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Glaucoma is a leading cause of blindness. Current glaucoma medications work by lowering intraocular pressure (IOP), a risk factor for glaucoma, but most treatments do not directly target the pathological changes leading to increased IOP, which can manifest as medication resistance as disease progresses. To identify physiological modulators of IOP, we performed genome- and exome-wide association analysis in >129,000 individuals with IOP measurements and extended these findings to an analysis of glaucoma risk. We report the identification and functional characterization of rare coding variants (including loss-of-function variants) in ANGPTL7 associated with reduction in IOP and glaucoma protection. We validated the human genetics findings in mice by establishing that Angptl7 knockout mice have lower (~2 mmHg) basal IOP compared to wild-type, with a trend towards lower IOP also in heterozygotes. Conversely, increasing mAngptl7 levels via injection into mouse eyes increases the IOP. We also show that acute gene silencing via siRNA knockdown of Angptl7 in adult mice lowers the IOP (~2-4 mmHg), reproducing the observations in knockout mice. Collectively, our data suggest that ANGPTL7 is important for IOP homeostasis and is amenable to therapeutic modulation to help maintain a healthy IOP that can prevent onset or slow the progression of glaucoma.

Table S1. Minnesota Grading System (MGS) â based on the AREDS grading system. Table S2. Demographic characteristics of all RPE samples used in the study including EWAS discovery cohort, independent sample cohort, and expression studies cohort. Table S3. Model parameters used for data analysis. Table S4. Bisulfite Pyrosequencing primer sequences. Table S5. Comparison of demographic characteristics between AMD cases and normal human donor RPE cells in our EWAS discovery cohort, independent sample cohort and combined cohorts. Table S6. RT-qPCR primer sequences. Table S7. Candidate Genes: Literature search results. Table S8. SNPs associated with candidate gene CpG site. Table S9. Histone modifications enriched at differentially methylated CpG loci in various cell types and cell lines represented in the ENCODE data. Table S10. Histone modifications enrichment in Differentially Methylated Regions in AMD. Table S11. Analysis of differentially methylated CpG loci in relation to proposed Cis-Regulatory Elements (CRE) in the RPE/Choroid from the data of T. Cherry et al. (doi: https://doi.org/10.1101/412361, http://biorxiv.org/cgi/content/short/412361v1 , Epigenomic Profiling and Single-Nucleus-RNA-Seq Reveal Cis-Regulatory Elements in Human Retina, Macula and RPE and Non-Coding Genetic Variation). Table S12. Target differentially methylated CpG probes in relation to ATAC-Seq peaks in identified in RPE tissue in Wang et al, 20187. (DOCX 115 kb)

Figure S1. Bar plot of mean p values. Figure S2. Beta value density distribution plots of Illumina Human Methylation450k BeadChip array before (A) and after (B) SWANN normalization. Figure S3. Correlation heatmap showing correlation between AMD (n=25) and Normal (n=19) donor RPE cells in our Illumina Human Methylation450k BeadChip array dataset. Figure S4. Highest ranked probes identified using the GapHunter algorithm. Figure S5. Univariate Analysis of EIF2AK3 and RIC3 CpG probes from the Illumina Human Methylation450k BeadChip array. Figure S6. Enhancer Signature Enrichment (H3K4me1) around differentially methylated cg18934822 within SKI. Figure S7. Reads Per Kilobase of Transcript per Million Mapped Reads (RPKM) for SKI, GTF2H4, TNXB and EIF2AK3 in normal and AMD RPE samples. (PDF 1120 kb)