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Glaucoma is a chronic and progressive disease, which affects more than 60 million people worldwide. Primary open-angle glaucoma (POAG) is one of the most common forms of glaucoma. For example, about 2.71 million people in the USA had primary open-angle glaucoma in 2011. Currently POAG is a major cause of irreversible vision loss. In patients with treated open-angle glaucoma the risk of blindness reached to be about 27 %. It is known that the death of optic nerve cells can be triggered by mechanical stress caused by increased intraocular pressure, which induces neuronal apoptosis and is observed in patients with POAG. Currently, there is a large number of scientific publications describing proteins and genes involved in the pathogenesis of POAG, including neuronal apoptosis and the cell response to mechanical stress. However, the molecular- genetic mechanisms underlying the pathophysiology of POAG are still poorly understood. Reconstruction of associative networks describing the functional interactions between these genes/proteins, including biochemical reactions, regulatory interactions, transport, etc., requires the use of methods of automated knowledge extraction from texts of scientific publications. The aim of the work was the analysis of associative networks, describing the molecular-genetic interactions between proteins and genes involved in cell response to mechanical stress (CRMS), neuronal apoptosis and pathogenesis of POAG using ANDSystem, our previous development for automated text analysis. It was shown that genes associated with POAG are statistically significantly more often represented among the genes involved in the interactions between CRMS and neuronal apoptosis than it was expected by random reasons, which can be an explanation for the effect of POAG leading to the retinal ganglion cell death.