Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density [Osteoclast_48H_samples]

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored the International Mouse Phenotyping Consortium (IMPC) for the analysis of skeletal data from 3,974 mutant strains for bone mineral density (BMD), a measure that is frequently altered in a range of bone pathologies including osteoporosis. A total of 200 genes were found to significantly affect BMD. This gene pool comprised 141 genes that were previously not known to have a function in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 BMD genes also caused skeletal abnormalities. Further, evidence suggested a direct role for several of the identified BMD genes in osteoclasts and osteoblasts, and candidate genes for further investigation were prioritized. Overall, the results add novel pathophysiological and molecular insight into bone health and disease We downloaded and re-analysed the gene expression profile of the datasets published by Wang et al., (2013) PMID: 23593435 with GEO accession number GSE43811. We re-analysed only the Osteoclastogenic cell line after 48h hours of induction compared to not inducted, to identify osteoclast specific genes and their expression levels after 48h of induction to apply GeneNet Schaefer and Strimmer algorythm for learning large-scale gene association networks (http://www.strimmerlab.org/software/genenet/). This method allows to statistically assess the reliability of a given network (nodes and edges) using gene expression data to compute q-values and posterior probabilities for each potential edge connecting in a pairwise manner the nodes of the network we built in silico using REACTOME ans STRING databases . Data pre-processing and annotation: The raw intensity measurements of the arrays downloaded from the GEO platform in .txt where normalized using limma Quantile based normalization algorithm producing the summaries at the probe/transcript level. The probes were gene and transcript annotated using the specific annotaton file MoGene-1_0-st-v1.na36.mm10.transcript.csv downloaded from https://www.thermofisher.com/es/es/home/life-science/microarray-analysis/microarray-data-analysis/genechip-array-annotation-files.html. Differential expression analysis (DEA): We carried on the differential expression analysis in R environment using limma. We provide the limma normalized expression data matrix to be used for GeneNet.