%0 Journal Article
%T PCAN: phenotype consensus analysis to support disease-gene association.
%A Godard P
%A Page M
%J BMC Bioinformatics
%V 17
%N 1
%D Dec 2016 7
%M 27923364
%F 3.307
%R 10.1186/s12859-016-1401-2
%X <B>BACKGROUND: </B>Bridging genotype and phenotype is a fundamental biomedical challenge that underlies more effective target discovery and patient-tailored therapy. Approaches that can flexibly and intuitively, integrate known gene-phenotype associations in the context of molecular signaling networks are vital to effectively prioritize and biologically interpret genes underlying disease traits of interest.<BR><B>RESULTS: </B>We describe Phenotype Consensus Analysis (PCAN); a method to assess the consensus semantic similarity of phenotypes in a candidate gene's signaling neighborhood. We demonstrate that significant phenotype consensus (p < 0.05) is observable for ~67% of 4,549 OMIM disease-gene associations, using a combination of high quality String interactions + Metabase pathways and use Joubert Syndrome to demonstrate the ease with which a significant result can be interrogated to highlight discriminatory traits linked to mechanistically related genes.<BR><B>CONCLUSIONS: </B>We advocate phenotype consensus as an intuitive and versatile method to aid disease-gene association, which naturally lends itself to the mechanistic deconvolution of diverse phenotypes. We provide PCAN to the community as an R package ( http://bioconductor.org/packages/PCAN/ ) to allow flexible configuration, extension and standalone use or integration to supplement existing gene prioritization workflows.