Modeling malaria genomics reveals transmission decline and rebound in Senegal

Significance Traditional methods for estimating malaria transmission based on mosquito sampling are not standardized and are unavailable in many countries in sub-Saharan Africa. Such studies are especially difficult to implement when transmission is low, and low transmission is the goal of malaria e...

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Published in:Proceedings of the National Academy of Sciences - PNAS Vol. 112; no. 22; pp. 7067 - 7072
Main Authors: Daniels, Rachel F., Schaffner, Stephen F., Wenger, Edward A., Proctor, Joshua L., Chang, Hsiao-Han, Wong, Wesley, Baro, Nicholas, Ndiaye, Daouda, Fall, Fatou Ba, Ndiop, Medoune, Ba, Mady, Milner, Danny A., Taylor, Terrie E., Neafsey, Daniel E., Volkman, Sarah K., Eckhoff, Philip A., Hartl, Daniel L., Wirth, Dyann F.
Format: Journal Article
Language:English
Published: United States National Academy of Sciences 02-06-2015
National Acad Sciences
Series:From the Cover
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Summary:Significance Traditional methods for estimating malaria transmission based on mosquito sampling are not standardized and are unavailable in many countries in sub-Saharan Africa. Such studies are especially difficult to implement when transmission is low, and low transmission is the goal of malaria elimination. Malaria-control efforts in Senegal have resulted in changes in population genomics evidenced by increased allele sharing among parasite genomes, often including genomic identity between independently sampled parasites. Fitting an epidemiological model to the observed data indicates falling transmission from 2006–2010 with a significant rebound in 2012–2013, an inference confirmed by incidence data. These results demonstrate that genomic approaches may help monitor transmission to assess initial and ongoing effectiveness of interventions to control malaria. To study the effects of malaria-control interventions on parasite population genomics, we examined a set of 1,007 samples of the malaria parasite Plasmodium falciparum collected in Thiès, Senegal between 2006 and 2013. The parasite samples were genotyped using a molecular barcode of 24 SNPs. About 35% of the samples grouped into subsets with identical barcodes, varying in size by year and sometimes persisting across years. The barcodes also formed networks of related groups. Analysis of 164 completely sequenced parasites revealed extensive sharing of genomic regions. In at least two cases we found first-generation recombinant offspring of parents whose genomes are similar or identical to genomes also present in the sample. An epidemiological model that tracks parasite genotypes can reproduce the observed pattern of barcode subsets. Quantification of likelihoods in the model strongly suggests a reduction of transmission from 2006–2010 with a significant rebound in 2012–2013. The reduced transmission and rebound were confirmed directly by incidence data from Thièès. These findings imply that intensive intervention to control malaria results in rapid and dramatic changes in parasite population genomics. The results also suggest that genomics combined with epidemiological modeling may afford prompt, continuous, and cost-effective tracking of progress toward malaria elimination.
Bibliography:http://dx.doi.org/10.1073/pnas.1505691112
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Reviewers: M.F., University of Notre Dame; and B.G., University of California, San Francisco.
Contributed by Daniel L. Hartl, March 23, 2015 (sent for review December 1, 2014; reviewed by Michael Ferdig and Bryan Greenhouse)
Author contributions: D.E.N., S.K.V., P.A.E., D.L.H., and D.F.W. conceived of the project; R.F.D., S.F.S., S.K.V., P.A.E., and D.F.W. designed research; F.B.F., M.N., M.B., D.A.M., and T.E.T. collected samples and data; R.F.D., S.F.S., E.A.W., D.N., F.B.F., M.N., M.B., D.A.M., and T.E.T. performed research; R.F.D., S.F.S., E.A.W., J.L.P., H.-H.C., W.W., N.B., and D.L.H. analyzed data; and R.F.D., S.F.S., E.A.W., J.L.P., D.N., D.E.N., S.K.V., P.A.E., D.L.H., and D.F.W. wrote the paper.
1R.F.D., S.F.S., and E.A.W. contributed equally to this work.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1505691112