The genetic predispositions of many complex human diseases, stress responses and ageing are proving difficult to uncover due to involvement of many different genes and because these gene groups interact with the environment. Our approach to resolving this problem is to identify gene networks by mapping the properties of genetic stress-responses onto graphical representations of the underlying network. This approach will be powered by taking advantage of natural genetic variation among individuals in respect of disease susceptibility and ageing. Compared to other network approaches, this focuses attention more specifically on functionally important gene-gene interactions and the gene regulatory networks.
For this we shall take advantage of a powerful model genetic system presented by the nematode worm, Caenorhabditis elegans. Approximately 200 genetic mosaic lines have been created by crossing genetically-divergent parental strains. These present a wide spectrum of responses to stress exposure, disease susceptibility and longevity. The gene regulatory properties of each line will be determined in response to stress treatment using DNA microarrays, thereby providing a detailed response profile for all lines across the 17,000 known genes. Detailed genetic mapping of these gene expression traits allows the identification of the regulatory genetic locus and ultimately the gene regulating the trait and associated genes.
The gene regulatory interactions that affect the relevant biomedical phenotypes will be mapped onto existing depictions of gene interactions. New network models will be developed which will suggest an additional set of gene perturbation tests, the outcome of which will further refine the network model. This iterative loop of directed gene perturbation experiments, network refinement and model prediction is a key means of leveraging our understanding of such complex systems. This functional, genetic approach contrasts with previous protein-protein interaction or gene co-expression interaction maps used to date. In particular, we seek to identify large-scale connectivity patterns between genes that re-occur at multiple sites across the network.