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      Dynamic genome-wide association analysis and identification of candidate genes involved in anaerobic germination tolerance in rice

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          Abstract

          Background

          In Asian rice production, an increasing number of countries now choose the direct seeding mode because of rising costs, labour shortages and water shortages. The ability of rice seeds to undergo anaerobic germination (AG) plays an important role in the success of direct seeding.

          Results

          In this study, we used 2,123,725 single nucleotide polymorphism (SNP) markers based on resequencing to conduct a dynamic genome-wide association study (GWAS) of coleoptile length (CL) and coleoptile diameter (CD) in 209 natural rice populations. A total of 26 SNP loci were detected in these two phenotypes, of which 5 overlapped with previously reported loci (S1_ 39674301, S6_ 20797781, S7_ 18722403, S8_ 9946213, S11_ 19165397), and two sites were detected repeatedly at different time points (S3_ 24689629 and S5_ 27918754). We suggest that these 7 loci (−log 10 ( P) value > 7.3271) are the key sites that affect AG tolerance. To screen the candidate genes more effectively, we sequenced the transcriptome of the flooding-tolerant variety R151 in six key stages, including anaerobic (AN) and the oxygen conversion point (AN-A), and obtained high-quality differential expression profiles. Four reliable candidate genes were identified: Os01g0911700 ( OsVP1), Os05g0560900 ( OsGA2ox8), Os05g0562200 ( OsDi19–1) and Os06g0548200. Then qRT-PCR and LC-MS/ MS targeting metabolite detection technology were used to further verify that the up-regulated expression of these four candidate genes was closely related to AG.

          Conclusion

          The four novel candidate genes were associated with gibberellin (GA) and abscisic acid (ABA) regulation and cell wall metabolism under oxygen-deficiency conditions and promoted coleoptile elongation while avoiding adverse effects, allowing the coleoptile to obtain oxygen, escape the low-oxygen environment and germinate rapidly. The results of this study improve our understanding of the genetic basis of AG in rice seeds, which is conducive to the selection of flooding-tolerant varieties suitable for direct seeding.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12284-020-00444-x.

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          Most cited references84

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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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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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Contributors
                LingSu2nlx@163.com
                15989091552@163.com
                1066055102@qq.com
                453276098@qq.com
                1392633058@qq.com
                ym1669218331@163.com
                920688599@qq.com
                hchong@scau.edu.cn
                jfwang@scau.edu.cn
                wanghui@scau.edu.cn
                chenlin@scau.edu.cn
                guo.tao@vip.163.com
                Journal
                Rice (N Y)
                Rice (N Y)
                Rice
                Springer US (New York )
                1939-8425
                1939-8433
                6 January 2021
                6 January 2021
                2021
                : 14
                : 1
                Affiliations
                GRID grid.20561.30, ISNI 0000 0000 9546 5767, National Engineering Research Center of Plant Space Breeding, , South China Agricultural University, ; Guangzhou, 510642 China
                Author information
                http://orcid.org/0000-0002-2707-1760
                Article
                444
                10.1186/s12284-020-00444-x
                7788155
                33409869
                7bdb2e46-462e-430e-981d-f218604cbc09
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 June 2020
                : 6 December 2020
                Funding
                Funded by: the Research and Development Plan for Key Areas in Guangdong province
                Award ID: No. 2018B020206002
                Award Recipient :
                Funded by: the National Key Research and Development Project
                Award ID: No. 2017YFD0100104
                Award Recipient :
                Funded by: the earmarked fund for China Agriculture Research System
                Award ID: No. CARS-01-17
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © The Author(s) 2021

                Agriculture
                rice,anaerobic germination tolerance,coleoptile,dynamic gwas,rna-seq,candidate gene
                Agriculture
                rice, anaerobic germination tolerance, coleoptile, dynamic gwas, rna-seq, candidate gene

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