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      Identification of genomic region(s) responsible for high iron and zinc content in rice

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          Abstract

          Micronutrient especially iron and zinc-enriched rice hold immense promise for sustainable and cost-effective solutions to overcome malnutrition. In this context, BC 2F 5 population derived from cross between RP-Bio226 and Sampada was used to localize genomic region(s)/QTL(s) for grain Fe (iron) and Zn (zinc) content together with yield and yield-related traits. Genotyping of mapping population with 108 SSR markers resulted in a genetic map of 2317.5 cM with an average marker distance of 21.5 cM. Mean grain mineral content in the mapping population across the two seasons ranged from 10.5–17.5 ppm for Fe and 11.3–22.1 ppm for Zn. Based on the multi-season phenotypic data together with genotypic data, a total of two major QTLs for Fe (PVE upto 17.1%) and three for Zn (PVE upto 34.2%) were identified. Comparative analysis across the two seasons has revealed four consistent QTLs for Fe ( qFe 1.1 , qFe 1.2 , qFe 6.1 and qFe 6.2 ) and two QTL for Zn content ( qZn 1.1 and qZn 6.2 ). Additionally, based on the previous and current studies three meta-QTLs for grain Fe and two for grain Zn have been identified. In-silico analysis of the identified QTL regions revealed the presence of potential candidate gene(s) such as, OsPOT, OsZIP4, OsFDR3, OsIAA5 etc., that were previously reported to influence grain Fe and Zn content. The identified QTLs could be utilized in developing high yielding, Fe and Zn denser varieties by marker assisted selection (MAS).

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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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            Iron uptake and transport in plants: the good, the bad, and the ionome.

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              Quantitative trait loci: a meta-analysis.

              This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only very few "actual" QTL locations. An application of the method is given using data from the maize database available online at http://www. agron.missouri.edu/.
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                Author and article information

                Contributors
                a.kumar@irri.org
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 May 2019
                31 May 2019
                2019
                : 9
                : 8136
                Affiliations
                [1 ]International Rice Research Institute (IRRI), South-Asia Hub, ICRISAT Campus, Hyderabad, 502324 India
                [2 ]GRID grid.464820.c, Indian Institute of Rice Research (IIRR), ; Hyderabad, 500030 India
                [3 ]ISNI 0000 0001 2259 7889, GRID grid.440987.6, Visva-Bharati University, ; Santiniketan, West Bengal 731235 India
                [4 ]HarvestPlus, ICRISAT Campus, Hyderabad, 502324 India
                [5 ]ISNI 0000 0001 0729 330X, GRID grid.419387.0, International Rice Research Institute, DAPO BOX 7777, ; Metro Manila, Philippines
                Author information
                http://orcid.org/0000-0002-5712-910X
                Article
                43888
                10.1038/s41598-019-43888-y
                6544658
                31148549
                c7ecb4f7-4068-432e-bc9f-dd3694e27d16
                © 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
                : 31 July 2018
                : 23 April 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001407, Department of Biotechnology, Ministry of Science and Technology (DBT);
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                plant breeding
                Uncategorized
                plant breeding

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