integrated approach, where HBCGM output is analyzed within the context of multiple ‘omic’ (metabolomic, proteomic, or gene expression) datasets, will become an increasingly important part of 21st century biomedical discovery. This requires a paradigm shift, since it is current practice to use genetic analysis to identify a single major candidate gene, which will then undergo subsequent testing. In contrast, the integrated approach uses genetic analysis to identify groups of potential candidate genes – and the causative factor may not even have the highest correlation – which are then filtered using other criteria. This approach is certainly not without precedent. It has been an accepted standard for human GWAS that a replicate analysis (validation study) must be performed in a different population “to separate true associations from the blizzard of false positives” [27]. HBCGM output has the same filtering requirement, but other types of data (rather than a replicate study) are used as the filtering mechanism. Given the large number of available inbred strains, it is also possible to perform a ‘replicate’ association study using a different set of strains.
