Overview
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Geneland is a computer program for statistical analysis of population genetics data.
Its main goal is to detect population structure in form of systematic variation of allele frequency that can be detected from
departure from Hardy-Weinberg and linkage equilibrium.
Geneland requires individual multilocus genetic data that are optionally geo-referenced.
It implements several models that can make use of both geographic
and genetic informations to estimate the number of populations in a dataset and
delineate their spatial organisation.
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Important areas of application include landscape genetics, conservation genetics, human genetics, anthropology and epidemiology.
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Geneland can handle all common types of co-dominant or dominant markers
(microsatellites, SNPs, AFLP, sequence data).
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Since version 4.0.0, the program can also process phenotypic data and therefore any combination of genetic, phenotypic and geographic
information.
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The program is released as an add-on to the free statistical program R and is currently available for Linux,
Mac-OS and Windows.
It includes a
fully clickable user interface requiring no particular knowledge of R.
Installation
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As of version 4.0.7, Geneland is no longer distributed on the Comprhensive R Archive Network (CRAN).
Files for installation can be found on the
Geneland distribution repository.
See manual (below) for details.
It is recommended to run Geneland on R version 3.4.x
Manual
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Information about installation and use can be found in the pdf document
Population genetic and morphometric data analysis using R and the Geneland program.
Reference papers
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G. Guillot, S. Renaud, R. Ledevin, J. Michaux, J. Claude
A Unifying Model for the Analysis of Phenotypic, Genetic and Geographic Data.
Systematic Biology, 61(5) 897-911, 2012.
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B. Guedj and G. Guillot
Estimating the location and shape of hybrid zones
Molecular Ecology Resources 11(6), 1119-1123, 2011.
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G. Guillot and F. Santos
Using AFLP markers and the Geneland program for the inference of population genetic structure.
Molecular Ecology Resources, 10(6), 1082-1084, 2010.
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G. Guillot and F. Santos
A computer program to simulate multilocus genotype data with spatially
auto-correlated allele frequencies. Molecular Ecology Resources,
9(4), 1112-1120, 2009.
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G. Guillot.
Inference of structure in subdivided populations at low levels of genetic
differentiation. The correlated allele frequencies model revisited. Bioinformatics,
24:2222-2228, 2008.
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G. Guillot, F. Santos and A. Estoup.
Analysing georeferenced population genetics data with Geneland:
a new algorithm to deal with null alleles and a
friendly graphical user interface. Bioinformatics, 24(11):1406-1407, 2008.
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G. Guillot, Mortier, F., Estoup, A.
Geneland: A program for landscape genetics. Molecular Ecology Notes, 5, 712-715, 2005.
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G. Guillot, Estoup, A., Mortier, F. Cosson, J.F.
A spatial statistical model for landscape genetics. Genetics, 170, 1261-1280, 2005.
Related method papers
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Violle, C., Reich,
P. B., Pacala, S. W., Enquist, B. J., & Kattge, J.
(2014). The emergence and promise of functional biogeography. Proceedings of the National Academy of Sciences, 201415442.
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Guillot G. and F. Rousset
Dismantling the Mantel tests. To appear in Methods in Ecology and Evolution.
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Blair C, Weigel DE, Balazik M, Keeley AT, Walker FM, Landguth E, Cushman S, Murphy M, Waits L, Balkenhol N..
A simulation-based evaluation of methods for inferring linear barriers to gene flow, Mol. Ecol. Resour. 2012
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Remais J.V., Xiao N., Akullian A., Qiu D., Blair D.
Genetic Assignment Methods for Gaining Insight into the Management of
Infectious Disease by Understanding Pathogen, Vector, and Host Movement.
PLoS Pathogens 7(4): e1002013.
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G. Guillot, R. Leblois, A. Coulon, A. Frantz
Statistical methods in spatial genetics. Molecular Ecology, to appear.
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G. Guillot
On the inference of spatial structure from population genetics data using the Tess program.
Bioinformatics 2009.
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M. Hansen and J. Hemmer-Hansen Landscape genetics goes to sea. Journal of Biology 6:6 2007.
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L. Excoffier and G. Heckel
Computer programs for population genetics data analysis: a survival guide.
Nature Reviews Genetics 7, 745-758, October 2006.
Selected applications
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Westengen O.T., et al.
Ethnolinguistic structuring of sorghum genetic diversity in
Africa and the role of local seed systems. Proceedings of the
National Academy of Sciences. (2014)
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Rieux A.,
T. Lenormand, J. Carlier, L. de Lapeyre de Bellair, V. Ravigné
Using neutral cline decay to estimate contemporary
dispersal: a generic tool and its application to a major
crop pathogen 2013
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M. Delętre, McKey D.B., Hodkinson T.R.
Marriage exchanges, seed exchanges, and the dynamics of manioc diversity.
PNAS 2011 108 (45) 18249-18254;
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Macholán M, Baird SJ, Dufková P, Munclinger P, Bímová BV, Piálek J.
Assessing multilocus introgression patterns: a case study on the mouse X chromosome in central Europe. Evolution, 65(5), 1428-1446, 2011.
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Beadell, JS; Hyseni, C; Abila, PP;
Azabo, R; Enyaru, JCK; Ouma, JO; Mohammed, YO; Okedi, LM; Aksoy, S; Caccone, A.
Phylogeography and Population Structure of Glossina fuscipes
fuscipes in Uganda: Implications for Control of Tsetse, PLoS Neglected Tropical Diseases web site, 2010
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J. A. Galarza, J. Carreras-Carbonell,
E.Macpherson, M.Pascual, S. Roques, G.F. Turner and C. Ricod
. The influence of oceanographic fronts and early-life-history traits
on connectivity among littoral fish species. PNAS, 106(5), 1473-1478, 2009.
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L. Joseph, G. Dolman, S. donnellan, K. M. Saint,
M. L. Berg, A.T. Bennet.
Where and when does a ring start and end? Testing the ring-species hypothesis
in a species complex of Australian parrots. Proc. Roy. Soc. B, 275: 2431-2440, 2008.
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Coulon A., Fitzpatrick J.W., Bowman R., Stith B.M.,
Makarewich C.A., Stenzler L.M. and Lovette I.J.
Congruent population structure inferred from dispersal behaviour and
intensive genetic surveys of the threatened Florida Scrub-Jay
(Aphelocoma coerulescens). Molecular Ecology, 17 1685-1701, 2008.
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U. Hannelius, E. Salmela,
T. Lappalainen, G. Guillot, C.M. Lindgren, U. von
Döbeln, P. Lahermo, P. and J. Kere
. Population
substructure in Finland and Sweden revealed by a small number of
unlinked autosomal SNPs. BMC Genetics, 9:54, 2008.
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B.N.Sacks, D.L. Bannasch, B.B. Chomel and H.B. Ernest
Coyotes Demonstrate How Habitat Specialization by Individuals of a Generalist Generalist Species Can Diversify Populations in a Heterogeneous
Ecoregion. Molecular Biology and Evolution 25(7):1384-1394, 2008.
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M. Fontaine, S. Baird, et al.
Rise of oceanographic barriers in continuous populations of a cetacean:
the genetic structure of harbour porpoises in Old World waters.
BMC Biology, 2007.doi:10.1186/1741-7007-5-30
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A. Coulon, G. Guillot
G., Cosson J.-F., Angibault J.M.A. Aulagnier S.,
Cargnelutti B., Galan M., Hewison A.J.M.
Genetic structure is influenced by lansdcape features. Empirical evidence from a roe deer
population. Molecular Ecology, 15 1669-1679, 2006.
Contact:
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b i o s t a t i s t i c s @ i-pri.org