Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent

Abstract

With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (F_ST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and F_ST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs. The spatiotemporal dispersal of organisms can inform efforts to conserve endangered species, to contain the spread of drug resistance, and to eliminate disease. As genomic data become increasingly more affordable and accessible via public depositories, the demand for methods capable of extracting fine-scale population structure from genomic data grows. However, to the best of our knowledge, there are no guidelines regarding the type of data and methods required to resolve local spatial trends over sexually recombining haploid organisms, such as the malaria parasite. The approach we present here compares relatedness based on identity by descent, which accounts for recombination while distinguishing genetic identity due to inheritance from genetic identity due to chance, to a classic population genetic measure of divergence, using data from sexually recombining malaria parasites. Using identity by descent, we uncover a significant decrease in highly related malaria parasites collected from proximal clinics on the Thai-Myanmar border, a region where human mobility is high. Our results demonstrate the power of analyses based on identity by descent to detect recent and local trends. Similar analyses could be used to inform the molecular epidemiology of other sexually recombining organisms.