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Glaucoma is a group of ocular disorders that will affect 111.8 million people globally by 2040 and can lead to permanent vision loss and blindness. However, current treatments are insufficient to modify the progression of the disease. Elevated intraocular pressure (IOP) is known as the leading risk factor of glaucoma, while some cases also occur without elevation in IOP. Therefore, it is crucial to analyze the molecular similarity and differences between IOP-dependent and IOP-independent glaucoma, which is a largely understudied topic. Using disease-associated genomic and transcriptomic sequencing datasets, this study aims to characterize the gene expression pattern and cellular composition in different pathogenic factors related to glaucoma. To achieve this research goal, I utilized multiple machine learning based bioinformatic tools to analyze genome wide association study (GWAS) data and a cross-species single-cell RNAsequencing (transcriptomics) dataset. The findings of this study revealed specific cell types and gene targets in different pathways in humans. Furthermore, new drug targets were identified based on the identified culprit disease-associated genes. The results of this study provided us a better understanding of the global and cell type specific mechanisms for different types of glaucoma. It can also facilitate future research and therapeutic options with a novel framework of sequencing data-based drug mining.
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