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

Autor:	Greatbatch CJ; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia., Lu Q; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia., Hung S; Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia., Tran SN; Department of Information and Communication Technology, University of Tasmania, Hobart, Tasmania, Australia., Wing K; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia., Liang H; Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia., Han X; Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia., Zhou T; Department of Ophthalmology, Flinders Medical Centre, Flinders University, Bedford Park, Australia., Siggs OM; Cellular Genomics Group, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia., Mackey DA; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.; Lions Eye Institute, Centre for Ophthalmology and Visual Science, University of Western Australia, Perth, Western Australia, Australia., Liu GS; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.; Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia., Cook AL; Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia., Powell JE; Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.; UNSW Cellular Genomics Futures Institute, UNSW, Sydney, New South Wales, Australia., Craig JE; Department of Ophthalmology, Flinders Medical Centre, Flinders University, Bedford Park, Australia., MacGregor S; Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia., Hewitt AW; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.; Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia.
Jazyk:	angličtina
Zdroj:	Ophthalmology science [Ophthalmol Sci] 2024 Mar 05; Vol. 4 (4), pp. 100504. Date of Electronic Publication: 2024 Mar 05 (Print Publication: 2024).
DOI:	10.1016/j.xops.2024.100504
Abstrakt:	Purpose: Genome-wide association studies 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 TMCs collected from human donors. Methods: Sixty-two genes at 55 loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a 5-channel fluorescent cell staining protocol. A convolutional neural network 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, standard deviation [SD] 0.030; and AUC 0.845, SD 0.020, respectively). Of 7 multigene loci, 5 had statistically significant differences in AUC ( P  < 0.05) between genes, allowing for pathological gene prioritization. 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. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. (© 2024 by the American Academy of Ophthalmology.)
Databáze:	MEDLINE
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