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

      Single-parent expression complementation contributes to phenotypic heterosis in maize hybrids

      research-article

      Read this article at

          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.

          Abstract

          The dominance model of heterosis explains the superior performance of F 1-hybrids via the complementation of deleterious alleles by beneficial alleles in many genes. Genes active in one parent but inactive in the second lead to single-parent expression (SPE) complementation in maize ( Zea mays L.) hybrids. In this study, SPE complementation resulted in approximately 700 additionally active genes in different tissues of genetically diverse maize hybrids on average. We established that the number of SPE genes is significantly associated with mid-parent heterosis (MPH) for all surveyed phenotypic traits. In addition, we highlighted that maternally (SPE_B) and paternally (SPE_X) active SPE genes enriched in gene co-expression modules are highly correlated within each SPE type but separated between these two SPE types. While SPE_B-enriched co-expression modules are positively correlated with phenotypic traits, SPE_X-enriched modules displayed a negative correlation. Gene ontology term enrichment analyses indicated that SPE_B patterns are associated with growth and development, whereas SPE_X patterns are enriched in defense and stress response. In summary, these results link the degree of phenotypic MPH to the prevalence of gene expression complementation observed by SPE, supporting the notion that hybrids benefit from SPE complementation via its role in coordinating maize development in fluctuating environments.

          Abstract

          The number of single-parent expression complementation patterns is significantly associated with mid-parent heterosis for all surveyed phenotypic traits in maize.

          Related collections

          Most cited references57

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de 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
              • Record: found
              • Abstract: found
              • Article: not found

              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
                Bookmark

                Author and article information

                Contributors
                Journal
                Plant Physiol
                Plant Physiol
                plphys
                Plant Physiology
                Oxford University Press
                0032-0889
                1532-2548
                July 2022
                21 April 2022
                21 April 2022
                : 189
                : 3
                : 1625-1638
                Affiliations
                Institute of Crop Science and Resource Conservation, Crop Functional Genomics, University of Bonn , 53113 Bonn, Germany
                Department of Statistics, Iowa State University , Ames, Iowa 50011-1210, USA
                Institute of Crop Science and Resource Conservation, Crop Bioinformatics, University of Bonn , 53115 Bonn, Germany
                Emmy Noether Group Root Functional Biology, Institute of Crop Science and Resource Conservation, University of Bonn , 53113 Bonn, Germany
                Institute of Crop Science, Biostatistics Unit, University of Hohenheim , 70599 Stuttgart, Germany
                Institute of Crop Science and Resource Conservation, Crop Bioinformatics, University of Bonn , 53115 Bonn, Germany
                Department of Statistics, Iowa State University , Ames, Iowa 50011-1210, USA
                Institute of Crop Science and Resource Conservation, Crop Functional Genomics, University of Bonn , 53113 Bonn, Germany
                Author notes
                Author for communication: hochholdinger@ 123456uni-bonn.de
                [†]

                Senior author.

                [‡]

                Present address for Meiling Liu: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, USA.

                Author information
                https://orcid.org/0000-0002-7286-0558
                https://orcid.org/0000-0001-7953-1506
                https://orcid.org/0000-0002-8924-9800
                https://orcid.org/0000-0003-1670-8428
                https://orcid.org/0000-0001-7813-2992
                https://orcid.org/0000-0002-1527-3752
                https://orcid.org/0000-0002-6045-1036
                https://orcid.org/0000-0002-5155-0884
                Article
                kiac180
                10.1093/plphys/kiac180
                9237695
                35522211
                8959637b-b527-4d19-9d68-bc0c21ec65f0
                © The Author(s) 2022. Published by Oxford University Press on behalf of American Society of Plant Biologists.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 09 December 2021
                : 23 March 2022
                : 06 May 2022
                Page count
                Pages: 14
                Funding
                Funded by: Deutsche Forschungsgemeinschaft (DFG);
                Award ID: HO2249/9-3
                Award ID: HO 2249/18-1
                Award ID: GRK2064
                Funded by: DFG, DOI 10.13039/100004807;
                Award ID: PI 377/19-2
                Funded by: National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health (NIH);
                Funded by: National Science Foundation (NSF)/NIGMS Mathematical Biology Program;
                Award ID: R01GM109458
                Categories
                Research Articles
                Genes, Development and Evolution
                AcademicSubjects/SCI02286
                AcademicSubjects/SCI02287
                AcademicSubjects/SCI01270
                AcademicSubjects/SCI01280
                AcademicSubjects/SCI02288

                Plant science & Botany
                Plant science & Botany

                Comments

                Comment on this article