Population Genetics Theory

描述(由申请者提供)：将学习四个理论种群遗传学领域。首先，我们将开发数学和统计框架来研究影响其他基因相互作用的基因的进化。这一理论有助于理解基因组在什么情况下可以预期成为其表型生产的模块化，以及不同基因的突变之间的协同应该在什么时候通过进化产生。我们将研究有害突变之间的相互作用模式是如何作为突变率、重组率和每个突变的适应度损失程度的函数而演变的。我们还将研究影响文化传播特征的基因之间统计关联的建立，这些基因通过传递或通过适应性而关联。第二个研究领域涉及流感等病原体的进化。在这里，我们建立模型来预测流行病和大流行期间突变的积累，以帮助指导疫苗接种策略。我们将开发算法，从多达1000个病毒序列中搜索潜在的重组子和重组子。在这里，我们的目标是设计一种统计检验，以表明识别出的重组子或重组子是否虚假或具有统计学意义。我们最近的生态位构建理论将为研究病原体可能如何进化以响应它们诱导的人类行为奠定基础，例如使用抗生素。最后的研究领域将开发多基因模型，用于基因组印记的性别连锁控制。这些将包括印记的顺式和反式修饰剂。育性选择以及性别特异性的存活率将被研究，以澄清多重父本的作用，这已被认为是基因组印记进化的驱动力。来自哺乳动物物种的基因组数据将使用统计学习工具进行分析，以预测哪些基因可能会被印记。还将寻找DMA序列属性和基于生活史特征的预测印记状态之间的相关性。

DESCRIPTION (provided by applicant): Four areas of theoretical population genetics will be studied. In the first, we will develop mathematical and statistical framework for studying the evolution of genes that affect how other sets of genes interact with one another. This theory helps in understanding conditions under which the genome can be expected to become modular in its production of phenotypes, as well as when synergy between mutations in different genes should be produced by evolution. We shall study how the pattern of interaction between deleterious mutations evolves as a function of the mutation rate, the recombination rate, and the degree of fitness loss of each mutation. We shall also study buildup of statistical association between genes that influence culturally transmitted traits that are associated either through transmission or through fitness. The second area of study concerns the evolution of pathogens such as influenza. Here we build models to predict the accumulation of mutations during epidemics and pandemics in a way that can assist in guiding vaccination strategies. We will develop algorithms that search for potential recombinants and reassortants among a collection of up to 1000 viral sequences. Here we aim to devise a statistical test to indicate whether identified recombinants or reassortants are spurious or statistically significant. Our recent theory of niche construction will form the basis of studies of how pathogens might evolve in response to human actions that they induce, such as use of antibiotics. The final research area will develop multiple-gene models for sex-linked control of genomic imprinting. These will include cis and trans modifiers of imprinting. Fertility selection as well as sex- specific viabilities will be studied in order to clarify the role of multiple paternities, which has been proposed as a driving force in the evolution of genomic imprinting. Genomic data from mammalian species will be analyzed using tools from statistical learning in order to predict which genes are likely to be imprinted. Correlations between DMA sequence properties and predicted imprinting status based on life history characteristics will also be sought.