Genetics is defined as the science of genes, heredity, and the variation of organisms. Gaining a real understanding of the variation of organisms as a function of genes and environment in a mechanistic sense, i.e understanding the genotype-phenotype map (GP map) - is a tremendous challenge that awaits technological, conceptual and methodological breakthroughs. But this is where we have to go if we aim for a future genetics theory that bridges the genotype-phenotype gap by both generic and specific causal explanatory models. The impact of a mature genetics theory such as this on production biology, evolutionary biology and biomedicine can hardly be overstated. Recent breakthroughs concerning large-scale, high-throughput genotyping and phenotyping instrumentation and methodological means to model very complex biological structures based on lower-level processes suggest that it is not premature to make heuristic use of this vision in terms of research programme objectives. The establishment of such a theory will of necessity have to involve the extensive use of mathematics, statistics, informatics and biological physics guided by biological data in the broader sense, it will force new developments within these disciplines, and it will have to involve very advanced eInfrastructures.
The cgptoolbox aims to facilitate researchers’ entry into cGP modeling by providing a cGP modelling framework in a population context, integrating and interfacing with existing VPR and VPH tools. The toolbox will provide the means for extensive explorative in silico studies as well as integration of patient-specific information in multiscale models to account for the individual’s genotype in the model parameterisation process. It adds to the VPH Toolkit by integrating genetic structure information, bioinformatic information and infrastructure and multiscale and multiphysics models and associated infrastructure. The strength of the cgptoolbox as a relevant research tool will be illustrated by specific examples of use:
- as an explorative tool for better understanding of key genetic concepts like dominance, epistasis, pleiotropy, penetrance and expressivity in biologically realistic complex trait situations and in a patient-specific perspective;
- to elucidate the fine structure of the distribution of individuals in a high-dimensional phenotypic landscape associated with a pathological condition as a function of genetic variation;
- as a test bed for developing new fine mapping methodologies within statistical genetics aimed at exploiting high-dimensional phenotypic information.
The cgptoolbox is a step towards providing computational tools for attaching GP maps of parameters to a multiscale modelling framework in order to handle patient-specific issues. We think this is an important delivery preparing for a future situation where acquisition of high-dimensional phenotypic data from patients become routine (phenomics) and the VPR and VPH communities have come closer to the key goal of achieving more integration across multiple spatial and temporal scales.