Deep Learning-based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology

PURPOSE Genome-wide association studies (GWAS) have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation. DESIGN Experimental study. SUBJECTS Primary trabecular meshwork cells collected from human donors. METHODS Sixty-two genes at fifty-five loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a five-channel fluorescent cell staining protocol. A convolutional neural network (CNN) was trained to distinguish between gene knockout and normal control cell images. The area under the receiver operator curve (AUC) metric was used to quantify morphological variation in gene knockouts to identify potential pathological perturbations. MAIN OUTCOME MEASURES Degree of morphological variation as measured by deep learning algorithm accuracy of differentiation from normal controls. RESULTS Cells where LTBP2 or BCAS3 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, SD 0.030; and AUC 0.845, SD 0.020, respectively). Of seven multi-gene loci, five had statistically significant differences in AUC (p<0.05) between genes, allowing for pathological gene prioritisation. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines). CONCLUSIONS We demonstrate a robust method for functionally interrogating genome-wide association signals using high-throughput microscopy and AI. Genetic variations inducing marked morphological variation can be readily identified, allowing for the gene-based dissection of loci associated with complex traits.