Biology subjects

Taylor, A. R.

Publications and source records attributed to Taylor, A. R..

11 recordsLinked to original sources

bioRxiv2024

Systematic bias in malaria parasite relatedness estimation

Genetic studies of malaria parasites increasingly feature estimates of relatedness. However, various aspects of malaria parasite relatedness estimation are not fully understood. For example, estimates of relatedness based on whole-genome-sequence (WGS) data often exceed those based on more sparse data types. We explore systematic bias in relatedness estimation using theoretical, numerical and empirical approaches. Specifically, we use a non-ancestral model of pairwise relatedness to derive theoretical results; a simulation model of ancestry to independently verify and expand our theoretical results; and data on parasites sampled from Guyana to explore how theoretical and numerical results translate empirically. We show that allele frequencies encode, locus-by-locus, relatedness averaged over the set of sampled parasites used to compute them. These sample allele frequencies are typically plugged into the models used to estimate pairwise relatedness. Consequently, models of pairwise relatedness are misspecified and pairwise relatedness values are systematically underestimated. However, systematic underestimation can be viewed as population-relatedness calibration, i.e., a way of generating measures of relative relatedness. Systematic underestimation is unavoidable when relatedness is estimated assuming independence between genetic markers. It is mitigated when estimated using WGS data under a hidden Markov model (HMM), which exploits linkage between proximal markers. Estimates of absolute relatedness generated under a HMM using relatively sparse data should be treated with caution because the extent to which underestimation is mitigated is unknowable. That said, analyses dependent on absolute values and high relatedness thresholds are relatively robust. In summary, practitioners have two options: resolve to use relative relatedness estimated under independence or try to estimate absolute relatedness under a HMM. We propose various practical tools to help practitioners evaluate their situation on a case-by-case basis. Author summaryMalaria genomic epidemiology is increasingly recognised as a tool for public health. Relatedness, which captures likeness derived from common ancestry, is a useful concept for malaria parasites. Analyses of malaria parasite relatedness are important for generating results on spatiotemporal scales relevant to disease control. Since shared ancestry is unobservable, relatedness must be estimated under a statistical model. However, not all aspects of malaria parasite estimation are fully understood, including the effects of different data types. In this work, we characterise systematic biases in estimates of malaria parasite relatedness. Our analysis is three-fold: we mathematically interrogate a non-ancestral model of relatedness to derive theoretical results; simulate parasite ancestries from first principles to yield numerical results; and perform an empirical case study of parasites sampled from Guyana. We show that bias may be particularly pronounced when using sparse marker data from inbred parasite populations, which are often found in pre-elimination settings. We chart out a practical roadmap to enable practitioners to assess epidemiological settings on a case-by-case basis. Our findings are relevant to applications in malaria genomic epidemiology that use relatedness directly or indirectly, including molecular surveillance and the genetic-based classification of treatment failure.

genetics↗

bioRxiv2022

Resolving drug selection and migration in an inbred South American Plasmodium falciparum population with identity-by-descent analysis

The human malaria parasite Plasmodium falciparum is globally widespread, but its prevalence varies significantly between and even within countries. Most population genetic studies in P. falciparum focus on regions of high transmission where parasite populations are large and genetically diverse, such as sub-Saharan Africa. Understanding population dynamics in low transmission settings, however, is of particular importance as these are often where drug resistance first evolves. Here, we use the Pacific Coast of Colombia and Ecuador as a model for understanding the population structure and evolution of Plasmodium parasites in small populations harboring low genetic diversity. The combination of low transmission and a high proportion of monoclonal infections means there are few outcrossing events and clonal lineages persist for long periods of time. Yet despite this, the population is evolutionarily labile and has successfully adapted to multiple drug regimes. Using 166 newly sequenced whole genomes, we measure relatedness between parasites, calculated as identity by descent (IBD), and find 17 distinct but highly related clonal lineages, six of which have persisted in the region for at least a decade. This inbred population structure is captured in more detail with IBD than with other common population structure analyses like PCA, ADMIXTURE, and distance-based trees. We additionally use patterns of intra-chromosomal IBD and an analysis of haplotypic variation to explore the role of recombination in spreading drug resistance mutations throughout the region. Two genes associated with chloroquine resistance, crt and aat1, show evidence of hard selective sweeps, while selection appears soft and/or incomplete at three other key resistance loci (dhps, mdr1, and dhfr). Overall, this work highlights the strength of IBD analyses for studying parasite population structure and resistance evolution in regions of low transmission, and emphasizes that drug resistance can evolve and spread in extremely small populations, as will occur in any region nearing malaria elimination.

