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      Genome-Wide Association Mapping of Root Traits in a Japonica Rice Panel

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          Abstract

          Rice is a crop prone to drought stress in upland and rainfed lowland ecosystems. A deep root system is recognized as the best drought avoidance mechanism. Genome-wide association mapping offers higher resolution for locating quantitative trait loci (QTLs) than QTL mapping in biparental populations. We performed an association mapping study for root traits using a panel of 167 japonica accessions, mostly of tropical origin. The panel was genotyped at an average density of one marker per 22.5 kb using genotyping by sequencing technology. The linkage disequilibrium in the panel was high (r 2>0.6, on average, for 20 kb mean distances between markers). The plants were grown in transparent 50 cm × 20 cm × 2 cm Plexiglas nailboard sandwiches filled with 1.5 mm glass beads through which a nutrient solution was circulated. Root system architecture and biomass traits were measured in 30-day-old plants. The panel showed a moderate to high diversity in the various traits, particularly for deep (below 30 cm depth) root mass and the number of deep roots. Association analyses were conducted using a mixed model involving both population structure and kinship to control for false positives. Nineteen associations were significant at P<1e-05, and 78 were significant at P<1e-04. The greatest numbers of significant associations were detected for deep root mass and the number of deep roots, whereas no significant associations were found for total root biomass or deep root proportion. Because several QTLs for different traits were co-localized, 51 unique loci were detected; several co-localized with meta-QTLs for root traits, but none co-localized with rice genes known to be involved in root growth. Several likely candidate genes were found in close proximity to these loci. Additional work is necessary to assess whether these markers are relevant in other backgrounds and whether the genes identified are robust candidates.

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions.

            The genetic improvement of drought resistance is essential for stable and adequate crop production in drought-prone areas. Here we demonstrate that alteration of root system architecture improves drought avoidance through the cloning and characterization of DEEPER ROOTING 1 (DRO1), a rice quantitative trait locus controlling root growth angle. DRO1 is negatively regulated by auxin and is involved in cell elongation in the root tip that causes asymmetric root growth and downward bending of the root in response to gravity. Higher expression of DRO1 increases the root growth angle, whereby roots grow in a more downward direction. Introducing DRO1 into a shallow-rooting rice cultivar by backcrossing enabled the resulting line to avoid drought by increasing deep rooting, which maintained high yield performance under drought conditions relative to the recipient cultivar. Our experiments suggest that control of root system architecture will contribute to drought avoidance in crops.
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              An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

              Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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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, USA )
                1932-6203
                2013
                5 November 2013
                : 8
                : 11
                : e78037
                Affiliations
                [1 ]Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR AGAP, Montpellier, France
                [2 ]Universidad Icesi, Departamento de Ciencias Biológicas, Cali, Colombia
                [3 ]Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UPR SCA, Montpellier, France
                [4 ]Diversity Arrays Technology Pty Ltd. (DArT P/L), Canberra, Australia
                United States Department of Agriculture, Agricultural Research Service, United States of America
                Author notes

                Competing Interests: AK is an employee of Diversity Arrays Technology Pty Ltd, which offers genome profiling service using the technologies described in this report. This fact, however, has not interfered whatsoever with the full, objective, transparent and unbiased presentation of the research results described in the manuscript nor alters the authors' adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: BC MD AA. Performed the experiments: BC AA TG AD SR JF NA. Analyzed the data: BC EG LR. Wrote the manuscript: BC NA. Selected the panel and multiplied the seeds: BC. Developed the phenotyping system: AA TG SR. Produced the genotyping data: AK. Did the bioinformatic work on genotyping data: GD AK.

                [¤]

                Current address: International Rice Research Institute (IRRI), Metro Manila, Philippines

                Article
                PONE-D-13-20612
                10.1371/journal.pone.0078037
                3818351
                3db070ba-4035-4ace-b5f3-147807005c7a
                Copyright @ 2013

                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
                : 19 May 2013
                : 6 September 2013
                Funding
                This research was funded by the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD-France) in the framework of its Orytage project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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