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Cayuela H, Rougemont Q, Prunier JG, Moore JS, Clobert J, Besnard A, Bernatchez L. Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review. Mol Ecol 2018; 27:3976-4010. [DOI: 10.1111/mec.14848] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 08/17/2018] [Accepted: 08/19/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Hugo Cayuela
- Institut de Biologie Intégrative et des Systèmes (IBIS); Université Laval; Québec City Québec Canada
| | - Quentin Rougemont
- Institut de Biologie Intégrative et des Systèmes (IBIS); Université Laval; Québec City Québec Canada
| | - Jérôme G. Prunier
- Station d'Ecologie Théorique et Expérimentale; Unité Mixte de Recherche (UMR) 5321; Centre National de la Recherche Scientifique (CNRS); Université Paul Sabatier (UPS); Moulis France
| | - Jean-Sébastien Moore
- Institut de Biologie Intégrative et des Systèmes (IBIS); Université Laval; Québec City Québec Canada
| | - Jean Clobert
- Station d'Ecologie Théorique et Expérimentale; Unité Mixte de Recherche (UMR) 5321; Centre National de la Recherche Scientifique (CNRS); Université Paul Sabatier (UPS); Moulis France
| | - Aurélien Besnard
- CNRS; PSL Research University; EPHE; UM, SupAgro, IRD; INRA; UMR 5175 CEFE; Montpellier France
| | - Louis Bernatchez
- Institut de Biologie Intégrative et des Systèmes (IBIS); Université Laval; Québec City Québec Canada
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Ringbauer H, Kolesnikov A, Field DL, Barton NH. Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns. Genetics 2018; 208:1231-1245. [PMID: 29311149 PMCID: PMC5844333 DOI: 10.1534/genetics.117.300638] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/23/2017] [Indexed: 11/18/2022] Open
Abstract
In continuous populations with local migration, nearby pairs of individuals have on average more similar genotypes than geographically well-separated pairs. A barrier to gene flow distorts this classical pattern of isolation by distance. Genetic similarity is decreased for sample pairs on different sides of the barrier and increased for pairs on the same side near the barrier. Here, we introduce an inference scheme that uses this signal to detect and estimate the strength of a linear barrier to gene flow in two dimensions. We use a diffusion approximation to model the effects of a barrier on the geographic spread of ancestry backward in time. This approach allows us to calculate the chance of recent coalescence and probability of identity by descent. We introduce an inference scheme that fits these theoretical results to the geographic covariance structure of bialleleic genetic markers. It can estimate the strength of the barrier as well as several demographic parameters. We investigate the power of our inference scheme to detect barriers by applying it to a wide range of simulated data. We also showcase an example application to an Antirrhinum majus (snapdragon) flower-color hybrid zone, where we do not detect any signal of a strong genome-wide barrier to gene flow.
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Affiliation(s)
- Harald Ringbauer
- Institute of Science and Technology Austria, Klosterneuburg A-3400, Austria
| | | | - David L Field
- Department of Botany and Biodiversity Research, University of Vienna, A-1030, Austria
| | - Nicholas H Barton
- Institute of Science and Technology Austria, Klosterneuburg A-3400, Austria
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3
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Vergara M, Basto MP, Madeira MJ, Gómez-Moliner BJ, Santos-Reis M, Fernandes C, Ruiz-González A. Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study. PLoS One 2015; 10:e0134257. [PMID: 26222680 PMCID: PMC4519273 DOI: 10.1371/journal.pone.0134257] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 07/07/2015] [Indexed: 11/20/2022] Open
Abstract
The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.
