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      Discovering consensus genomic regions in wheat for root-related traits by QTL meta-analysis

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      Scientific Reports
      Nature Publishing Group UK
      Agricultural genetics, Plant breeding

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          Abstract

          Root system architecture is crucial for wheat adaptation to drought stress, but phenotyping for root traits in breeding programmes is difficult and time-consuming owing to the belowground characteristics of the system. Identifying quantitative trait loci (QTLs) and linked molecular markers and using marker-assisted selection is an efficient way to increase selection efficiency and boost genetic gains in breeding programmes. Hundreds of QTLs have been identified for different root traits in the last few years. In the current study, consensus QTL regions were identified through QTL meta-analysis. First, a consensus map comprising 7352 markers was constructed. For the meta-analysis, 754 QTLs were retrieved from the literature and 634 of them were projected onto the consensus map. Meta-analysis grouped 557 QTLs in 94 consensus QTL regions, or meta-QTLs (MQTLs), and 18 QTLs remained as singletons. The recently published genome sequence of wheat was used to search for gene models within the MQTL peaks. As a result, gene models for 68 of the 94 Root_MQTLs were found, 35 of them related to root architecture and/or drought stress response. This work will facilitate QTL cloning and pyramiding to develop new cultivars with specific root architecture for coping with environmental constraints.

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          Cellular mechanisms for heavy metal detoxification and tolerance

          J.L. Hall (2002)
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            AtNAC2, a transcription factor downstream of ethylene and auxin signaling pathways, is involved in salt stress response and lateral root development.

            An NAC-type transcription factor gene AtNAC2 was identified from Arabidopsis thaliana when expression patterns of the genes from a microarray analysis were examined. The AtNAC2 expression was induced by salt stress and this induction was reduced in magnitude in the transgenic Arabidopsis plants overexpressing tobacco ethylene receptor gene NTHK1. AtNAC2 is localized in the nucleus and has transcriptional activation activity. It can form a homodimer in yeast. AtNAC2 was highly expressed in roots and flowers, but less expressed in other organs examined. In addition to the salt induction, the AtNAC2 can also be induced by abscisic acid (ABA), ACC and NAA. The salt induction was enhanced in the ethylene overproducer mutant eto1-1, but suppressed in the ethylene-insensitive mutants etr1-1 and ein2-1, and in the auxin-insensitive mutant tir1-1when compared with that in wild-type plants. However, the salt induction of AtNAC2 was not significantly affected in the ABA-insensitive mutants abi2-1, abi3-1 and abi4-1. These results indicate that the salt response of AtNAC2 requires ethylene signaling and auxin signaling pathways but does not require ABI2, ABI3 and ABI4, intermediates of the ABA signaling pathway. Overexpression of AtNAC2 in transgenic Arabidopsis plants resulted in promotion of lateral root development. AtNAC2 also promoted or inhibited downstream gene expressions. These results indicate that AtNAC2 may be a transcription factor incorporating the environmental and endogenous stimuli into the process of plant lateral root development.
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              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.
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                Author and article information

                Contributors
                josemiguel.soriano@irta.cat
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 July 2019
                22 July 2019
                2019
                : 9
                : 10537
                Affiliations
                ISNI 0000 0001 1943 6646, GRID grid.8581.4, Sustainable Field Crops Programme, IRTA (Institute for Food and Agricultural Research and Technology), ; Lleida, Spain
                Article
                47038
                10.1038/s41598-019-47038-2
                6646344
                31332216
                48cd01f6-0e65-47a4-aed6-0beb03734d37
                © The Author(s) 2019

                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
                : 19 October 2018
                : 9 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness);
                Award ID: AGL2015-65351-R
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100007652, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (National Institute for Agricultural and Food Research and Technology);
                Award ID: RTA2015-00072-C03
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                agricultural genetics,plant breeding
                Uncategorized
                agricultural genetics, plant breeding

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