Mapping and predicting genetic interactions in C. elegans development
by
Andrew Fraser(Donnelly Centre, Toronto University)
→
Europe/Stockholm
RB35 (RB35)
RB35
RB35
Seminar room RB35 (Roslagstullsbacken 35, the SBC house)
Description
Most heritable traits, including susceptibility to disease, are affected by the interactions between multiple genes; however, we still understand little about how genes interact to generate phenotypes, and it is certain that we have identified only a small fraction of the phenotypically relevant genetic interactions in any organism. C. elegans is a simple, tractable animal model; if we can understand how genes interact in this animal, this will have key lessons for far more complex animals like humans. I will report two approaches that we are taking to understand genetic interactions in the worm; the first is the systematic experimental mapping of genetic interactions in a relatively unbiased manner; the second is the computational prediction of genetic interactions via integration of diverse large datasets.
In the first approach, we have used high throughput RNAi screening to identify genetic interactions between genes that regulate development of the metazoan C. elegans. We identify ~350 genetic interactions for genes that function in the EGF, Notch, Wnt and other signalling pathways. This is the first global genetic interaction map constructed for any animal and will act as a platform for future mechanistic studies. The genetic interaction network contains several highly connected hub genes; loss of these genes enhances the phenotypic consequences of mutations in components of the majority of examined signalling pathways. The hub genes all encode components of chromatin-modifying complexes, and we find that their activity as genetic buffers appears conserved in other animals. We propose that these chromatin-modifying complexes may function as general buffers of genetic variation and that alterations in their activity may play a significant role in human genetic disease.
In the second, computational approach, we use a modified Bayesian approach to integrate a range of large-scale datasets into a single genetic interaction network. Each dataset identifies functional linkages between individual genes; they include physical interaction maps, co-expression data, and informatic linkages such as gene fusion events. While each dataset is incomplete and noisy, this statistical-based integration yields a network that predicts over 100,000 linkages between genes and covers over 60% of the worm proteome. We show that we can use this network to make accurate predictions of gene interactions including those that are highly tissue-specific. Integrating such noisy, complex datasets to generate accurate predictions of the effects of gene perturbation, as we have here, holds great promise for human biology where predicting the effects of drugs or inherited mutations will be a key problem in the future.
