MouseFinder: Candidate disease genes from mouse phenotype data

✍ Scribed by Chao-Kung Chen; Christopher J. Mungall; Georgios V. Gkoutos; Sandra C. Doelken; Sebastian Köhler; Barbara J. Ruef; Cynthia Smith; Monte Westerfield; Peter N. Robinson; Suzanna E. Lewis; Paul N. Schofield; Damian Smedley

• Book ID: 102859996
• Publisher: John Wiley and Sons
• Year: 2012
• Tongue: English
• Weight: 517 KB
• Volume: 33
• Category: Article
• ISSN: 1059-7794
• DOI: 10.1002/humu.22051

✦ Synopsis

Mouse phenotype data represents a valuable resource for the identification of disease-associated genes, especially where the molecular basis is unknown and there is no clue to the candidate gene's function, pathway involvement or expression pattern. However, until recently these data have not been systematically used due to difficulties in mapping between clinical features observed in humans and mouse phenotype annotations. Here, we describe a semantic approach to solve this problem and demonstrate highly significant recall of known disease-gene associations and orthology relationships. A Web application (MouseFinder; www.mousemodels.org) has been developed to allow users to search the results of our whole-phenome comparison of human and mouse. We demonstrate its use in identifying ARTN as a strong candidate gene within the 1p34.1-p32 mapped locus for a hereditary form of ptosis.