The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria
Abstract
Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Furthermore, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.
Data availability
Metadata on samples is available from ftp://ngs.sanger.ac.uk/production/pf3k/release_5/pf3k_release_5_metadata_20170804.txt.gz. Sequence data (aligned to Plasmodium falciparum strain 3D7 v3.1 reference genome sequences, for details see ftp://ftp.sanger.ac.uk/pub/project/pathogens/gff3/2015-08/Pfalciparum.genome.fasta.gz) is available from ftp://ngs.sanger.ac.uk/production/pf3k/release_5/5.1/. Diagnostic plots for the deconvolution of all samples can be found at https://github.com/mcveanlab/mixedIBD-Supplement and deconvolved haplotypes can be accessed at ftp://ngs.sanger.ac.uk/production/pf3k/technical_working/release_5/mixedIBD_paper_haplotypes/. Code implementing the algorithms described in this paper, DEploidIBD, is available at https://github.com/mcveanlab/DEploid.
-
The Pf3k Project (2016): pilot data release 5Wellcome Trust Sanger public ftp site, 5.1 Data.
Article and author information
Author details
Funding
Wellcome (206194)
- Jacob Almagro-Garcia
Wellcome (090770)
- Jacob Almagro-Garcia
- Richard D. Pearson
- Roberto Amato
- Alistair Miles
- Dominic Kwiatkowski
Wellcome (100956/Z/13/Z)
- Sha Joe Zhu
- Gil McVean
Li Ka Shing Foundation (NA)
- Gil McVean
Wellcome (204911)
- Jacob Almagro-Garcia
- Richard D. Pearson
- Roberto Amato
- Alistair Miles
- Dominic Kwiatkowski
Medical Research Council (G0600718)
- Jacob Almagro-Garcia
- Richard D. Pearson
- Roberto Amato
- Alistair Miles
- Dominic Kwiatkowski
Department for International Development (M006212)
- Jacob Almagro-Garcia
- Richard D. Pearson
- Roberto Amato
- Alistair Miles
- Dominic Kwiatkowski
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Eduardo Franco, McGill University, Canada
Version history
- Received: August 9, 2018
- Accepted: July 10, 2019
- Accepted Manuscript published: July 12, 2019 (version 1)
- Version of Record published: August 6, 2019 (version 2)
Copyright
© 2019, Zhu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,131
- views
-
- 301
- downloads
-
- 50
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Epidemiology and Global Health
We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
-
- Ecology
- Epidemiology and Global Health
Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.