8.8 years ago by
Boston, MA USA
Khader, this is an excellent question and I have been giving this some thought on and off throughout the day. Researchers often full into ruts - doing analysis such as yours with GO and KEGG (or Reactome or similar pathways) just like everyone else who has presented at conference or in print, or writing grants in a similar style with similar trendy approaches for similar reasons.
So, is there something else you can do?
With the emerging data linking GWAS to eQTL, I would suggest looking at GWAS or similar genetic signals (could be from human GWAS or could be from mouse/rat data and their phenotype affecting, say blood pressure). This is not that different, admittedly, because it boils down to enrichment analysis, but from a different set of genes. Here is a great paper that shows that many cardiovascular disease GWAS signals are also eQTLs in pertinent tissues. Similarly, which rodent phenotypes would you find interesting? Go get those genes involved in that phenotype from the MGI or Rat genome databases and analyze for enrichment in your data. Imagining being able to say that of Y rodent genes whose knock-out/knock-down phenotypes show increased blood pressure, X of those human orthologs also show decreased expression in the data. That could be powerful if X/Y is high a statistically significant.