Exploring how space, time, and sampling impact our ability to measure genetic structure across Plasmodium falciparum populations

Rohan Arambepola; Sophie Bérubé; Betsy Freedman; Steve M Taylor; Wendy Prudhomme O'Meara; Andrew A Obala; Amy Wesolowski

doi:10.3389/fepid.2023.1058871

Exploring how space, time, and sampling impact our ability to measure genetic structure across Plasmodium falciparum populations

Front Epidemiol. 2023 Feb 17:3:1058871. doi: 10.3389/fepid.2023.1058871. eCollection 2023.

Authors

Rohan Arambepola¹, Sophie Bérubé¹, Betsy Freedman², Steve M Taylor^{2

3}, Wendy Prudhomme O'Meara^{2

3}, Andrew A Obala⁴, Amy Wesolowski¹

Affiliations

¹ Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Batlimore, MD, United States.
² Division of Infectious Diseases, Duke University Medical Center, Durham, NC, United States.
³ Duke Global Health Institute, Durham, NC, United States.
⁴ College of Health Sciences, Moi University, Eldoret, Kenya.

Abstract

A primary use of malaria parasite genomics is identifying highly related infections to quantify epidemiological, spatial, or temporal factors associated with patterns of transmission. For example, spatial clustering of highly related parasites can indicate foci of transmission and temporal differences in relatedness can serve as evidence for changes in transmission over time. However, for infections in settings of moderate to high endemicity, understanding patterns of relatedness is compromised by complex infections, overall high forces of infection, and a highly diverse parasite population. It is not clear how much these factors limit the utility of using genomic data to better understand transmission in these settings. In particular, further investigation is required to determine which patterns of relatedness we expect to see with high quality, densely sampled genomic data in a high transmission setting and how these observations change under different study designs, missingness, and biases in sample collection. Here we investigate two identity-by-state measures of relatedness and apply them to amplicon deep sequencing data collected as part of a longitudinal cohort in Western Kenya that has previously been analysed to identify individual-factors associated with sharing parasites with infected mosquitoes. With these data we use permutation tests, to evaluate several hypotheses about spatiotemporal patterns of relatedness compared to a null distribution. We observe evidence of temporal structure, but not of fine-scale spatial structure in the cohort data. To explore factors associated with the lack of spatial structure in these data, we construct a series of simplified simulation scenarios using an agent based model calibrated to entomological, epidemiological and genomic data from this cohort study to investigate whether the lack of spatial structure observed in the cohort could be due to inherent power limitations of this analytical method. We further investigate how our hypothesis testing behaves under different sampling schemes, levels of completely random and systematic missingness, and different transmission intensities.

Keywords: Plasmodium falciparum; agent - based modeling; malaria; relatedness; simulation; study design.

Grants and funding

AW is supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and by the National Institute of Allergy and Infectious Diseases (R01AI146849). SB, RA, and AW are all supported by the National Institute of Health Director's New Innovator Award, grant number DP2LM013102-0 and by the National Institute of Allergy and Infectious Diseases (1R01A1160780-01). This work was supported by NIAID (R21AI126024 to WPO and R01AI146849 to WPO and SMT).