PURPOSE. Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability. METHODS. We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis. RESULTS. We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) ''drive'' the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases. We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0.0001) across KEGG, Reactome, GO, and NetPath pathways. CONCLUSIONS. We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD.

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Age-related macular degeneration (AMD) is a leading cause of blindness in the world. While dozens of independent genomic variants are associated with AMD, about one-third of AMD heritability is still unexplained. To identify novel variants and loci for AMD, we analyzed Illumina HumanExome chip data from 87 Amish individuals with early or late AMD, 79 unaffected Amish individuals, and 15 related Amish individuals with unknown AMD affection status. We retained 37,428 polymorphic autosomal variants across 175 samples for association and linkage analyses. After correcting for multiple testing (n = 37,428), we identified four variants significantly associated with AMD: rs200437673 (LCN9, p = 1.50 × 10−11), rs151214675 (RTEL1, p = 3.18 × 10−8), rs140250387 (DLGAP1, p = 4.49 × 10−7), and rs115333865 (CGRRF1, p = 1.05 × 10−6). These variants have not been previously associated with AMD and are not in linkage disequilibrium with the 52 known AMD-associated variants reported by the International AMD Genomics Consortium based on physical distance. Genome-wide significant linkage peaks were observed on chromosomes 8q21.11–q21.13 (maximum recessive HLOD = 4.03) and 18q21.2–21.32 (maximum dominant HLOD = 3.87; maximum recessive HLOD = 4.27). These loci do not overlap with loci previously linked to AMD. Through gene ontology enrichment analysis with ClueGO in Cytoscape, we determined that several genes in the 1-HLOD support interval of the chromosome 8 locus are involved in fatty acid binding and triglyceride catabolic processes, and the 1-HLOD support interval of the linkage region on chromosome 18 is enriched in genes that participate in serine-type endopeptidase inhibitor activity and the positive regulation of epithelial to mesenchymal transition. These results nominate novel variants and loci for AMD that require further investigation.Electronic supplementary materialThe online version of this article (10.1007/s00439-019-02050-4) contains supplementary material, which is available to authorized users.

Glaucoma is the leading cause of irreversible blindness worldwide. Primary open-angle glaucoma (POAG), the most common glaucoma subtype, is more prevalent and severe in individuals of African ancestry. Unfortunately, this ancestral group has been historically under-represented among genetic studies of POAG. Moreover, both genetic and polygenic risk scores (GRS, PRS) that are typically based on genetic data from European-descent populations are not transferable to individuals without a majority of European ancestry. Given the aspirations of leveraging genetic information for precision medicine, GRS and PRS demonstrate clinical potential but fall short, in part due to the lack of diversity in these studies. Prioritizing diversity in the discovery of risk variants will improve the performance and utility of GRS and PRS-derived risk estimation for disease stratification, which could bring about earlier POAG intervention and treatment for a disease that often goes undetected until significant damage has occurred.