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Shannath L Merbs, Verity Frances Oliver, Andrew Jaffe, Anand Swaroop, Kari E Branham, Elizabeth Campochiaro, Donald J Zack, Jiang Qian; Blood DNA Methylation Analysis Reveals Differential Methylation in AMD. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4996.
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Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. Genome wide association studies (GWAS) have successfully identified risk alleles, yet a large proportion of disease is not explained by these loci. Adverse environmental exposures, such as smoking, are known to increase an individual’s risk of AMD independent of genetic risk. We hypothesized that epigenetic factors, specifically aberrant DNA methylation, contribute to the development or progression of AMD.
Genome-wide DNA methylation analysis was performed by CIDR on trios of peripheral blood samples of patients with geographic atrophy (GA; n=100), neovascularization (NV, n=100) and sex- and age-matched controls (within 1 year, n=100) using the Infinium HumanMethylation450 BeadChip. These samples comprised part of the AMD-MMAP (Michigan, Mayo, AREDS, Pennsylvania) GWAS. Methylated (M) and unmethylated (U) channel intensities were normalized separately using a modified version of quantile normalization in the minfi Bioconductor package.
Using a cutoff of p<10E-5, 16 genes in the NV group and 9 genes in the GA group contained at least 1 differentially methylated probe (DMP) compared to the control group. One of the genes that were in the NV group, which was not in the GA group, contained 2 significant DMPs in ARMS2, a gene previously implicated in AMD by GWAS. The top 2 DMPs in the GA group were within one gene, ENTPD1. When the cutoff was lowered to p<0.01, 11 of the 28 genes associated with the 19 SNP risk alleles contained at least one DMP in both groups combined. The IL17RC promoter, which had previously been identified as differentially methylated in peripheral monocytes of AMD patients, was not differentially methylated in these samples.
Through global methylation analysis, we identified new genes that may be associated with AMD, including for the first time a gene preferentially associated with GA. We also found a significant overlap between differentially methylated genes in peripheral blood and candidate genes from GWAS analyses such as ARMS2. Methylation changes in the blood may be useful for AMD biomarkers. Future work will include the integration of the genotypic and epigenetic data.
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