A Network and Pathway Analysis of Genes Associated With Atrial Fibrillation

Background: Atrial fibrillation (AF) is affected by both environmental and genetic factors. Previous genetic association studies, especially genome-wide association studies, revealed a large group of AF-associated genes. However, little is known about the functions and interactions of these genes. Moreover, established genetic variants of AF contribute modestly to AF variance, implying that numerous additional AF-associated genetic variations need to be identified. Hence, a systematic network and pathway analysis is needed. Methods: We retrieved all AF-associated genes from genetic association studies in various databases and performed integrative analyses including pathway enrichment analysis, pathway crosstalk analysis, network analysis, and microarray meta-analysis. Results: We collected 254 AF-associated genes from genetic association studies in various databases. Pathway enrichment analysis revealed the top biological pathways that were enriched in the AF-associated genes related to cardiac electromechanical activity. Pathway crosstalk analysis showed that numerous neuro-endocrine-immune pathways connected AF with various diseases including cancers, inflammatory diseases, and cardiovascular diseases. Furthermore, an AF-specific subnetwork was constructed with the prize-collecting Steiner forest algorithm based on the AF-associated genes, and 24 novel genes that were potentially associated with AF were inferred by the subnetwork. In the microarray meta-analysis, six of the 24 novel genes (APLP1, CREB1, CREBBP, PRMT1, IRAK1, and PLXND1) were expressed differentially in patients with AF and sinus rhythm. Conclusions: AF is not only an isolated disease with abnormal electrophysiological activity but might also share a common genetic basis and biological process with tumors and inflammatory diseases as well as cardiovascular diseases. Moreover, the six novel genes inferred from network analysis might help detect the missing AF risk loci.