evolutionary biology↗

bioRxiv2022

The effect of cytoskeleton inhibitors on coccolith morphology in Coccolithus braarudii and Scyphosphaera apsteinii

The calcite platelets of coccolithophores (Haptophyta), the coccoliths, are among the most elaborate biomineral structures. How these unicellular algae accomplish the complex morphogenesis of coccoliths is still largely unknown. It has long been proposed that the cytoskeleton plays a central role in shaping the growing coccoliths. Previous studies have indicated that disruption of the microtubule network led to defects in coccolith morphogenesis in Emiliania huxleyi and Coccolithus braarudii. Disruption of the actin network also led to defects in coccolith morphology in E. huxleyi, but its impact on coccolith morphology in C. braarudii was less clear, as coccolith secretion was largely inhibited under the conditions used. A more detailed examination of the role of actin and microtubule networks is therefore required to address the wider role of the cytoskeleton in coccolith morphogenesis. In this study, we have examined coccolith morphology in C. braarudii and Scyphosphaera apsteinii following treatment with the microtubule inhibitors vinblastine and colchicine (S. apsteinii only) and the actin inhibitor cytochalasin B. We found that all cytoskeleton inhibitors induced coccolith malformations, strongly suggesting that both microtubules and actin filaments are instrumental in morphogenesis. By demonstrating the requirement for the microtubule and actin networks in coccolith morphogenesis in diverse species, our results suggest that both of these cytoskeletal elements are likely to play conserved roles in defining coccolith morphology.

microbiology↗

bioRxiv2021

Distinguishing gene flow between malaria parasite populations

Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report evaluates numerically the ability to distinguish different levels of gene flow between malaria populations, using different amounts of real and simulated data, where data are simulated using parameters that approximate different epidemiological conditions. Specifically, using Plasmodium falciparum whole genome sequence data and sequence data simulated for a metapopulation with different migration rates and effective population sizes, we compare two estimators of gene flow, explore the number of genetic markers and number of individuals required to reliably rank highly connected locations, and describe how these thresholds change given different effective population sizes and migration rates. Our results have implications for the design and implementation of malaria genomic surveillance efforts.

genomics↗

bioRxiv2020

Identity-by-descent relatedness estimates with uncertainty characterise departure from isolation-by-distance between Plasmodium falciparum populations on the Colombian-Pacific coast

Characterising connectivity between geographically separated biological populations is a common goal in many fields. Recent approaches to understanding connectivity between malaria parasite populations, with implications for disease control efforts, have used estimates of relatedness based on identity-by-descent (IBD). However, uncertainty around estimated relatedness has not been accounted for to date. IBD-based relatedness estimates with uncertainty were computed for pairs of monoclonal Plasmodium falciparum samples collected from five cities on the Colombian-Pacific coast where long-term clonal propagation of P. falciparum is frequent. The cities include two official ports, Buenaventura and Tumaco, that are separated geographically but connected by frequent marine traffic. The fraction of highly-related sample pairs (whose classification accounts for uncertainty) was greater within cities versus between. However, based on both the fraction of highly-related sample pairs and on a threshold-free approach (Wasserstein distances between parasite populations) connectivity between Buenaventura and Tumaco was disproportionally high. Buenaventura-Tumaco connectivity was consistent with three separate transmission events involving parasites from five different clonal components (groups of statistically indistinguishable parasites identified under a graph theoretic framework). To conclude, P. falciparum population connectivity on the Colombian-Pacific coast abides by accessibility not isolation-by-distance, potentially implicating marine traffic in malaria transmission with opportunities for targeted intervention. Further investigations are required to test this and alternative hypotheses. For the first time in malaria epidemiology, we account for uncertainty around estimated relatedness (an important consideration for future studies that plan to use genotype versus whole genome sequence data to estimate IBD-based relatedness); we also use a threshold-free approach to compare parasite populations, and identify clonal components in a statistically principled manner. The approaches we employ could be adapted to other recombining organisms with mixed mating systems, thus have broad relevance.

