Identifying functional genes and pathways towards a unifying model for atrial fibrillation

Rationale: Genome wide association studies (GWAS) have associated >100 genetic loci with atrial fibrillation (AF), yet the biological pathways of AF remain elusive. Objective: To determine candidate causal genes associated with AF risk loci and their coexpression partners, modules, biologic and mechanistic pathways. Methods and Results: Cis-expression quantitative trait loci (eQTLs) were identified for candidate genes near AF risk single nucleotide polymorphisms (SNPs) in human left atrial tissues. Genes were categorized into 3 sets according to likelihood of being a causative AF gene: 1) All Candidate Genes (with significant eQTLs or previously prioritized); 2) Any eQTL Genes (with ≥1 significant eQTL); and 3) Top GWAS SNP eQTL Genes (top SNP within the top 10 eQTL SNPs). Coexpression partners were identified for each candidate gene. Weighted gene coexpression network analysis (WGCNA) identified modules and modules with overrepresentation of candidate AF genes. Ingenuity Pathway Analysis (IPA) was applied to the coexpression partners of each candidate gene, and IPA and gene set enrichment analysis (GSEA) to each WGCNA module. 166 AF-risk SNPs were located in 135 distinct loci. The All Candidate Genes group contained 233, the Any eQTL Genes group 131 (83 novel), and the Top GWAS SNP eQTL Genes group 37 genes. IPA identified mitochondrial dysfunction, oxidative stress, epithelial adherens junction signaling, and sirtuin signaling as the most frequent pathways. WGCNA characterized 64 modules; candidate AF genes were overrepresented in 8. Modules were represented by cell injury, death, stress, developmental, metabolic/mitochondrial, transcription/translation, and immune activation/inflammation regulatory pathways. Conclusions: AF candidate gene coexpression analyses suggest significant roles for cellular stress and remodeling in AF. We propose a dual risk model for AF: Genetic susceptibility to AF may not manifest until later in life, when cellular stressors overwhelm adaptive responses. These analyses provide a resource for further functional studies on potential causal AF genes.