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      Whole-genome resequencing reveals world-wide ancestry and adaptive introgression events of domesticated cattle in East Asia

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          Abstract

          Cattle domestication and the complex histories of East Asian cattle breeds warrant further investigation. Through analysing the genomes of 49 modern breeds and eight East Asian ancient samples, worldwide cattle are consistently classified into five continental groups based on Y-chromosome haplotypes and autosomal variants. We find that East Asian cattle populations are mainly composed of three distinct ancestries, including an earlier East Asian taurine ancestry that reached China at least ~3.9 kya, a later introduced Eurasian taurine ancestry, and a novel Chinese indicine ancestry that diverged from Indian indicine approximately 36.6–49.6 kya. We also report historic introgression events that helped domestic cattle from southern China and the Tibetan Plateau achieve rapid adaptation by acquiring ~2.93% and ~1.22% of their genomes from banteng and yak, respectively. Our findings provide new insights into the evolutionary history of cattle and the importance of introgression in adaptation of cattle to new environmental challenges in East Asia.

          Abstract

          There are various indigenous cattle breeds in East Asia which have a complex history. Here, the authors analyse the genomes of 49 modern breeds and eight ancient samples and identify three distinct ancestries and multiple adaptive introgressions from other bovine species.

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          New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

          PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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            Fast model-based estimation of ancestry in unrelated individuals.

            Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
              • Record: found
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              Bayesian Phylogenetics with BEAUti and the BEAST 1.7

              Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk

                Author and article information

                Contributors
                yu.jiang@nwafu.edu.cn
                leichuzhao1118@nwafu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 June 2018
                14 June 2018
                2018
                : 9
                : 2337
                Affiliations
                [1 ]ISNI 0000 0004 1760 4150, GRID grid.144022.1, Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, , Northwest A&F University, ; Yangling, 712100 China
                [2 ]ISNI 0000 0001 0307 1240, GRID grid.440588.5, Center for Ecological and Environmental Sciences, , Northwestern Polytechnical University, ; Xi’an, 710129 China
                [3 ]Shaanxi Provincial Institute of Archaeology, Xi’an, 710054 China
                [4 ]GRID grid.440773.3, Key Laboratory of Plateau Lake Ecology and Environment Change, , Yunnan University, ; Kunming, 650504 China
                [5 ]ISNI 0000 0004 1760 4150, GRID grid.144022.1, State Key Laboratory of Crop Stress Biology in Arid Areas, Yangling Branch of China Wheat Improvement Center, College of Agronomy, , Northwest A&F University, ; Yangling, 712100 China
                [6 ]GRID grid.262246.6, Academy of Animal Science and Veterinary Medicine, , Qinghai University, ; Xining, 810016 China
                [7 ]ISNI 0000 0001 2181 583X, GRID grid.260987.2, Agricultural College, , Ningxia University, ; Yinchuan, 750021 China
                [8 ]Institute of Animal Science and Veterinary Medicine, Henan Academy of Agriculture Science, Zhengzhou, 450002 China
                [9 ]Institute of Animal Science and Veterinary Medicine, Anhui Academy of Agriculture Science, Hefei, 230001 China
                [10 ]Bozhou Comprehensive Experimental Station, National Beef Cattle and Yak Industrial Technology System, Bozhou, 236000 China
                [11 ]ISNI 0000 0000 8571 0482, GRID grid.32566.34, State Key Laboratory of Grassland Agroecosystems, College of Pastoral Agriculture Science and Technology, , Lanzhou University, ; Lanzhou, 730000 China
                [12 ]GRID grid.410696.c, Faculty of Animal Science and Technology, , Yunnan Agricultural University, ; Kunming, 650201 China
                [13 ]ISNI 0000000119573309, GRID grid.9227.e, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, , Chinese Academy of Sciences, ; Kunming, 650223 China
                [14 ]ISNI 0000 0001 0526 1937, GRID grid.410727.7, CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, , Chinese Academy of Agriculture Sciences (CAAS), ; Beijing, 100193 China
                [15 ]GRID grid.419369.0, International Livestock Research Institute (ILRI), ; Nairobi, 00100 Kenya
                [16 ]ISNI 0000 0004 1936 9705, GRID grid.8217.c, Smurfit Institute of Genetics, , Trinity College Dublin, ; D02 DK07 Dublin, Ireland
                Author information
                http://orcid.org/0000-0001-5902-0901
                http://orcid.org/0000-0003-4821-3585
                Article
                4737
                10.1038/s41467-018-04737-0
                6002414
                29904051
                6b0b2176-ede9-48e5-8d22-be925d94a0bc
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 September 2017
                : 10 May 2018
                Funding
                Funded by: Natural Science Foundation of Qinghai Province of China (2017-ZJ-906)
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31501918
                Award Recipient :
                Funded by: Yunnan Modern Agriculture Beef Cattle Industry Technology System (2017KJTX0015)
                Funded by: National Thousand Youth Talents Plan
                Funded by: National Beef Cattle and Yak Industrial Technology System (CARS-37)
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