New approaches to the representation and analysis of phenotype knowledge in human diseases and their animal models.
Animals, Disease, Disease Models, Animal, Genomics, Humans, Knowledge, Mutation, Phenotype
Brief Funct Genomics 2011 Sep; 10(5):258-65.
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.
Schofield, Paul N; Sundberg, John P; Hoehndorf, Robert; and Gkoutos, Georgios V, "New approaches to the representation and analysis of phenotype knowledge in human diseases and their animal models." (2011). Faculty Research 2011. 144.