C T

Plasmodium vivax, traditionally overlooked has experienced a notable increase in cases in East Africa. This study investigated the geographical origin and genetic diversity of P. vivax in Sudan using 14 microsatellite markers. A total of 113 clinical P. vivax samples were collected from two different ecogeographical zones, New Halfa and Khartoum, in Sudan. Additionally, 841 geographical samples from the database were incorporated for a global genetic analysis to discern genetic relationships among P. vivax isolates on regional and worldwide scales. On the regional scale, our findings revealed 91 unique and 8 shared haplotypes among the Sudan samples, showcasing a remarkable genetic diversity compared to other geographical isolates and supporting the hypothesis that P. vivax originated from Africa. On a global scale, distinct genetic clustering of P. vivax isolates from Africa, South America, and Asia (including Papua New Guinea and Solomon Island) was observed, with limited admixture among the three clusters. Principal component analysis emphasized the substantial contribution of African isolates to the observed global genetic variation. The Sudanese populations displayed extensive genetic diversity, marked by significant multi-locus linkage disequilibrium, suggesting an ancestral source of P. vivax variation globally and frequent recombination among the isolates. Notably, the East African P. vivax exhibited similarity with some Asian isolates, indicating potential recent introductions. Overall, our results underscore the effectiveness of utilizing microsatellite markers for implementing robust control measures, given their ability to capture extensive genetic diversity and linkage disequilibrium patterns.

**1. Introduction**

Plasmodium vivax malaria is the most predominant form of human
malaria (Battle et al., 2014)yet is classified as a neglected tropical dis-
ease, and there are mounting evidence indicating a rising trend in the
overall disease burden, economic impact, and severity associated with
P. vivax (Elgoraish et al., 2019; Price et al., 2007). Malaria caused by
P. vivax poses a significant re-emerging disease threat in many African,
South American, and Asian countries (Bahk et al., 2018; Baird, 2000;
Faulde et al., 2007; Joshi et al., 2008; Leclerc et al., 2004). The

understanding of genetic diversity and population structure in P. vivax
falls behind that of P. falciparum, primarily because suitable genetic
markers for the P. vivax genome have been lacking (Carlton et al., 2008)
and its inability to be cultured for long-term in the laboratory except in
non-human primates (Roobsoong et al., 2015). The comprehensive
sequencing of the P. vivax genome in 2008 constituted a significant
asset, catalyzing the imperative investigation of this parasite (Carlton
et al., 2008). Subsequent studies revealed greater genetic diversity of
P. vivax in contrast to P. falciparum (Fola et al., 2017; Kale et al., 2021;
Neafsey et al., 2012).

<footnote><sup>a</sup> Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan</footnote>

<footnote>b Faculty of Medical Laboratory Sciences, Sinnar University, Sudan</footnote>

<footnote>Department of Bioinformatics and Genomics, University of North Carolina, Charlotte, NC 28223, USA</footnote>

<footnote>d Department of Microbiology and Immunology, Drexel University, Philadelphia, PA 19129, USA</footnote>

<footnote>e Department of Biology, Mai Nefhi College of Science, Mai-Nefhi, Eritrea</footnote>

<footnote>Laboratoire MIVEGEC (Université de Montpellier-CNRS-IRD), CREES, 34394 Montpellier, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>** Corresponding author at: Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan.
E-mail addresses: el855@drexel.edu (E. Lo), mahdi@iend.org (M.M. Abdel Hamid).</footnote>
A recent study conducted in Sudan (Elgoraish et al., 2019) showed a rise in *P. vivax* prevalence constituting 26.6% of the suspected malaria cases. The proportion of positive results for *P. vivax* was 13.3%, with significant variations between the states and residence status (Elgoraish et al., 2019). These findings underscore the stability of transmission, presenting new challenges to malaria treatment and control efforts in Sudan (Charani et al., 2019). The genetic identity of local *P. vivax* populations is paramount to anticipating and implementing effective malaria control measures, particularly in regions prone to population displacement e.g., due to political conflicts (Arnott et al., 2012). The genetic diversity of *P. vivax* plays a crucial role in the parasite's ability to evade host immune responses and develop resistance to antimalarial drugs (Habtamu et al., 2022). By unravelling the specific genetic makeup of local *P. vivax* strains through STR analysis, we can gain insights into factors such as drug resistance and transmission dynamics that are crucial for optimizing control strategies. These genetic insights enable us to tailor interventions to address the unique challenges posed by the local parasite populations, thereby complementing and enhancing the broad strategies suggested by WHO. In the backdrop of the ongoing challenging political situation in Sudan, where limited funding constrains scientific research endeavours, this study employs microsatellite markers to investigate the genetic diversity of *P. vivax*.

Microsatellite markers offer an efficient method for discerning population structure and unravelling the geographical origin and genetic diversity of isolates (Barry et al., 2015; Congpuong and Ubalee, 2017; Havryliuk and Ferreira, 2009; Iwagami et al., 2012; Kittichai et al., 2017; Koepfli et al., 2015). Microsatellites have been instrumental in describing the temporal dynamics of *P. vivax* infections by tracking genetic changes and diversity over time (Gunawardena et al., 2010; Imwong et al., 2007). This helps in understanding the evolution and spread of the parasite in various regions, which is crucial for developing effective control and treatment strategies. The tumultuous political environment has posed substantial obstacles to comprehensive and well-funded research initiatives (Charani et al., 2019). However, microsatellite studies offer a cost-effective and resource-efficient approach, rendering them particularly well-suited for environments characterized by financial constraints and unpredictable circumstances. Despite the prevailing challenges, the specific advantages of microsatellite analysis, including its high resolution, sensitivity to genetic variation, and capacity for elucidating population dynamics, make it an invaluable tool for exploring the intricate genetic landscape of *P. vivax* in Sudan. This methodology not only adapts to the current resource limitations imposed by the political context but also holds the potential to yield crucial insights into the epidemiology and evolution of *P. vivax* in the region.

