Evolution of Gene Expression in Yeast

描述（由申请人提供）：基因表达的遗传差异是常见的，并有助于物种之间、同一物种的个体之间，有时甚至是同一个体的细胞之间的表型多样性。这种多样性包括健康个体之间的“正常”变异以及导致疾病的“异常”变异。尽管它们的重要性，负责基因表达差异的遗传变化和影响它们的进化过程还没有得到很好的理解。其中一个原因是，人们对调控变异的突变输入知之甚少，选择和漂移正是基于这种突变输入。 在野生环境中产生调节多态性和趋异的模式。该项目将通过以下方式帮助填补这一知识空白：（1）收集和表征影响焦点基因活性的新突变，（2）识别和表征物种内作为多态性分离的遗传变异，以及（3）比较这些突变的特性与适应性以及突变和多态性之间的特性。这些属性将包括：突变类型（编码或非编码），效应大小，网络位置，显性和多效性。利用这些信息，不同的假设将测试这些突变特性对新的调控突变的进化命运的影响。这项工作将促进我们的理解的分子机制的监管（和大概表型）的差异，以及提供一个经验基础，在未来开发更现实的监管进化模型。

DESCRIPTION (provided by applicant): Heritable differences in gene expression are common and contribute to phenotypic diversity between species, between individuals of the same species, and sometimes even between cells of the same individual. This diversity includes "normal" variation among healthy individuals as well as "abnormal" variation causing disease. Despite their importance, the genetic changes responsible for difference in gene expression and the evolutionary processes that affect them are not well understood. One reason for this is that little is known about the mutational input for regulatory variation, upon which selection and drift act to produce patterns of regulatory polymorphism and divergence in the wild. This project will help fill this knowledge gap by (1) collecting and characterizing new mutations affecting activity of a focal gene, (2) identifying and characterizing genetic variants segregating as polymorphisms within a species, and (3) comparing properties of these mutations with fitness and between the sets of mutations and polymorphisms. These properties will include: the type of mutation (coding or noncoding), effect size, network position, dominance, and pleiotropy. Using this information, diverse hypotheses will be tested about the effects of these mutational properties on the evolutionary fate of new regulatory mutations. This work will advance our understanding of the molecular mechanisms underlying regulatory (and presumably phenotypic) differences, as well as provide an empirical foundation for developing more realistic models of regulatory evolution in the future.