Statistical Models for Mapping Genetic and Environmental Effects Regulating Shape Variation

进化和自然选择在微生物、植物、昆虫、动物和人类之间产生了非凡的形状和形式多样性。这个项目开发了新的统计方法来了解遗传和环境因素如何结合联合收割机来影响植物和动物的形态。叶片形态是植物对环境刺激敏感的一种重要现象，对植物的生长发育具有重要意义。研究叶子的形状可以更好地理解生态学、进化和大气-生物圈的相互作用和影响（例如，气候变化）。 该项目的重点是物种拟南芥，一种小型开花欧亚植物，由于其基因组的性质和遗传数据的可用性而常用于植物遗传学研究。该项目促进了统计学、图像分析、遗传学、生物学和计算机科学方面的研究人员和学生之间的跨学科合作和培训。该项目所产生的工具适用于古气候学、人类学、农业、发育生物学、进化、生态学和生物医学等更广泛的数据密集型和分析研究。在该项目中，拟南芥植物在生长室中的受控条件下培养，照片记录了成熟叶片的形状。然后使用高维曲线来创建单个叶子形状的精确表示。每个形状的唯一描述，一个两阶段的统计模型，集成贝叶斯套索和功能数据分析，被用来检测重要的基因，调节形状。选择一个子集的遗传标记，单核苷酸多态性（SNP），显着影响形状和响应环境因素。然后使用高维曲线建模来获得候选SNP标记的详细遗传和生物学功能的知识。将评估叶片形状的高维数据分析与数千个SNP标记的检查相结合需要计算密集型方法，无论是在算法还是数据管理方面。开发的工具将以用户友好的R和Matlab软件包提供，这些软件包专为高性能计算环境而设计，供对形状分析有类似兴趣的研究人员广泛使用。

Evolution and natural selection have produced an extraordinary diversity in shape and form among microbes, plants, insects, animals, and humans. This project develops new statistical methods for understanding how genetics and environmental factors combine to influence plant and animal morphology. Leaf morphology is a key plant phenomenon that is sensitive to environmental stimulus and is important for plant growth. Studying leaf shape leads to a better understanding of ecology, evolution and atmosphere-biosphere interactions and implications (e.g., climate change). This project focuses on the species Arabidopsis thaliana, a small flowering Eurasian plant that is commonly used in plant genetics studies because of the nature of its genome and the availability of its genetic data. This project promotes interdisciplinary cooperation and training between investigators and students in statistics, image analysis, genetics, biology, and computer science. The tools produced by this project are applicable for broader data-intensive and analytic research in paleoclimatology, anthropology, agriculture, developmental biology, evolution, ecology, and biomedicine.In this project, the Arabidpsis thaliana plant is cultivated under controlled conditions in a growth chamber, and photographs document the shapes of mature leaves. Then high-dimensional curves are used to create an accurate representation of individual leaf shape. With each shape uniquely described, a two-stage statistical model, integrating Bayesian Lasso and Functional Data Analysis, is utilized to detect significant genes that regulate shape. A subset of genetic markers, single nucleotide polymorphisms (SNPs), which significantly effect shape and respond to environmental factors, is selected. Then the high-dimensional curve modeling is used to gain knowledge of the detailed genetic and biological functions of the candidate SNP markers. Combining high-dimensional data analysis that assesses leaf shape with the examination of thousands of SNP markers requires computationally intensive methods, both with respect to algorithms and data management. The tools developed will be made available in user-friendly R and Matlab packages designed for high-performance computing environments, for broad use by researchers with similar interests in shape analysis.