genetics↗

bioRxiv2020

A cautionary note on the use of machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results. Author summaryGenetic epidemiology studies of malaria attempt to characterise what is happening in malaria parasite populations. In particular, they are an important tool to track the spread of drug resistance and to validate genetic makers of drug resistance. To make sense of parasite genetic data, researchers usually characterise the population structure using statistical methods. This is most often done as a two step process. The first is a data reduction step, whereby the data are summarised into a distance matrix (each entry represents the genetic distance between two isolates) and then the distance matrix is input into an unsupervised machine learning algorithm. Principal coordinates analysis and hierarchical agglomerative clustering are the two most popular unsupervised machine learning algorithms used for this purpose in malaria genetic epidemiology. We illustrate that this procedure is sensitive to the choice of genetic distance and to the specification of the algorithms. These unsupervised methods are useful for exploratory data analysis but cannot be used to infer historical events. We provide some guidance on how to make genetic epidemiology analyses more transparent and reproducible.

genetics↗

bioRxiv2019

Comparative molecular life history of spontaneous canine and human gliomas

Sporadic gliomas in companion dogs provide a window on the interaction between tumorigenic mechanisms and host environment. We compared the molecular profiles of canine gliomas with those of human pediatric and adult gliomas to characterize evolutionarily conserved mammalian mutational processes in gliomagenesis. Employing whole genome-, exome-, transcriptome-and methylation-sequencing of 81 canine gliomas, we found alterations shared between canine and human gliomas such as the receptor tyrosine kinases, p53 and cell cycle pathways, and IDH1 R132. Canine gliomas showed high similarity with human pediatric gliomas per robust aneuploidy, mutational rates, relative timing of mutations, and DNA methylation patterns. Our cross-species comparative genomic analysis provides unique insights into glioma etiology and the chronology of glioma-causing somatic alterations.\n\nSignificanceDiffuse gliomas are the most common malignant brain tumors, with high-grade tumors carrying a dismal prognosis. Preclinical models have proven themselves as poor predictors of clinical efficacy. Spontaneous glioma in dogs provides an attractive alternative model, because of their comparable tumor microenvironment and tumor life history. We determined the similarities and differences between human and canine gliomas through genomic profiling, and leveraged our datasets to identify conserved somatic drivers, mutational processes and temporal ordering of somatic glioma events across species. We show that canine gliomas resemble human gliomas at (epi-)genetic levels and are more reminiscent of pediatric than adult disease, thus rationalizing sporadic canine glioma as a preclinical model tailored to measuring treatment efficacies in patients with canine or human glioma.

cancer biology↗

bioRxiv2019

No evidence for disruption of global patterns of nest predation in shorebirds

Kubelka et al. (Science, 9 November 2018, p. 680-683) claim that climate change has disrupted patterns of nest predation in shorebirds. They report that predation rates have increased since the 1950s, especially in the Arctic. We describe methodological problems with their analyses and argue that there is no solid statistical support for their claims.

ecology↗

bioRxiv2019

Estimating relatedness between malaria parasites

1.Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. It remains unclear how to compare different studies, therefore, and which measures to use. Here, we systematically compare measures based on identity-by-state and identity-by-descent using a globally diverse data set of malaria parasites, Plasmodium falciparum and Plasmodium vivax, and provide marker requirements for estimates based on identity-by-descent. We formally show that the informativeness of polyallelic markers for relatedness inference is maximised when alleles are equifrequent. Estimates based on identity-by-state are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on identity-by-descent. To generate reliable estimates, we recommend approximately 200 biallelic or 100 polyallelic markers. Confidence intervals illuminate inference across studies based on different sets of markers. These marker requirements, unlike many thus far reported, are immediately applicable to haploid malaria parasites and other haploid eukaryotes. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology, and will provide a basis for statistically informed prospective study design and surveillance strategies.

genetics↗

bioRxiv2018

Estimating the probable cause of recurrence in Plasmodium vivax malaria: relapse, reinfection or recrudescence?