The primary objective of this study was to analyze the genetic diversity and population structure of *Plasmodium vivax* isolates from Sudan. We utilized 14 highly polymorphic tri- and tetranucleotide microsatellite markers (Karunaweera et al., 2007) to examine 113 *P. vivax* isolates from New Halfa (eastern Sudan) and Khartoum (central Sudan). The microsatellite data aimed to identify unique haplotypes and separate the clustering of isolates according to their geographical origins, thereby providing insights into the genetic landscape and evolutionary dynamics of *P. vivax* populations. Genetic analysis of *P. vivax* using microsatellites is robust to address questions on the geographical origin and spread of the parasites and contribute to the current malaria control and elimination effort in Sudan.

## 2. Methods

### 2.1. Sample collection and ethics statement

*P. vivax* microsatellite typing was conducted on 113 field isolates collected from two malaria endemic areas in Sudan: 21 from New Halfa, eastern Sudan (15°20′N 35°35′00′E) (2013) and 92 from rural Khartoum, central Sudan (15°53′11.2″N 32°31′39.9″E) (2013 and 2015) (Ali

Albsheer et al., 2021). A field isolate is defined in this study as a sample of parasites derived from a single-infected patient collected on a single occasion. All patients were febrile with 37.5 °C or above and were diagnosed as having *P. vivax* malaria by microscopic examination of Giemsa-stained thick and thin blood smears. Finger-prick blood samples, for molecular analysis, were collected on Whatman 3 mm filter paper (Whatman International, Ltd., Maidstone, England), dried, and kept in a dark place. Filter paper samples were coded to keep the anonymity of the patients and kept at Parasitology and Medical Entomology laboratory at the Institute of Endemic Diseases, the University of Khartoum. Samples were obtained and analyzed after approval from the Ethics Committee of the Institute of Endemic Diseases, University of Khartoum.

### 2.2. Microsatellite typing of *P. vivax*

DNA was extracted from dried blood spots collected on Whatman 3 filter paper using DNeasy Blood and Tissue kit (Qiagen, France) following the manufacturer's instructions, and eluted in 100 µL TE (10 mM Tris-Cl, 1 mM EDTA, pH 8.0) buffer. For each DNA sample, whole genome amplification was carried out using an Illustra-GenomiPhi™ V2 DNA Amplification Kit (GE Healthcare, Uppsala, Sweden) following the manufacturer's protocols. The product (DNA + Sample buffer) was denatured by heating at 95 °C for 3 min and then kept on ice. 9 µL of Reaction Buffer (Illustra™ GenomiPhi™ V2 DNA Amplification Kit - GE Healthcare, Uppsala, Sweden) and 1 µL of enzyme Phi 29 was added and incubated at 30 °C for 2 h for genome amplification. The amplification was stopped by placing the samples at 65 °C for 10 min, before being stored at -20 °C. The product DNA was used as a template for the PCR-based amplification of 14 polymorphic microsatellite markers distributed among 10 of the 14 chromosomes of *P. vivax*. The PCR protocol was adapted from Karunawera et al. (Karunaweera et al., 2007) as follows: a 20 µL reaction mix was made of 0.3 µL of each primer (10 µM), with the forward being labeled with a fluorochrome (6-FAM, VIC, NED or PET), 0.1 µL template DNA, 0.5 µL dNTP mix (10 pmol/µL), 0.7 µL of MgCl₂ (50 mM), 2.5 of 10× buffer, 0.2 µL Taq polymerase (5 UI/µL, Invitrogen). In this analysis, fourteen highly polymorphic microsatellite markers were initially selected (MS1, MS2, MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS12, MS15, MS16 and MS20)) (Karunaweera et al., 2007). MS2 exhibits tetranucleotide repeat arrays, while all remaining markers showcase tandem repeats of trinucleotide motifs. Amplifications were carried out in a thermal cycler: 40 cycles of denaturation at 94 °C for the 30s, the annealing temperature of each locus for 1 min at 58 °C and extension at 72 °C for 30s, followed by a final extension at 72 °C for 7 min. The reaction products were visualized on a 1.5% agarose gel stained with EZ-vision. Fluorescence-labeled PCR products were sized on Applied Biosystems ABI3500XL (GenSeq platform, Montpellier), with a Genescan 500 LIZ internal size standard.

### 2.3. Locus selection

In addition to the 113 *P. vivax* samples from Sudan, 841 global samples were obtained from the plasmoDB database for global genetic analysis (http://plasmodb.org) to analyze the ancestry of the isolates and to have a regional and global perspective of the parasites (Aurrecoechea et al., 2009). Of these 841 global samples, only 665 were used in the analyses, as the remaining samples were not compatible with our dataset due to the presence of loci not included in our sample data set. Initially, 14 loci were selected for analysis. However, to ensure compatibility and consistency with the loci available in the database, only 11 loci were ultimately considered for analyses. This adjustment was necessary to maintain alignment with the existing database. The specific loci analyzed were as follows: MS1, MS2, MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS11, MS12. Given the haploid nature of blood-stage malaria parasites, the presence of additional alleles at a locus indicated co-infections with distinct clones(Arnott et al., 2012; Haubold and Hudson, 2000; Karunaweera et al., 2008). In such cases, the
predominant allele was considered for computing allele frequencies and defining haplotypes. Samples with missing data at any locus were excluded. Linkage disequilibrium (LD) was estimated using the $\bar{r}_d$ statistic(Agapow and Burt, 2000), computed with the poppr R package (Kamvar et al., 2014), both with and without clonal correction. The analysis set was further refined by removing a locus (MS10) showing substantial pairwise LD, resulting in a final dataset of 665 individuals across 18 populations.