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Affiliation(s)
- María Vergara
- Department of Zoology and Animal Cell Biology, Zoology Laboratory, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
- Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Mafalda P. Basto
- CE3C—Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - María José Madeira
- Department of Zoology and Animal Cell Biology, Zoology Laboratory, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
- Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Benjamín J. Gómez-Moliner
- Department of Zoology and Animal Cell Biology, Zoology Laboratory, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
- Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Margarida Santos-Reis
- CE3C—Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Carlos Fernandes
- CE3C—Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Aritz Ruiz-González
- Department of Zoology and Animal Cell Biology, Zoology Laboratory, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
- Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
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Duforet-Frebourg N, Blum MGB. Nonstationary patterns of isolation-by-distance: inferring measures of local genetic differentiation with Bayesian kriging. Evolution 2014; 68:1110-23. [PMID: 24372175 PMCID: PMC4285919 DOI: 10.1111/evo.12342] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Accepted: 12/13/2013] [Indexed: 11/27/2022]
Abstract
Patterns of isolation-by-distance (IBD) arise when population differentiation increases with increasing geographic distances. Patterns of IBD are usually caused by local spatial dispersal, which explains why differences of allele frequencies between populations accumulate with distance. However, spatial variations of demographic parameters such as migration rate or population density can generate nonstationary patterns of IBD where the rate at which genetic differentiation accumulates varies across space. To characterize nonstationary patterns of IBD, we infer local genetic differentiation based on Bayesian kriging. Local genetic differentiation for a sampled population is defined as the average genetic differentiation between the sampled population and fictive neighboring populations. To avoid defining populations in advance, the method can also be applied at the scale of individuals making it relevant for landscape genetics. Inference of local genetic differentiation relies on a matrix of pairwise similarity or dissimilarity between populations or individuals such as matrices of FST between pairs of populations. Simulation studies show that maps of local genetic differentiation can reveal barriers to gene flow but also other patterns such as continuous variations of gene flow across habitat. The potential of the method is illustrated with two datasets: single nucleotide polymorphisms from human Swedish populations and dominant markers for alpine plant species.
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Affiliation(s)
- Nicolas Duforet-Frebourg
- Laboratoire TIMC-IMAG, Centre National de la Recherche Scientifique, Université Joseph Fourier, Grenoble, France
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A new eigenfunction spatial analysis describing population genetic structure. Genetica 2013; 141:479-89. [DOI: 10.1007/s10709-013-9747-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Accepted: 10/18/2013] [Indexed: 10/26/2022]
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Population structure in a comprehensive genomic data set on human microsatellite variation. G3-GENES GENOMES GENETICS 2013; 3:891-907. [PMID: 23550135 PMCID: PMC3656735 DOI: 10.1534/g3.113.005728] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Over the past two decades, microsatellite genotypes have provided the data for landmark studies of human population-genetic variation. However, the various microsatellite data sets have been prepared with different procedures and sets of markers, so that it has been difficult to synthesize available data for a comprehensive analysis. Here, we combine eight human population-genetic data sets at the 645 microsatellite loci they share in common, accounting for procedural differences in the production of the different data sets, to assemble a single data set containing 5795 individuals from 267 worldwide populations. We perform a systematic analysis of genetic relatedness, detecting 240 intra-population and 92 inter-population pairs of previously unidentified close relatives and proposing standardized subsets of unrelated individuals for use in future studies. We then augment the human data with a data set of 84 chimpanzees at the 246 loci they share in common with the human samples. Multidimensional scaling and neighbor-joining analyses of these data sets offer new insights into the structure of human populations and enable a comparison of genetic variation patterns in chimpanzees with those in humans. Our combined data sets are the largest of their kind reported to date and provide a resource for use in human population-genetic studies.
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Diniz-Filho JAF, Bini LM. Thirty-five years of spatial autocorrelation analysis in population genetics: an essay in honour of Robert Sokal (1926-2012). Biol J Linn Soc Lond 2012. [DOI: 10.1111/j.1095-8312.2012.01987.x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Luis Mauricio Bini
- Departamento de Ecologia, Instituto de Ciências Biológicas; Universidade Federal de Goiás; CP 131 Campus II 74001-970; Goiânia; GO; Brazil
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Safner T, Miller MP, McRae BH, Fortin MJ, Manel S. Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics. Int J Mol Sci 2011; 12:865-89. [PMID: 21541031 PMCID: PMC3083678 DOI: 10.3390/ijms12020865] [Citation(s) in RCA: 115] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Revised: 01/18/2011] [Accepted: 01/19/2011] [Indexed: 11/25/2022] Open
Abstract
Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.
