Who is Who in Phylogenetic Networks

Publications related to 'population genetics'   Order by:   Type | Year

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Associated keywords

abstract-network characterization distinguishability explicit-network from-sequences from-splits galled-tree generation haplotype-network heuristic likelihood median-network MedianJoining NeighborNet phylogenetic-network phylogeny population-genetics Program-AdmixTools Program-Network Program-PopART Program-Spectronet Program-SplitsTree Program-T-REX Program-TCS Program-TreeMix Program-Treevolve reconstruction reticulogram semidirected-network software split-decomposition statistical-model survey uniqueness visualization

Article (Journal)

1 Hans-Jürgen Bandelt, Peter Forster, Bryan C. Sykes and Martin Richards. Mitochondrial portraits of human population using median networks. In GEN, Vol. 141:743-753, 1995. Keywords: from splits, median network, population genetics, Program Spectronet, reconstruction, visualization. Note: http://www.genetics.org/cgi/content/abstract/141/2/743.         2 David A. Morrison. Networks in phylogenetic analysis: new tools for population biology. In IJP, Vol. 35:567-582, 2005. Keywords: median network, NeighborNet, phylogenetic network, phylogeny, population genetics, Program Network, Program Spectronet, Program SplitsTree, Program T REX, Program TCS, reconstruction, reticulogram, split decomposition, survey. Note: http://hem.fyristorg.com/acacia/papers/networks.pdf.         3 Joseph K. Pickrell and Jonathan K. Pritchard. Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data. In PLoS Genetics, Vol. 8(11):e1002967, 2012. Keywords: explicit network, heuristic, likelihood, phylogenetic network, phylogeny, population genetics, Program TreeMix. Note: http://dx.doi.org/10.1371/journal.pgen.1002967.         Toggle abstract "Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In our model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data, we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry (9% and 25%, respectively) to wolves subsequent to domestication and that East Asian toy breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy breeds and "ancient" Asian breeds. Software implementing the model described here, called TreeMix, is available at http://treemix.googlecode.com. © 2012 Pickrell, Pritchard." 4 Nick J. Patterson, Priya Moorjani, Yontao Luo, Swapan Mallick, Nadin Rohland, Yiping Zhan, Teri Genschoreck, Teresa Webster and David Reich. Ancient Admixture in Human History. In Genetics, Vol. 192(3):1065-1093, 2012. Keywords: explicit network, phylogenetic network, phylogeny, population genetics, Program AdmixTools. Note: http://genetics.med.harvard.edu/reich/Reich_Lab/Welcome_files/2012_Patterson_AncientAdmixture_Genetics.pdf.         Toggle abstract "Population mixture is an important process in biology. We present a suite of methods for learning about population mixtures, implemented in a software package called ADMIXTOOLS, that support formal tests for whether mixture occurred and make it possible to infer proportions and dates of mixture. We also describe the development of a new single nucleotide polymorphism (SNP) array consisting of 629,433 sites with clearly documented ascertainment that was specifically designed for population genetic analyses and that we genotyped in 934 individuals from 53 diverse populations. To illustrate the methods, we give a number of examples that provide new insights about the history of human admixture. The most striking finding is a clear signal of admixture into northern Europe, with one ancestral population related to present-day Basques and Sardinians and the other related to present-day populations of northeast Asia and the Americas. This likely reflects a history of admixture between Neolithic migrants and the indigenous Mesolithic population of Europe, consistent with recent analyses of ancient bones from Sweden and the sequencing of the genome of the Tyrolean "Iceman." © 2012 by the Genetics Society of America." 5 Jessica W. Leigh and David Bryant. PopART: full-feature software for haplotype network construction. In Methods in Ecology and Evolution, Vol. 6(9):1110-1116, 2015. Keywords: abstract network, from sequences, haplotype network, MedianJoining, phylogenetic network, phylogeny, population genetics, Program PopART, Program TCS, software. Note: http://dx.doi.org/10.1111/2041-210X.12410.

Misc

6 Nicholas C. Grassly and Andrew Rambaut. Treevolve, a program to simulate the evolution of DNA sequences under different population dynamic scenarios. 1999. Keywords: generation, phylogenetic network, phylogeny, population genetics, Program Treevolve, software. Note: http://evolve.zoo.ox.ac.uk/software/treevolve/manual.php.         7 Elizabeth Gross, Leo van Iersel, Remie Janssen, Mark Jones, Colby Long and Yukihiro Murakami. Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes. 2020. Keywords: characterization, distinguishability, explicit network, galled tree, phylogenetic network, population genetics, semidirected network, statistical model, uniqueness. Note: https://arxiv.org/abs/2007.08782.