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      Genome-Wide QTL Mapping for Wheat Processing Quality Parameters in a Gaocheng 8901/Zhoumai 16 Recombinant Inbred Line Population

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          Abstract

          Dough rheological and starch pasting properties play an important role in determining processing quality in bread wheat ( Triticum aestivum L.). In the present study, a recombinant inbred line (RIL) population derived from a Gaocheng 8901/Zhoumai 16 cross grown in three environments was used to identify quantitative trait loci (QTLs) for dough rheological and starch pasting properties evaluated by Mixograph, Rapid Visco-Analyzer (RVA), and Mixolab parameters using the wheat 90 and 660 K single nucleotide polymorphism (SNP) chip assays. A high-density linkage map constructed with 46,961 polymorphic SNP markers from the wheat 90 and 660 K SNP assays spanned a total length of 4121 cM, with an average chromosome length of 196.2 cM and marker density of 0.09 cM/marker; 6596 new SNP markers were anchored to the bread wheat linkage map, with 1046 and 5550 markers from the 90 and 660 K SNP assays, respectively. Composite interval mapping identified 119 additive QTLs on 20 chromosomes except 4D; among them, 15 accounted for more than 10% of the phenotypic variation across two or three environments. Twelve QTLs for Mixograph parameters, 17 for RVA parameters and 55 for Mixolab parameters were new. Eleven QTL clusters were identified. The closely linked SNP markers can be used in marker-assisted wheat breeding in combination with the Kompetitive Allele Specific PCR (KASP) technique for improvement of processing quality in bread wheat.

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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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            A high-density, SNP-based consensus map of tetraploid wheat as a bridge to integrate durum and bread wheat genomics and breeding.

            Consensus linkage maps are important tools in crop genomics. We have assembled a high-density tetraploid wheat consensus map by integrating 13 data sets from independent biparental populations involving durum wheat cultivars (Triticum turgidum ssp. durum), cultivated emmer (T. turgidum ssp. dicoccum) and their ancestor (wild emmer, T. turgidum ssp. dicoccoides). The consensus map harboured 30 144 markers (including 26 626 SNPs and 791 SSRs) half of which were present in at least two component maps. The final map spanned 2631 cM of all 14 durum wheat chromosomes and, differently from the individual component maps, all markers fell within the 14 linkage groups. Marker density per genetic distance unit peaked at centromeric regions, likely due to a combination of low recombination rate in the centromeric regions and even gene distribution along the chromosomes. Comparisons with bread wheat indicated fewer regions with recombination suppression, making this consensus map valuable for mapping in the A and B genomes of both durum and bread wheat. Sequence similarity analysis allowed us to relate mapped gene-derived SNPs to chromosome-specific transcripts. Dense patterns of homeologous relationships have been established between the A- and B-genome maps and between nonsyntenic homeologous chromosome regions as well, the latter tracing to ancient translocation events. The gene-based homeologous relationships are valuable to infer the map location of homeologs of target loci/QTLs. Because most SNP and SSR markers were previously mapped in bread wheat, this consensus map will facilitate a more effective integration and exploitation of genes and QTL for wheat breeding purposes.
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              Comparative DNA sequence analysis of wheat and rice genomes.

              The use of DNA sequence-based comparative genomics for evolutionary studies and for transferring information from model species to crop species has revolutionized molecular genetics and crop improvement strategies. This study compared 4485 expressed sequence tags (ESTs) that were physically mapped in wheat chromosome bins, to the public rice genome sequence data from 2251 ordered BAC/PAC clones using BLAST. A rice genome view of homologous wheat genome locations based on comparative sequence analysis revealed numerous chromosomal rearrangements that will significantly complicate the use of rice as a model for cross-species transfer of information in nonconserved regions.
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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
                19 July 2016
                2016
                : 7
                : 1032
                Affiliations
                [1] 1National Wheat Improvement Center, Institute of Crop Science, Chinese Academy of Agricultural Sciences Beijing, China
                [2] 2Department of Plant Genetics & Breeding/State Key Laboratory for Agrobiotechnology, China Agricultural University Beijing, China
                [3] 3Wheat and Maize Research Center, Institute of Cotton Research, Chinese Academy of Agricultural Sciences Anyang, China
                [4] 4International Maize and Wheat Improvement Center (CIMMYT) China office Beijing, China
                Author notes

                Edited by: Soren K. Rasmussen, University of Copenhagen, Denmark

                Reviewed by: Zvi Peleg, The Hebrew University of Jerusalem, Israel; Yueming Yan, Capital Normal University, China

                *Correspondence: Zhonghu He zhhecaas@ 123456163.com

                This article was submitted to Crop Science and Horticulture, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2016.01032
                4949415
                27486464
                9f74c1db-fc6f-46ea-9aeb-fd1a0525382c
                Copyright © 2016 Jin, Wen, Liu, Zhai, Zhang, Yan, Liu, Xia and He.

                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) or licensor 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
                : 24 February 2016
                : 30 June 2016
                Page count
                Figures: 1, Tables: 5, Equations: 0, References: 80, Pages: 16, Words: 11948
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 31461143021
                Award ID: 31371623
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                high-density linkage map,mixograph,mixolab,qtl mapping,quality parameters,rapid visco-analyzer,single nucleotide polymorphism,triticum aestivum

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