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Affiliation(s)
- Toni Safner
- Laboratory of Alpine Ecology, Equipe Population Genomics and Biodiversity, UMR CNRS 5553, BP 53, University Joseph Fourier, 38041 Grenoble Cedex 9, France; E-Mail:
- Department of Plant Breeding, Genetics and Biometrics, Faculty of Agriculture, University of Zagreb, Svetosimunska 25, 10000 Zagreb, Croatia
| | - Mark P. Miller
- Department of Biology, Utah State University, 5305 Old Main Hill, Logan, UT 84321, USA; E-Mail:
| | - Brad H. McRae
- The Nature Conservancy, 1917 1st Ave, Seattle, WA 98101, USA; E-Mail:
| | - Marie-Josée Fortin
- Department of Ecology & Evolutionary Biology, University of Toronto, Ontario, M6R 2R8, Canada; E-Mail:
| | - Stéphanie Manel
- Laboratory of Alpine Ecology, Equipe Population Genomics and Biodiversity, UMR CNRS 5553, BP 53, University Joseph Fourier, 38041 Grenoble Cedex 9, France; E-Mail:
- Laboratory of Population Environment Development, UMR 151 UP/IRD, University Aix-Marseille I, 3 place Victor Hugo, 13331 Marseille Cedex 03, France
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Fine-scale genetic structure of mainland invasive Rattus rattus populations: implications for restoration of forested conservation areas in New Zealand. CONSERV GENET 2010. [DOI: 10.1007/s10592-010-0085-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Abstract
Landscape genetics has seen rapid growth in number of publications since the term was coined in 2003. An extensive literature search from 1998 to 2008 using keywords associated with landscape genetics yielded 655 articles encompassing a vast array of study organisms, study designs and methodology. These publications were screened to identify 174 studies that explicitly incorporated at least one landscape variable with genetic data. We systematically reviewed this set of papers to assess taxonomic and temporal trends in: (i) geographic regions studied; (ii) types of questions addressed; (iii) molecular markers used; (iv) statistical analyses used; and (v) types and nature of spatial data used. Overall, studies have occurred in geographic regions proximal to developed countries and more commonly in terrestrial vs. aquatic habitats. Questions most often focused on effects of barriers and/or landscape variables on gene flow. The most commonly used molecular markers were microsatellites and amplified fragment length polymorphism (AFLPs), with AFLPs used more frequently in plants than animals. Analysis methods were dominated by Mantel and assignment tests. We also assessed differences among journals to evaluate the uniformity of reporting and publication standards. Few studies presented an explicit study design or explicit descriptions of spatial extent. While some landscape variables such as topographic relief affected most species studied, effects were not universal, and some species appeared unaffected by the landscape. Effects of habitat fragmentation were mixed, with some species altering movement paths and others unaffected. Taken together, although some generalities emerged regarding effects of specific landscape variables, results varied, thereby reinforcing the need for species-specific work. We conclude by: highlighting gaps in knowledge and methodology, providing guidelines to authors and reviewers of landscape genetics studies, and suggesting promising future directions of inquiry.
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Affiliation(s)
- Andrew Storfer
- School of Biological Sciences, Washington State University, Pullman, WA 99164, USA.
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Abstract
The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls.
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Affiliation(s)
- Gilles Guillot
- Department of Informatics and Mathematical Modelling, Technical University of Denmark, Copenhagen, Denmark.
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Sloan CD, Duell EJ, Shi X, Irwin R, Andrew AS, Williams SM, Moore JH. Ecogeographic genetic epidemiology. Genet Epidemiol 2009; 33:281-9. [PMID: 19025788 DOI: 10.1002/gepi.20386] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Complex diseases such as cancer and heart disease result from interactions between an individual's genetics and environment, i.e. their human ecology. Rates of complex diseases have consistently demonstrated geographic patterns of incidence, or spatial "clusters" of increased incidence relative to the general population. Likewise, genetic subpopulations and environmental influences are not evenly distributed across space. Merging appropriate methods from genetic epidemiology, ecology and geography will provide a more complete understanding of the spatial interactions between genetics and environment that result in spatial patterning of disease rates. Geographic information systems (GIS), which are tools designed specifically for dealing with geographic data and performing spatial analyses to determine their relationship, are key to this kind of data integration. Here the authors introduce a new interdisciplinary paradigm, ecogeographic genetic epidemiology, which uses GIS and spatial statistical analyses to layer genetic subpopulation and environmental data with disease rates and thereby discern the complex gene-environment interactions which result in spatial patterns of incidence.
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Affiliation(s)
- Chantel D Sloan
- Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, New Hampshire, USA
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CRIDA A, MANEL S. wombsoft: anrpackage that implements the Wombling method to identify genetic boundary. ACTA ACUST UNITED AC 2007. [DOI: 10.1111/j.1471-8286.2007.01753.x] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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