June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Identification of central corneal thickness-associated genes by a bioinformatic approach
Author Affiliations & Notes
  • Isaac Alejandro Vidal Paredes
    School of Medicine, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
  • Juan Carlos Angeles-Ando
    School of Medicine, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
  • Judith Zavala
    School of Medicine, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
  • Maricruz Sepulveda-Villegas
    School of Medicine, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
  • Victor Treviño
    School of Medicine, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
  • Jorge E Valdez
    School of Medicine, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
  • Footnotes
    Commercial Relationships   Isaac Vidal Paredes, None; Juan Carlos Angeles-Ando, None; Judith Zavala, None; Maricruz Sepulveda-Villegas, None; Victor Treviño, None; Jorge Valdez, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4700. doi:
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      Isaac Alejandro Vidal Paredes, Juan Carlos Angeles-Ando, Judith Zavala, Maricruz Sepulveda-Villegas, Victor Treviño, Jorge E Valdez; Identification of central corneal thickness-associated genes by a bioinformatic approach. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4700.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Despite the importance of central corneal thickness (CCT) in ocular disease, such as open-angle glaucoma, the genetic factors which determine it are still not fully understood. Screening for CCT-associated genetic mutations will allow us to better identify and manage patients at risk. Nevertheless, few gene panels are available. To identify all reported genes associated to CCT, we used a bioinformatic approach to find reported and unreported mutations that could be used for screening purposes and possible treatment options.

Methods : A systematic review and functional analysis was performed in order to identify CCT-associated genes, comprehensive of all genetic variants reported to date. Abstracts were procured from a PubMed search, and facilitated by the PubTerm web tool, manually curated, annotated, and classified according to the type of genetic evidence associated with CCT. Genetic variants of interest were then compared in multiple public online databases such as ClinVar from NCBI, and HGMD. Thereupon, enrichment analyses for CCT-associated genes were performed on all reported relevant genetic variants in both DAVID and EnrichR.

Results : Using Pubterm tool, 206 abstracts were retrieved, involving 226 genes. 52 non-human genes were excluded. From the 174 human genes, 97 were labeled as not mutated and 71 showed genetic alterations. From the non-mutated genes, 29 accounted for negative evidence of mutation, 6 were unrelated and 62 had annotation errors. From the genes with genetic alterations, 31 had specific alterations in a related disease and 40 were genes with experimental evidence of variants in CCT. From the last group, 2 genes (COL8A2 and PRDM5) were identified as having clear mutations influencing CCT. For the remaining 38 genes classified as other genetic evidence of genetic alterations, the genetic alterations corresponded to SNPs identified by GWASs studies.

Conclusions : PubTerm bioinformatic tool was able to identify several unreported genetic alterations that may contribute to our understanding regarding the role of CCT in several ocular pathologies. This may, in turn, be instrumental in the creation of new genetic panels for screening of patients at risk, aiding medical professionals in their clinical setting and public health officials towards making informed decisions for prevention strategies.

This is a 2020 ARVO Annual Meeting abstract.

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