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The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: A simulation study.

ABSTRACT: The ongoing large-scale increase in the total amount of genetic data for viruses and other pathogens has led to a situation in which it is often not possible to include every available sequence in a phylogenetic analysis and expect the procedure to complete in reasonable computational time. This raises questions about how a set of sequences should be selected for analysis, particularly if the data are used to infer more than just the phylogenetic tree itself. The design of sampling strategies for molecular epidemiology has been a neglected field of research. This article describes a large-scale simulation exercise that was undertaken to select an appropriate strategy when using the GMRF skygrid, one of the Bayesian skyline family of coalescent methods, in order to reconstruct past population dynamics. The simulated scenarios were intended to represent sampling for the population of an endemic virus across multiple geographical locations. Large phylogenies were simulated under a coalescent or structured coalescent model and sequences simulated from these trees; the resulting datasets were then downsampled for analyses according to a variety of schemes. Variation in results between different replicates of the same scheme was not insignificant, and as a result, we recommend that where possible analyses are repeated with different datasets in order to establish that elements of a reconstruction are not simply the result of the particular set of samples selected. We show that an individual stochastic choice of sequences can introduce spurious behaviour in the median line of the skygrid plot and that even marginal likelihood estimation can suggest complicated dynamics that were not in fact present. We recommend that the median line should not be used to infer historical events on its own. Sampling sequences with uniform probability with respect to both time and spatial location (deme) never performed worse than sampling with probability proportional to the effective population size at that time and in that location and frequently was superior. As a result, we recommend this approach in the design of future studies. We also confirm that the inclusion of many recent sequences from a single geographical location in an analysis tends to result in a spurious bottleneck effect in the reconstruction and caution against interpreting this as genuine.

SUBMITTER: Hall MD

PROVIDER: S-EPMC4989886 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: A simulation study.

Hall Matthew D MD Woolhouse Mark E J ME Rambaut Andrew A

Virus evolution 20160101 1

The ongoing large-scale increase in the total amount of genetic data for viruses and other pathogens has led to a situation in which it is often not possible to include every available sequence in a phylogenetic analysis and expect the procedure to complete in reasonable computational time. This raises questions about how a set of sequences should be selected for analysis, particularly if the data are used to infer more than just the phylogenetic tree itself. The design of sampling strategies fo ...[more]

PMID: 27774296

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Project description:Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman's coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary period were related to climate change. [Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics.

Project description:Background. In coalescent theory, computer programs often use importance sampling to calculate likelihoods and other statistical quantities. An importance sampling scheme can exploit human intuition to improve statistical efficiency of computations, but unfortunately, in the absence of general computer frameworks on importance sampling, researchers often struggle to translate new sampling schemes computationally or benchmark against different schemes, in a manner that is reliable and maintainable. Moreover, most studies use computer programs lacking a convenient user interface or the flexibility to meet the current demands of open science. In particular, current computer frameworks can only evaluate the efficiency of a single importance sampling scheme or compare the efficiencies of different schemes in an ad hoc manner. Results. We have designed a general framework (http://coalescent.sourceforge.net; language: Java; License: GPLv3) for importance sampling that computes likelihoods under the standard neutral coalescent model of a single, well-mixed population of constant size over time following infinite sites model of mutation. The framework models the necessary core concepts, comes integrated with several data sets of varying size, implements the standard competing proposals, and integrates tightly with our previous framework for calculating exact probabilities. For a given dataset, it computes the likelihood and provides the maximum likelihood estimate of the mutation parameter. Well-known benchmarks in the coalescent literature validate the accuracy of the framework. The framework provides an intuitive user interface with minimal clutter. For performance, the framework switches automatically to modern multicore hardware, if available. It runs on three major platforms (Windows, Mac and Linux). Extensive tests and coverage make the framework reliable and maintainable. Conclusions. In coalescent theory, many studies of computational efficiency consider only effective sample size. Here, we evaluate proposals in the coalescent literature, to discover that the order of efficiency among the three importance sampling schemes changes when one considers running time as well as effective sample size. We also describe a computational technique called "just-in-time delegation" available to improve the trade-off between running time and precision by constructing improved importance sampling schemes from existing ones. Thus, our systems approach is a potential solution to the "2(8) programs problem" highlighted by Felsenstein, because it provides the flexibility to include or exclude various features of similar coalescent models or importance sampling schemes.

Project description:The amount and patterns of phylodiversity in a community are often used to draw inferences about the local and historical factors affecting community assembly and can be used to prioritize communities and locations for conservation. Because measures of phylodiversity are based on the topology and branch lengths of phylogenetic trees, which are affected by the number and diversity of taxa in the tree, these analyses may be sensitive to changes in taxon sampling and tree reconstruction methods.To investigate the effects of taxon sampling and tree reconstruction methods on measures of phylodiversity, we investigated the community phylogenetics of the Ordway-Swisher Biological Station (Florida), which is home to over 600 species of vascular plants. We studied the effects of (a) the number of taxa included in the regional phylogeny; (b) random versus targeted sampling of species to assemble the regional species pool; (c) including only species from specific clades rather than broad sampling; (d) using trees reconstructed directly for the taxa under study compared to trees pruned from a larger reconstructed tree; and (e) using phylograms compared to chronograms.We found that including more taxa in a study increases the likelihood of observing significantly nonrandom phylogenetic patterns. However, there were no consistent trends in the phylodiversity patterns based on random taxon sampling compared to targeted sampling, or within individual clades compared to the complete dataset. Using pruned and reconstructed phylogenies resulted in similar patterns of phylodiversity, while chronograms in some cases led to significantly different results from phylograms.The methods commonly used in community phylogenetic studies can significantly impact the results, potentially influencing both inferences of community assembly and conservation decisions. We highlight the need for both careful selection of methods in community phylogenetic studies and appropriate interpretation of results, depending on the specific questions to be addressed.

Dataset Information

The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: A simulation study.

Publications

The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: A simulation study.

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