## 2.4. Population genetic analyses

To assess genetic diversity, Stoddart and Taylor's index, G (Stoddart and Taylor, 1988), Simpson's index, $\lambda$ (Kamvar et al., 2014), and Nei's unbiased gene diversity, $H_{exp}$ (Nei, 1978) were calculated. Genotypic evenness was assessed using the $E_5$ metric (Ludwig and Reynolds, 1988) computed with the poppr R package (Kamvar et al., 2014). Principal component analysis (PCA) was employed to explore the number of genetic clusters among samples using R (Lüdecke et al., 2021). This analysis transformed the microsatellite data into orthogonal (uncorrelated) components that representing the information content of the data set. By ranking the components in order of the amount of variance and selecting the highest ranked for visualization, most of the information content of a dataset can be represented in relatively few dimensions. In this case, PCA was performed on a binary matrix of the microsatellite data, with one column for each allele and one row for each sample. The results were visualized with the GGally R package (Schloerke et al., 2021).

In addition to PCA, the Bayesian clustering technique, *rmaverick* (Verity and Nichols, 2016), was utilized to assign individuals to clusters. This method estimates a set of admixture coefficients for each individual (i.e., proportions of membership in K clusters) that best explains observed deviations from Hardy-Weinberg or linkage disequilibrium in a Bayesian framework, with posterior parameter distributions found with a Markov chain Monte Carlo technique. This method employs a "Metropolis-coupled" Markov chains (Verity and Nichols, 2016) for improved parameter estimation. Parameter value estimation occurs simultaneously in these different chains, with information passing between chains to improve mixing and increase the likelihood of model convergence (Verity and Nichols, 2016). The results were visualized with pophelper (Francis, 2017) and scatterpie (Yu et al., 2017) R packages.

## 2.5. Genetic relatedness estimation

To enhance our understanding of the genetic origin and diversity of these isolates on a regional and global scale, we included an additional 841 samples from various geographic locations in our analysis. Genetic relatedness among individuals was assessed using the R package *Dcifer*, which models relatedness between malaria infections based on observed alleles, population allele frequencies, and complexity of infection (Agnandji et al., 2012). *Dcifer* distinguishes identity-by-descent (IBD) from identity-by-state (IBS) by utilizing allele frequency information, assuming unlinked loci — a condition met after filtering out linkage disequilibrium (LD) from the dataset. *Dcifer*-generated relatedness estimates between sample pairs were aggregated to the population level using two summary statistics. The first statistic computed was mean relatedness, providing an average measure of relatedness within a given population. The second statistic determined the fraction of sample pairs that exhibited significant relatedness, with significance assessed using a likelihood ratio approach and a null hypothesis of unrelated infections. Notably, *p*-values obtained from this step were used solely for aggregating relatedness at the population level and were not corrected for multiple hypothesis testing.

Spatially proximate sites were merged into a single population, maintaining analytical integrity. Populations with fewer than 15 samples were excluded to ensure robust statistical representation.

Population-level relatedness values were visualized through both heat-maps and networks. The network visualization was facilitated by the ggraph R package (Pedersen et al., 2017), providing a graphical representation of genetic relatedness relationships among populations.

# 3. Results

## 3.1. Linkage disequilibrium and genetic diversity

Analysis of linkage disequilibrium (LD) in the dataset revealed significant values before any loci were removed due to pairwise LD (with clonal correction: $\bar{r}_d = 0.023$, $p = 0.001$; without clonal correction: $\bar{r}_d = 0.0167$, $p = 0.001$). Genetic diversity metrics exhibited inconsistencies with no discernible patterns of diversity differences based on geography (Table 1).

## 3.2. Principal component and clustering analyses

Principal Component Analysis (PCA) results did not reveal a clear clustering pattern in the data (Supplemental Fig. 1). Nevertheless, log-likelihood evaluations based on the *Rmaverick* model indicated that number of clusters (K values) of two, three, and four were most likely (Supplemental Fig. 2). Detailed inspection of results for these K values revealed that the "no admixture" model was more likely than the "admixture" model. Admixture coefficients at the individual level exhibited distinct geographic patterns, with samples from Africa showing a clear differentiation from Southeast Asian samples (Fig. 1). South America, particularly Brazil, emerged as even more distinct, with the four-cluster model assigning most Brazil samples exclusively to one cluster. Maps of mean admixture coefficients further highlighted an evident east-west divide between African and Southeast Asian populations, and Brazilian samples stood apart even within South America (Fig. 2; Supplemental Figs. 3 and 4).

