Enabling Cold Tolerant Maize Using Genomic and Machine Vision Phenomic Approaches

派：埃德加·斯伯丁(威斯康星大学麦迪逊分校)Copis：Nathan Springer(明尼苏达大学)；Irina Makarevitch(Hamline大学)；Tessa Durham-Brooks(多恩学院)；A.Mark Settle(佛罗里达大学)合作者：Natalia de Leon(威斯康星大学麦迪逊分校)；Nathan Miller(威斯康星大学麦迪逊分校)；Jeffery Gustin(佛罗里达大学)；Gokhan Hacisalihoglu(佛罗里达农业与农业大学；机械大学)在美国北部的大部分地区，寒冷的春季气温可能会阻碍种植的玉米种子成功转化为茁壮成长的幼苗。春季天气变化无常，不利于农民提早种植。该项目旨在识别自然发生的基因变异，这些变异使一些玉米植株在生命周期的这个关键时期比其他玉米植株更耐冷。基因变异将首先使用实验室正在开发的全新成像和计算工具进行识别。通过使用基于计算机的方法对植物对寒冷的反应进行成像和测量，该项目将获得有关种子如何在不同的寒冷条件下吸收水分、发芽、从土壤中萌发和生长的信息，这些信息本来是模糊的。分子水平的差异将使用将寒冷反应与遗传变异联系起来的基因组方法来识别。然后，将通过比较携带不同版本已识别遗传元素的植物的生长和产量，在早期种植的玉米田里测试关于这些耐寒候选植物的遗传假设。来自不同机构的本科生，包括农村和城市地区，将通过直接参与各级数据生成和分析来接受培训。学生的关键作用将导致一个具有高度整合的研究和教育成果的项目。例如，学生主导的实验将在教育玉米田地进行，进一步促进学生学习科学方法，同时提供关键的研究数据。该项目旨在通过识别玉米抗寒的遗传基础，并通过培训下一代将这些知识应用于现实世界的情况，对美国农业的一个重要限制产生积极影响。该研究计划整合了不同种群中的自然变异、先进的基因组学技术和定制的机器视觉方法，以捕捉系统层面上对寒冷胁迫的复杂反应的理解。群体将包括重组自交系、近等基因系和加倍单倍体，并包括嵌套的协会图谱群体和威斯康星州多样性小组的父母。玉米的耐冷等位基因将通过全基因组关联和QTL分析进行鉴定，有关冷反应表型的遗传结构和遗传模式的假说将得到严格检验。目标1将改进机器视觉方法来测量发芽表型，而目标2将专注于幼苗生长过程中的低温胁迫。目的3将确定与冷处理相关的转录组和染色质反应，以确定影响耐受性的表观遗传调控基因和靶点。目的4将在田间试验中确定冷应激反应的不同等位基因对生活史性状的影响，以确定早季耐冷性是否转化为产量的提高。数据将通过项目网站http://phytomorph.wisc.edu，获得，序列数据将上传到国家生物信息学研究所，数据集将通过玉米社区数据库http://maizegdb.org和数量性状基因档案http://qtlarchive.org.获得

PI: Edgar Spalding (University of Wisconsin-Madison)CoPIs: Nathan Springer (University of Minnesota); Irina Makarevitch (Hamline University); Tessa Durham-Brooks (Doane College); A. Mark Settles (University of Florida)Collaborators: Natalia de Leon (University of Wisconsin-Madison); Nathan Miller (University of Wisconsin-Madison); Jeffery Gustin (University of Florida); Gokhan Hacisalihoglu (Florida Agricultural & Mechanical University)Throughout much of the northern United States, cold spring temperatures can impede the successful transition of planted corn seed into vigorously growing seedlings. Increasingly variable spring weather works against the benefits to the farmer of planting early. This project is designed to identify naturally occurring variations in genes that make some corn plants more resistant to cold than others at this critical period in the life cycle. Genetic variants will be identified first using all-new imaging and computational tools being developed in the laboratory. By imaging and measuring plant responses to cold using computer-based methods, the project will access otherwise obscure information about how seeds take up water, germinate, emerge from the soil and grow under varying cold conditions. Molecular level differences will be identified using genomic methods that associate cold responses to genetic variants. Genetic hypotheses about these cold hardy candidates will then be tested in early-planted corn fields by comparing growth and yield in plants carrying different versions of the identified genetic elements. Undergraduate students from diverse institutions, including rural and urban locations, will be trained through direct participation at all levels of data generation and analysis. The critical role of students will result in a project with highly integrated research and education outcomes. For example, student-lead experiments will be conducted on educational corn field sites, further advancing student learning about scientific methods while at the same time providing critical research data. The project stands to make a positive impact on an important limitation to US agriculture by identifying the genetic basis of cold-resistance in corn and by training the next generation to apply the knowledge to real world situations.The research plan integrates natural variation in diverse populations, advanced genomics techniques, and custom machine-vision methods to capture a systems-level understanding of the complex responses to cold stress. Populations will include recombinant inbred lines, near isogenic lines and doubled haploids, and includes parents of the Nested Association Mapping population and the Wisconsin Diversity Panel. Cold tolerant alleles in maize will be identified with genome wide association and QTL analyses, and hypotheses about the genetic architecture and inheritance patterns of cold response phenotypes will be rigorously tested. Aim 1 will refine machine vision methods for measuring germination phenotypes, while Aim 2 will focus on chilling stress during seedling growth. Aim 3 will determine transcriptome and chromatin responses associated with cold treatment to identify genes and targets of epigenetic regulation that affect tolerance. Aim 4 will determine the impacts of contrasting alleles for cold stress response on life cycle traits in field experiments to determine if early season cold tolerance translates to improved yield. Data will be available through the project website http://phytomorph.wisc.edu, sequence data will be uploaded to NCBI, and datasets will be accessible through the maize community database http://maizegdb.org and QTL archive http://qtlarchive.org.