A comprehensive and comparative phenotypic analysis of the collaborative founder strains identifies new and known phenotypes.
Heike KollmusHelmut FuchsChristoph LenggerHamed HaselimashhadiMolly A BogueManuela A ÖstereicherMarion HorschThure AdlerJuan Antonio Aguilar-PimentelOana Veronica AmarieLore BeckerJohannes BeckersJulia Calzada-WackLillian GarrettWolfgang HansSabine M HölterTanja Klein-RodewaldHolger MaierPhilipp Mayer-KuckukGregor MillerKristin MorethFrauke NeffBirgit RathkolbIldikó RáczJan RozmanNadine SpielmannIrina TreiseDirk BuschJochen GrawThomas KlopstockEckhard WolfWolfgang WurstAli Önder YildirimJeremy MasonArturo Torresnull nullRudi BallingTerry MehaanValerie Gailus-DurnerKlaus SchughartMartin Hrabě de AngelisPublished in: Mammalian genome : official journal of the International Mammalian Genome Society (2020)
The collaborative cross (CC) is a large panel of mouse-inbred lines derived from eight founder strains (NOD/ShiLtJ, NZO/HILtJ, A/J, C57BL/6J, 129S1/SvImJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Here, we performed a comprehensive and comparative phenotyping screening to identify phenotypic differences and similarities between the eight founder strains. In total, more than 300 parameters including allergy, behavior, cardiovascular, clinical blood chemistry, dysmorphology, bone and cartilage, energy metabolism, eye and vision, immunology, lung function, neurology, nociception, and pathology were analyzed; in most traits from sixteen females and sixteen males. We identified over 270 parameters that were significantly different between strains. This study highlights the value of the founder and CC strains for phenotype-genotype associations of many genetic traits that are highly relevant to human diseases. All data described here are publicly available from the mouse phenome database for analyses and downloads.
Keyphrases
- escherichia coli
- lung function
- genome wide
- cystic fibrosis
- endothelial cells
- chronic obstructive pulmonary disease
- quality improvement
- air pollution
- gene expression
- high throughput
- bone mineral density
- big data
- body composition
- single cell
- deep learning
- postmenopausal women
- induced pluripotent stem cells
- drug discovery
- atopic dermatitis
- artificial intelligence