Towards causally cohesive genotype-phenotype modelling for characterization of the soft-tissue mechanics of the heart in normal and pathological geometries.
Towards causally cohesive genotype-phenotype modelling for characterization of the soft-tissue mechanics of the heart in normal and pathological geometries.
复制标题
建立因果内聚的基因型-表型模型，用于表征正常和病理几何形状中心脏的软组织力学。
DOI：
10.1098/rsif.2014.1166
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Vik,JonOlav
中科院分区：
文献类型：
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作者：
Nordbø,Øyvind;Gjuvsland,ArneB;Nermoen,Anders;Land,Sander;Niederer,Steven;Lamata,Pablo;Lee,Jack;Smith,NicolasP;Omholt,StigW;Vik,JonOlav
对个体变异的科学理解是个性化医疗的关键，通过计算生理学整合基因型和表型信息。遗传效应通常是依赖于环境的，在遗传背景或生理状态（如疾病）之间存在差异。在这里，我们分析silicogenotype-phenotype地图（GP地图）的一个软组织力学模型的被动膨胀阶段的心跳，对比的微观结构和其他低级别的参数的影响，假设是遗传的影响，在正常的，同心肥大和偏心肥大的几何形状。对于大量的参数的情况下，代表模拟遗传变异的低水平参数，我们计算的表型描述膨胀过程中的心脏变形。GP图的特征在于每个表型相对于每个参数的方差分解。正如假设的那样，同心几何形状允许更多的低水平参数有助于形状表型的变化。此外，整体刚度和纤维刚度的相对重要性在几何形状之间不同。否则，GP图对于不同的心脏几何形状在很大程度上是相似的，在本研究中包括的参数之间几乎没有遗传相互作用。我们认为，个性化医疗可以受益于因果关系的凝聚力的基因型-表型模型的组合，和战略表型，捕捉效果修饰剂没有明确包括在机械模型。
A scientific understanding of individual variation is key to personalized medicine, integrating genotypic and phenotypic information via computational physiology. Genetic effects are often context-dependent, differing between genetic backgrounds or physiological states such as disease. Here, we analysein silicogenotype–phenotype maps (GP map) for a soft-tissue mechanics model of the passive inflation phase of the heartbeat, contrasting the effects of microstructural and other low-level parameters assumed to be genetically influenced, under normal, concentrically hypertrophic and eccentrically hypertrophic geometries. For a large number of parameter scenarios, representing mock genetic variation in low-level parameters, we computed phenotypes describing the deformation of the heart during inflation. The GP map was characterized by variance decompositions for each phenotype with respect to each parameter. As hypothesized, the concentric geometry allowed more low-level parameters to contribute to variation in shape phenotypes. In addition, the relative importance of overall stiffness and fibre stiffness differed between geometries. Otherwise, the GP map was largely similar for the different heart geometries, with little genetic interaction between the parameters included in this study. We argue that personalized medicine can benefit from a combination of causally cohesive genotype–phenotype modelling, and strategic phenotyping that captures effect modifiers not explicitly included in the mechanistic model.