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      Identification of a major-effect QTL associated with pre-harvest sprouting in cucumber ( Cucumis sativus L.) using the QTL-seq method

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          Abstract

          Background

          Cucumber ( Cucumis sativus L.) is cultivated worldwide, and it is essential to produce enough high-quality seeds to meet demand. Pre-harvest sprouting (PHS) in cucumber is a critical problem and causes serious damage to seed production and quality. Nevertheless, the genetic basis and molecular mechanisms underlying cucumber PHS remain unclear. QTL-seq is an efficient approach for rapid quantitative trait loci (QTL) identification that simultaneously takes advantage of bulked-segregant analysis (BSA) and whole-genome resequencing. In the present research, QTL-seq analysis was performed to identify QTLs associated with PHS in cucumber using an F 2 segregating population.

          Results

          Two QTLs that spanned 7.3 Mb on Chromosome 4 and 0.15 Mb on Chromosome 5 were identified by QTL-seq and named qPHS4.1 and qPHS5.1, respectively. Subsequently, SNP and InDel markers selected from the candidate regions were used to refine the intervals using the extended F 2 populations grown in the 2016 and 2017 seasons. Finally, qPHS4.1 was narrowed to 0.53 Mb on chromosome 4 flanked by the markers SNP-16 and SNP-24 and was found to explain 19–22% of the phenotypic variation in cucumber PHS. These results reveal that qPHS4.1 is a major-effect QTL associated with PHS in cucumber. Based on gene annotations and qRT-PCR expression analyses, Csa4G622760 and Csa4G622800 were proposed as the candidate genes.

          Conclusions

          These results provide novel insights into the genetic mechanism controlling PHS in cucumber and highlight the potential for marker-assisted selection of PHS resistance breeding.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-021-07548-8.

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          Most cited references48

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          Kenneth Livak, Thomas D. Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
          Article 0 comments Cited 24394 times – based on 0 reviews     Review now
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Heng Li, Richard Durbin (2009)
            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)
              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
              Article 0 comments Cited 5812 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Mingming Cao: caoming2013@126.com
                Shuju Li: lishuju1964@126.com
                Qiang Deng: dengqiang022@126.com
                Huizhe Wang: wanghuizhe@126.com
                Ruihuan Yang: yruihuan@126.com
                Journal
                Journal ID (nlm-ta): BMC Genomics
                Journal ID (iso-abbrev): BMC Genomics
                Title: BMC Genomics
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2164
                Publication date (Electronic): 7 April 2021
                Publication date PMC-release: 7 April 2021
                Publication date Collection: 2021
                Volume: 22
                Electronic Location Identifier: 249
                Affiliations
                [1 ]State Key Laboratory of Vegetable Germplasm Innovation, Tianjin Key Laboratory of Vegetable Breeding Enterprise, Tianjin Kernel Cucumber Research Institute, Tianjin, 300192 China
                [2 ]GRID grid.464465.1, ISNI 0000 0001 0103 2256, Institute of Cucumber Research, Tianjin Academy of Agricultural Sciences, ; Tianjin, 300192 China
                Article
                Publisher ID: 7548
                DOI: 10.1186/s12864-021-07548-8
                PMC ID: 8028694
                PubMed ID: 33827431
                SO-VID: a084336b-07cc-402e-a0fe-f3a9a4e8ffd9
                Copyright © © The Author(s) 2021
                License:

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 29 December 2020
                Date accepted : 17 March 2021
                Categories
                Subject: Research Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                ScienceOpen disciplines: Genetics
                Keywords: cucumber,pre-harvest sprouting,qtl-seq,qphs4.1
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: cucumber, pre-harvest sprouting, qtl-seq, qphs4.1

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