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Leah Owen, Patrice Marie Hicks, Denise J Morgan, Jeeyun Ahn, Giuliana Silvestri, Joan W Miller, Kyu Hyung Park, maria G Kotoula, Ivana K Kim, Lindsay A Farrer, Neena B Haider, Margaret M DeAngelis; HTRA1 gene interaction network informs Age-Related Macular Degeneration (AMD) risk. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4895.
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© ARVO (1962-2015); The Authors (2016-present)
The pathomechanisms that underlie AMD are incompletely understood. HTRA1 genetic variation is the most significantly associated with AMD; however, functional implications of this are unclear. Our prior work demonstrates that HTRA1 informs AMD risk through interaction with ROBO1 and RORα and suggests a broader interaction network. We sought to better define this network using expression, genetic, and in silico analysis in human peripheral and retinal tissues.
Tag-SNPs were bioinformatically identified within genes previously shown to be differently expressed in peripheral blood leukocytes of 9 sibling pairs extremely discordant for AMD (RGS13, CXCL13, RPS6KA2, ABCA1 andNLRP2). SNPs were genotyped within leukocyte genomic DNA then tested for association with AMD within a previously characterized discovery cohort. These data were analyzed in meta-analysis using 4 ethnically diverse human cohorts (n = 2,224) as well as for gene-gene interaction using a cases-only methodology relative to our previously described gene-gene interaction network (ARMS2, HTRA1, ROBO1, RORα). We assessed potential relevance to the local disease environment and regulatory function using RT-PCR in human retinal tissues. Additional pathway and eQTL analysis were performed in silico.
We demonstrated significant gene-gene interactions between established SNPs in ARMS2, HTRA1, ROBO1, and RORα and SNPs within ABCA1, RGS13, and RPS6KA2 (p<.01). Pathway analysis using multiple database platforms identified lipid metabolism, small molecule metabolism, immune regulation, and neuronal development as pathways with potential relevance to our interaction network. In silico analysis demonstrated HTRA1rs2672598 genotypes were associated with the expression of ROBO1 (p=0.0138) and RGS13rs3795617 genotypes were associated with the expression of RGS13 (p=0.0128), representing trans and cis-eQTLs respectively. Furthermore, interrogation of 27 eQTL databases showed that significant SNPs in RGS13, RPS6KA2, andABCA1were eQTLs for 27 genes, 5 of which were found to have significant differential expression in our microarray data.
Our multi-faceted approach, integrating both human expression and genetic data, builds on a previously suggested interaction network for HTRA1 and allows for greater insight into its functional role in AMD pathogenesis. Further, we describe a novel association of RGS13 variation with AMD disease.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.
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