Phylogeny and Genome Evolution of the Andropogoneae (Poaceae)

美国最重要的粮食和生物燃料作物都是密切相关的，属于一个被称为Andropogoneae的植物群。 玉米是美元价值最高的作物，与高粱和甘蔗、潜在的生物燃料芒草以及大约1000种其他野生物种有关。 因为玉米和高粱的整个基因组已经测序，我们可以将这些作物的信息转化为野生物种，相反，我们可以研究野生物种，以帮助开发更好的作物。 该项目将使用DNA序列来确定群体成员之间的关系，然后使用这些信息来评估他们的基因组是如何进化的。 基因组加倍在这个群体中非常常见，可能使该物种能够殖民新的栖息地并发展新的生长形式。 这项工作将阐明作物育种者迫切需要养活地球的未开发的遗传资源。该项目的数据可用于作物改良，用于确定新的生物燃料来源，并用于了解美国和非洲草原对气候变化的反应。 对本科生和研究生进行草分类学、DNA数据分析和植物基因组进化方面的培训是该项目的另一部分。 该项目的教育组成部分包括本科实验室助理的参与，在UMSL高中生暑期项目的参与，并在植物学会的PlantingScience计划的参与。 此外，我们将围绕该项目的分析组件开发研究生计算课程，以便开始研究生可以直接对序列数据的分析做出贡献。

The most important crops in the U.S. for food and biofuel are all closely related, members of a plant group known as Andropogoneae. Corn (maize), the crop with the highest dollar value, is related to sorghum and sugarcane, the potential biofuel Miscanthus and about 1000 other wild species. Because the entire genomes of corn and sorghum have been sequenced, we can translate information from these crops to wild species and conversely, we can study wild species to help develop better crops. This project will use DNA sequences to determine how members of the group are related to each other, and then will use that information to assess how their genomes have evolved. Genome doubling is very common in this group, and may have allowed the species to colonize new habitats and to develop new growth forms. This work will illuminate untapped genetic resources that crop breeders desperately need to feed the planet.Data from this project can be used for crop improvement, for identifying new sources of biofuels, and for understanding the response of American and African grasslands to climate change. Training of undergraduate and graduate students in grass taxonomy, analysis of DNA data, and plant genome evolution is another part of this project. Education components of the project include involvement of undergraduate lab assistants, participation in the summer program for high school students at UMSL, and involvement in the Botanical Society's PlantingScience program. In addition, we will develop a graduate computational course built around the analytical components of this project, so that beginning graduate students can contribute directly to the analysis of sequence data.