The clustering patterns obtained at the optimal K = 3 (Fig. 1) showed a clear structure of *P. vivax* isolates from Africa, South America, and Asia (including Papua New Guinea and Solomon Island) with substantial admixture in all three clusters. Specific proportions of cluster membership were observed for distinct populations within each cluster, providing insights into the genetic composition of *P. vivax* in various regions. Cluster 1 (Red) predominantly included *P. vivax* isolates from Peru (90.8%), Brazil (95.4%), and Central America (50.3%). Cluster 2 (Green) encompassed *P. vivax* isolates from New Halfa (84.5%), Khartoum (81.5%), other parts of Sudan (89.9%), and other African countries

<table><caption>**Table 1**
Population names, sample sizes, and genetic diversity metrics.</caption><thead><tr><th>Pop. ID</th><th>Location</th><th>N</th><th>G</th><th>lambda</th><th>H_exp</th><th>E_5</th></tr></thead><tbody><tr><td>AF</td><td>Africa (other)</td><td>10</td><td>8.333</td><td>0.88</td><td>0.873</td><td>0.952</td></tr><tr><td>AR</td><td>Armenia/Azerbaijan</td><td>9</td><td>7.364</td><td>0.864</td><td>0.678</td><td>0.948</td></tr><tr><td>BR</td><td>Brazil</td><td>120</td><td>81.818</td><td>0.988</td><td>0.738</td><td>0.826</td></tr><tr><td>CA</td><td>Central America</td><td>27</td><td>27</td><td>0.963</td><td>0.823</td><td>1</td></tr><tr><td>CM</td><td>Cambodia</td><td>87</td><td>87</td><td>0.989</td><td>0.866</td><td>1</td></tr><tr><td>HF</td><td>New Halfa</td><td>11</td><td>11</td><td>0.909</td><td>0.811</td><td>1</td></tr><tr><td>ID</td><td>Indonesia</td><td>5</td><td>5</td><td>0.8</td><td>0.95</td><td>1</td></tr><tr><td>IN</td><td>India</td><td>8</td><td>8</td><td>0.875</td><td>0.839</td><td>1</td></tr><tr><td>KH</td><td>Khartoum</td><td>64</td><td>30.118</td><td>0.967</td><td>0.763</td><td>0.732</td></tr><tr><td>MA</td><td>Madagascar</td><td>53</td><td>53</td><td>0.981</td><td>0.79</td><td>1</td></tr><tr><td>PE</td><td>Peru</td><td>62</td><td>36.962</td><td>0.973</td><td>0.708</td><td>0.841</td></tr><tr><td>PH</td><td>Papua New Guinea (highlands)</td><td>29</td><td>29</td><td>0.966</td><td>0.815</td><td>1</td></tr><tr><td>PL</td><td>Papua New Guinea (lowlands)</td><td>45</td><td>45</td><td>0.978</td><td>0.826</td><td>1</td></tr><tr><td>SO</td><td>Solomon Islands</td><td>22</td><td>22</td><td>0.955</td><td>0.822</td><td>1</td></tr><tr><td>SU</td><td>Sudan (other)</td><td>54</td><td>29.16</td><td>0.966</td><td>0.773</td><td>0.8</td></tr><tr><td>UZ</td><td>Uzbekistan</td><td>15</td><td>3.689</td><td>0.729</td><td>0.548</td><td>0.667</td></tr><tr><td>VI</td><td>Vietnam</td><td>44</td><td>29.333</td><td>0.966</td><td>0.839</td><td>0.821</td></tr><tr><td>Total</td><td>All</td><td>665</td><td>464.035</td><td>0.998</td><td>0.874</td><td>0.84</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Stacked bar plot indicating the individual admixture coefficients estimated by *rmaverick* for each K value from two through four. Bars are sorted first by region and then by population. The vertical dashed white lines correspond to the divisions between samples from different regions. See Table 1 for full population names.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Map of pie charts showing the mean admixture coefficients for each population, as estimated by *rmaverick* with four clusters. The area of each pie is scaled according to the number of samples drawn from that population. Country boundaries are given for context.</figcaption></figure>

(77.3%), including Madagascar (77.4%). Cluster 3 (Blue) comprised *P. vivax* isolates from Cambodia (54.4%), Vietnam (76.4%), Indonesia (51.2%), Papua New Guinea lowland (92.5%), Papua New Guinea highland (89.4%), and Solomon Island (79.9%). Isolates from Armenia, India, and Uzbekistan demonstrated unique clustering patterns, indicating distinct genetic profiles. The Armenian isolates primarily aligned with clusters associated with Central Asian genetic characteristics, while Indian isolates exhibited a diverse membership across multiple clusters, reflecting the country's broad geographical and genetic diversity. The Uzbek isolates formed a separate group, highlighting their unique genetic composition compared to other Asian and African isolates. These distinct cluster memberships underscore the complex genetic structuring of *P. vivax* across different regions and the potential influence of historical and contemporary migration and transmission dynamics. When the number of clusters was set to 4 or 5, the distinction between

countries was limited, emphasizing the robustness of the optimal K = 3 clusters, except for the unique clustering pattern observed for *P. vivax* isolates from Khartoum.

### 3.3. Population structure validation

Principal component analysis (PCA; Fig. 2), NJ tree, and clustering analyses corroborated the structuring patterns observed at the K = 3 level, revealing clear geographical clustering of all analyzed populations. The phylogenetic tree (Fig. 3) constructed using PAST highlighted the significant contribution of isolates from Africa and Sudan to the observed global genetic variation. Further, AMOVA analysis of the 17 populations showed that the majority of the genetic variation lies within populations (89%), with 11% variation observed among populations. (See Fig. 4.)
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Heatmap showing relatedness between populations summarized as both (A) the mean of sample pair relatedness and (B) the fraction of significantly related sample pairs. Color swatches along the diagonal show within-population relatedness. In both plots, the color scale has been log₁₀-transformed.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Network showing the mean relatedness between populations, with country boundaries for context. The size of each point is scaled according to the number of samples available for that population.</figcaption></figure>

**4. Discussion**

The incidence of *P. vivax* cases in Sudan has increased by a factor of five within a span of six years (Elgoraish et al., 2019). This resurgence and escalation of *P. vivax* infections may be linked to the parasite's capacity to employ alternative mechanisms for invading human erythrocytes, bypassing the Duffy antigen (Suliman et al., 2016). Notably, this phenomenon is also correlated with an elevated risk of severe cases,

underscoring the imperative for conducting further investigations into a parasite that was previously considered benign and overlooked.

