Genetic diversity leads to variation of individual traits. Systems genetics studies leverage this diversity to identify associations between genes and traits of interest. Revealing the molecular pathways associated with particular traits, such as metabolic disease susceptibility or aging, can in turn help to identify druggable targets for the prevention and treatment of diseases. By nature, mining genetic diversity requires large-scale studies, and deciphering molecular mechanisms increasingly relies on high-throughput “omics” technologies. Promoting the FAIR principles is therefore essential to enable the integration of data across studies and between “omics” layers, necessary in the field. Researchers, including us, largely rely on model populations of genetic diversity such as the mouse BXD or Collaborative Cross panels and our group has endeavored to create fully transparent and open pipelines to mine the numerous datasets derived from such populations. Our approach integrates the novel RENKU platform with specialized and general public data repositories to document data collection and analysis, share complete datasets, code and computing environments as well as results - including in the form of dashboards/shiny apps. Beyond this, version control over the entire pipelines enables the full reproducibility, quality assessment and continuous improvement of our research. Overall, we believe that this initiative will provide impetus for a larger application of FAIR principles in population genetics, facilitating large-scale, multi-omics analyses and eventually accelerate the rate of druggable target discoveries in fields including aging and metabolism.