Recent work has demonstrated that phenome scans are a powerful way to link sequence variants to sets of phenotypes in clinical cohorts67. Here we have extended this approach to a murine cohort for which we have been generating cellular and molecular traits from many tissues and cell types and for which we can generate data on gene-by-environment interactions81029. The variety and depth of phenotype data that we have assembled over the past decade for the BXD cohort make this the largest coherent multiscalar data set for any segregating population. Of course, there are an almost unlimited numbers of ways to extend this BXD phenome—from much more extensive gene-environment interaction (GXE) studies to single-cell omics, but at the current size, the phenome is certainly large enough to explore the utility of PheWAS in an experimental population. We demonstrate that phenome scans can be effective at linking sequence variants to a range of phenotypes and can be used to identify novel and unexpected genome-to-phenome relations, or to validate hypothesized associations from independent studies. Coupling mouse and human PheWAS cohorts also shows great promise, and provides an efficient method to validate and translate key genome-to-phenome relations.
