Genotype-Phenotype Maps and Signatures of Selection in Genomic Sequences

基因型-表型图谱和基因组序列选择特征

• 批准号: 221857062
• 负责人: Professor Dr. Peter Florian Stadler
• 金额: --
• 依托单位: Lehrstuhl für Bioinformatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/221857062?language=en
• 关键词: Genotype; Phenotype; Maps; Signatures; Selection

Natural selection acts on the phenotype while genetic variation is generated at the genotypic level. The connection between these two levels is the genotype-phenotype map (GP-map), which entails both molecular interactions of gene products and the regulatory networks built upon them. Since GP-map is a major determinant of the fitness-effect of genetic variants, its structural features strongly impact both on the observable variation within a population and on the pattern of substitutions separating the genomic sequences of distinct species. The goal of this project is to systematically study this influence of the GP map. To this end, we will devise a suitable mathematical framework, construct and analyze probabilistic models for concrete scenaria, and perform large-scale computer simulations. Since genotypic fitness is understood as the composition of the GP map and a phenotypic fitness function, the GP map generically introduced neutrality into the fitness landscape. The geometric structure of these neutral sets affects substitution patterns under stabilizing selection as well as the accessibility of advantageous innovations. As a practical outcome we aim at the development of statistical procedures to assess selective constraints in the large-scale genome-wide data sets that are becoming widely available.

自然选择作用于表型，而遗传变异产生于基因水平。这两个层次之间的联系是基因-表型图谱(GP-MAP)，它既需要基因产物的分子相互作用，也需要建立在它们之上的调控网络。由于GP-MAP是遗传变异适合度效应的主要决定因素，其结构特征对种群内可观察到的变异和分离不同物种基因组序列的替换模式都有很大的影响。本项目的目标是系统地研究GP图的这种影响。为此，我们将设计一个合适的数学框架，构建和分析具体场景的概率模型，并进行大规模的计算机模拟。由于基因适合度被理解为GP图和表型适应度函数的组合，GP图通常将中性引入适应度图景。这些中性集的几何结构影响稳定选择下的替代模式以及优势创新的可及性。作为一个实际结果，我们的目标是开发统计程序，以评估正在变得广泛可用的大规模全基因组数据集中的选择性限制。