title: Phenotype mining for functional genomics and gene discovery.
abstract: In gene prediction, studying phenotypes is highly valuable for reducing the number of locus candidates in association studies and to aid disease gene candidate prioritization. This is due to the intrinsic nature of phenotypes to visibly reflect genetic activity, making them potentially one of the most useful data types for functional studies. However, systematic use of these data has begun only recently. 'Comparative phenomics' is the analysis of genotype-phenotype associations across species and experimental methods. This is an emerging research field of utmost importance for gene discovery and gene function annotation. In this chapter, we review the use of phenotype data in the biomedical field. We will give an overview of phenotype resources, focusing on PhenomicDB--a cross-species genotype-phenotype database--which is the largest available collection of phenotype descriptions across species and experimental methods. We report on its latest extension by which genotype-phenotype relationships can be viewed as graphical representations of similar phenotypes clustered together ('phenoclusters'), supplemented with information from protein-protein interactions and Gene Ontology terms. We show that such 'phenoclusters' represent a novel approach to group genes functionally and to predict novel gene functions with high precision. We explain how these data and methods can be used to supplement the results of gene discovery approaches. The aim of this chapter is to assist researchers interested in understanding how phenotype data can be used effectively in the gene discovery field.
authors.affiliation: Research Laboratories, Bayer Schering Pharma AG, 13442, Berlin, Germany. philip.groth@bayer.com
authors.forename: Philip
authors.initials: P
authors.lastname: Groth
authors.forename: Ulf
authors.initials: U
authors.lastname: Leser
authors.forename: Bertram
authors.initials: B
authors.lastname: Weiss
mesh_terms: D000818:Animals; D057225:Data Mining; D016208:Databases, Factual; D056726:Genetic Association Studies; D023281:Genomics / Q000379:methods*; D006801:Humans; D010641:Phenotype; D012984:Software