This observation underscores the fact that highly expressed genes or genes that undergo large changes in expression are not necessarily the best predictors of complex phenotypic traits, such as egg quality. The heat map also revealed some variation in expression of discrete genes between individual fish within egg quality groups, implying that certain regulatory pathways leading to transcriptome dysfunction may be activated to different extents among females or that differences in these pathways are not universally shared in all fish of either group. Nonetheless, as noted, the collective minor changes in expression of many score of genes show a particularly powerful relationship to egg quality, measured as embryo developmental competence. Additionally, the majority of differentially expressed gene transcripts were down-regulated on average in the ovary of females producing poor egg quality spawns, suggesting that the basis of this phenomenon may be insufficiency as opposed to overcompensation of maternal effects. In human and non-human primates, some oocytes with reduced developmental competence contain gene transcripts that fail to undergo proper post-transcriptional regulation during ovary maturation. Our results indicate that developmental competence of eggs can be predicted from the stockpile of transcripts already present within the ovary before maturation, therefore such dysregulation may be the result of complex programming occurring throughout the different stages of oocyte growth. Traditional linear-based analyses of microarray data aiming to identify a few candidate genes whose expression is related to egg quality may offer limited resolution. In the present study, the collective expression of at least 233 UniGenes was required to accurately predict egg quality, whereas using only 100 genes was less predictive. Furthermore, none of the differences in ovary gene expression identified by the ANN were found to be statistically significant by ANOVA once adjustments for multiple tests were considered. Sensitivity analyses, which extract the sensitivity of the output to changes in individual inputs, showed that no single gene accounted for more than 2% of the variation in egg quality explained via ANN modeling. These findings clearly indicate that the predictive power of the transcriptome is not due to individual genes, but rather to the information contained in their collective, coordinated behaviors, perhaps more appropriately evaluated as a transcriptomic “fingerprint”. Genes can be assigned more than one Gene Ontology class, as the proteins they encode may perform multiple functions in more than one pathway. However function or dysfunction of a particular gene in a physiological process influencing egg quality may be relegated to a specific subset of GOs. As multiple gene pathways may be functionally linked, a holistic analytical approach is required for proper biological interpretation.