PURPOSE: Use of genome-wide association studies (GWASs) in combination with transcriptomic arrays of different tissues or cell lines can reveal relevant cellular profiles and significantly improve understanding of the mechanisms of diseases. However, due to difficulty of access, few ocular transcriptomics datasets are available. This work aimed to create and make available an expression library platform that can be used with popular and versatile tools such as the linkage disequilibrium score (LDSC) regression techniques to identify specific cell lines enriched in ocular diseases. METHODS: We used transcriptome information from six publicly available single-cell and single-nucleus RNA sequence datasets to obtain matrices of normalized gene expression and estimated enrichment of the 10% most expressed transcripts in each cell line. We tested for type 1 error using simulated GWAS datasets and then used LDSC regression analyses to study the enrichment in different eye diseases. RESULTS: Gene expression databases for over 197 ocular cell lines were generated. Simulations of 1000 random GWASs of varying sample sizes showed no genomic inflation. Cell line-specific analyses of GWAS results found that genes near single nucleotide polymorphisms (SNPs) associated with refractive error were significantly enriched in cones (P = 0.008), rods (P = 0.002) and peripheral retina Müller cells (P = 0.002), juxtacanalicular cribriform cells (P = 0.0017), stromal keratocytes (P = 0.0018), and one beam-cell subpopulation (P = 0.0028) in primary open-angle glaucoma, emphasizing the importance of intraocular pressure in its etiology. CONCLUSIONS: We have built a structured ocular transcriptomics library to estimate cell line enrichment among association signals from genome-wide association studies that may be extended by incorporating other datasets. We identified cells that appear important in the genetics of common eye diseases.