Our study based on microsatellite markers and clustering analyses offers a comprehensive understanding of *P. vivax* genetic diversity. The results reveal distinct clustering patterns at the optimal K = 3, highlighting clear genetic structures among *P. vivax* isolates from Africa, South America, and Asia, including Papua New Guinea and the Solomon Islands. These findings underscore the complex interplay between
genetic diversity and geographic origins, emphasizing the need for
tailored malaria control measures informed by regional genetic char-
acteristics. The observed clustering dynamics demonstrate substantial
admixture within all three clusters, reflecting the intricate genetic re-
lationships among global *P. vivax* populations. Notably, Cluster 1 ex-
hibits a predominant representation of South American isolates, while
Cluster 2 includes isolates from Sudan and other parts of Africa. Cluster
3, marked by isolates from Southeast Asia and the Pacific, further ac-
centuates the geographic specificity of genetic patterns. These results
align with the broader literature on *P. vivax*, emphasizing regional
variations in genetic diversity and the impact of geographic factors on
parasite evolution (Battle et al., 2014; Diez Benavente et al., 2017).

Utilizing genetic markers, particularly microsatellites (MS), proves
to be indispensable for unravelling the population structure of parasites
due to their inherent polymorphic nature (Havryliuk and Ferreira,
2009). Microsatellites, recognized for their neutrality and ubiquitous
presence throughout genomes, serve as highly effective alternatives to
polymorphic antigenic genes in the study of malaria parasites, yielding
insights into the evolutionary history of global populations (Sutton,
2013). The unconstrained polymorphic nature of MS loci amplifies the
detection of multi-clonal infections, enriching our comprehension of
endemicity history and transmission stability within specific global re-
gions (Branch et al., 2011; Havryliuk and Ferreira, 2009). In our study,
we sought to delineate the genetic diversity of *P. vivax* populations from
blood samples collected in New Halfa and Khartoum in Sudan using MS
genotyping and analysis. Both populations exhibited substantial mi-
crosatellite polymorphisms, characterized by heightened heterozygosity
(Havryliuk and Ferreira, 2009). These findings contribute to the ongoing
debate about the evolutionary origins of *Plasmodium vivax*, particularly
in the context of the ‘Out of Africa’ and ‘Out of Asia’ hypotheses. The
‘Out of Africa’ hypothesis suggests that *P. vivax* originated in Africa and
spread globally, supported by the high genetic diversity seen in African
populations. Conversely, the ‘Out of Asia’ hypothesis suggests that
*P. vivax* originated in Asia and expanded worldwide through human
migration and trade routes (Rougeron et al., 2020). The observed
elevated genetic diversity in our study aligns with other studies, lending
support to the proposition that Africa is the origin of *P. vivax* (Culleton
and Carter, 2012; Liu et al., 2014)although this contention is disputed
(Escalante et al., 2005), reflecting a complex history of ancient migra-
tions and recent introductions. This study emphasizes the need for more
genetic studies to unravel *P. vivax’s* evolutionary and migration pat-
terns. Our findings, indicate significant genetic diversity and unique
clustering of Sudanese isolates, underscore the importance of regional
genetic data in understanding the global dissemination of *P. vivax*. The
utilization of 11 highly polymorphic microsatellite markers unveiled a
population structure of *P. vivax* isolates, with Asian and Latin American
isolates clustering together, and isolates from Africa clustering with
Southeast Asian isolates. Further differentiation emerged with the
incorporation of a third population (K = 3), elucidating distinct clusters
for isolates from Brazil and the Solomon Islands/Papua New Guinea.
Configuring the number of clusters to 4 or 5 resulted in limited sepa-
ration between countries, except for isolates from Khartoum forming a
distinct cluster from those in New Halfa and other parts of Africa. This
pattern could be explained by our previous study where, in Khartoum,
Duffy-negatives constituted 9.5% of *P. vivax* infections, whereas in New
Halfa Duffy-negatives accounted for 14.3% of these infections (Lo et al.,
2021). This underscores the significance of designing studies at sub-
regional or sub-population levels in this part of Africa, mirroring
human population studies (Liu et al., 2014). The observed genetic as-
sociation between East African and Asian isolates implies a potential
recent re-introduction of *P. vivax* from Asia to East Africa through
human migration.

This study has some limitations. First, the sample sizes for certain
populations included in the analysis were not sufficiently large, neces-
sitating cautious interpretation of the corresponding results. Second, the
limitation of using microsatellites in population genetic studies include

their reduced ability to estimate relatedness among substantially
diverged samples and the challenges associated with their neutrality
assumptions and potential locus-specific influences on genetic diversity
(Koepfli and Mueller, 2017). Thirdly, due to the absence of a clear a
priori outgroup, we opted to use the most diverged sample (BR103) from
the most diverged population (Brazil) as the outgroup for rooting the
phylogenetic tree, as determined by our other analyses. Additionally,
our phylogenetic analysis lacked temporal awareness, as it did not
incorporate the variable times of sample collection. The assertion that
*P. vivax* in Southeast Asia serves as a transmission source may be an
artifact stemming from different sample collection times. Assessing the
likelihood of this possibility is challenging due to a second, potentially
more significant limitation: it is inherently impossible to ascertain with
certainty the period measured by the transmission network analysis. The
population structure of *P. vivax* is shaped by both ancient population
movements and recent transmission patterns, and our analysis likely
captures a composite of these two signals. Future research that can
discern modern transmission patterns from ancient migrations stands as
a crucial next step. Despite the limitations inherent in using micro-
satellites for studying parasite population genetics (Ellegren, 2004), the
population structure generated using these markers exhibits promise in
identifying origins (Gunawardena et al., 2010) and furnishing valuable
information on parasite genetic structure (Severini et al., 2002).

