Effective population sizes and migration rates in fragmented populations of an endangered insect (Coenagrion mercuriale: Odonata)

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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Accounting for Gene Flow from Unsampled ‘Ghost’ Populations while Estimating Evolutionay History under the Isolation with Migration Model

10.1101/733600 ◽

2019 ◽

Author(s):

Arun Sethuraman ◽

Melissa Lynch

Keyword(s):

Gene Flow ◽

Evolutionary History ◽

Population Genomics ◽

Divergence Times ◽

Effective Population ◽

Migration Rates ◽

Isolation With Migration ◽

Population Sizes ◽

And Migration ◽

Erroneous Inferences

AbstractUnsampled or extinct ‘ghost’ populations leave signatures on the genomes of individuals from extant, sampled populations, especially if they have exchanged genes with them over evolutionary time. This gene flow from ‘ghost’ populations can introduce biases when estimating evolutionary history from genomic data, often leading to data misinterpretation and ambiguous results. Here we assess these biases while accounting, or not accounting for gene flow from ‘ghost’ populations under the Isolation with Migration (IM) model. We perform extensive simulations under five scenarios with no gene flow (Scenario A), to extensive gene flow to- and from- an unsampled ‘ghost’ population (Scenarios B, C, D, and E). Estimates of evolutionary history across all scenarios A-E (effective population sizes, divergence times, and migration rates) indicate consistent a) under-estimation of divergence times between sampled populations, (b) over-estimation of effective population sizes of sampled populations, and (c) under-estimation of migration rates between sampled populations, with increased gene flow from the unsampled ‘ghost’ population. Without accounting for an unsampled ‘ghost’, summary statistics like FST are under-estimated, and π is over-estimated with increased gene flow from the‘ghost’. To show this persistent issue in empirical data, we use a 355 locus dataset from African Hunter-Gatherer populations and discuss similar biases in estimating evolutionary history while not accounting for unsampled ‘ghosts’. Considering the large effects of gene flow from these ‘ghosts’, we propose a multi-pronged approach to account for the presence of unsampled ‘ghost’ populations in population genomics studies to reduce erroneous inferences.

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Estimating Effective Population Size and Migration Rates From Genetic Samples Over Space and Time

Genetics ◽

10.1093/genetics/163.1.429 ◽

2003 ◽

Vol 163 (1) ◽

pp. 429-446 ◽

Cited By ~ 2

Author(s):

Jinliang Wang ◽

Michael C Whitlock

Keyword(s):

Population Size ◽

Effective Population Size ◽

Joint Estimation ◽

Large Sample Size ◽

Effective Population ◽

Likelihood Methods ◽

Marker Allele ◽

Migration Rates ◽

Space And Time ◽

And Migration

Abstract In the past, moment and likelihood methods have been developed to estimate the effective population size (Ne) on the basis of the observed changes of marker allele frequencies over time, and these have been applied to a large variety of species and populations. Such methods invariably make the critical assumption of a single isolated population receiving no immigrants over the study interval. For most populations in the real world, however, migration is not negligible and can substantially bias estimates of Ne if it is not accounted for. Here we extend previous moment and maximum-likelihood methods to allow the joint estimation of Ne and migration rate (m) using genetic samples over space and time. It is shown that, compared to genetic drift acting alone, migration results in changes in allele frequency that are greater in the short term and smaller in the long term, leading to under- and overestimation of Ne, respectively, if it is ignored. Extensive simulations are run to evaluate the newly developed moment and likelihood methods, which yield generally satisfactory estimates of both Ne and m for populations with widely different effective sizes and migration rates and patterns, given a reasonably large sample size and number of markers.

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Genetic Differentiation and Estimation of Effective Population Size and Migration Rates in Two Sympatric Ecotypes of the Marine Snail Littorina saxatilis

Journal of Heredity ◽

10.1093/jhered/esi064 ◽

2005 ◽

Vol 96 (4) ◽

pp. 460-464 ◽

Cited By ~ 8

Author(s):

J. Fernández ◽

J. Galindo ◽

B. Fernández ◽

A. Pérez-Figueroa ◽

A. Caballero ◽

...

