Abstract 4118735: Interpretable deep learning translation of GWAS findings for drug repurposing in Atrial Fibrillation

Introduction: Translating human genetic findings, such as genome-wide association studies (GWAS) to pathobiology and the discovery of therapeutic target remains a major challenge for Atrial Fibrillation (AF). We previously published a network topology-based deep learning framework to identify disease-associated genes (NETTAG). Hypothesis: By using a deep learning framework, we can efficiently identify AF risk genes, druggable targets, and candidates of repurposable drugs. Aims: To identify potential AF related genes and repurposable drug candidates using our deep learning framework. Methods: First, we collected the reported quantitative trait loci (QTLs) for AF from human heart tissues. Then, we identified the overlaps between the QTLs and the previously reported 150 AF GWAS loci in the latest meta-analysis. We previously built a comprehensive human protein-protein interactome using 18 publicly available databases, containing 351,444 unique PPIs and 17,706 proteins. Using the human protein-protein interactome and the overlaps between AF GWAS hits and QTLs, we prioritized genes and defined the genes with the top 1 % predicted score as AF risk genes (afRGs) using the NETTAG. Then, we assembled drugs from the Drugbank database relating 2,938 FDA-approved drugs or clinically investigated molecules. Using network proximity approaches to evaluate the closest distance between afRGs and a drug’s targets within the human protein-protein interactome, we computationally predicted drugs for AF using Z scores <-2.0. Results: We first collected the overlaps between AF GWAS hits and QTLs, which constituted 27 expression and 12 splicing QTLs. Via NETTAG, we identified 176 afRGs. Among the 176 predicted afRGs, 12 proteins (gene products of afRGs) have been identified as known drug targets with FDA-approved medicines. In total, 1,275 targets have been widely investigated as therapeutic targets for treating AF. Using the closest-based network proximity approach, we computationally identified 49 candidate drugs. These included drugs both reportedly potentially treating AF, such as Pioglitazone (Z=2.29), Telmisartan (Z=-2.52), Sildenafil (Z=-2.86), and those have never been reported before, such as Balsalazide (Z=-2.32). Conclusion: Using a deep learning methodology that utilized GWAS and QTL findings, we identified risk genes and repurposing drug candidates for AF.