5. Conclusions

Microsatellite typing remains an important tool to study *P. vivax*
population structure on a global scale, and differences in diversity
reflect transmission intensity and isolation of parasite populations as
was reported elsewhere. Microsatellite-based analysis of *P. vivax* para-
sites from Sudan showed extensive genetic diversity co-occurring with
significant multilocus linkage disequilibrium. These parasite pop-
ulations were seen to cluster according to their geographic locations and
ancestry. As inferred elsewhere the predictive power of ancestry with
this microsatellite data as a model is promising and could be useful in
identifying the origin of *P. vivax* malaria cases and enabling meaningful
control and preventive measures to be implemented. Moreover, dis-
secting the parasite population into subpopulations and sub-regions
especially in this part of the world would give a meaningful interpre-
tation in the origin and structuring of *P. vivax* parasites in the future.

Funding

This research received was funded by Third World Academy of Sci-
ences (Research Grant Agreement (RGA) No. 19-225 RG/BIO/AF/AC G
- FR3240310127. The funders had no role in study design, data collec-
tion and analysis, decision to publish, or preparation of the manuscript.

CRediT authorship contribution statement

**Musab M. Ali Albsheer:** Writing – review & editing, Supervision, Investigation, Formal analysis, Data curation, Conceptualization. **Alfred Hubbard:** Writing – original draft, Validation, Software, Formal analysis, Data curation. **Cheikh Cambel Dieng:** Writing – review & editing, Methodology, Data curation. **Eyoab Iyasu Gebremeskel:** Writing – original draft, Data curation, Conceptualization. **Safaa Ahmed:** Writing – review & editing, Investigation, Conceptualization. **Virginie Rougeron:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. **Muntaser E. Ibrahim:** Writing – review & editing, Writing – original draft, Methodology, Conceptualization. **Eugenia Lo:** Writing – review & editing, Validation, Supervision, Investigation, Funding acquisition, Conceptualization. **Muzamil M. Abdel Hamid:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Funding acquisition, Conceptualization.
Declaration of competing interest

The authors declare no conflict of interest.

Data availability

Data will be made available on request.

Acknowledgements

We are greatly indebted to the patients for participating in the study by donating blood and the staffs and technicians from the Institute of Endemic Diseases, University of Khartoum for assisting sample collection; the communities and hospitals for their support and willingness to participate in this research. Authors would like also to thank IRD, CNRS-INEE, and the ANR Tremplin EVAD 2017 for generation of the data.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.meegid.2024.105643.

References

Agapow, P., Burt, A., 2000. Multilocus 1.2. Analysing Multi-Locus Population Genetic Data. Computer Software and Documentation. Department of Biology, Imperial College, Silwood Park, Ascot.

Agnandji, S.T., Lell, B., Fernandes, J.F., Abossolo, B.P., Methogo, B.G., Kabwende, A.L., Vansadia, P., 2012. A phase 3 trial of RTS,S/AS01 malaria vaccine in African infants. N. Engl. J. Med. 367 (24), 2284-2295. https://doi.org/10.1056/NEJMoa1208394.

Ali Albsheer, M.M., Lovet, A.A., Eltom, S.B., Omereltinai, L., Mohamed, N., Muneer, M. S., Abdel Hamid, M.M., 2021. Prevalence of glucose-6-phosphate dehydrogenase deficiency (G6PDd), CareStart qualitative rapid diagnostic test performance, and genetic variants in two malaria-endemic areas in Sudan. PLoS Negl. Trop. Dis. 15 (10), e0009720.

Arnott, A., Barry, A.E., Reeder, J.C., 2012. Understanding the population genetics of plasmodium vivax is essential for malaria control and elimination. Malar. J. 11, 14. https://doi.org/10.1186/1475-2875-11-14.

Aurrecochea, C., Brestelli, J., Brunk, B.P., Dommer, J., Fischer, S., Gajria, B., Harb, O.S., 2009. PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res. 37 (suppl 1), D539-D543.

Bakh, Y.Y., Lee, H.W., Na, B.K., Kim, J., Jin, K., Hong, Y.S., Kim, T.S., 2018. Epidemiological characteristics of re-emerging vivax malaria in the Republic of Korea (1993-2017). Korean J. Parasitol. 56 (6), 531-543. https://doi.org/10.3347/kjp.2018.56.6.531.

Baird, J.K., 2000. Resurgent malaria at the millennium: control strategies in crisis. Drugs 59 (4), 719-743. https://doi.org/10.2165/00003495-200059040-00001.

Barry, A.E., Waltmann, A., Koepfli, C., Barnadas, C., Mueller, I., 2015. Uncovering the transmission dynamics of plasmodium vivax using population genetics. Pathogens and Global Health 109 (3), 142-152.

Battle, K.E., Karhunen, M.S., Bhatt, S., Gething, P.W., Howes, R.E., Golding, N., Hay, S.I., 2014. Geographical variation in plasmodium vivax relapse. Malar. J. 13, 144. https://doi.org/10.1186/1475-2875-13-144.

