Statistical Models for Studying the Genetic Architecture of Dynamic Traits

研究动态性状遗传结构的统计模型

• 批准号: 0540745
• 负责人: George Casella
• 金额: --
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-15 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540745&HistoricalAwards=false
• 关键词: Statistical; Models; Studying; Genetic; Architecture

Statistical Models for Studying the Genetic Architecture of Dynamic Traits (0540745)Rongling Wu and George CasellaAbstractMany traits of economic and biomedical importance, such as seed size, milk yield, cancer growth, HIV/AID progression and drug response, change their phenotypes with time or other variables in a certain pattern. Traditional statistical models for genetic studies of these traits by analyzing phenotypic data measured at a single point are too simple to take into account their developmental or dynamic trajectories. In this project, Wu and Casella will develop a collection of statistical models for detecting genetic loci that govern dynamic complex traits, in which mathematical aspects of trait formation and development are integrated into a statistical framework for genetic mapping. Wu and Casella's models provide a useful platform to ask, disseminate and address biologically meaningful questions regarding the interplay between gene action and development across a range of physiological or ecological contexts. The depth and scope of scientific inference about the genetic architecture of complex traits can be greatly improved by using their models. With the availability of a complete reference sequence of the entire genome for humans and other species, Wu and Casella's models will be useful to extract genetic information and unravel genetic secrets hidden in increasingly accumulating data that are often high-dimensional and display complex dynamic structure.

研究动态性状遗传结构的统计模型（0540745）吴荣玲和乔治Casella摘要许多具有经济和生物医学重要性的性状，如种子大小、产奶量、癌症生长、HIV/AID进展和药物反应，以一定的模式随时间或其他变量改变其表型。 传统的统计模型通过分析在单个点测量的表型数据来进行这些性状的遗传研究过于简单，无法考虑它们的发育或动态轨迹。在这个项目中，Wu和Casella将开发一系列统计模型，用于检测控制动态复杂性状的遗传位点，其中性状形成和发展的数学方面被整合到遗传作图的统计框架中。Wu和Casella的模型提供了一个有用的平台，可以询问、传播和解决有关基因作用与发育之间在一系列生理或生态背景下相互作用的生物学意义问题。通过使用它们的模型，可以大大提高关于复杂性状遗传结构的科学推断的深度和广度。随着人类和其他物种整个基因组的完整参考序列的可用性，Wu和Casella的模型将有助于提取遗传信息，并揭开隐藏在日益积累的数据中的遗传秘密，这些数据通常是高维的，并显示复杂的动态结构。