ArticleFrontiers in physiology2011
Genotype-Phenotype Map Characteristics of an In silico Heart Cell.
Article in Frontiers in physiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Creation and application of virtual patient cohorts of heart models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits.Journal of applied genetics · 2017Article
- Towards causally cohesive genotype-phenotype modelling for characterization of the soft-tissue mechanics of the heart in normal and pathological geometries.Journal of the Royal Society, Interface · 2015Article
- Cell-specific cardiac electrophysiology models.PLoS computational biology · 2015Article
- Quantifying inter-species differences in contractile function through biophysical modelling.The Journal of physiology · 2015Article
- Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.BMC systems biology · 2014Article
- Bridging the genotype-phenotype gap: what does it take?The Journal of physiology · 2013Review
- Effect of regulatory architecture on broad versus narrow sense heritability.PLoS computational biology · 2013Article
- Interpreting genetic effects through models of cardiac electromechanics.American journal of physiology. Heart and circulatory physiology · 2012Review
- Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models.BMC systems biology · 2012Article
- Parameters in dynamic models of complex traits are containers of missing heritability.PLoS computational biology · 2012Article
- Virtual patients and sensitivity analysis of the Guyton model of blood pressure regulation: towards individualized models of whole-body physiology.PLoS computational biology · 2012Article
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Authors and funding
8 authors.
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Abstract
Understanding the causal chain from genotypic to phenotypic variation is a tremendous challenge with huge implications for personalized medicine. Here we argue that linking computational physiology to genetic concepts, methodology, and data provides a new framework for this endeavor. We exemplify this causally cohesive genotype-phenotype (cGP) modeling approach using a detailed mathematical model of a heart cell. In silico genetic variation is mapped to parametric variation, which propagates through the physiological model to generate multivariate phenotypes for the action potential and calcium transient under regular pacing, and ion currents under voltage clamping. The resulting genotype-to-phenotype map is characterized using standard quantitative genetic methods and novel applications of high-dimensional data analysis. These analyses reveal many well-known genetic phenomena like intralocus dominance, interlocus epistasis, and varying degrees of phenotypic correlation. In particular, we observe penetrance features such as the masking/release of genetic variation, so that without any change in the regulatory anatomy of the model, traits may appear monogenic, oligogenic, or polygenic depending on which genotypic variation is actually present in the data. The results suggest that a cGP modeling approach may pave the way for a computational physiological genomics capable of generating biological insight about the genotype-phenotype relation in ways that statistical-genetic approaches cannot.
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