Branch, O.H., Sutton, P.L., Barnes, C., Castro, J.C., Hussin, J., Awadalla, P., Hijar, G., 2011. Plasmodium falciparum genetic diversity maintained and amplified over 5 years of a low transmission endemic in the Peruvian Amazon. Mol. Biol. Evol. 28 (7), 1973-1986.

Carlton, J.M., Adams, J.H., Silva, J.C., Bidwell, S.L., Lorenzi, H., Caler, E., Fraser-Liggett, C.M., 2008. Comparative genomics of the neglected human malaria parasite plasmodium vivax. Nature 455 (7214), 757-763. https://doi.org/10.1038/nature07327.

Charani, E., Cunningham, A.J., Yousif, A.H., Ahmed, M.S., Ahmed, A.E., Babiker, S., Elbashir, M.I., 2019. In transition: current health challenges and priorities in Sudan. *BMJ Glob. Health* 4 (4), e001723.

Congpuong, K., Ubalee, R., 2017. Population genetics of plasmodium vivax in four high malaria endemic areas in Thailand. Korean J. Parasitol. 55 (5), 465-472. https://doi.org/10.3347/kjp.2017.55.5.465.

Culleton, R., Carter, R., 2012. African plasmodium vivax: distribution and origins. Int. J. Parasitol. 42 (12), 1091-1097.

Diez Benavente, E., Ward, Z., Chan, W., Mohareb, F.R., Sutherland, C.J., Roper, C., Clark, T.G., 2017. Genomic variation in plasmodium vivax malaria reveals regions under selective pressure. PLoS One 12 (5), e0177134. https://doi.org/10.1371/journal.pone.0177134.

Elgoraish, A.G., Elzaki, S.E.G., Ahmed, R.T., Ahmed, A.I., Fadlalmula, H.A., Abdalgader Mohamed, S., El-Sayed, B.B., 2019. Epidemiology and distribution of plasmodium vivax malaria in Sudan. Trans. R. Soc. Trop. Med. Hyg. 113 (9), 517-524. https://doi.org/10.1093/trstmh/trz044.

Ellegren, H., 2004. Microsatellites: simple sequences with complex evolution. Nat. Rev. Genet. 5 (6), 435-445.

Escalante, A.A., Cornejo, O.E., Freeland, D.E., Poe, A.C., Durrego, E., Collins, W.E., Lal, A.A., 2005. A monkey's tale: the origin of plasmodium vivax as a human malaria parasite. Proc. Natl. Acad. Sci. 102 (6), 1980-1985.

Faulde, M.K., Hoffmann, R., Fazilat, K.M., Hoerauf, A., 2007. Malaria reemergence in northern Afghanistan. Emerg. Infect. Dis. 13 (9), 1402-1404. https://doi.org/10.3201/eid1309.061325.

Fola, A.A., Harrison, G.L.A., Hazairin, M.H., Barnadas, C., Hetzel, M.W., Iga, J., Barry, A. E., 2017. Higher complexity of infection and genetic diversity of plasmodium vivax than plasmodium falciparum across all malaria transmission zones of Papua New Guinea. Am. J. Trop. Med. Hyg. 96 (3), 630-641. https://doi.org/10.4269/ajtmh.16-0716.

Francis, R.M., 2017. Pophelper: an R package and web app to analyse and visualize population structure. Mol. Ecol. Resour. 17 (1), 27-32.

Gunawardena, S., Karunaweera, N.D., Ferreira, M.U., Phone-Kyaw, M., Pollack, R.J., Alifrangis, M., Wirth, D.F., 2010. Geographic structure of plasmodium vivax: microsatellite analysis of parasite populations from Sri Lanka, Myanmar, and Ethiopia. Am. J. Trop. Med. Hyg. 82 (2), 235-242. https://doi.org/10.4269/ajtmh.2010.09.0588.

Habtamu, K., Petros, B., Yan, G., 2022. Plasmodium vivax: the potential obstacles it presents to malaria elimination and eradication. Trop Dis Travel Med Vaccines 8 (1), 27. https://doi.org/10.1186/s40794-022-00185-3.

Haubold, B., Hudson, R.R., 2000. LIAN 3.0: detecting linkage disequilibrium in multilocus data. Bioinformatics 16 (9), 847-849.

Havryliuk, T., Ferreira, M.U., 2009. A closer look at multiple-clone plasmodium vivax infections: detection methods, prevalence and consequences. Mem. Inst. Oswaldo Cruz 104 (1), 67-73. https://doi.org/10.1590/s0074-02762009000100011.

Imwong, M., Nair, S., Pukrittayakamee, S., Sudimack, D., Williams, J.T., Mayxay, M., Anderson, T.J., 2007. Contrasting genetic structure in plasmodium vivax populations from Asia and South America. Int. J. Parasitol. 37 (8-9), 1013-1022. https://doi.org/10.1016/j.ijpara.2007.02.010.

Iwagami, M., Fukumoto, M., Hwang, S.Y., Kim, S.H., Kho, W.G., Kano, S., 2012. Population structure and transmission dynamics of plasmodium vivax in the Republic of Korea based on microsatellite DNA analysis. PLoS Negl. Trop. Dis. 6 (4), e1592. https://doi.org/10.1371/journal.pntd.0001592.

Joshi, H., Prajapati, S.K., Verma, A., Kang'a, S., Carlton, J.M., 2008. Plasmodium vivax in India. Trends Parasitol. 24 (5), 228-235. https://doi.org/10.1016/j.pt.2008.01.007.

Kale, S., Pande, V., Singh, O.P., Carlton, J.M., Mallick, P.K., 2021. Genetic diversity in two leading plasmodium vivax malaria vaccine candidates AMA1 and MSP119 at three sites in India. PLoS Negl. Trop. Dis. 15 (8), e0009652.

