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 (FST) 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 FST, 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.

Estimating relatedness between malaria parasites

“…, m) single nucleotide polymorphism (SNP) data from monoclonal P. falciparum samples ( Table 1). All data are published [50,51,29,30,52,8]. They were obtained either from sparse genome-wide panels of select markers, called barcodes, or from dense whole genome sequencing (WGS) data sets (reviewed in [53]); full details of sample collection and data generation can be found via the citations above and references therein.…”

“…Besides mapping SNP positions to the P. falciparum 3d7 v3 reference genome and recoding heteroallelic calls as missing (since all samples with fewer than 10 heteroallelic SNP calls were classified monoclonal by [50]), we did not post-process the Colombian data in any way. Thailand 93-SNP and WGS samples were used exactly as described in [8]. Data derived from [29,30] (i.e.…”

“…Because relatedness is broken down by outbreeding, it can change with each generation [7]. On a population-level, studies of malaria parasite relatedness thus provide a sensitive measure of recent gene flow [8], generating insight on an operationally relevant scale for disease control efforts [9].…”

Patterns of population structure and complex haplotype sharing among field isolates of the green alga Chlamydomonas reinhardtii

“…This approach infers identical by descent tracts shared between pairs of individuals as genomic regions that are identical by state (allowing for genotyping error), based on SNP allele frequencies, the distance between SNPs in bases, and a genome‐wide recombination rate. Additionally, the program estimates the expected proportion of the genome inherited identical by descent between pairs ( IBD ) based on the average per‐SNP probability of identity by descent, independent of the designation of tracts (Taylor et al, ). hmmibd was run independently for each N. American lineage (NA1/NA2), assuming a recombination rate of 1.2 × 10 –5 cM/bp (Liu et al, ) and otherwise default parameters.…”

Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

“…Technological advancements that allow better tracking of human mobility, such as wearable proximity censors [56][57][58] or fitness trackers, cell phone location data [59][60][61][62] , or large-scale transit networks 63,64 , provide one source of population structure data. Advances in genetic epidemiology allow us to retrospectively trace the spread of infection between individual hosts or communities using pathogen phylogenies [65][66][67][68][69] , which also provides information on population structure. Spatial heterogeneity has been understood by population geneticists to be an important factor in the spread of genes since the days of Fisher 70 and later Kimura 71 .…”