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      Dissecting the old Mediterranean durum wheat genetic architecture for phenology, biomass and yield formation by association mapping and QTL meta-analysis

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          Abstract

          Association mapping was used to identify genome regions affecting yield formation, crop phenology and crop biomass in a collection of 172 durum wheat landraces representative of the genetic diversity of ancient local durum varieties from the Mediterranean Basin. The collection was genotyped with 1,149 DArT markers and phenotyped in Spanish northern and southern locations during three years. A total of 245 significant marker trait associations (MTAs) ( P<0.01) were detected. Some of these associations confirmed previously identified quantitative trait loci (QTL) and/or candidate genes, and others are reported for the first time here. Eighty-six MTAs corresponded with yield and yield component traits, 70 to phenology and 89 to biomass production. Twelve genomic regions harbouring stable MTAs (significant in three or more environments) were identified, while five and two regions showed specific MTAs for northern and southern environments, respectively. Sixty per cent of MTAs were located on the B genome and 29% on the A genome. The marker wPt-9859 was detected in 12 MTAs, associated with six traits in four environments and the mean across years. To refine QTL positions, a meta-analysis was performed. A total of 477 unique QTLs were projected onto a durum wheat consensus map and were condensed to 71 meta-QTLs and left 13 QTLs as singletons. Sixty-one percent of QTLs explained less than 10% of the phenotypic variance confirming the high genetic complexity of the traits analysed.

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          A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase.

          Grain weight is one of the most important components of grain yield and is controlled by quantitative trait loci (QTLs) derived from natural variations in crops. However, the molecular roles of QTLs in the regulation of grain weight have not been fully elucidated. Here, we report the cloning and characterization of GW2, a new QTL that controls rice grain width and weight. Our data show that GW2 encodes a previously unknown RING-type protein with E3 ubiquitin ligase activity, which is known to function in the degradation by the ubiquitin-proteasome pathway. Loss of GW2 function increased cell numbers, resulting in a larger (wider) spikelet hull, and it accelerated the grain milk filling rate, resulting in enhanced grain width, weight and yield. Our results suggest that GW2 negatively regulates cell division by targeting its substrate(s) to proteasomes for regulated proteolysis. The functional characterization of GW2 provides insight into the mechanism of seed development and is a potential tool for improving grain yield in crops.
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            A rapid DNA isolation procedure for small quantities of fresh leaf tissues

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              Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield.

              Increases in the yield of rice, a staple crop for more than half of the global population, are imperative to support rapid population growth. Grain weight is a major determining factor of yield. Here, we report the cloning and functional analysis of THOUSAND-GRAIN WEIGHT 6 (TGW6), a gene from the Indian landrace rice Kasalath. TGW6 encodes a novel protein with indole-3-acetic acid (IAA)-glucose hydrolase activity. In sink organs, the Nipponbare tgw6 allele affects the timing of the transition from the syncytial to the cellular phase by controlling IAA supply and limiting cell number and grain length. Most notably, loss of function of the Kasalath allele enhances grain weight through pleiotropic effects on source organs and leads to significant yield increases. Our findings suggest that TGW6 may be useful for further improvements in yield characteristics in most cultivars.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                25 May 2017
                2017
                : 12
                : 5
                : e0178290
                Affiliations
                [1 ]Field Crops Programme, IRTA ( Institute for Food and Agricultural Research and Technology, ), Lleida, Spain
                [2 ]Biometrics, Wageningen University and Research Centre, Wageningen, The Netherlands
                [3 ]Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States of America
                Università Politecnica delle Marche, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: JMS CR MES.

                • Data curation: JMS MM MR CR.

                • Formal analysis: JMS MM MR MES CR.

                • Funding acquisition: JMS CR.

                • Investigation: JMS MM MR MES CR.

                • Methodology: JMS MM CR.

                • Project administration: CR.

                • Resources: JMS CR.

                • Software: JMS MM.

                • Supervision: CR.

                • Validation: JMS CR.

                • Visualization: JMS CR.

                • Writing – original draft: JMS.

                • Writing – review & editing: JMS MM MES CR.

                Author information
                http://orcid.org/0000-0003-1965-6504
                Article
                PONE-D-17-08332
                10.1371/journal.pone.0178290
                5444813
                28542488
                977a7d85-7892-4060-b51e-7139e81906c0
                © 2017 Soriano et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 March 2017
                : 10 May 2017
                Page count
                Figures: 3, Tables: 3, Pages: 19
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: AGL-2006-09226-C02-01
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: AGL2015-65351-R
                Award Recipient :
                This research was funded by the projects AGL-2006-09226-C02-01 (CR) and AGL2015-65351-R (JMS) from the Spanish Ministry of Economy and Competitiveness ( http://www.mineco.gob.es) and CERCA Programme / Generalitat de Catalunya ( http://cerca.cat/). Jose Miguel Soriano was hired by the INIA-CCAA program funded by Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria and Generalitat de Catalunya ( http://www.inia.es).
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Plants
                Grasses
                Wheat
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Quantitative Trait Loci
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromosomes
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Gene Mapping
                Research and Analysis Methods
                Molecular Biology Techniques
                Gene Mapping
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Meta-Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Meta-Analysis
                Biology and Life Sciences
                Agriculture
                Crop Science
                Crops
                Cereal Crops
                Biology and Life Sciences
                Genetics
                Plant Genetics
                Crop Genetics
                Biology and Life Sciences
                Plant Science
                Plant Genetics
                Crop Genetics
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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                Uncategorized

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