Genomic structure and diversity of Plasmodium falciparum in Southeast Asia reveal recent parasite migration patterns

Shetty, Amol C.; Jacob, Christopher G; Huang, Fang; Li, Yao; Agrawal, Sonia; Saunders, David; Lon, Chanthap; Fukuda, Mark M.; Ringwald, Pascal; Ashley, Elizabeth A.; Han, Kay Thwe; Hlaing, Tin Maung; Nyunt, Myaing M.; Silva, Joana C.; Stewart, Kathleen; Plowe, Christopher V.; O’Connor, Timothy D.; Takala-Harrison, Shannon

doi:10.1038/s41467-019-10121-3

Cited by 46 publications

(77 citation statements)

References 66 publications

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“…As discussed later, Southeast Asia is the global epicentre of antimalarial drug resistance, and these observations add to a growing body of evidence that Southeast Asian parasites have acquired a wide range of genomic features that are likely due to natural selection rather than genetic drift. 23,40 It is noteworthy that the level of genetic differentiation between western and eastern parts of Southeast Asia (genome-wide FST 0.05) is greater than between West Africa and East Africa (0.02) although the geographic distances are much greater in Africa. This is likely due to the lower intensity of malaria transmission in Southeast Asia, and in particular the presence of a malaria-free corridor running through Thailand, which act as barriers to gene flow across the region.…”

Section: Geographic Patterns Of Population Differentiation and Gene Flowmentioning

confidence: 99%

“…This is likely due to the lower intensity of malaria transmission in Southeast Asia, and in particular the presence of a malaria-free corridor running through Thailand, which act as barriers to gene flow across the region. 23,40 Genes with high levels of geographic differentiation…”

Section: Geographic Patterns Of Population Differentiation and Gene Flowmentioning

confidence: 99%

“…28 The data have also been used to study gene deletions that cause failure of rapid diagnostic tests 29 ; to characterise genetic variation in malaria vaccine antigens 30,31 ; to screen for new vaccine candidates 32 ; to investigate specific host-parasite interactions 33,34 ; and to describe the evolutionary adaptation and diversification of local parasite populations. 7,9,12,[35][36][37][38][39][40] The Pf Community Project data also provide an important resource for developing and testing new analytical and computational methods. A key area of methods development is quantification of within-host diversity 7,[41][42][43][44][45][46] , estimation of inbreeding 7,47 , and deconvolution of mixed infections into individual strains.…”

Section: Introductionmentioning

confidence: 99%

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An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

Pearson

Amato

Kwiatkowski

2019

Preprint

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MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed. Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination. MethodsAll samples in this study were derived from blood samples obtained from patients with P. falciparum malaria, collected with informed consent from the patient or a parent or guardian. At each location, sample collection was approved by the appropriate local and institutional ethics committees. The following local and institutional committees gave ethical approval for the partner studies:Standard laboratory protocols were used to determine DNA quantity and proportion of human DNA in each sample as previously described 7,56 .

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Section: Geographic Patterns Of Population Differentiation and Gene Flowmentioning

confidence: 99%

Section: Geographic Patterns Of Population Differentiation and Gene Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

Pearson

Amato

Kwiatkowski

2019

Preprint

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“…Similarly, relatedness may be greater among parasites with comparable antimalarial resistance: a recent study of South East Asian P. falciparum parasites found greater relatedness in the recent past among parasites with artemisinin resistance mutations versus without [49] 1 . This study used size-stratified IBD segments to date relatedness [49]. 1 On the Colombian-Pacific coast, IBD segment size inference could help identify some recently related parasites.…”

Section: Discussionmentioning

confidence: 99%

Identity-by-descent relatedness estimates with uncertainty characterise departure from isolation-by-distance betweenPlasmodium falciparumpopulations on the Colombian-Pacific coast

