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      Resequencing 545 ginkgo genomes across the world reveals the evolutionary history of the living fossil

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          Abstract

          As Charles Darwin anticipated, living fossils provide excellent opportunities to study evolutionary questions related to extinction, competition, and adaptation. Ginkgo ( Ginkgo biloba L.) is one of the oldest living plants and a fascinating example of how people have saved a species from extinction and assisted its resurgence. By resequencing 545 genomes of ginkgo trees sampled from 51 populations across the world, we identify three refugia in China and detect multiple cycles of population expansion and reduction along with glacial admixture between relict populations in the southwestern and southern refugia. We demonstrate multiple anthropogenic introductions of ginkgo from eastern China into different continents. Further analyses reveal bioclimatic variables that have affected the geographic distribution of ginkgo and the role of natural selection in ginkgo’s adaptation and resilience. These investigations provide insights into the evolutionary history of ginkgo trees and valuable genomic resources for further addressing various questions involving living fossil species.

          Abstract

          Ginkgo is one of the living fossils from the plant kingdom. Here, authors conduct population genomics analyses to reveal its refugia and demographic history, and provide evidence of multiple anthropogenic introductions of ginkgo from eastern China into different continents.

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          Most cited references40

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          SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data

          Abstract Quality control (QC) and preprocessing are essential steps for sequencing data analysis to ensure the accuracy of results. However, existing tools cannot provide a satisfying solution with integrated comprehensive functions, proper architectures, and highly scalable acceleration. In this article, we demonstrate SOAPnuke as a tool with abundant functions for a “QC-Preprocess-QC” workflow and MapReduce acceleration framework. Four modules with different preprocessing functions are designed for processing datasets from genomic, small RNA, Digital Gene Expression, and metagenomic experiments, respectively. As a workflow-like tool, SOAPnuke centralizes processing functions into 1 executable and predefines their order to avoid the necessity of reformatting different files when switching tools. Furthermore, the MapReduce framework enables large scalability to distribute all the processing works to an entire compute cluster. We conducted a benchmarking where SOAPnuke and other tools are used to preprocess a ∼30× NA12878 dataset published by GIAB. The standalone operation of SOAPnuke struck a balance between resource occupancy and performance. When accelerated on 16 working nodes with MapReduce, SOAPnuke achieved ∼5.7 times the fastest speed of other tools.
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            Inferring human population size and separation history from multiple genome sequences

            The availability of complete human genome sequences from populations across the world has given rise to new population genetic inference methods that explicitly model their ancestral relationship under recombination and mutation. So far, application of these methods to evolutionary history more recent than 20-30 thousand years ago and to population separations has been limited. Here we present a new method that overcomes these shortcomings. The Multiple Sequentially Markovian Coalescent (MSMC) analyses the observed pattern of mutations in multiple individuals, focusing on the first coalescence between any two individuals. Results from applying MSMC to genome sequences from nine populations across the world suggest that the genetic separation of non-African ancestors from African Yoruban ancestors started long before 50,000 years ago, and give information about human population history as recently as 2,000 years ago, including the bottleneck in the peopling of the Americas, and separations within Africa, East Asia and Europe.
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              Genomic scans for selective sweeps using SNP data.

              Detecting selective sweeps from genomic SNP data is complicated by the intricate ascertainment schemes used to discover SNPs, and by the confounding influence of the underlying complex demographics and varying mutation and recombination rates. Current methods for detecting selective sweeps have little or no robustness to the demographic assumptions and varying recombination rates, and provide no method for correcting for ascertainment biases. Here, we present several new tests aimed at detecting selective sweeps from genomic SNP data. Using extensive simulations, we show that a new parametric test, based on composite likelihood, has a high power to detect selective sweeps and is surprisingly robust to assumptions regarding recombination rates and demography (i.e., has low Type I error). Our new test also provides estimates of the location of the selective sweep(s) and the magnitude of the selection coefficient. To illustrate the method, we apply our approach to data from the Seattle SNP project and to Chromosome 2 data from the HapMap project. In Chromosome 2, the most extreme signal is found in the lactase gene, which previously has been shown to be undergoing positive selection. Evidence for selective sweeps is also found in many other regions, including genes known to be associated with disease risk such as DPP10 and COL4A3.
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                Author and article information

                Contributors
                cxfu@zju.edu.cn
                liuxin@genomics.cn
                xuxun@genomics.cn
                gesong@ibcas.ac.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 September 2019
                13 September 2019
                2019
                : 10
                : 4201
                Affiliations
                [1 ]ISNI 0000 0004 1759 700X, GRID grid.13402.34, Laboratory of Systematic & Evolutionary Botany and Biodiversity, College of Life Sciences, , Zhejiang University, ; Hangzhou, 310058 Zhejiang China
                [2 ]BGI-Qingdao, BGI-Shenzhen, Qingdao, 266555 Shandong China
                [3 ]ISNI 0000000119573309, GRID grid.9227.e, State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, , Chinese Academy of Sciences, ; Beijing, 100093 China
                [4 ]ISNI 0000 0004 1797 8419, GRID grid.410726.6, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [5 ]BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083 Guangdong China
                [6 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, BGI-Shenzhen, ; Shenzhen, 518083 Guangdong China
                [7 ]State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China
                [8 ]ISNI 0000 0001 0722 6377, GRID grid.254230.2, Department of Environment and Forest Resources, , Chungnam National University, ; Daejeon, 34134 Korea
                [9 ]ISNI 0000 0004 0370 1101, GRID grid.136304.3, College of Liberal Arts and Sciences, , Chiba University, ; Chiba, 263-8522 Japan
                [10 ]James D. Watson Institute of Genome Sciences, Hangzhou, 310058 Zhejiang China
                [11 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, BGI-Fuyang, , BGI-Shenzhen, ; Fuyang, 236009 Anhui China
                [12 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, China National GeneBank, BGI-Shenzhen, ; Shenzhen, 518120 Zhejiang China
                Author information
                http://orcid.org/0000-0003-4393-8472
                http://orcid.org/0000-0002-7050-0540
                http://orcid.org/0000-0003-1092-3186
                http://orcid.org/0000-0001-9294-1403
                http://orcid.org/0000-0003-2860-9611
                http://orcid.org/0000-0003-3256-2940
                http://orcid.org/0000-0002-5338-5173
                http://orcid.org/0000-0002-7683-5579
                Article
                12133
                10.1038/s41467-019-12133-5
                6744486
                31519986
                0283c68a-40fa-4311-a7ac-0e86bedaafd4
                © The Author(s) 2019

                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
                : 8 November 2018
                : 23 August 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31870190; 31461123001; J1310002
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                evolutionary genetics,genome evolution,genetic variation,plant evolution
                Uncategorized
                evolutionary genetics, genome evolution, genetic variation, plant evolution

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