13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Meta-QTL analysis and identification of candidate genes for quality, abiotic and biotic stress in durum wheat

      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.

          Abstract

          The genetic improvement of durum wheat and enhancement of plant performance often depend on the identification of stable quantitative trait loci (QTL) and closely linked molecular markers. This is essential for better understanding the genetic basis of important agronomic traits and identifying an effective method for improving selection efficiency in breeding programmes. Meta-QTL analysis is a useful approach for dissecting the genetic basis of complex traits, providing broader allelic coverage and higher mapping resolution for the identification of putative molecular markers to be used in marker-assisted selection. In the present study, extensive QTL meta-analysis was conducted on 45 traits of durum wheat, including quality and biotic and abiotic stress-related traits. A total of 368 QTL distributed on all 14 chromosomes of genomes A and B were projected: 171 corresponded to quality-related traits, 127 to abiotic stress and 71 to biotic stress, of which 318 were grouped in 85 meta-QTL (MQTL), 24 remained as single QTL and 26 were not assigned to any MQTL. The number of MQTL per chromosome ranged from 4 in chromosomes 1A and 6A to 9 in chromosome 7B; chromosomes 3A and 7A showed the highest number of individual QTL (4), and chromosome 7B the highest number of undefined QTL (4). The recently published genome sequence of durum wheat was used to search for candidate genes within the MQTL peaks. This work will facilitate cloning and pyramiding of QTL to develop new cultivars with specific quantitative traits and speed up breeding programs.

          Related collections

          Most cited references85

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

          The transcriptional landscape of polyploid wheat

          The coordinated expression of highly related homoeologous genes in polyploid species underlies the phenotypes of many of the world's major crops. Here we combine extensive gene expression datasets to produce a comprehensive, genome-wide analysis of homoeolog expression patterns in hexaploid bread wheat. Bias in homoeolog expression varies between tissues, with ~30% of wheat homoeologs showing nonbalanced expression. We found expression asymmetries along wheat chromosomes, with homoeologs showing the largest inter-tissue, inter-cultivar, and coding sequence variation, most often located in high-recombination distal ends of chromosomes. These transcriptionally dynamic genes potentially represent the first steps toward neo- or subfunctionalization of wheat homoeologs. Coexpression networks reveal extensive coordination of homoeologs throughout development and, alongside a detailed expression atlas, provide a framework to target candidate genes underpinning agronomic traits in wheat.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Durum wheat genome highlights past domestication signatures and future improvement targets

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

              BioMercator: integrating genetic maps and QTL towards discovery of candidate genes.

              Breeding programs face the challenge of integrating information from genomics and from quantitative trait loci (QTL) analysis in order to identify genomic sequences controlling the variation of important traits. Despite the development of integrative databases, building a consensus map of genes, QTL and other loci gathered from multiple maps remains a manual and tedious task. Nevertheless, this is a critical step to reveal co-locations between genes and QTL. Another important matter is to determine whether QTL linked to same traits or related ones is detected in independent experiments and located in the same region, and represents a single locus or not. Statistical tools such as meta-analysis can be used to answer this question. BioMercator has been developed to automate map compilation and QTL meta-analysis, and to visualize co-locations between genes and QTL through a graphical interface. Available upon request (http://moulon/~bioinfo/BioMercator/). Free of charge for academic use.
                Bookmark

                Author and article information

                Contributors
                josemiguel.soriano@irta.cat
                ilaria.marcotuli@uniba.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 June 2021
                4 June 2021
                2021
                : 11
                : 11877
                Affiliations
                [1 ]GRID grid.8581.4, ISNI 0000 0001 1943 6646, Sustainable Field Crops Programme, , IRTA (Institute for Food and Agricultural Research and Technology), ; 25198 Lleida, Spain
                [2 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Department of Agricultural and Environmental Science, , University of Bari ‘Aldo Moro’, ; Via G. Amendola 165/A, 70126 Bari, Italy
                Article
                91446
                10.1038/s41598-021-91446-2
                8178383
                34088972
                d43f272f-218c-45e3-b754-b0ed4fe210e9
                © 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 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
                : 8 March 2021
                : 25 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia e Innovación;
                Award ID: PID2019-109089RB-C31
                Award Recipient :
                Funded by: CERCA programme/Generalitat de Catalunya
                Funded by: FundRef http://dx.doi.org/10.13039/501100003407, Ministero dell’Istruzione, dell’Università e della Ricerca;
                Award ID: PON-AIM AIM1812334
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100014439, Partnership for Research and Innovation in the Mediterranean Area;
                Award ID: CerealMed
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                quantitative trait,plant breeding
                Uncategorized
                quantitative trait, plant breeding

                Comments

                Comment on this article