Both authors contributed equally to this work.
MouseFinder: Candidate disease genes from mouse phenotype data†
Article first published online: 16 MAR 2012
© 2012 Wiley Periodicals, Inc.
Special Issue: Deep Phenotyping for Precision Medicine
Volume 33, Issue 5, pages 858–866, May 2012
How to Cite
Chen, C.-K., Mungall, C. J., Gkoutos, G. V., Doelken, S. C., Köhler, S., Ruef, B. J., Smith, C., Westerfield, M., Robinson, P. N., Lewis, S. E., Schofield, P. N. and Smedley, D. (2012), MouseFinder: Candidate disease genes from mouse phenotype data. Hum. Mutat., 33: 858–866. doi: 10.1002/humu.22051
For the Deep Phenotyping Special Issue
- Issue published online: 13 APR 2012
- Article first published online: 16 MAR 2012
- Accepted manuscript online: 13 FEB 2012 11:57AM EST
- Manuscript Accepted: 20 JAN 2012
- Manuscript Received: 7 NOV 2011
- Director, Office of Science, Office of Basic Energy Sciences, U.S. Department of Energy. Grant Number: DE-AC02-05CH11231
- NIH R01. Grant Number: HG004838-02
- candidate disease genes;
- model organism;
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. Hum Mutat 33:858–866, 2012. © 2012 Wiley Periodicals, Inc.