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      Genome wide association study reveals new genes for resistance to striped stem borer in rice ( Oryza sativa L.)

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          Abstract

          Rice is one of the most important food crops in the world and is important for global food security. However, damage caused by striped stem borer (SSB) seriously threatens rice production and can cause significant yield losses. The development and use of resistant rice varieties or genes is currently the most effective strategy for controlling SSB. We genotyped 201 rice samples using 2849855 high-confidence single nucleotide polymorphisms (SNPs). We conducted a genome-wide association study (GWAS) based on observed variation data of 201 rice cultivars resistant to SSB. We obtained a quantitative trait locus (QTL)- qRSSB4 that confers resistance to SSB. Through annotation and analysis of genes within the qRSSB4 locus, as well as qRT-PCR detection in resistant rice cultivars, we ultimately selected the candidate gene LOC_Os04g34140 (named OsRSSB4) for further analysis. Next, we overexpressed the candidate gene OsRSSB4 in Nipponbare through transgenic methods, resulting in OsRSSB4 overexpressing lines ( OsRSSB4OE). In addition, we evaluated the insect resistance of OsRSSB4OE lines using wild type (Nipponbare) as a control. The bioassay experiment results of live plants showed that after 20 days of inoculation with SSB, the withering heart rate of OsRSSB4OE-34 and OsRSSB4OE-39 lines was only 8.3% and 0%, with resistance levels of 1 and 0, respectively; however, the withering heart rate of the wild-type reached 100%, with a resistance level of 9. The results of the in vitro stem bioassay showed that, compared with the wild-type, the average corrected mortality rate of the SSB fed on the OsRSSB4OE line reached 94.3%, and the resistance reached a high level. In summary, we preliminarily confirmed that OsRSSB4 positively regulates the defense of rice against SSB. This research findings reveal new SSB resistance gene resources, providing an important genetic basis for SSB resistance breeding in rice crops.

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          The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

          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.
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            Fast and accurate long-read alignment with Burrows–Wheeler transform

            Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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              Genome-wide Efficient Mixed Model Analysis for Association Studies

              Linear mixed models have attracted considerable recent attention as a powerful and effective tool for accounting for population stratification and relatedness in genetic association tests. However, existing methods for exact computation of standard test statistics are computationally impractical for even moderate-sized genome-wide association studies. To deal with this several approximate methods have been proposed. Here, we present an efficient exact method that makes these approximations unnecessary in many settings. This method is roughly n times faster than the widely-used exact method EMMA, where n is the sample size, making exact genome-wide association analysis computationally practical for large numbers of individuals.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2795740Role:
                Role: Role:
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                URI : https://loop.frontiersin.org/people/2810266Role:
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                URI : https://loop.frontiersin.org/people/972521Role: Role: Role: Role:
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                13 September 2024
                2024
                : 15
                : 1466857
                Affiliations
                [1] 1 State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University , Chengdu, China
                [2] 2 College of Agronomy, Sichuan Agricultural University , Chengdu, China
                [3] 3 Guangan Vocational & Technical College , Guangan, China
                Author notes

                Edited by: Zhenbin Hu, Agricultural Research Service (USDA), United States

                Reviewed by: Yuzhou Xu, Kansas State University, United States

                Jiancai Li, Shanghai Institutes for Biological Sciences (CAS), China

                *Correspondence: Aiping Zheng, aipingzh@ 123456163.com

                †These authors have contributed equally to this work

                Article
                10.3389/fpls.2024.1466857
                11427250
                39345976
                3fb47846-00b8-4bdc-950d-4e177c57f07d
                Copyright © 2024 Xiang, Liu, He, Li, Ofori, Ghani Kandhro, Zheng, Yi, Li, Huang and Zheng

                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
                : 18 July 2024
                : 27 August 2024
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 57, Pages: 11, Words: 5267
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Sichuan Province International Science and Technology Innovation Cooperation (No. 2024YFHZ0299), the Sichuan Science and Technology Program (No.2022YFH0031), and the Chengdu Science and Technology Bureau (No. 2024-YF05-02168-SN). The funding agent only provided the financial support and did not involve in the design of the experiment, collection, interpretation and analysis of data and in the drafting of the manuscript.
                Categories
                Plant Science
                Original Research
                Custom metadata
                Functional and Applied Plant Genomics

                Plant science & Botany
                rice,striped stem borer,gwas,resistance genes,snp
                Plant science & Botany
                rice, striped stem borer, gwas, resistance genes, snp

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