Abstract
Gene function prediction has proven valuable in formulating testable hypotheses. It is particularly useful for exploring biological processes that are experimentally intractable, such as meiotic initiation and progression in the human fetal ovary. In this study, we developed the first functional gene network for the human fetal ovary, HFOnet, by probabilistically integrating multiple genomic features using a naïve Bayesian model. We demonstrated that this network could accurately recapture known functional connections between genes, as well as predict new connections. Our findings suggest that known meiosis-specific genes (i.e., with functions only in meiotic processes in the germ cells) make either no or a few functional connections but are highly clustered with neighbor genes. In contrast, known nonspecific meiotic genes (i.e., with functions in both meiotic and nonmeiotic processes in the germ cells and somatic cells) exhibit numerous connections but low clustering coefficients, indicating their role as central modulators of diverse pathways, including those in meiosis. We also predicted novel genes that may be involved in meiotic initiation and DNA repair. This global functional network provides a much-needed framework for exploring gene functions and pathway components in early human female meiosis that are difficult to tackle by traditional in vivo mammalian genetics.