| Summary: | More reliable methods are needed to uncover novel biomarkers associated with atrial fibrillation (AF). Our objective is to identify significant network modules and newly AF-associated genes by integrative genetic analysis approaches. The single nucleotide polymorphisms with nominal relevance significance from the AF-associated genome-wide association study (GWAS) data were converted into the GWAS discovery set using ProxyGeneLD, followed by merging with significant network modules constructed by weighted gene coexpression network analysis (WGCNA) from one expression profile data set, composed of left and right atrial appendages (LAA and RAA). In LAA, two distinct network modules were identified (blue: p = 0.0076; yellow: p = 0.023). Five AF-associated biomarkers were identified (ERBB2, HERC4, MYH7, MYPN, and PBXIP1), combined with the GWAS test set. In RAA, three distinct network modules were identified and only one AF-associated gene LOXL1 was determined. Using human LAA tissues by real-time quantitative polymerase chain reaction, the differentially expressive results of ERBB2, MYH7, and MYPN were observed (p < 0.05). This study first demonstrated the feasibility of fusing GWAS with expression profile data by ProxyGeneLD and WGCNA to explore AF-associated genes. In particular, two newly identified genes ERBB2 and MYPN via this approach contribute to further understanding the occurrence and development of AF, thereby offering preliminary data for subsequent studies.
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