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      Large-scale mitochondrial DNA analysis reveals new light on the phylogeography of Central and Eastern-European Brown hare ( Lepus europaeus Pallas, 1778)

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          Abstract

          European brown hare, Lepus europaeus, from Central and Eastern European countries (Hungary, Poland, Serbia, Lithuania, Romania, Georgia and Italy) were sampled, and phylogenetic analyses were carried out on two datasets: 1.) 137 sequences (358 bp) of control region mtDNA; and 2.) 105 sequences of a concatenated fragment (916 bp), including the cytochrome b, tRNA-Thr, tRNA-Pro and control region mitochondrial DNA. Our sequences were aligned with additional brown hare sequences from GenBank. A total of 52 and 51 haplotypes were detected within the two datasets, respectively, and assigned to two previously described major lineages: Anatolian/Middle Eastern (AME) and European (EUR). Furthermore, the European lineage was divided into two subclades including South Eastern European (SEE) and Central European (CE). Sympatric distribution of the lineages of the brown hare in South-Eastern and Eastern Europe revealed contact zones there. BAPS analysis assigned sequences from L. europaeus to five genetic clusters, whereas CE individuals were assigned to only one cluster, and AME and SEE sequences were each assigned to two clusters. Our findings uncover numerous novel haplotypes of Anatolian/Middle Eastern brown hare outside their main range, as evidence for the combined influence of Late Pleistocene climatic fluctuations and anthropogenic activities in shaping the phylogeographic structure of the species. Our results support the hypothesis of a postglacial brown hare expansion from Anatolia and the Balkan Peninsula to Central and Eastern Europe, and suggest some slight introgression of individual haplotypes from L. timidus to L. europaeus.

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          Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations.

          We consider the distribution of pairwise sequence differences of mitochondrial DNA or of other nonrecombining portions of the genome in a population that has been of constant size and in a population that has been growing in size exponentially for a long time. We show that, in a population of constant size, the sample distribution of pairwise differences will typically deviate substantially from the geometric distribution expected, because the history of coalescent events in a single sample of genes imposes a substantial correlation on pairwise differences. Consequently, a goodness-of-fit test of observed pairwise differences to the geometric distribution, which assumes that each pairwise comparison is independent, is not a valid test of the hypothesis that the genes were sampled from a panmictic population of constant size. In an exponentially growing population in which the product of the current population size and the growth rate is substantially larger than one, our analytical and simulation results show that most coalescent events occur relatively early and in a restricted range of times. Hence, the "gene tree" will be nearly a "star phylogeny" and the distribution of pairwise differences will be nearly a Poisson distribution. In that case, it is possible to estimate r, the population growth rate, if the mutation rate, mu, and current population size, N0, are assumed known. The estimate of r is the solution to ri/mu = ln(N0r) - gamma, where i is the average pairwise difference and gamma approximately 0.577 is Euler's constant.
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            Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

            Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at .
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              Bayesian identification of admixture events using multilocus molecular markers.

              Bayesian statistical methods for the estimation of hidden genetic structure of populations have gained considerable popularity in the recent years. Utilizing molecular marker data, Bayesian mixture models attempt to identify a hidden population structure by clustering individuals into genetically divergent groups, whereas admixture models target at separating the ancestral sources of the alleles observed in different individuals. We discuss the difficulties involved in the simultaneous estimation of the number of ancestral populations and the levels of admixture in studied individuals' genomes. To resolve this issue, we introduce a computationally efficient method for the identification of admixture events in the population history. Our approach is illustrated by analyses of several challenging real and simulated data sets. The software (baps), implementing the methods introduced here, is freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: Writing – original draft
                Role: Data curationRole: Writing – review & editing
                Role: Data curation
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Project administrationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Resources
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                4 October 2018
                2018
                : 13
                : 10
                : e0204653
                Affiliations
                [1 ] Department of Fisheries and Environmental Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran
                [2 ] Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia
                [3 ] Institute of Animal Husbandry, Biotechnology and Nature Conservation, University of Debrecen, Debrecen, Hungary
                [4 ] Department of Biology, Faculty of Natural Sciences, Vytautas Magnus University, Kaunas, Lithuania
                [5 ] Department of Veterinary Medicine, University of Sassari, Sassari, Italy
                [6 ] Institutes for Agricultural Research and Educational Farm, University of Debrecen, Debrecen, Hungary
                [7 ] Research and Development Station for Bovine Arad, Academy for Agricultural and Forestry Sciences, Arad, Romania
                [8 ] Tbilisi Zoo, Tbilisi, Georgia
                [9 ] Polish Hunting Association, Research Station, Czempiń, Poland
                National Cheng Kung University, TAIWAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5441-5303
                Article
                PONE-D-18-04820
                10.1371/journal.pone.0204653
                6171851
                30286121
                daf7f4e4-d53c-42e8-a188-2babf35f0535
                © 2018 Ashrafzadeh et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 13 February 2018
                : 12 September 2018
                Page count
                Figures: 6, Tables: 2, Pages: 20
                Funding
                Funded by: János Bolyai Research Scholarship of the Hungarian Academy of Sciences
                Award ID: BO/00223/14/4
                Award Recipient :
                Funded by: European Union and the European Social Fund
                Award ID: EFOP-3.6.3-VEKOP-16-2017-00008
                Award Recipient :
                This work was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. The publication is supported by the EFOP-3.6.3-VEKOP-16-2017-00008 project. The project is co-financed by the European Union and the European Social Fund.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Leporids
                Hares
                People and Places
                Geographical Locations
                Europe
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Haplotypes
                Biology and Life Sciences
                Biogeography
                Phylogeography
                Ecology and Environmental Sciences
                Biogeography
                Phylogeography
                Earth Sciences
                Geography
                Biogeography
                Phylogeography
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Phylogeography
                Biology and Life Sciences
                Genetics
                Population Genetics
                Phylogeography
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Phylogeography
                Biology and life sciences
                Genetics
                DNA
                Forms of DNA
                Mitochondrial DNA
                Biology and life sciences
                Biochemistry
                Nucleic acids
                DNA
                Forms of DNA
                Mitochondrial DNA
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Biology and Life Sciences
                Genetics
                Population Genetics
                Biology and Life Sciences
                Population Biology
                Population Genetics
                People and places
                Geographical locations
                Europe
                European Union
                Italy
                Custom metadata
                All sequences are available from the Genbank database (accession number(s) MG865671-MG865724 for CR and MG841060- MG841112 for the Cyt b + tRNA-Thr + tRNA-Pro + CR region.

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