Computational Methods for Detecting Natural Selection using Comparative Population Genomic Data

使用比较群体基因组数据检测自然选择的计算方法

• 批准号: 0516310
• 负责人: Carlos Bustamante
• 金额: --
• 依托单位: Cornell Univ - State: AWDS MADE PRIOR MAY 2010
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0516310&HistoricalAwards=false
• 关键词: Computational; Methods; Detecting; Natural; Selection

A central problem in evolutionary genetics is identifying genetic differences among populations of plants and animals that are biologically significant. For example, of all the genetic differences between humans and chimpanzees a few are biologically important and confer our ability to speak and walk upright while others have no discernable effect and are due to the random fixation of chance mutations since the divergence from a common ancestor. Likewise, there is tremendous interest in identifying mutations segregating in natural populations that are deleterious / disruptive of biological function. Since even for closely related species there are likely to be millions of genetic differences (most of which are likely non-significant), computational tools are needed for identifying those that are most important or interesting. The goal of this project is to develop statistical models for comparing patterns of genetic variation within and between species in order to identify which changes fall into adaptive (beneficial), neutral, or deleterious categories. The scope of the project is focused on development of mathematical models and statistical methods for fitting the models to the growing amount of genetic data. This project will focus on analyzing variation within populations of humans, Drosophila (fruit fly), and other ``model'' organisms where large data sets are being collected by other groups.The broader significance of this work is the development of computer programs that can be used by other scientists to apply the methods/ models developed to their data. Doctoral and post-doctoral students will also be trained as a result of working on these research projects. Special emphasis will be placed on involving historically underrepresented minority graduate students in statistical genetic research. The scientific importance of this work is the potential discovery of meaningful genetic differences between species including humans and their closest relatives.

进化遗传学的一个中心问题是确定植物和动物种群之间具有生物学意义的遗传差异。 例如，在人类和黑猩猩之间的所有遗传差异中，有几个在生物学上是重要的，赋予我们说话和直立行走的能力，而其他人则没有明显的影响，是由于从共同祖先分化而来的随机突变的随机固定。 同样，人们对鉴定在自然群体中分离的对生物功能有害/破坏的突变非常感兴趣。由于即使是密切相关的物种也可能存在数百万个遗传差异（其中大多数可能不重要），因此需要计算工具来识别那些最重要或最有趣的差异。该项目的目标是开发统计模型，用于比较物种内部和物种之间的遗传变异模式，以确定哪些变化属于适应性（有益），中性或有害类别。 该项目的范围侧重于开发数学模型和统计方法，以便使模型适应日益增多的遗传数据。这个项目将集中于分析人类、果蝇和其他"模型“生物种群内的变异，这些生物种群的大量数据集是由其他小组收集的。这项工作的更广泛意义是开发计算机程序，供其他科学家使用，将开发的方法/模型应用于他们的数据。 博士生和博士后学生也将通过这些研究项目接受培训。 将特别强调让历史上代表性不足的少数民族研究生参与统计遗传学研究。 这项工作的科学重要性在于可能发现物种之间有意义的遗传差异，包括人类及其近亲。