scholarly journals Coalescent inference using serially sampled, high-throughput sequencing data from intra-host HIV infection

2015 ◽  
Author(s):  
Kevin Dialdestoro ◽  
Jonas Andreas Sibbesen ◽  
Lasse Maretty ◽  
Jayna Raghwani ◽  
Astrid Gall ◽  
...  
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
High Throughput ◽  
Deep Sequencing ◽  
Evolutionary Dynamics ◽  
Viral Population ◽  
Single Infection ◽  
Sequencing Data ◽  
Effective Population ◽  
Population Size Estimates

ABSTRACTHuman immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput “deep” sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different timepoints during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intra-host viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this paper we develop a new method for inference using HIV deep sequencing data using an approach based on importance sampling of ancestral recombination graphs under a multi-locus coalescent model. The approach further extends recent progress in the approximation of so-calledconditional sampling distributions, a quantity of key interest when approximating co-alescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different timepoints and missing data without extra computational difficulty. We apply our method to a dataset of HIV-1, in which several hundred sequences were obtained from an infected individual at seven timepoints over two years. We find mutation rate and effective population size estimates to be comparable to those produced by the software BEAST. Additionally, our method is able to produce local recombination rate estimates. The software underlying our method, Coalescenator, is freely available.

scholarly journals Contrasting Trends of Population Size Change for Two Eurasian Owlet Species—Athene brama and Glaucidium radiatum From South Asia Over the Late Quaternary

2020 ◽  
Vol 8 ◽  
Author(s):  
Meghana Natesh ◽  
K. L. Vinay ◽  
Samriddha Ghosh ◽  
Rajah Jayapal ◽  
Shomita Mukherjee ◽  
...  
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Late Quaternary ◽  
Habitat Associations ◽  
Interglacial Period ◽  
Effective Population ◽  
Size Change ◽  
Last Glacial ◽  
Population Size Estimates ◽  
The Last Glacial

Climatic oscillations over the Quaternary have had a lasting impact on species’ distribution, evolutionary history, and genetic composition. Many species show dramatic population size changes coinciding with the last glacial period. However, the extent and direction of change vary across biogeographic regions, species-habitat associations, and species traits. Here we use genomic data to assess population size changes over the late Quaternary using the Pairwise Sequential Markovian Coalescent (PSMC) approach in two Eurasian Owlet species—the Spotted Owlet, Athene brama, and the Jungle Owlet, Glaucidium radiatum. While Spotted Owlets are typically associated with open habitats, Jungle Owlets are found in deciduous forests and scrublands. We find that the effective population size for the Spotted Owlet increased after the Interglacial period till the Last Glacial Maxima and subsequently declined toward the Mid-Holocene. On the other hand, effective population size estimates for the Jungle Owlet increased gradually throughout this period. These observations are in line with climatic niche model-based predictions for range size change for both species from a previous study and suggest that habitat associations at the local scale are important in determining responses to past climatic and vegetational changes. The Spotted Owlet result also aligns well with the expectation of open habitat expansion during the arid Glacial Maxima, whereas for the Jungle Owlet the contrasting expectation does not hold. Therefore, assessing the impacts of glacial history on population trajectories of multiple species with different habitat associations is necessary to understand the impacts of past climate on South Asian taxa.

scholarly journals Demography and natural selection have shaped genome-wide variation in the widely distributed conifer Norway Spruce (Picea abies)

2019 ◽  
Author(s):  
Xi Wang ◽  
Carolina Bernhardsson ◽  
Pär K. Ingvarsson
Keyword(s):  
Genetic Diversity ◽  
Natural Selection ◽  
Picea Abies ◽  
Population Size ◽  
Effective Population Size ◽  
Norway Spruce ◽  
Demographic History ◽  
Sequencing Data ◽  
Effective Population ◽  
Genome Wide

