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，用于研究影响其他基因组相互作用的基因进化。这一理论有助于理解基因组在产生表型时预期会变得模块化的条件，以及不同基因突变之间的协同作用何时应该通过进化产生。我们将研究有害突变之间的相互作用模式如何作为突变率、重组率和每个突变的适应度损失程度的函数而演变。我们还将研究影响文化传播性状的基因之间统计关联的建立，这些性状通过传播或适应性相关联。第二个研究领域涉及病原体的演变，如流感。在这里，我们建立模型来预测流行病和大流行病期间突变的积累，以帮助指导疫苗接种策略。我们将开发算法，在多达1000个病毒序列的集合中搜索潜在的重组体和重组体。在这里，我们的目标是设计一个统计测试，以表明是否确定重组或revertants是假的或统计学显着。我们最近的生态位构建理论将成为研究病原体如何进化以应对它们诱导的人类行为（如使用抗生素）的基础。最后的研究领域将开发多基因模型的性连锁控制的基因组印记。这些将包括顺式和反式印迹改性剂。将研究生育力选择以及性别特异性活力，以澄清多个父系的作用，这已被提出作为基因组印记进化的驱动力。将使用统计学习工具分析哺乳动物物种的基因组数据，以预测哪些基因可能被印记。也将寻求基于生活史特征的DMA序列属性和预测的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.