Abstract TP133: A Network Approach to Identifying New Candidates for Ischemic Stroke

Abstract

Introduction: Recent success identifying genetic variation in ischemic stroke (IS) has yet to translate into actionable targets or pathways. Weighted Gene Co-Expression Network Analysis (WGCNA) uses an unsupervised approach to cluster genes into modules based on co-expression profiles. Modules likely contain biologically related genes. The correlation between a gene’s expression and the module’s overall expression pattern (eigengene) defines connectivity. Genes with high connectivity may inform and supplement genome-wide association studies (GWAS). Methods: Gene Expression in Carotid Artery GSE43292 (GEO) resource contains publically available Affymetrix Human GeneChip Gene 1.0 ST data from 34 carotid atherosclerotic plaques and paired normal carotid tissue from the same individual. WGCNA resulted in 16 co-expression modules tested against the atherosclerosis phenotype. Significant modules had strong correlation between module eigengene and atherosclerosis. We took the top genes from significantly associated modules and looked them up in two existing IS genetic datasets: Stroke Genetic Network (SiGN) (17,000 IS cases & 32,000 controls) and METASTROKE (10,307 cases & 19,326 controls). We conducted a secondary GWAS informed by WGCNA for only known functional variants in METASTROKE with replication in SiGN. Results: Five of 16 modules showed evidence of association with two standouts (“Turquoise” and “Brown”) both enriched for genes associated with vascular disease related traits (carotid IMT, coronary artery disease, etc.) and contain genes associated with IS, especially large artery atherosclerotic stroke (including HDAC9). We took the top 20 genes from these modules, not currently known to associate with IS, and looking them up in SiGN and METASTROKE, we found association with variants in three genomic regions (SLC7A7, ATXN2/SH2B3, ALDH2). The informed GWAS approach identified ADAMTSL3 as a potential novel translational target. Conclusion: Leveraging expression data in a convergent genomics approach holds promise to supplement on-going GWAS and accelerate identification of potential translational targets. Gene ontology analyses of genes from the top modules are underway.