Despite its great potential, high-throughput functional genomic data are rarely integrated and applied to characterizing the genomic basis of fertility. We obtained and reprocessed over 30 functional genomics datasets from human and mouse germ cells to perform genome-wide prediction of genes underlying various reproductive phenotypes in both species. Genes involved in male fertility are easier to predict than their female analogs. Of the multiple genomic data types examined, protein-protein interactions are by far the most informative for gene prediction, followed by gene expression, and then epigenetic marks. As an application of our predictions, we show that copy number variants (CNVs) disrupting predicted fertility genes are more strongly associated with gonadal dysfunction in male and female case-control cohorts when compared to all gene-disrupting CNVs (OR = 1.64, p < 1.64 × 10(-8) vs. OR = 1.25, p < 4 × 10(-6)). Using gender-specific fertility gene annotations further increased the observed associations (OR = 2.31, p < 2.2 × 10(-16)). We provide our gene predictions as a resource with this article.
Keywords: fertility genes; machine learning; ovary; systems biology; testis.
© 2015 American Society of Andrology and European Academy of Andrology.