THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites

Abstract

As many malaria-endemic countries move towards elimination of Plasmodium falciparum, the most virulent human malaria parasite, effective tools for monitoring malaria epidemiology are urgent priorities. P. falciparum population genetic approaches offer promising tools for understanding transmission and spread of the disease, but a high prevalence of multi-clone or polygenomic infections can render estimation of even the most basic parameters, such as allele frequencies, challenging. A previous method, COIL, was developed to estimate complexity of infection (COI) from single nucleotide polymorphism (SNP) data, but relies on monogenomic infections to estimate allele frequencies or requires external allele frequency data which may not available. Estimates limited to monogenomic infections may not be representative, however, and when the average COI is high, they can be difficult or impossible to obtain. Therefore, we developed THE REAL McCOIL, Turning HEterozygous SNP data into Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection using Likelihood, to incorporate polygenomic samples and simultaneously estimate allele frequency and COI. This approach was tested via simulations then applied to SNP data from cross-sectional surveys performed in three Ugandan sites with varying malaria transmission. We show that THE REAL McCOIL consistently outperforms COIL on simulated data, particularly when most infections are polygenomic. Using field data we show that, unlike with COIL, we can distinguish epidemiologically relevant differences in COI between and within these sites. Surprisingly, for example, we estimated high average COI in a peri-urban subregion with lower transmission intensity, suggesting that many of these cases were imported from surrounding regions with higher transmission intensity. THE REAL McCOIL therefore provides a robust tool for understanding the molecular epidemiology of malaria across transmission settings.

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

“…7,9,12,[35][36][37][38][39][40] The Pf Community Project data also provide an important resource for developing and testing new analytical and computational methods. A key area of methods development is quantification of within-host diversity 7,[41][42][43][44][45][46] , estimation of inbreeding 7,47 , and deconvolution of mixed infections into individual strains. 48,49 The data have also been used to develop and test methods for estimating identity by descent 50,51 , imputation 52 , typing structural variants 53 , designing other SNP genotyping platforms 54 and data visualisation 8,55 .…”

Mapping malaria by combining parasite genomic and epidemiologic data

“…In high transmission settings, the complexity of infection (COI) is very high, making it difficult to calculate genetic relatedness between parasites within polyclonal infections or estimate allele frequencies across polyclonal infections. Methods are available to phase parasite genetic data within polyclonal infections [36], while THE REAL McCOIL [37] has been developed to infer allele frequencies and COI simultaneously, allowing downstream calculation of F ST . However, to fully characterize genetic structure at fine scales in high transmission settings, new methods are needed that estimate IBD and other relatedness measures to infer ancestry between polyclonal infections.…”

The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites

“…As a result, phylogenetic and population genetic methods normally applied to the analysis of non-recombining organisms cannot be used. An emerging field of promising new approaches, such as THE REAL McCOIL, hmmIBD, or isoRelate, are being developed to explicitly address these characteristics of malaria infection (18,(22)(23)(24).…”