Background: Diabesity defines the concurrent manifestation of type 2 diabetes (T2D) and obesity (BMI≥30 kg/m ² ) in the development of cardiovascular diseases, although the genetic basis for this joint phenotype remain poorly understood. Objective: This study aimed to identify the overlapping genetic patterns for diabesity incidence in 3,231 self-reported African American (AA) and 8,252 European Americans (EA) participated in four cohorts of the Trans-Omics for Precision Medicine (TOPMed) consortium. Methods: Using marker set enrichment analysis (MSEA) of whole genome sequencing data, specific gene sets (pathways) and key driver (KD) genes (important hub genes overrepresented in a network of pathways) were identified for diabesity incidence. Using multi-tissue and multi-species gene expression signatures as molecular indicators of drug functions, their potential drug signatures were also examined. Results: Testing genome-wide significance (P-value < 10 ^-8 ) identified seven independent loci, six of which were replicated in the T2D Knowledge Portal (https://t2d.hugeamp.org/ )( NPFFR1, TRIO, G6PD, BCL11A, IGF1, and TCF7L2, P<0.05) for diabetes and/or such obesity-related traits as blood pressure, lipids, glucose and insulin levels. One novel variant for diabesity, rs144540309, is an intronic region of GPAT3 (G>A, AA MAF = 0.004, beta=3.66 and P=1.00e-8) whose enzyme plays important roles in dietary lipid absorption, enteric and hepatic lipid homeostasis, and entero-endocrine hormone production, along with 12 KEGG/Reactome/Biocarta pathways enriched for diabesity in AAs and 11 for EAs. In AAs, the top three pathways (ranked by P-value and false discovery rate [FDR]) were mitotic spindle checkpoint, resolution of sister chromatid cohesion, and rho GTPases activate formins, along with six KD genes ( NCKAP1L, CDCA8 , BUB1 , IRF5 , FYB , and C15orf23 ); in EAs, colorectal cancer, prostate cancer, and beta Catenin independent WNT signaling were the top 3 pathways (FDR≤0.25) including LEF1 . Top repositioned drugs derived from diabesity-related gene sets (FDR≤0.25) included Benzbromarone, Fenofibrate, Interleukin-1β, and antihypertensive. Conclusion: Our study supports the notion that both pathway and network-based analytical approaches may identify novel signals from gene sets for highly clustering clinical phenotypes such as diabetes and obesity and improve their target validation for intervention.