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      Genetic basis and adaptation trajectory of soybean from its temperate origin to tropics

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          Abstract

          Soybean ( Glycine max) serves as a major source of protein and edible oils worldwide. The genetic and genomic bases of the adaptation of soybean to tropical regions remain largely unclear. Here, we identify the novel locus Time of Flowering 16 ( Tof16), which confers delay flowering and improve yield at low latitudes and determines that it harbors the soybean homolog of LATE ELONGATED HYPOCOTYL ( LHY). Tof16 and the previously identified J locus genetically additively but independently control yield under short-day conditions. More than 80% accessions in low latitude harbor the mutations of tof16 and j, which suggests that loss of functions of Tof16 and J are the major genetic basis of soybean adaptation into tropics. We suggest that maturity and yield traits can be quantitatively improved by modulating the genetic complexity of various alleles of the LHY homologs, J and E1. Our findings uncover the adaptation trajectory of soybean from its temperate origin to the tropics.

          Abstract

          How soybean, a temperate origin crop, adapted to a tropical environment remains unclear. Here, the authors report Tof16, an ortholog of LHY, and the previously identified J locus, control soybean yield under short-day condition and loss of function of these two genes contributes to the adaptation to tropics.

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          Most cited references38

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Contributors
                jim.weller@utas.edu.au
                lusijia@gzhu.edu.cn
                kongfj@gzhu.edu.cn
                liubh@gzhu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 September 2021
                14 September 2021
                2021
                : 12
                : 5445
                Affiliations
                [1 ]GRID grid.411863.9, ISNI 0000 0001 0067 3588, Innovative Center of Molecular Genetics and Evolution, School of Life Sciences, Guangzhou University, ; Guangzhou, China
                [2 ]GRID grid.458493.7, ISNI 0000 0004 1799 2093, The Innovative Academy of Seed Design, Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, ; Harbin, China
                [3 ]GRID grid.464388.5, ISNI 0000 0004 1756 0215, Soybean Research Institute, National Engineering Research Center for Soybean, Jilin Academy of Agricultural Sciences, ; Changchun, China
                [4 ]GRID grid.256922.8, ISNI 0000 0000 9139 560X, State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, ; Kaifeng, China
                [5 ]GRID grid.162110.5, ISNI 0000 0000 9291 3229, School of Computer Science and Technology, Wuhan University of Technology, ; Wuhan, China
                [6 ]GRID grid.1009.8, ISNI 0000 0004 1936 826X, School of Natural Sciences, University of Tasmania, ; Hobart, Tasmania Australia
                Author information
                http://orcid.org/0000-0002-8085-1678
                http://orcid.org/0000-0001-5595-2058
                http://orcid.org/0000-0002-2671-7443
                http://orcid.org/0000-0001-9344-9162
                http://orcid.org/0000-0002-7788-4268
                http://orcid.org/0000-0003-0661-5332
                http://orcid.org/0000-0003-2423-8286
                http://orcid.org/0000-0002-3110-0915
                http://orcid.org/0000-0001-7138-1478
                http://orcid.org/0000-0003-3491-8293
                Article
                25800
                10.1038/s41467-021-25800-3
                8440769
                34521854
                387cbfbc-6d1c-4b80-a604-cf8fb485b4df
                © The Author(s) 2021

                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
                : 4 June 2021
                : 31 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100010905, National Science Foundation of China | Major Research Plan;
                Award ID: 32090064
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                evolutionary genetics,agricultural genetics,genetic variation,plant domestication

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