Abstract
Background: The first models of malaria transmission assumed a completely mixed and homogeneous population of parasites. Recent models include spatial heterogeneity and variably mixed populations. However, there are few empiric estimates of parasite mixing with which to parametize such models. Methods: Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 P. falciparum isolates from two Kenyan sites and one Gambian site to determine the spatio-temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and relatedness in space and time for parasite pairs. Results: We show that there are no discrete geographically restricted parasite sub-populations, but instead we see a diffuse spatio-temporal structure to parasite genotypes. Genetic relatedness of sample pairs is predicted by relatedness in space and time.
Conclusions: Our findings suggest that targeted malaria control will benefit the surrounding community, but unfortunately also that emerging drug resistance will spread rapidly through the population.
| Original language | English (US) |
|---|---|
| Article number | A2 |
| Journal | Wellcome Open Research |
| Volume | 2 |
| DOIs | |
| State | Published - Jan 1 2017 |
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ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Medicine (miscellaneous)
Cite this
Micro-epidemiological structuring of Plasmodium falciparum parasite populations in regions with varying transmission intensities in Africa. / Omedo, Irene; Mogeni, Polycarp; Bousema, Teun; Rockett, Kirk; Amambua-Ngwa, Alfred; Oyier, Isabella; Stevenson, Jennifer Claire; Baidjoe, Amrish Y.; de Villiers, Etienne P.; Fegan, Greg; Ross, Amanda; Hubbart, Christina; Jeffreys, Anne; Williams, Thomas N.; Kwiatkowski, Dominic; Bejon, Philip.
In: Wellcome Open Research, Vol. 2, A2, 01.01.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Micro-epidemiological structuring of Plasmodium falciparum parasite populations in regions with varying transmission intensities in Africa.
AU - Omedo, Irene
AU - Mogeni, Polycarp
AU - Bousema, Teun
AU - Rockett, Kirk
AU - Amambua-Ngwa, Alfred
AU - Oyier, Isabella
AU - Stevenson, Jennifer Claire
AU - Baidjoe, Amrish Y.
AU - de Villiers, Etienne P.
AU - Fegan, Greg
AU - Ross, Amanda
AU - Hubbart, Christina
AU - Jeffreys, Anne
AU - Williams, Thomas N.
AU - Kwiatkowski, Dominic
AU - Bejon, Philip
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Background: The first models of malaria transmission assumed a completely mixed and homogeneous population of parasites. Recent models include spatial heterogeneity and variably mixed populations. However, there are few empiric estimates of parasite mixing with which to parametize such models. Methods: Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 P. falciparum isolates from two Kenyan sites and one Gambian site to determine the spatio-temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and relatedness in space and time for parasite pairs. Results: We show that there are no discrete geographically restricted parasite sub-populations, but instead we see a diffuse spatio-temporal structure to parasite genotypes. Genetic relatedness of sample pairs is predicted by relatedness in space and time.Conclusions: Our findings suggest that targeted malaria control will benefit the surrounding community, but unfortunately also that emerging drug resistance will spread rapidly through the population.
AB - Background: The first models of malaria transmission assumed a completely mixed and homogeneous population of parasites. Recent models include spatial heterogeneity and variably mixed populations. However, there are few empiric estimates of parasite mixing with which to parametize such models. Methods: Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 P. falciparum isolates from two Kenyan sites and one Gambian site to determine the spatio-temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and relatedness in space and time for parasite pairs. Results: We show that there are no discrete geographically restricted parasite sub-populations, but instead we see a diffuse spatio-temporal structure to parasite genotypes. Genetic relatedness of sample pairs is predicted by relatedness in space and time.Conclusions: Our findings suggest that targeted malaria control will benefit the surrounding community, but unfortunately also that emerging drug resistance will spread rapidly through the population.
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U2 - 10.12688/wellcomeopenres.10784.1
DO - 10.12688/wellcomeopenres.10784.1
M3 - Article
VL - 2
JO - Wellcome Open Research
JF - Wellcome Open Research
SN - 2398-502X
M1 - A2
ER -