New approaches to the representation and analysis of phenotype knowledge in human diseases and their animal models.

Document Type

Article

Publication Date

9-2011

Keywords

Animals, Disease, Disease Models, Animal, Genomics, Humans, Knowledge, Mutation, Phenotype

JAX Location

Reprint Collection

JAX Source

Brief Funct Genomics 2011 Sep; 10(5):258-65.

PMID

21987712

Volume

10

Issue

5

First Page

258

Last Page

265

ISSN

2041-2657

Abstract

The systematic investigation of the phenotypes associated with genotypes in model organisms holds the promise of revealing genotype-phenotype relations directly and without additional, intermediate inferences. Large-scale projects are now underway to catalog the complete phenome of a species, notably the mouse. With the increasing amount of phenotype information becoming available, a major challenge that biology faces today is the systematic analysis of this information and the translation of research results across species and into an improved understanding of human disease. The challenge is to integrate and combine phenotype descriptions within a species and to systematically relate them to phenotype descriptions in other species, in order to form a comprehensive understanding of the relations between those phenotypes and the genotypes involved in human disease. We distinguish between two major approaches for comparative phenotype analyses: the first relies on evolutionary relations to bridge the species gap, while the other approach compares phenotypes directly. In particular, the direct comparison of phenotypes relies heavily on the quality and coherence of phenotype and disease databases. We discuss major achievements and future challenges for these databases in light of their potential to contribute to the understanding of the molecular mechanisms underlying human disease. In particular, we discuss how the use of ontologies and automated reasoning can significantly contribute to the analysis of phenotypes and demonstrate their potential for enabling translational research.

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