Population Genetics Theory

描述（由申请人提供）：将研究理论群体遗传学的四个领域。首先，我们将开发数学和统计框架来研究影响其他基因组如何相互作用的基因进化。这一理论有助于理解在何种条件下基因组可以在表型的产生中变得模块化，以及在什么情况下不同基因的突变应该通过进化产生协同作用。我们将研究有害突变之间的相互作用模式如何演变为突变率、重组率和每个突变的适应度损失程度的函数。我们还将研究影响文化传播特征的基因之间的统计关联的建立，这些特征要么通过遗传，要么通过适应性相关联。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，以一种有助于指导疫苗接种策略的方式。我们将开发算法，搜索潜在的重组和重组的收集多达1000个病毒序列。在这里，我们的目的是设计一个统计测试，以表明是否确定重组或重组是假的或统计显著。我们最近的生态位构建理论将形成病原体如何进化以响应人类行为的研究基础，例如抗生素的使用。最后的研究方向将是发展基因组印记的性别连锁控制多基因模型。这些将包括顺式和反式改性剂的印记。为了阐明多父性在基因组印记进化中的作用，我们将研究生育选择和性别特异性生存能力。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被印迹。还将寻求DMA序列特性与基于生活史特征的预测印迹状态之间的相关性。

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.