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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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University of Michigan Ann Arbor
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