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序列已经被确定，为发现构成关键农业性状的基因提供了蓝图。然而，一个巨大的挑战是识别同一作物相关品种之间的DNA差异，这是植物育种者利用微妙的性状差异来提高生产力的原因。这种性状变异的一个主要原因是‘基因组结构变异’，即DNA片段被删除、插入或重新排列，从而导致基因表达的变化。本项目将重点研究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，为作物改良创造结构变异。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率，从而改善美国农业。该项目的一个主要部分还将包括对年轻科学家进行可应用于农业的植物基因组研究基本原理的培训。这些知识也将通过纽约市中心那些没有机会获得研究机会的学校的外联计划来分享。项目人员将开展实践教学活动，强调植物基因组学和新的基因组编辑技术对改良作物和满足21世纪农业需求的重要性。对植物基因组结构变异的范围和多样性的有限知识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、发育和数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)。番茄由于其相对较小和高质量的参考基因组以及可获得重新测序的基因组而提供了一个强大的系统。通过将SV检测算法应用于来自数百个种质的现有短读Illumina测序数据，将选择40多个基因组，捕捉大部分预测的SV多样性，以使用最新的长读测序技术(PacBio和10X基因组学)建立新的参考基因组。根据这些数据，将生成经过验证的SVS的概要，并将其与正在进行的全基因组关联研究整合在一起。将使用CRISPR/Cas9基因编辑和定量表型分析来表征重要的与基因相关的SVS，包括那些通过全基因组转录谱测量的影响基因活性的SVS，重点是推动作物生产力的生殖性状。同时，CRISPR/Cas9基因编辑将用于在两个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质量基因的SV突变集合，目的是实现驯化和比较发育遗传学研究的主要步骤。该项目将极大地扩展我们对番茄基因组多样性的了解，并为剖析其他作物的SVS提供一个路线图，这样的知识可以被利用来提高生产力。

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.