AbstractUnder the neutral theory, species with larger effective population sizes are expected to harbour higher genetic diversity. However, across a wide variety of organisms, the range of genetic diversity is orders of magnitude more narrow than the range of effective population size. This observation has become known as Lewontin’s paradox and although aspects of this phenomenon have been extensively studied, the underlying causes for the paradox remain unclear. Norway spruce (Picea abies) is a widely distributed conifer species across the northern hemisphere and it consequently plays a major role in European forestry. Here, we use whole-genome re-sequencing data from 35 individuals to perform population genomic analyses in P. abies in an effort to understand what drives genome-wide patterns of variation in this species. Despite having a very wide geographic distribution and an enormous current population size, our analyses find that genetic diversity of P.abies is low across a number of populations (p=0.005-0.006). To assess the reasons for the low levels of genetic diversity, we infer the demographic history of the species and find that it is characterised by several re-occurring bottlenecks with concomitant decreases in effective population size can, at least partly, provide an explanation for low polymorphism we observe in P. abies. Further analyses suggest that recurrent natural selection, both purifying and positive selection, can also contribute to the loss of genetic diversity in Norway spruce by reducing genetic diversity at linked sites. Finally, the overall low mutation rates seen in conifers can also help explain the low genetic diversity maintained in Norway spruce.

scholarly journals Recombination increases human immunodeficiency virus fitness, but not necessarily diversity

2008 ◽  
Vol 89 (6) ◽  
pp. 1467-1477 ◽  
Author(s):  
N. N. V. Vijay ◽  
Vasantika ◽  
Rahul Ajmani ◽  
Alan S. Perelson ◽  
Narendra M. Dixit
Keyword(s):  
Human Immunodeficiency Virus ◽  
Population Size ◽  
Effective Population Size ◽  
Accurate Determination ◽  
Viral Fitness ◽  
Viral Population ◽  
Multiple Infections ◽  
Effective Population ◽  
Immunodeficiency Virus

Recombination can facilitate the accumulation of mutations and accelerate the emergence of resistance to current antiretroviral therapies for human immunodeficiency virus (HIV) infection. Yet, since recombination can also dissociate favourable combinations of mutations, the benefit of recombination to HIV remains in question. The confounding effects of mutation, multiple infections of cells, random genetic drift and fitness selection that underlie HIV evolution render the influence of recombination difficult to unravel. We developed computer simulations that mimic the genomic diversification of HIV within an infected individual and elucidate the influence of recombination. We find, interestingly, that when the effective population size of HIV is small, recombination increases both the diversity and the mean fitness of the viral population. When the effective population size is large, recombination increases viral fitness but decreases diversity. In effect, recombination enhances (lowers) the likelihood of the existence of multi-drug resistant strains of HIV in infected individuals prior to the onset of therapy when the effective population size is small (large). Our simulations are consistent with several recent experimental observations, including the evolution of HIV diversity and divergence in vivo. The intriguing dependencies on the effective population size appear due to the subtle interplay of drift, selection and epistasis, which we discuss in the light of modern population genetics theories. Current estimates of the effective population size of HIV have large discrepancies. Our simulations present an avenue for accurate determination of the effective population size of HIV in vivo and facilitate establishment of the benefit of recombination to HIV.

scholarly journals Inferring pandemic growth rates from sequence data

2012 ◽  
Vol 9 (73) ◽  
pp. 1797-1808 ◽  
Author(s):  
Eric de Silva ◽  
Neil M. Ferguson ◽  
Christophe Fraser
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Simulated Data ◽  
Parametric Methods ◽  
Sampling Strategies ◽  
Size Estimation ◽  
Effective Population ◽  
Population Size Estimates ◽  
Non Parametric

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

scholarly journals The effective population size and mutation rate of influenza A virus in acutely infected individuals

2020 ◽  
Author(s):  
John T. McCrone ◽  
Robert J. Woods ◽  
Arnold S. Monto ◽  
Emily T. Martin ◽  
Adam S. Lauring
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Mutation Rate ◽  
Influenza A ◽  
Evolutionary Dynamics ◽  
Sequence Data ◽  
Influenza Viruses ◽  
Influenza A Viruses ◽  
Effective Population

AbstractThe global evolutionary dynamics of influenza viruses ultimately derive from processes that take place within and between infected individuals. Recent work suggests that within-host populations are dynamic, but an in vivo estimate of mutation rate and population size in naturally infected individuals remains elusive. Here we model the within-host dynamics of influenza A viruses using high depth of coverage sequence data from 200 acute infections in an outpatient, community setting. Using a Wright-Fisher model, we estimate a within-host effective population size of 32-72 and an in vivo mutation rate of 3.4×10−6 per nucleotide per generation.

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