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      Construction of a high density linkage map and genome dissection of bruchid resistance in zombi pea ( Vigna vexillata (L.) A. Rich)

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          Abstract

          Zombi pea ( Vigna vexillata) is a legume crop that is resistant to several biotic and abiotic stresses. Callosobruchus maculatus and Callosobruchus chinensis are serious stored-insect pests of legume crops. We constructed a high-density linkage map and performed quantitative trait loci (QTLs) mapping for resistance to these insect species in zombi pea. An F 2 population of 198 individuals from a cross between ‘TVNu 240’ (resistant) and ‘TVNu 1623’ (susceptible) varieties was used to construct a linkage map of 6,529 single nucleotide polymorphism markers generated from sequencing amplified fragments of specific loci. The map comprised 11 linkage groups, spanning 1,740.9 cM, with an average of 593.5 markers per linkage group and an average distance of 0.27 cM between markers. High levels of micro-synteny between V. vexillata and cowpea ( Vigna unguiculata), mungbean ( Vigna radiata), azuki bean ( Vigna angularis) and common bean ( Phaseolus vulgaris) were found. One major and three minor QTLs for C. chinensis resistance and one major and one minor QTLs for C. maculatus resistance were identified. The major QTLs for resistance to C. chinensis and C. maculatus appeared to be the same locus. The linkage map developed in this study will facilitate the identification of useful genes/QTLs in zombi pea.

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          SLAF-seq: An Efficient Method of Large-Scale De Novo SNP Discovery and Genotyping Using High-Throughput Sequencing

          Large-scale genotyping plays an important role in genetic association studies. It has provided new opportunities for gene discovery, especially when combined with high-throughput sequencing technologies. Here, we report an efficient solution for large-scale genotyping. We call it specific-locus amplified fragment sequencing (SLAF-seq). SLAF-seq technology has several distinguishing characteristics: i) deep sequencing to ensure genotyping accuracy; ii) reduced representation strategy to reduce sequencing costs; iii) pre-designed reduced representation scheme to optimize marker efficiency; and iv) double barcode system for large populations. In this study, we tested the efficiency of SLAF-seq on rice and soybean data. Both sets of results showed strong consistency between predicted and practical SLAFs and considerable genotyping accuracy. We also report the highest density genetic map yet created for any organism without a reference genome sequence, common carp in this case, using SLAF-seq data. We detected 50,530 high-quality SLAFs with 13,291 SNPs genotyped in 211 individual carp. The genetic map contained 5,885 markers with 0.68 cM intervals on average. A comparative genomics study between common carp genetic map and zebrafish genome sequence map showed high-quality SLAF-seq genotyping results. SLAF-seq provides a high-resolution strategy for large-scale genotyping and can be generally applicable to various species and populations.
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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 simple and efficient method for DNA extraction from grapevine cultivars andVitis species

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                Author and article information

                Contributors
                wanglixia03@caas.cn
                agrpks@ku.ac.th
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 August 2019
                12 August 2019
                2019
                : 9
                : 11719
                Affiliations
                [1 ]ISNI 0000 0001 0944 049X, GRID grid.9723.f, Department of Agronomy, Faculty of Agriculture at Kamphaeng Saen, , Kasetsart University, Kamphaeng Saen Campus, ; Nakhon Pathom, 73140 Thailand
                [2 ]ISNI 0000 0001 0944 049X, GRID grid.9723.f, Center of Advanced Studies for Agriculture and Food, , Kasetsart University Institute of Advanced Studies, Kasetsart University, ; Bangkok, 10900 Thailand
                [3 ]Center of Excellence on Agricultural Biotechnology: (AG-BIO/PERDO-CHE), Bangkok, 10900 Thailand
                [4 ]ISNI 0000 0001 0526 1937, GRID grid.410727.7, Institute of Crop Sciences, , Chinese Academy of Agricultural Sciences, ; Beijing, 100081 China
                Article
                48239
                10.1038/s41598-019-48239-5
                6690978
                31406222
                bc638f65-93e9-4e0d-93bc-2aea2597ca7f
                © 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
                : 1 October 2018
                : 30 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004539, Kasetsart University (KU);
                Funded by: FundRef https://doi.org/10.13039/501100005196, Chinese Academy of Agricultural Sciences (CAAS);
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                agricultural genetics,plant breeding
                Uncategorized
                agricultural genetics, plant breeding

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