Multi-omics data are instrumental in obtaining a comprehensive picture of complex biological systems. This is particularly useful for women's health conditions, such as endometriosis which has been historically understudied despite having a high prevalence (around 10% of women of reproductive age). Subsequently, endometriosis has limited genetic characterization: current genome-wide association studies explain only 11% of its 47% total estimated heritability. Graph representations provide an intuitive and meaningful way to relate concepts across diverse data sources and address fundamental sparsity and dimensionality challenges with multi-omics data analysis. Here we present DRIVE-KG (Disease Risk Inference and Variant Exploration-Knowledge Graph), which uses a heterogeneous graph representation to integrate biological data from multi-omics datasets: dbSNP, NCBI Human Gene, Omics Pred, GTEx, and Open Targets. We drew directly from the knowledge captured in these data, using nodes to represent genes, single nucleotide polymorphisms, proteins, and phenotypes, and edges to represent relationships between these concepts. We trained two models using DRIVE-KG: a link prediction model to suggest associations between SNPs and two pilot phenotypes (endometriosis and obesity), and a graph convolutional network (GCN) to classify patient-level endometriosis status. We conducted the patient-level classification using data from 1,441 Penn Medicine BioBank participants with gold standard chart-reviewed endometriosis status. The link prediction model uncovered 66 high-confidence (score ≥ 0.95) previously unreported SNP-endometriosis associations. Many of these variants were linked to obesity/body mass index traits (24.2%), lipid metabolism (6%), and depressive disorders (4.5%), showing agreement with emerging hypotheses about endometriosis etiology. In contrast, 11% of the 149 high confidence, candidate SNP-obesity associations (score ≥ 0.9888) were in LD with known obesity associations. The GCN to classify patient endometriosis status had an AUPRC of 0.738 compared to 0.679 for a genetic risk score. Despite this moderate improvement, we found that the GCN learned meaningful stratification of underlying adenomyosis signal and severe grades of endometriosis. We have demonstrated that heterogeneous integration of multi-omics data is valuable for diverse downstream tasks-including discovery and clinical prediction-particularly for understudied diseases where traditional genomic approaches are insufficient.