Keyword(s):

Genetic Differentiation ◽

Population Size ◽

Effective Population Size ◽

Littorina Saxatilis ◽

Effective Population ◽

Marine Snail ◽

Migration Rates ◽

And Migration

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Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations

Molecular Ecology ◽

10.1111/j.1365-294x.2004.02101.x ◽

2004 ◽

Vol 13 (4) ◽

pp. 827-836 ◽

Cited By ~ 200

Author(s):

Peter Beerli

Keyword(s):

Migration Rates ◽

Population Sizes ◽

And Migration

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Bayesian phylodynamics of avian influenza virus H9N2 in Asia with time-dependent predictors of migration

10.1101/450064 ◽

2018 ◽

Author(s):

Jing Yang ◽

Nicola F. Müller ◽

Remco Bouckaert ◽

Bing Xu ◽

Alexei J. Drummond

Keyword(s):

Time Series ◽

Avian Influenza ◽

Wild Bird ◽

Time Dependent ◽

Poultry Production ◽

Geographic Proximity ◽

Effective Population ◽

Migration Rates ◽

Bird Populations ◽

Population Sizes

AbstractModel-based phylodynamic approaches recently employed generalized linear models (GLMs) to uncover potential predictors of viral spread. Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent. However, these studies mainly focused on predictors that are assumed to be constant through time. Here we inferred the phylodynamics of H9N2 viruses isolated in 12 Asian countries and regions under both discrete trait analysis (DTA) and structured coalescent (MASCOT) approaches. Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread. We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures. This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives. Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time and these effective population dynamics within each country were predicted by a GLM. International annual poultry trade is a strongly supported predictor of virus migration rates. There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated. In time-dependent MASCOT models, national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China and Bangladesh. Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction. We demonstrated the contribution of poultry trade and geographic proximity (potentially unheralded wild bird movements) to avian influenza spread in Asia. To gain a better understanding of the drivers of H9N2 spread, we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries.Author summaryWhat drives the geographic dispersal and genetic diversity of H9N2 avian influenza virus in Asia? We used two model-based approaches, DTA and MASCOT, to reconstruct the phylogeographic dynamics of the virus. Further, multiple potential predictors were used to inform the virus spread and population dynamics by GLMs. Here, we maximised the power of time-series predictors in Bayesian phylodynamic prediction. For the first time, we were able to quantify the contribution of both time-series and constant predictors to both migration rates and effective population sizes in a structured population. We identified a positive association of international poultry trade and national poultry production time-series with virus migration rates and effective population sizes respectively. We also identify geographic proximity as a strongly supported driver to virus migration rates and this points to the potential role of wild bird populations in virus dispersal across countries. Our study is a practical exemplar of the use of temporal information in predictors to model heterogeneous spatial diffusion and population dynamic processes and provides direction to H9N2 control efforts in Asia.

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Maximum-Likelihood Estimation of Migration Rates and Effective Population Numbers in Two Populations Using a Coalescent Approach

Genetics ◽

10.1093/genetics/152.2.763 ◽

1999 ◽

Vol 152 (2) ◽

pp. 763-773 ◽

Cited By ~ 38

Author(s):

Peter Beerli ◽

Joseph Felsenstein

Keyword(s):

Maximum Likelihood ◽

Mutation Rate ◽

Likelihood Estimation ◽

Effective Population ◽

Migration Rates ◽

Coalescent Approach ◽

Population Sizes ◽

Two Populations ◽

Better Than ◽

Population Numbers

Abstract A new method for the estimation of migration rates and effective population sizes is described. It uses a maximum-likelihood framework based on coalescence theory. The parameters are estimated by Metropolis-Hastings importance sampling. In a two-population model this method estimates four parameters: the effective population size and the immigration rate for each population relative to the mutation rate. Summarizing over loci can be done by assuming either that the mutation rate is the same for all loci or that the mutation rates are gamma distributed among loci but the same for all sites of a locus. The estimates are as good as or better than those from an optimized FST-based measure. The program is available on the World Wide Web at http://evolution.genetics.washington.edu/lamarc.html/.

