a false negative result, which explains why investigators have concluded that murine association studies can’t work. In contrast, HBCGM results are viewed within the context of integrated analysis of a biomedical trait. As a consequence, a less stringent filtering criterion is used to evaluate HBCGM results, which increases the number of false positives but ensures that the true positives are retained. Then, the causative genetic candidates are selected from among the many correlated genes by applying orthogonal criteria [16], such as gene expression and metabolomic [17] or curated biologic data [18], or using the genomic regions delimited by prior QTL analyses [19, 20]. This integrated approach evaluates genetic candidates using multiple criteria, even though less stringent cutoffs are used for identification of genetic candidates. This has proven to be a better method for murine genetic analysis, than that of a typical GWAS that is performed using a single highly stringent criterion to identify candidates.
