Investigative ophthalmology & visual science · 2016
Disease-Phenotype Deconvolution in Genetic Eye Diseases Using Online Mendelian Inheritance in Man.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Pandey Priyanka, Acharya Moulinath
Аннотация
PURPOSE: Capturing organ-specific phenomes in genetic diseases is an uphill task for the eye as it comprises tissue types derived from all three germinal layers. We attempted to deconstruct genetic eye diseases (GEDs) into primary phenotypic features, to understand the complex genome-phenome relationship in GEDs.
METHODS: Using phenotype, molecular basis, and gene description features in OMIM as a primary resource, we analyzed gene-phenotype information. All ocular and systemic phenotypes were categorized and ranked based on occurrence. Clustering was performed on shared ocular features to identify genetic interactions and the largest cluster of each phenotype was used for functional analyses.
RESULTS: We collected 527 GEDs associated with 440 unique protein-coding genes. We indexed 787 ocular and 3094 systemic features, for an average of 2.17 ocular and 8.14 systemic features, respectively, per disease unit. The most common ocular features included nystagmus, hypertelorism, and myopia, while neurological and skeletal are the most common systemic groups associated with GEDs. Functional analyses revealed pathways relevant to GEDs (e.g., extracellular matrix organization in ONH3 [glaucoma]) and protein metabolism in EOM35 (nystagmus) phenotype clusters.
CONCLUSIONS: Our work imparts a structure in dissecting GEDs into unique phenotypes to study the relationship between genes and diseases involving the eye.
METHODS: Using phenotype, molecular basis, and gene description features in OMIM as a primary resource, we analyzed gene-phenotype information. All ocular and systemic phenotypes were categorized and ranked based on occurrence. Clustering was performed on shared ocular features to identify genetic interactions and the largest cluster of each phenotype was used for functional analyses.
RESULTS: We collected 527 GEDs associated with 440 unique protein-coding genes. We indexed 787 ocular and 3094 systemic features, for an average of 2.17 ocular and 8.14 systemic features, respectively, per disease unit. The most common ocular features included nystagmus, hypertelorism, and myopia, while neurological and skeletal are the most common systemic groups associated with GEDs. Functional analyses revealed pathways relevant to GEDs (e.g., extracellular matrix organization in ONH3 [glaucoma]) and protein metabolism in EOM35 (nystagmus) phenotype clusters.
CONCLUSIONS: Our work imparts a structure in dissecting GEDs into unique phenotypes to study the relationship between genes and diseases involving the eye.