Nonparametric Binary Classification to Distinguish Closely Related versus Unrelated Plasmodium falciparum Parasites

Abstract

Assessing genetic relatedness of Plasmodium falciparum genotypes is a key component of antimalarial efficacy trials. Previous methods have focused on determining a priori definitions of the level of genetic similarity sufficient to classify two infections as sharing the same strain. However, factors such as mixed-strain infections, allelic suppression, imprecise typing methods, and heterozygosity complicate comparisons of apicomplexan genotypes. Here, we introduce a novel method for nonparametric statistical testing of relatedness for P. falciparum parasites. First, the background distribution of genetic distance between unrelated strains is computed. Second, a threshold genetic distance is computed from this empiric distribution of distances to demarcate genetic distances that are unlikely to have arisen by chance. Third, the genetic distance between paired samples is computed, and paired samples with genetic distances below the threshold are classified as related. The method is designed to work with any arbitrary genetic distance definition. We validated this procedure using two independent approaches to calculating genetic distance. We assessed the concordance of the novel nonparametric classification with a gold-standard Bayesian approach for 175 pairs of recurrent P. falciparum episodes from previously published malaria efficacy trials with microsatellite data from five studies in Guinea and Angola. The novel nonparametric approach was 98% sensitive and 84–89% specific in correctly identifying related genotypes compared with a gold-standard Bayesian algorithm. The approach provides a unified and systematic method to statistically assess relatedness of P. falciparum parasites using arbitrary genetic distance methodologies.

Microsatellites reveal high polymorphism and high potential for use in antimalarial efficacy studies in areas with different transmission intensities in mainland Tanzania

“…The findings showed that four markers (Poly-α, C2M34, C3M69 and TA1) had high diversity and could be adopted as validated markers for use in TES in Tanzania. As recently recommended by the WHO [53] and a previous study that showed that a combination of four microsatellite markers with sufficient diversity are needed in TES [54,55], these microsatellite markers can be included in the revised workflow for TES in Tanzania. The new panel should replace the old system based on genotyping of msp1, msp2 and glurp for distinguishing recrudescent from new infections in ongoing TES in Tanzania.…”

Retrospective evaluation of an integrated molecular-epidemiological approach to cyclosporiasis outbreak investigations – United States, 2021

“…Distance matrix calculation for the isolates passing the inclusion criteria (n = 655) was performed alongside a genetically diverse reference population of 1169 genotypes generated between 2018 and 2020 [1][2][3]. Cluster membership of isolates was determined using a recently described statistical framework for genetically linking pairs of isolates and applying a stringency setting of 99.5% [15]. Descriptions of the algorithms underpinning distance matrix calculation and genetic clustering are provided in the Supplementary Material.…”

Optimizing hierarchical tree dissection parameters using historic epidemiologic data as ‘ground truth’

“…A pairwise distance matrix was calculated from these Cyclospora spp. genotypes using Barratt's heuristic definition of genetic distance as previously described [3,19,20]. This matrix was hierarchically clustered using Ward's method implemented via the agnes function in the R package 'cluster' [21].…”

“…Consequently, investigators must dissect hierarchical trees into discrete genetic groupings (i.e., partitions) to facilitate prioritization of discrete genetic groups for subsequent epidemiologic investigation. Usually, the value of some tree-dissection parameter (e.g., a SNP distance threshold) is empirically selected by investigators to facilitate tree dissection, hopefully yielding partitions where all (or most) grouped isolates are representatives of the same strain [1][2][3]. In epidemiologic contexts, the objective is always to select a parameter value for tree dissection that groups isolates with a high likelihood of belonging to the same strain, and thus, have a high probability of being associated with a common source.…”