Sunday, September 11, 2011

Mining the diseasome

Mining the diseasome: Over the last ten years, genome-wide association studies (GWAS) have reported over 4000 single nucleotide polymorphisms associated to more than 200 traits. Despite providing us with a slightly better understanding of the genetic architecture of common diseases, generating avalanches of new hypotheses, and fostering timid progress in pharmacogenomics, genetic associations studies haven't yet revolutionized clinical practice. Hence, although such studies are still published at a remarkable pace, the notion of 'post-GWAS' functional characterization of risk loci is gradually gaining in popularity. Indeed, deciphering the function of disease-associated genetic variants is likely to get us closer to achieving an understanding of disease architecture that will ultimately be translatable into clinical applications. Despite this gradual change in research priorities, the field of medical genomics remains fairly conservative: the 'single gene single disease' paradigm largely prevails, to the detriment of the avant-garde notion of 'diseasome' and of human disease network (HDN) in particular, and attempts to truly integrate clinical information (e.g., age at onset or reduction in life span) and molecular data are scarce. Here we call for a revival of the notion of disease network, and recall how superimposing layers of clinical data and biological information to such networks may help identify novel disease genes.

(Via BioData Mining - Latest articles.)