title: Linking tissues to phenotypes using gene expression profiles.
abstract: Despite great biological and computational efforts to determine the genetic causes underlying human heritable diseases, approximately half (3500) of these diseases are still without an identified genetic cause. Model organism studies allow the targeted modification of the genome and can help with the identification of genetic causes for human diseases. Targeted modifications have led to a vast amount of model organism data. However, these data are scattered across different databases, preventing an integrated view and missing out on contextual information. Once we are able to combine all the existing resources, will we be able to fully understand the causes underlying a disease and how species differ. Here, we present an integrated data resource combining tissue expression with phenotypes in mouse lines and bringing us one step closer to consequence chains from a molecular level to a resulting phenotype. Mutations in genes often manifest in phenotypes in the same tissue that the gene is expressed in. However, in other cases, a systems level approach is required to understand how perturbations to gene-networks connecting multiple tissues lead to a phenotype. Automated evaluation of the predicted tissue-phenotype associations reveals that 72-76% of the phenotypes are associated with disruption of genes expressed in the affected tissue. However, 55-64% of the individual phenotype-tissue associations show spatially separated gene expression and phenotype manifestation. For example, we see a correlation between 'total body fat' abnormalities and genes expressed in the 'brain', which fits recent discoveries linking genes expressed in the hypothalamus to obesity. Finally, we demonstrate that the use of our predicted tissue-phenotype associations can improve the detection of a known disease-gene association when combined with a disease gene candidate prediction tool. For example, JAK2, the known gene associated with Familial Erythrocytosis 1, rises from the seventh best candidate to the top hit when the associated tissues are taken into consideration. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm/phenotype/list.
authors.affiliation: Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
authors.forename: Anika
authors.initials: A
authors.lastname: Oellrich
authors.forename: Damian
authors.initials: D
authors.lastname: Smedley
mesh_terms: D000818:Animals; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D063990:Gene Ontology; D016678:Genome / Q000235:genetics; D006801:Humans; D020407:Internet; D051379:Mice; D009928:Organ Specificity / Q000235:genetics*; D010641:Phenotype; D014584:User-Computer Interface