Mouse phenome as biological resource

Abstract

Tanaka’s research leverages outputs from the International Mouse Phenotyping Consortium (IMPC), which targets building a catalogue of mammalian gene function. His work on mouse phenotype, mice being mammals frequently used in scientific experiments to gather insight into human conditions, is an important step forward for learning about diseases and human body function. ‘This research presents reference resource data on both comprehensive phenotype associations and biological system associations in mice for the first time in the world,’ states Tanaka. Thus far, Tanaka’s studies for IMPC have achieved many noteworthy outcomes. They have developed a significant amount of data, a new biological resource for future scientists that delivers high-quality phenotype associations across the mouse phenome acquired by analysis of phenotyping data. ‘These data consist of 3,100 mutant strains, 113 phenotyping tests and 2,050 measured parameters annotated into 532 phenotypes with ontology terms,’ confirms Tanaka. For simpler analysis, they introduced a new concept, a set of phenotype-phenotype association pairs or PPAP, an analysis unit or phenotypic module. There are 345 sets which define the association connections between an arbitrarily selected phenotype and its associated phenotypes. Each PPAP is expressed in an application made especially for more straightforward access. They also fashioned a different way to analyse the sets by converting the PPAPs to pathway-like configurations. This configuration, also available on the application they developed, allows us to chart flows from phenotypes that occurred less frequently in mice to more frequently. ‘Phenotypes can also experience abnormalities frequently, less frequently or normally and are documented in the application as such,’ explains Tanaka. ‘This computer application tool has proved vital in creating a more digestible, visual representation of phenotypic relationships.’ Tanaka’s research has developed a putative phenome-wide association network by merging each of pathway-like configurations, and also presented results of structural analysis for the overall phenotypic network. They discovered that the entire network could be subdivided into seven different groups by hierarchical clustering of phenotypes within the PPAPs. Through categorising phenotypes into these seven groups, they are able to better understand both relationships among phenotypes and their biological systems.