Within-household clustering of genetically related Plasmodium falciparum infections in a moderate transmission area of Uganda.
Jessica Briggs, Alison Kuchta, Max Murphy, Sofonias Tessema, Emmanuel Arinaitwe, John Rek, Anna Chen, Joaniter I Nankabirwa, Chris Drakeley, David Smith, Teun Bousema, Moses Kamya, Isabel Rodriguez-Barraquer, Sarah Staedke, Grant Dorsey, Philip J Rosenthal, Bryan Greenhouse
Author Information
Jessica Briggs: Department of Medicine, University of California San Francisco, San Francisco, CA, USA. jessica.briggs@ucsf.edu. ORCID
Alison Kuchta: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Max Murphy: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Sofonias Tessema: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Emmanuel Arinaitwe: Infectious Diseases Research Collaboration, Kampala, Uganda.
John Rek: Infectious Diseases Research Collaboration, Kampala, Uganda.
Anna Chen: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Joaniter I Nankabirwa: Infectious Diseases Research Collaboration, Kampala, Uganda.
Chris Drakeley: Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK.
David Smith: Institute for Health Metrics & Evaluation, University of Washington, Seattle, WA, USA.
Teun Bousema: Department of Medical Microbiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
Moses Kamya: Infectious Diseases Research Collaboration, Kampala, Uganda.
Isabel Rodriguez-Barraquer: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Sarah Staedke: Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK.
Grant Dorsey: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Philip J Rosenthal: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Bryan Greenhouse: Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
BACKGROUND: Evaluation of genetic relatedness of malaria parasites is a useful tool for understanding transmission patterns, but patterns are not easily detectable in areas with moderate to high malaria transmission. To evaluate the feasibility of detecting genetic relatedness in a moderate malaria transmission setting, relatedness of Plasmodium falciparum infections was measured in cohort participants from randomly selected households in the Kihihi sub-county of Uganda (annual entomological inoculation rate of 27 infectious bites per person). METHODS: All infections detected via microscopy or Plasmodium-specific loop mediated isothermal amplification from passive and active case detection during August 2011-March 2012 were genotyped at 26 microsatellite loci, providing data for 349 samples from 230 participants living in 80 households. Pairwise genetic relatedness was calculated using identity by state (IBS). RESULTS: As expected, genetic diversity was high (mean heterozygosity [H] = 0.73), and the majority (76.5 %) of samples were polyclonal. Despite the high genetic diversity, fine-scale population structure was detectable, with significant spatiotemporal clustering of highly related infections. Although the difference in malaria incidence between households at higher (mean 1127 metres) versus lower elevation (mean 1015 metres) was modest (1.4 malaria cases per person-year vs. 1.9 per person-year, respectively), there was a significant difference in multiplicity of infection (2.2 vs. 2.6, p = 0.008) and, more strikingly, a higher proportion of highly related infections within households (6.3 % vs. 0.9 %, p = 0.0005) at higher elevation compared to lower elevation. CONCLUSIONS: Genetic data from a relatively small number of diverse, multiallelic loci reflected fine scale patterns of malaria transmission. Given the increasing interest in applying genetic data to augment malaria surveillance, this study provides evidence that genetic data can be used to inform transmission patterns at local spatial scales even in moderate transmission areas.