Taylor

Echeverry

Anderson

et al. 2020

Preprint

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18Characterising connectivity between geographically separated biological populations is a common goal in 19 many fields. Recent approaches to understanding connectivity between malaria parasite populations, with 20 implications for disease control efforts, have used estimates of relatedness based on identity-by-descent (IBD). 21 However, uncertainty around estimated relatedness has not been accounted for to date. IBD-based relat-22 edness estimates with uncertainty were computed for pairs of monoclonal Plasmodium falciparum samples 23 collected from five cities on the Colombian-Pacific coast where long-term clonal propagation of P. falciparum 24 is frequent. The cities include two official ports, Buenaventura and Tumaco, that are separated geographically 25 but connected by frequent marine traffic. The fraction of highly-related sample pairs (whose classification 26 accounts for uncertainty) was greater within cities versus between. However, based on both the fraction 27 of highly-related sample pairs and on a threshold-free approach (Wasserstein distances between parasite 28 populations) connectivity between Buenaventura and Tumaco was disproportionally high. Buenaventura- 29Tumaco connectivity was consistent with three separate transmission events involving parasites from five 30 different clonal components (groups of statistically indistinguishable parasites identified under a graph theo-31 retic framework). To conclude, P. falciparum population connectivity on the Colombian-Pacific coast abides 32 by accessibility not isolation-by-distance, potentially implicating marine traffic in malaria transmission with 33 opportunities for targeted intervention. Further investigations are required to test this and alternative hy-34 potheses. For the first time in malaria epidemiology, we account for uncertainty around estimated relatedness 35 (an important consideration for future studies that plan to use genotype versus whole genome sequence data 36 to estimate IBD-based relatedness); we also use a threshold-free approach to compare parasite populations, 37 and identify clonal components in a statistically principled manner. The approaches we employ could be 38 adapted to other recombining organisms with mixed mating systems, thus have broad relevance. 39 1 In many research fields genetic data are used to help characterise connectivity between geographically dis-41 parate biological populations, with numerous applications in conservation, agriculture, and public health. 42Patterns of genetic similarity between pathogen populations help us understand how the disease spreads. 43 Patterns of relatedness (a measure of genetic similarity) between malaria parasites in different human pop-44 ulations, for instance, help characterise the connectivity between them, thus guide the design of targeted 45 public health interventions [1]. 46 Several methods are employed to measure genetic similarity and thus characterise connectivity. Phylo-47 genetic methods, in which genetic distances between individuals are measured in unit...

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“…In our previous research, we have applied EEMS as well as approaches based on identity-by-descent to investigate migration patterns and population structure of Plasmodium falciparum in the Greater Mekong Subregion [20], an area of emerging multidrug resistance being targeted for malaria elimination [21]. EEMS maps are visuallyintuitive and may be useful to malaria elimination programs by identifying defined geographic areas that can be targeted with interventions.…”

Section: Introductionmentioning

confidence: 99%

Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces

Shetty

Lon

et al. 2020

Int J Health Geogr

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Background: Understanding the genetic structure of natural populations provides insight into the demographic and adaptive processes that have affected those populations. Such information, particularly when integrated with geospatial data, can have translational applications for a variety of fields, including public health. Estimated effective migration surfaces (EEMS) is an approach that allows visualization of the spatial patterns in genomic data to understand population structure and migration. In this study, we developed a workflow to optimize the resolution of spatial grids used to generate EEMS migration maps and applied this optimized workflow to estimate migration of Plasmodium falciparum in Cambodia and bordering regions of Thailand and Vietnam. Methods:The optimal density of EEMS grids was determined based on a new workflow created using density clustering to define genomic clusters and the spatial distance between genomic clusters. Topological skeletons were used to capture the spatial distribution for each genomic cluster and to determine the EEMS grid density; i.e., both genomic and spatial clustering were used to guide the optimization of EEMS grids. Model accuracy for migration estimates using the optimized workflow was tested and compared to grid resolutions selected without the optimized workflow. As a test case, the optimized workflow was applied to genomic data generated from P. falciparum sampled in Cambodia and bordering regions, and migration maps were compared to estimates of malaria endemicity, as well as geographic properties of the study area, as a means of validating observed migration patterns. Results:Optimized grids displayed both high model accuracy and reduced computing time compared to grid densities selected in an unguided manner. In addition, EEMS migration maps generated for P. falciparum using the optimized grid corresponded to estimates of malaria endemicity and geographic properties of the study region that might be expected to impact malaria parasite migration, supporting the validity of the observed migration patterns. Conclusions:Optimized grids reduce spatial uncertainty in the EEMS contours that can result from user-defined parameters, such as the resolution of the spatial grid used in the model. This workflow will be useful to a broad range of EEMS users as it can be applied to analyses involving other organisms of interest and geographic areas.© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'

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Genomic structure and diversity of Plasmodium falciparum in Southeast Asia reveal recent parasite migration patterns

Cited by 46 publications

References 66 publications

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

Identity-by-descent relatedness estimates with uncertainty characterise departure from isolation-by-distance betweenPlasmodium falciparumpopulations on the Colombian-Pacific coast

Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces

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