BackgroundRelapse of Plasmodium vivax infection is the main cause of vivax malaria in many parts of Asia. However at the individual patient level, recurrence of a blood stage infection following treatment within the endemic area can be either a relapse (from dormant liver-stage parasites), a recrudescence (blood-stage treatment failure), or a reinfection (following a new mosquito inoculation). Each requires a different prevention strategy, but previously they could not be distinguished reliably. Time-of-event and genetic data provide complimentary information about the cause of P. vivax recurrence, but the optimum approach to genotyping and analysis remains uncertain.\n\nMethodsIndividual-level data from two large drug trials in acute vivax malaria patients (Vivax History: VHX; Best Primaquine Dose: BPD) conducted on the Thailand-Myanmar border with follow-up of one year were pooled (n=1299). A total of 710 isolates from both acute and recurrent P. vivax episodes were genotyped using 3-9 highly polymorphic microsatellite markers. These pooled data were analyzed using a novel population statistical model incorporating an assessment of genetic relatedness, treatment drug administered, and the time-to-recurrence.\n\nResults99% of genotyped recurrences in individuals who did not receive primaquine (n=365) were estimated to be relapses. In comparison, 14% of genotyped recurrences (n=121) were estimated to be relapses following high-dose supervised primaquine. By comparing episodes across individuals (90194 comparisons), the false-positive rate of relapse identification using genetic data alone was estimated to be 2.2%. We estimated the true failure rate after high-dose primaquine (7mg/kg total dose) to be 2.6% in this epidemiological context, substantially lower the reinfection unadjusted estimate of 12%. Simulation studies show that 9 highly polymorphic microsatellite markers suffice to discriminate between recurrence states. Drug exposures reflected by plasma carboxy-primaquine concentrations were not predictive of treatment failure, but did identify non-adherence.\n\nConclusionUsing this novel statistical model, relapse of P. vivax malaria could be distinguished reliably from reinfection. This showed that in this population supervised high-dose primaquine could avert up to 99% of relapses. In low transmission settings, microsatellite genotyping combined with time-to-event data can accurately discriminate between the different causes of recurrent P. vivax malaria.\n\nAuthor summaryOne hundred years ago, Plasmodium vivax, the most globally diverse cause of human malaria, was present across most of the old-world, throughout tropical and temperate climes. Its main evolutionary advantage over Plasmodium falciparum, responsible for the most deadly type of human malaria, is its ability to stay dormant in the liver, emerging weeks to years later causing recurring illness and continuing transmission. The dormant liver-stage parasites are called hypnozoites. A recurrent infection can either be hypnozoite-derived (a relapse), a blood-stage treatment failure (recrudescence), or from a new mosquito bite (a reinfection). At a population level, each requires a different preventative strategy but no widely applicable methodology existed to discriminate between the three possible states: relapse, recrudescence and reinfection. Parasite genetic data alone cannot resolve the different possibilities. We developed a novel probabilistic framework which uses both epidemiological and genetic data to determine the most likely cause of vivax recurrence at the individual level. We applied this method to the largest available pooled clinical trial data from Southeast Asia incorporating information from highly polymorphic microsatellite markers, drug treatments received and time-to-recurrent illness. This analysis provides a tool for estimating probabilities of relapse, recrudescence and reinfection and, importantly, shows that hypnozoiticidal high-dose primaquine radical cure is much more effective than previously thought.

epidemiology↗

bioRxiv2017

hmmIBD: software to infer pairwise identity by descent between haploid genotypes

SummaryWe introduce hmmIBD, software to estimate pairwise identity by decent between haploid genomes, such as those of the malaria parasite, sampled from one or more populations. We verified hmmIBD using simulated data, benchmarked it against a previously published method for detecting IBD within populations, and demonstrated its utility using Plasmodium falciparum data from Cambodia and Ghana.\n\nSupplementary informationSupplementary data include Appendices S1, S2 and S3, and are available online.\n\nAvailability and ImplemetationSource code written in C99/C11-compliant C and requiring no external libraries, is freely available for download at https://github.com/glipsnort/hmmIBD/releases, alongside test datasets.\n\nContactsfs@broadinstitute.org

bioinformatics↗