Unbiased forward genetic screens for mutations causing increased gross chromosomal rearrangement (GCR) rates in Saccharomyces cerevisiae are hampered by the difficulty in reliably using qualitative GCR assays to detect mutants with small but significantly increased GCR rates. We therefore developed a bioinformatic procedure using genome-wide functional genomics screens to identify and prioritize candidate GCR-suppressing genes on the basis of the shared drug sensitivity suppression and similar genetic interactions as known GCR suppressors. The number of known suppressors was increased from 75 to 110 by testing 87 predicted genes, which identified unanticipated pathways in this process. This analysis explicitly dealt with the lack of concordance among high-throughput datasets to increase the reliability of phenotypic predictions. Additionally, shared phenotypes in one assay were imperfect predictors for shared phenotypes in other assays, indicating that although genome-wide datasets can be useful in aggregate, caution and validation methods are required when deciphering biological functions via surrogate measures, including growth-based genetic interactions.