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      Genome-Wide Linkage Mapping Reveals QTLs for Seed Vigor-Related Traits Under Artificial Aging in Common Wheat ( Triticum aestivum)

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          Abstract

          Long-term storage of seeds leads to lose seed vigor with slow and non-uniform germination. Time, rate, homogeneity, and synchrony are important aspects during the dynamic germination process to assess seed viability after storage. The aim of this study is to identify quantitative trait loci (QTLs) using a high-density genetic linkage map of common wheat ( Triticum aestivum) for seed vigor-related traits under artificial aging. Two hundred and forty-six recombinant inbred lines derived from the cross between Zhou 8425B and Chinese Spring were evaluated for seed storability. Ninety-six QTLs were detected on all wheat chromosomes except 2B, 4D, 6D, and 7D, explaining 2.9–19.4% of the phenotypic variance. These QTLs were clustered into 17 QTL-rich regions on chromosomes 1AL, 2DS, 3AS (3), 3BS, 3BL (2), 3DL, 4AS, 4AL (3), 5AS, 5DS, 6BL, and 7AL, exhibiting pleiotropic effects. Moreover, 10 stable QTLs were identified on chromosomes 2D, 3D, 4A, and 6B ( QaMGT.cas-2DS.2, QaMGR.cas-2DS.2, QaFCGR.cas-2DS.2, QaGI.cas-3DL, QaGR.cas-3DL, QaFCGR.cas-3DL, QaMGT.cas-4AS, QaMGR.cas-4AS, QaZ.cas-4AS, and QaGR.cas-6BL.2). Our results indicate that one of the stable QTL-rich regions on chromosome 2D flanked by IWB21991 and IWB11197 in the position from 46 to 51 cM, presenting as a pleiotropic locus strongly impacting seed vigor-related traits under artificial aging. These new QTLs and tightly linked SNP markers may provide new valuable information and could serve as targets for fine mapping or markers assisted breeding.

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          A modified algorithm for the improvement of composite interval mapping.

          Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.
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            Active oxygen species and antioxidants in seed biology

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              How and why to measure the germination process?

              In the last two centuries, papers have been published including measurements of the germination process. High diversity of mathematical expressions has made comparisons between papers and some times the interpretation of results difficult. Thus, in this paper is included a review about measurements of the germination process, with an analysis of the several mathematical expressions included in the specific literature, recovering the history, sense, and limitations of some germination measurements. Among the measurements included in this paper are the germinability, germination time, coefficient of uniformity of germination (CUG), coefficient of variation of the germination time (CVt), germination rate (mean rate, weighted mean rate, coefficient of velocity, germination rate of George, Timson’s index, GV or Czabator’s index; Throneberry and Smith’s method and its adaptations, including Maguire’s rate; ERI or emergence rate index, germination index, and its modifications), uncertainty associated to the distribution of the relative frequency of germination (U), and synchronization index (Z). The limits of the germination measurements were included to make the interpretation and decisions during comparisons easier. Time, rate, homogeneity, and synchrony are aspects that can be measured, informing the dynamics of the germination process. These characteristics are important not only for physiologists and seed technologists, but also for ecologists because it is possible to predict the degree of successful of a species based on the capacity of their harvest seed to spread the germination through time, permitting the recruitment in the environment of some part of the seedlings formed.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                27 July 2018
                2018
                : 9
                : 1101
                Affiliations
                [1] 1Key Laboratory of Plant Molecular Physiology, Institute of Botany, The Chinese Academy of Sciences , Beijing, China
                [2] 2College of Life Science, University of Chinese Academy of Sciences , Beijing, China
                [3] 3National Wheat Improvement Center, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences , Beijing, China
                [4] 4Crop Research Institute, Heilongjiang Academy of Agricultural Sciences , Harbin, China
                [5] 5Zhoukou Academy of Agricultural Sciences , Zhoukou, China
                Author notes

                Edited by: Petr Smýkal, Palacký University Olomouc, Czechia

                Reviewed by: Hui Liu, The University of Western Australia, Australia; Andreas Börner, Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK), Germany; Xueqing Huang, Fudan University, China; Yong Xiang, Chinese Academy of Agricultural Sciences, China

                *Correspondence: Hong Cao, caohong@ 123456ibcas.ac.cn Yongxiu Liu, yongxiu@ 123456ibcas.ac.cn

                This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2018.01101
                6073742
                e0bd46fa-d17d-4603-b3f7-ed57dc05766f
                Copyright © 2018 Zuo, Liu, Gao, Yin, Wang, Chen, Li, Xu, Chen, Li, Li, Xia, Cao and Liu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 July 2017
                : 09 July 2018
                Page count
                Figures: 1, Tables: 2, Equations: 1, References: 56, Pages: 11, Words: 0
                Funding
                Funded by: Chinese Academy of Sciences 10.13039/501100002367
                Award ID: XDA08010303
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 31371242
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                controlled deterioration,linkage analysis,longevity,90k snp array,seed storability,triticum aestivum

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