Mouse to Human
The mouse to human web resource provides an easy-to-use web-based tool to determine which mouse genes have relevance to human osteoporosis and bone fracture. Knowing which genes have relevance in humans will facilitate translation of experimental laboratory results to human clinical research, and also identify genes that should be prioritized for in-depth laboratory investigation.
We estimated gene-based scores using full GWAS results based on 426,824 UK Biobank participants with bone mineral density estimated from quantitative heel ultrasounds (eBMD) and 53,184/373,611 fracture cases/controls (Morris, Kemp et al. 2019) available for download from the GEFOS website here. We used MAGMA to estimate gene scores based on the most significant variant within a gene region (50 kb upstream and downstream of each gene) adjusted for potential confounders, such as gene size, gene density, and linkage disequilibrium (LD) between variants (de Leeuw, Mooij et al. 2015).
Gene-based results for all genes are provided in an interactive searchable table (Full table tab). Users can search the table based on human or mouse gene symbols or NCBI IDs. Clicking on NCBI gene IDs takes users to the corresponding NCBI Gene page. Batch queries for multiple genes can be performed. Homology between human and mouse genes was based on NCBI's homologene resource.
This resource is a collaborative effort between Serra Kaya and Tamara Alliston at UCSF and Daniel Evans at UCSF/CPMCRI. This resource was supported by the UCSF Core Center for Musculoskeletal Biology and Medicine (CCMBM) of the National Institute of Health's National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) under the award number P30AR075055 and the National Institute of Dental and Craniofacial Research (NIDCR) under the award number R01DE019284.
Morris, J.A., Kemp, J.P., Youlten, S.E. et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet 51, 258–266 (2019). https://doi.org/10.1038/s41588-018-0302-x
de Leeuw CA, Mooij JM, Heskes T, Posthuma D (2015) MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Comput Biol 11(4): e1004219. https://doi.org/10.1371/journal.pcbi.1004219