Nordbø Øyvind, Gjuvsland Arne B, Nermoen Anders, Land Sander, Niederer Steven, Lamata Pablo, Lee Jack, Smith Nicolas P, Omholt Stig W, Vik Jon Olav
Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway.
Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway.
J R Soc Interface. 2015 May 6;12(106). doi: 10.1098/rsif.2014.1166.
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 analyse in silico genotype-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.
对个体差异的科学理解是个性化医疗的关键，即通过计算生理学整合基因型和表型信息。基因效应通常依赖于背景，在不同的遗传背景或生理状态（如疾病）之间存在差异。在此，我们针对心跳被动充盈期的软组织力学模型分析了计算机模拟的基因型-表型图谱（GP图谱），对比了在正常、向心性肥厚和离心性肥厚几何结构下，假定受基因影响的微观结构和其他低水平参数的效应。对于大量代表低水平参数模拟基因变异的参数情景，我们计算了描述心脏在充盈过程中变形的表型。GP图谱通过每个表型相对于每个参数的方差分解来表征。如所假设的，向心性几何结构允许更多低水平参数对形状表型的变异产生影响。此外，整体刚度和纤维刚度的相对重要性在不同几何结构之间有所不同。否则，不同心脏几何结构的GP图谱在很大程度上是相似的，本研究中所包含的参数之间几乎没有基因相互作用。我们认为，个性化医疗可以从因果关系紧密的基因型-表型建模与战略性表型分析的结合中受益，战略性表型分析能够捕捉机制模型中未明确包含的效应修饰因子。