Kamvar, Z.N., Tabima, J.F., Grünwald, N.J., 2014. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281.

Karunaweera, N., Ferreira, M.U., Hartl, D., Wirth, D., 2007. Fourteen polymorphic microsatellite DNA markers for the human malaria parasite plasmodium vivax. Mol. Ecol. Notes 7 (1), 172-175.

Karunaweera, N.D., Ferreira, M.U., Munasinghe, A., Barnwell, J.W., Collins, W.E., King, C.L., Wirth, D.F., 2008. Extensive microsatellite diversity in the human malaria parasite plasmodium vivax. Gene 410 (1), 105-112. https://doi.org/10.1016/j.gene.2007.11.022.

Kittichai, V., Koepfli, C., Nguitragool, W., Sattabongkot, J., Cui, L., 2017. Substantial population structure of plasmodium vivax in Thailand facilitates identification of the sources of residual transmission. PLoS Negl. Trop. Dis. 11 (10), e0005930 https://doi.org/10.1371/journal.pntd.0005930.

Koepfli, C., Mueller, I., 2017. Malaria epidemiology at the clone level. Trends Parasitol. 33 (12), 974-985. https://doi.org/10.1016/j.pt.2017.08.013.

Koepfli, C., Rodrigues, P.T., Antao, T., Orjuela-Sánchez, P., Van den Eede, P., Abboa, D., Felger, I., 2015. Plasmodium vivax diversity and population structure across four continents. PLoS Negl. Trop. Dis. 9 (6), e0003872 https://doi.org/10.1371/journal.pntd.0003872.

Leclerc, M.C., Menegon, M., Cligny, A., Noyer, J.L., Mammadov, S., Aliyev, N., Severini, C., 2004. Genetic diversity of plasmodium vivax isolates from Azerbaijan. Malar. J. 3, 40. https://doi.org/10.1186/1475-2875-3-40.

Liu, W., Li, Y., Shaw, K.S., Learn, G.H., Plenderleith, L.J., Malenke, J.A., Smith, A.G., 2014. African origin of the malaria parasite plasmodium vivax. Nat. Commun. 5 (1), 3346.

Lo, E., Russo, G., Pestana, K., Kepple, D., Abagero, B.R., Dongho, G.B.D., Yewhalaw, D., 2021. Contrasting epidemiology and genetic variation of plasmodium vivax infecting Duffy-negative individuals across Africa. Int. J. Infect. Dis. 108, 63-71.

Lüdecke, D., Ben-Shachar, M.S., Patil, I., Waggoner, P., Makowski, D., 2021. Performance: an R package for assessment, comparison and testing of statistical models. J Open Source Software 6 (60).

Ludwig, J.A., Reynolds, J.F., 1988. Statistical Ecology: A Primer in Methods and Computing, Vol. 1. John Wiley & Sons.

Neafsey, D.E., Galinsky, K., Jiang, R.H., Young, L., Sykes, S.M., Saif, S., Carlton, J.M., 2012. The malaria parasite plasmodium vivax exhibits greater genetic diversity than plasmodium falciparum. Nat. Genet. 44 (9), 1046-1050. https://doi.org/10.1038/ng.2373.

Nei, M., 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89 (3), 583-590.

Pedersen, T.L., Pedersen, M., LazyData, T., Rcpp, I., Rcpp, L., 2017. Package 'ggraph'. Retrieved January, 1, 2018.

Price, R.N., Tjitra, E., Guerra, C.A., Yeung, S., White, N.J., Anstey, N.M., 2007. Vivax malaria: neglected and not benign. Am. J. Trop. Med. Hyg. 77 (6 Suppl), 79-87.

Roobsoong, W., Tharinjaroen, C.S., Rachaphaew, N., Chobson, P., Schofield, L., Cui, L., Sattabongkot, J., 2015. Improvement of culture conditions for long-term in vitro
culture of plasmodium vivax. Malar. J. 14, 297. https://doi.org/10.1186/s12936-015-0815-z.

Rougeron, V., Elguero, E., Arnathau, C., Acuña Hidalgo, B., Durand, P., Houze, S., Prugnolle, F., 2020. Human plasmodium vivax diversity, population structure and evolutionary origin. PLoS Negl. Trop. Dis. 14 (3), e0008072 https://doi.org/10.1371/journal.pntd.0008072.

Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., 2021. Ggally: extension to 'ggplot2' [R package version 2.1. 1 computer software manual].

Severini, C., Menegon, M., Gradoni, L., Majori, G., 2002. Use of the plasmodium vivax merozoite surface protein 1 gene sequence analysis in the investigation of an introduced malaria case in Italy. Acta Trop. 84 (2), 151–157.

Stoddart, J.A., Taylor, J.F., 1988. Genotypic diversity: estimation and prediction in samples. Genetics 118 (4), 705–711.

Suliman, M.M.A., Hamad, B.M., Albasheer, M.M.A., Elhadi, M., Amin Mustafa, M., Elobied, M., Hamid, M.M.A., 2016. Molecular evidence of high proportion of plasmodium vivax malaria infection in White Nile area in Sudan. J. Parasitol. Res. 2016.

Sutton, P.L., 2013. A call to arms: on refining plasmodium vivax microsatellite marker panels for comparing global diversity. Malar. J. 12 (1), 1–12.

Verity, R., Nichols, R., 2016. Documentation for MAVERICK Software: Version 1.0. Genetics.

Yu, G., Smith, D.K., Zhu, H., Guan, Y., Lam, T.T.Y., 2017. Ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8 (1), 28–36.