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

Taylor, Aimee R; Echeverry, Diego F.; Anderson, Timothy J.; Neafsey, Daniel E.; Buckee, Caroline O.

doi:10.1101/2020.04.10.035303

Cited by 5 publications

(8 citation statements)

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“…In malaria parasite population genetics the choice of genetic distance has been given less consideration than in human population genetics. Recent work has demonstrated the utility of assessing genetic relatedness based on IBD (and its correlate IBS) and using this to measure genetic distance between proximal malaria parasite populations [ 29 , 30 ]. Output of analyses using an IBD-based genetic distance are interpretable insofar as IBD-based genetic distance explicitly references recent evolutionary events driven by recombination.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

“…Other alternatives, such as network analytic methods (e.g. [ 30 ]) were not explored in this work. We suggest researchers apply PCoA to multiple distance matrices (e.g.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…In malaria parasite population genetics the choice of genetic distance has been given less consideration than in human population genetics. Recent work has demonstrated the utility of assessing genetic relatedness based on IBD (and its correlate IBS) and using this to measure genetic distance between proximal parasites populations [29,30] The emergence and spread of a single multidrug resistant P. falciparum lineage across four countries in the eastern Greater Mekong sub-region (GMS) [16,17] (Panel 1) is of serious public health concern. It leaves few options for effective antimalarial therapy in the region.…”

Section: Discussionmentioning

confidence: 99%

“…monitor changes in transmission (we do not know of an example yet where IBD measures from barcodes have been used to do this; although genome-wide measures derived from WGS data have [54]), and connectivity between locations on a population level (e.g. [29,30]). For inter-population analyses, one must convert many inter-individual IBD measures into inter-population IBD measures.…”

Section: Code and Data Availabilitymentioning

confidence: 99%

“…the fraction whose relatedness is greater than 0.5 [29]). More recently, approaches from optimal transport have been used to in a threshold-free manner that essentially amounts to calculating the cost of transporting a distribution of parasites samples from population to another [30]. There is still a need for a more theoretical analysis of which approach is optimal and in what setting.…”

Section: Code and Data Availabilitymentioning

confidence: 99%

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

Watson

Taylor

Ashley

et al. 2020

Preprint

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1Genetic surveillance of malaria parasites supports malaria control programmes, treatment 2 guidelines and elimination strategies. Surveillance studies often pose questions about malaria 3 parasite ancestry (e.g. about the spread of antimalarial resistance) and employ methods that 4 characterise parasite population structure. Many of the methods used to characterise struc-5 ture are algorithms developed in machine learning (ML) and depend on a genetic distance 6 matrix, e.g. principal coordinates analysis (PCoA) and hierarchical agglomerative clustering 7 (HAC). However, PCoA and HAC are sensitive to both the definition of genetic distance and 8 algorithmic specification. Importantly, neither algorithm generates an inferred malaria para-9 site ancestry. As such, PCoA and HAC can support (e.g. via exploratory visualisation and 10 hypothesis generation), but not answer comprehensively, key questions about malaria para-11 site ancestry. We illustrate the sensitivity of PCoA and HAC using 393 P. falciparum whole 12 genome sequences collected from Cambodia and neighbouring regions (where antimalarial re-13 sistance has emerged and spread recently) and we provide tentative guidance for the use of 14 PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for 15 fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scien-16 tific question; (ii) clear justification of methods used along with discussion of any inferential 17 limitations; (iii) publicly available genetic distance matrices; and (iv) sensitivity analyses. To 18 bridge the inferential disconnect between the output of the ML algorithms and the scientific 19 questions of interest, tailor-made statistical models are needed to infer malaria parasite an-20 cestry. In the absence of such models speculative reasoning should feature only as discussion 21 but not as results. 22 24 ular surveillance to understand how the parasites causing malaria are spreading, to track drug 25 resistance and identify its emergence, and to understand where to target interventions. Malaria 26 parasite genetic data have been accrued increasingly rapidly in recent years, but the analytical 27 methods for making sense of them lag behind. In particular, the methods used currently to in-28 fer malaria parasite ancestry, which is often central to the questions posed by studies of malaria 29 parasite genetic epidemiology, have significant limitations. An important goal in malaria parasite 30 genetic epidemiology is inference of the full ancestral recombination graph. However, there are no 31 scalable methods directly applicable to malaria parasites. Instead, the first step towards inferring 32 1 ancestry involves characterisation of contemporary population genetic structure using computa-33 tionally tractable methods. Many questions of clinical and public health relevance, for example, 34 interpreting reduced haplotype diversity as a selective sweep, rely on methods that first characterise 35 the underlying population structure w...

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Distinguishing gene flow between malaria parasite populations

Brown

Taylor

Arogbokun³

et al. 2021

Preprint

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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.

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Identity-by-descent relatedness estimates with uncertainty characterise departure from isolation-by-distance betweenPlasmodium falciparumpopulations on the Colombian-Pacific coast

Cited by 5 publications

References 59 publications

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

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

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

Distinguishing gene flow between malaria parasite populations

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