PROJECT 5

该项目的目标是将遗传学理论与多尺度生理模型联系起来的挑战，以解决和理解群体背景下的生理基因组学。个性化医疗的许多挑战反映了对基因型-表型（GP）图谱（见图1.1）缺乏了解，即不同遗传变异（基因型）星座在不同长度和时间尺度上的聚集表型效应。 我们的方法的动机是这样一个事实，即理解一个复杂的性状的GP地图可能需要遗传变异的表型效应的机械模型描述，即先进的多通道生理模型与遗传信息的明确联系。我们使用术语因果内聚基因型-表型模型来描述遗传变异如何在各种系统水平直至组织、器官和整个生物体水平上表现为表型变异的模型[19]。因果内聚基因型表型模型是从复合映射中出现的计算机表示，首先从基因型到模型参数，其次从模型到表型，表型又可以是覆盖模型结构中的参数。(See图1.1.）这种方法使得解决一系列复杂现象成为可能，如遗传显性，上位性，互作性，多效性，隐性变异和基因型/环境相互作用，其方式超出了经典统计方法的范围[16-22]。结合该项目中开发的非常先进的模型，这种方法将提供有关遗传变异和复杂疾病之间关系的新的重要信息。

This project targets the challenge of connecting genetics theory with multi-scale physiological models, to address and understand physiological genomics in a population context. Many challenges in personalized medicine reflect a lack of understanding of the genotype-to-phenotype (GP) map (see Figure 1.1), i.e. the aggregated phenotypic effects across different length and time scales of different constellations of genetic variation (genotypes). Our approach is motivated by the fact that understanding the GP map for a complex trait is likely to require mechanistic model descriptions of the phenotypic effects of genetic variation, i.e. advanced multiseale physiological models with an explicit link to genetic information. We use the term causally cohesive genotype-phenotype models for models describing how genetic variation manifests in phenotypic variation at various systemic levels up to the tissue, organ and whole-organism level [19]. Causally cohesive genotype phenotype models are in silico representations that emerge from a composite mapping, first from genotypes to model parameters, and second from models to phenotypes, which may in turn be parameters in overlying model structures. (See Figure 1.1.) This approach makes it possible to address a whole range of complex phenomena, like genetic dominance, epistasis, penetrance, plelotropy, cryptic variation and genotype/environment interactions in ways that are beyond reach of classical statistical approaches [16-22]. In combination with the very advanced models to be developed in this project, this approach will provide novel and important information about the relationships between genetic variation and complex diseases.