3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      eQTLs play critical roles in regulating gene expression and identifying key regulators in rice

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          The regulation of gene expression plays an essential role in both the phenotype and adaptation of plants. Transcriptome sequencing enables simultaneous identification of exonic variants and quantification of gene expression. Here, we sequenced the leaf transcriptomes of 287 rice accessions from around the world and obtained a total of 177 853 high‐quality single nucleotide polymorphisms after filtering. Genome‐wide association study identified 44 354 expression quantitative trait loci (eQTLs), which regulate the expression of 13 201 genes, as well as 17 local eQTL hotspots and 96 distant eQTL hotspots. Furthermore, a transcriptome‐wide association study screened 21 candidate genes for starch content in the flag leaves at the heading stage. HS002 was identified as a significant distant eQTL hotspot with five downstream genes enriched for diterpene antitoxin synthesis. Co‐expression analysis, eQTL analysis, and linkage mapping together demonstrated that bHLH026 acts as a key regulator to activate the expression of downstream genes. The transgenic assay revealed that bHLH026 is an important regulator of diterpenoid antitoxin synthesis and enhances the disease resistance of rice. These findings improve our knowledge of the regulatory mechanisms of gene expression variation and complex regulatory networks of the rice genome and will facilitate genetic improvement of cultivated rice varieties.

          Related collections

          Most cited references56

          • Record: found
          • Abstract: found
          • Article: not found

          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
                Bookmark

                Author and article information

                Contributors
                gwwang@mail.hzau.edu.cn
                Journal
                Plant Biotechnol J
                Plant Biotechnol J
                10.1111/(ISSN)1467-7652
                PBI
                Plant Biotechnology Journal
                John Wiley and Sons Inc. (Hoboken )
                1467-7644
                1467-7652
                10 September 2022
                December 2022
                : 20
                : 12 ( doiID: 10.1111/pbi.v20.12 )
                : 2357-2371
                Affiliations
                [ 1 ] National Key Laboratory of Crop Genetic Improvement Hubei Hongshan Laboratory, Huazhong Agricultural University Wuhan China
                [ 2 ] Institute of Plant Science Paris‐Saclay (IPS2) CNRS, INRAE, University Paris‐Saclay Orsay France
                Author notes
                [*] [* ] Correspondence (Tel +86‐15827398206; fax +86‐27‐87280916; email gwwang@ 123456mail.hzau.edu.cn )
                Author information
                https://orcid.org/0000-0003-4379-1116
                Article
                PBI13912 PBI-00698-2022.R2
                10.1111/pbi.13912
                9674320
                36087348
                ef88734e-df96-411f-9a25-ceac03ceda04
                © 2022 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 11 August 2022
                : 17 June 2022
                : 13 August 2022
                Page count
                Figures: 6, Tables: 1, Pages: 2371, Words: 12697
                Funding
                Funded by: Earmarked Fund for China Agriculture Research System , doi 10.13039/501100010038;
                Award ID: CARS‐01‐03
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 31871224
                Funded by: National Special Key Project for Transgenic Breeding
                Award ID: 2016ZX08009001
                Funded by: Huazhong Agricultural University , doi 10.13039/501100007925;
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                December 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.1 mode:remove_FC converted:18.11.2022

                Biotechnology
                population rna‐seq,expression quantitative trait loci (eqtls),diterpenoid antitoxin,key regulators,transcriptome‐wide association study (twas)

                Comments

                Comment on this article