Risclab

Ah I see. One last question for the case of gene interactions as specified in BioGRID where mutliple gene variants determine the outcome of a specific phenotype. Whats the general approach for these. I mean conducting epistasis analysis or exploiting naturally occuring perturbation (eQTL data) is statistical framwork is an oppurtunity to infer these. However, how about simple gene expression analysis or gene perturbation screens? It is possible to infer these interactions without exact genetic information? And how about statistical methods for them? Boolean networks, Bayesian networks, SEM. What are the current methodologies?

Ah ok. Its basically an abstraction into the gene space? So thats the same when I consider epistatic interactions? So what are the statistical methods to infer these gene interaction networks? Can I infer these networks from expression data alone?

Ah so the genetic interaction is more like an functional relationship, where I ignore the protein interactions and just look at the effect of the genes in terms of the phenotype?

Hello, thank you for your answer. So when Im talking about gene network its just a gene regulatory network transferred to the gene space? Lets consider for example the Biogrid database. It lists physical and genetical interactions. Whats the difference?

Hello there, its hard for me to grasp what is the difference between genetic interaction networks and gene regulatory networks? I mean after all the interaction happens about the interaction via proteins? Another question Im having is the inference of genetic interaction networks. What are the statistical tools for it and more importantly what data is necessary for it? I mean genetical genomics is a prospering field, but is the inference possible with expression data alone? Thanks R