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Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations

Virus Evolution ◽

10.1093/ve/vez030 ◽

2019 ◽

Vol 5 (2) ◽

Cited By ~ 4

Author(s):

Nicola F Müller ◽

Gytis Dudas ◽

Tanja Stadler

Keyword(s):

Population Dynamics ◽

Phylogenetic Trees ◽

Sequence Data ◽

Structured Populations ◽

Model Parameters ◽

Effective Population ◽

Genetic Sequence ◽

Migration Rates ◽

Population Sizes ◽

Structured Coalescent

Abstract Population dynamics can be inferred from genetic sequence data by using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume migration rates and effective population sizes to be constant through time in structured populations. When considering rates to vary through time in structured populations, the number of parameters to infer increases rapidly and the available data might not be sufficient to inform these. Additionally, it is often of interest to know what predicts these parameters rather than knowing the parameters themselves. Here, we introduce a method to infer the predictors for time-varying migration rates and effective population sizes by using a generalized linear model (GLM) approach under the marginal approximation of the structured coalescent. Using simulations, we show that our approach is able to reliably infer the model parameters and its predictors from phylogenetic trees. Furthermore, when simulating trees under the structured coalescent, we show that our new approach outperforms the discrete trait GLM model. We then apply our framework to a previously described Ebola virus dataset, where we infer the parameters and its predictors from genome sequences while accounting for phylogenetic uncertainty. We infer weekly cases to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This approach is implemented as part of the BEAST2 package MASCOT, which allows us to jointly infer population dynamics, i.e. the parameters and predictors, within structured populations, the phylogenetic tree, and evolutionary parameters.

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Gene Flow Among Populations of the Malaria Vector, Anopheles gambiae, in Mali, West Africa

Genetics ◽

10.1093/genetics/157.2.743 ◽

2001 ◽

Vol 157 (2) ◽

pp. 743-750

Author(s):

Charles Taylor ◽

Yeya T Touré ◽

John Carnahan ◽

Douglas E Norris ◽

Guimogo Dolo ◽

...

Keyword(s):

Population Structure ◽

Gene Flow ◽

Anopheles Gambiae ◽

Sensu Stricto ◽

The Other ◽

Effective Population ◽

Chromosomal Form ◽

Migration Rates ◽

Anopheles Gambiae Complex ◽

And Migration

Abstract The population structure of the Anopheles gambiae complex is unusual, with several sibling species often occupying a single area and, in one of these species, An. gambiae sensu stricto, as many as three “chromosomal forms” occurring together. The chromosomal forms are thought to be intermediate between populations and species, distinguishable by patterns of chromosome gene arrangements. The extent of reproductive isolation among these forms has been debated. To better characterize this structure we measured effective population size, Ne, and migration rates, m, or their product by both direct and indirect means. Gene flow among villages within each chromosomal form was found to be large (Nem > 40), was intermediate between chromosomal forms (Nem ≈ 3–30), and was low between species (Nem ≈ 0.17–1.3). A recently developed means for distinguishing among certain of the forms using PCR indicated rates of gene flow consistent with those observed using the other genetic markers.

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Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations

10.1101/342329 ◽

2018 ◽

Cited By ~ 3

Author(s):

Nicola F. Müller ◽

Gytis Dudas ◽

Tanja Stadler

Keyword(s):

Population Dynamics ◽

Phylogenetic Trees ◽

Ebola Virus ◽

Sequence Data ◽

Geographic Distance ◽

Structured Populations ◽

Effective Population ◽

Genetic Sequence ◽

Migration Rates ◽

Population Sizes

AbstractPopulation dynamics can be inferred from genetic sequence data using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume the parameters describing the dynamics to be constant through time in structured populations. Inference methods allowing for structured populations and parameters to vary through time involve many parameters which have to be inferred. Each of these parameters might be however only weakly informed by data. Here we introduce an approach that uses so-called predictors, such as geographic distance between locations, within a generalized linear model to inform the population dynamic parameters, namely the time-varying migration rates and effective population sizes under the marginal approximation of the structured coalescent. By using simulations, we show that we are able to reliably infer the parameters from phylogenetic trees. We then apply this framework to a previously described Ebola virus dataset. We infer incidence to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This allows us to show not only on simulated data, but also on real data, that we are able to identify reasonable predictors. Overall, we provide a novel method that allows to identify predictors for migration rates and effective population sizes and to use these predictors to quantify migration rates and effective population sizes. Its implementation as part of the BEAST2 software package MASCOT allows to jointly infer population dynamics within structured populations, the phylogenetic tree, and evolutionary parameters.

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