Research PGR: Structural variant landscapes in tomato genomes and their role in natural variation, domestication and crop improvement

在过去的二十年里，许多作物的基因组DNA序列已经确定，为发现关键农业性状的基因提供了蓝图。然而，一个巨大的挑战是确定同一作物的相关品种之间的DNA差异，这是植物育种家用来提高生产力的微妙性状变异的原因。这种性状变异的主要贡献者是“基因组结构变异”，其中DNA片段被删除，插入或重排，导致基因表达的变化。该项目将重点关注结构变异如何促进番茄的驯化和育种。一个相关的目标是扩大和开发新的分子工具，以创造作物改良的结构变异。该项目将通过提供新的知识和工具来有效和可预测地提高作物生产力，从而改善美国农业。该项目的一个主要部分还将包括对青年科学家进行可应用于农业的植物基因组研究基本原则方面的培训。 这些知识也将通过在没有研究机会的纽约市中心学校的外展计划分享。项目人员将开展实践教学活动，突出植物基因组学和新的基因组编辑技术对改善作物和满足21世纪农业需求的重要性。对植物基因组结构变异的程度和多样性的有限了解阻碍了将基因与重要作物表型联系起来的能力。该项目将结合新的长读测序技术、计算生物学、发育和定量遗传学以及基因组编辑，以前所未有的规模阐明和操纵结构变异（SV）。番茄提供了一个强大的系统，由于其相对较小和高质量的参考基因组和重测序基因组的可用性。通过将SV检测算法应用于来自数百个加入物的现有短读序Illumina测序数据，将选择40多个基因组，捕获大多数预测的SV多样性，以使用最新的长读序测序技术（PacBio和10 X Genomics）建立新的参考基因组。根据这些数据，将生成经验证的SV的概要，并将其与正在进行的全基因组关联研究相结合。将使用CRISPR/Cas9基因编辑和定量表型分析来表征重要的基因相关SV，包括那些影响通过全基因组转录谱测量的基因活性的SV，重点关注驱动作物生产力的生殖性状。与此同时，CRISPR/Cas9基因编辑将用于在两种具有农业潜力的相关野生茄科植物中产生已知产量和果实品质基因的SV突变集合，目标是实现驯化和比较发育遗传学研究的重大步骤。该项目将大大扩展我们对番茄基因组多样性的了解，并为解剖其他作物中的SV提供路线图，这些知识可以用于提高生产力。

Genome DNA sequences for many crops have been determined in the last two decades, providing the blueprints to discover genes that underlie key agricultural traits. However, a great challenge is identifying the differences in DNA between related varieties of the same crop, which are responsible for the subtle trait variation that plant breeders exploit to improve productivity. A major contributor to this trait variation is 'genome structural variation' where pieces of DNA are deleted, inserted, or rearranged resulting in changes in gene expression. This project will focus on how structural variation contributed to domestication and breeding of tomatoes. A related goal is to expand and develop new molecular tools to create structural variation for crop improvement. This project will improve US agriculture by providing new knowledge and tools to efficiently and predictably enhance crop productivity. A major part of the project will also include training of young scientists in fundamental principles of plant genome research that can be applied to agriculture. This knowledge will also be shared through outreach programs in inner city New York schools that do not have access to research opportunities. Project personnel will develop hands-on teaching activities that will highlight the importance of plant genomics and new genome editing technologies to improve crops and meet the agricultural needs of the 21st century.Limited knowledge on the extent and diversity of structural variation in plant genomes is hindering the ability to link genes to important crop phenotypes. This project will unite new long-read sequencing technologies, computational biology, developmental and quantitative genetics, and genome editing to elucidate and manipulate structural variation (SV) at a scale never before achieved for a major crop. Tomato provides a powerful system due to its relatively small and high quality reference genome and availability of resequenced genomes. By applying SV-detection algorithms to existing short-read Illumina sequencing data from hundreds of accessions, more than 40 genomes will be selected, capturing the majority of predicted SV diversity, to establish new reference genomes using the latest long-read sequencing technology (PacBio and 10X Genomics). From these data, a compendium of validated SVs will be generated and integrated with ongoing genome-wide association studies. Significant gene-associated SVs, including those affecting gene activity measured by genome-wide transcript profiling, will be characterized using CRISPR/Cas9 gene editing and quantitative phenotypic analyses, focusing on reproductive traits that drive crop productivity. In parallel, CRISPR/Cas9 gene editing will be used to generate a collection of SV mutations in known yield and fruit quality genes in two related wild Solanaceae with agricultural potential, with the goal of achieving major steps towards domestication and for comparative developmental genetics studies. This project will greatly expand our knowledge of genomic diversity in tomato, and provide a road map for dissecting SVs in other crops, where such knowledge can be exploited to improve productivity.