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      Identifying the patterns and drivers of Puumala hantavirus enzootic dynamics using reservoir sampling

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          Abstract

          Hantaviruses are zoonotic hemorrhagic fever viruses for which prevention of human spillover remains the first priority in disease management. Tailored intervention measures require an understanding of the drivers of enzootic dynamics, commonly inferred from distorted human incidence data. Here, we use longitudinal sampling of approximately three decades of Puumala orthohantavirus (PUUV) evolution in isolated reservoir populations to estimate PUUV evolutionary rates, and apply these to study the impact of environmental factors on viral spread. We find that PUUV accumulates genetic changes at a rate of ∼10 −4 substitutions per site per year and that land cover type defines the dispersal dynamics of PUUV, with forests facilitating and croplands impeding virus spread. By providing reliable short-term PUUV evolutionary rate estimates, this work facilitates the evaluation of spatial risk heterogeneity starting from timed phylogeographic reconstructions based on virus sampling in its animal reservoir, thereby side-stepping the need for difficult-to-collect human disease incidence data.

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          Most cited references76

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          Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

          A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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            Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci.

            Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we present a generalization of the GMRF model that allows for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA). We analyze a multilocus alignment of HIV-1 CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA data set.
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              Isolation by resistance.

              Brad McRae (2006)
              Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few tools are available to incorporate data on landscape composition into population genetic studies. Analyses of isolation by distance have typically either assumed spatial homogeneity for convenience or applied theoretically unjustified distance metrics to compensate for heterogeneity. Here I propose the isolation-by-resistance (IBR) model as an alternative for predicting equilibrium genetic structuring in complex landscapes. The model predicts a positive relationship between genetic differentiation and the resistance distance, a distance metric that exploits precise relationships between random walk times and effective resistances in electronic networks. As a predictor of genetic differentiation, the resistance distance is both more theoretically justified and more robust to spatial heterogeneity than Euclidean or least cost path-based distance measures. Moreover, the metric can be applied with a wide range of data inputs, including coarse-scale range maps, simple maps of habitat and nonhabitat within a species' range, or complex spatial datasets with habitats and barriers of differing qualities. The IBR model thus provides a flexible and efficient tool to account for habitat heterogeneity in studies of isolation by distance, improve understanding of how landscape characteristics affect genetic structuring, and predict genetic and evolutionary consequences of landscape change.
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                Author and article information

                Journal
                Virus Evol
                Virus Evol
                vevolu
                Virus Evolution
                Oxford University Press
                2057-1577
                January 2019
                22 April 2019
                22 April 2019
                : 5
                : 1
                : vez009
                Affiliations
                [1 ]KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
                [2 ]Evolutionary Ecology Group, Department of Biology, University of Antwerp, Antwerp, Belgium
                [3 ]Epidemiology of Infectious Diseases, Belgian Institute of Health, Sciensano, Brussels, Belgium
                [4 ]Spatial Epidemiology Lab (spELL), Université Libre de Bruxelles, Bruxelles, Belgium
                Author notes
                Corresponding author: E-mail: piet.maes@ 123456kuleuven.be
                Author information
                http://orcid.org/0000-0003-2826-5353
                http://orcid.org/0000-0001-9558-1052
                http://orcid.org/0000-0001-6547-5283
                http://orcid.org/0000-0001-9118-8608
                Article
                vez009
                10.1093/ve/vez009
                6476162
                31024739
                0d0c1177-63d9-4418-9cd2-97c8a46f35b4
                © The Author(s) 2019. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 12
                Funding
                Funded by: KU Leuven 10.13039/501100004040
                Award ID: IDO/07/005
                Funded by: Research Foundation Flanders - Fonds voor Wetenschappelijk Onderzoek Vlaanderen
                Categories
                Research Article

                puumala orthohantavirus; bunyavirales,bank vole,evolutionary rate,landscape genetics,zoonosis

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