1. ¹Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen, Toernooiveld 1, 6525ED Nijmegen, the Netherlands
2. ²Department of Bioinformatics, Wageningen University and Research Centre, Dreijenlaan 3, 6703HA Wageningen, the Netherlands
3. ³Department of Human Genetics, University Medical Centre Nijmegen, Geert Grooteplein 10, 6525GA Nijmegen, the Netherlands

Correspondence: Professor HG Brunner, Department of Human Genetics, University Medical Centre Nijmegen, Geert Grooteplein 10, 6525GA Nijmegen, The Netherlands. Tel: +31 24 361 4017; Fax: +31 24 366 8752; E-mail: H.Brunner@antrg.umcn.nl

Received 23 August 2005; Revised 12 December 2005; Accepted 5 January 2006; Published online 22 February 2006.

Abstract

A number of large-scale efforts are underway to define the relationships between genes and proteins in various species. But, few attempts have been made to systematically classify all such relationships at the phenotype level. Also, it is unknown whether such a phenotype map would carry biologically meaningful information. We have used text mining to classify over 5000 human phenotypes contained in the Online Mendelian Inheritance in Man database. We find that similarity between phenotypes reflects biological modules of interacting functionally related genes. These similarities are positively correlated with a number of measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein–protein interaction. Phenotype grouping reflects the modular nature of human disease genetics. Thus, phenotype mapping may be used to predict candidate genes for diseases as well as functional relations between genes and proteins. Such predictions will further improve if a unified system of phenotype descriptors is developed. The phenotype similarity data are accessible through a web interface at http://www.cmbi.ru.nl/MimMiner/.