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The Genetics of Keratoconus: A Review.

ABSTRACT: Keratoconus is the most common ectatic disorder of the corneal. Genetic and environmental factors may contribute to its pathogenesis. The focus of this article is to summarize current research into the complex genetics of keratoconus. We discuss the evidence of genetic etiology including family-based linkage studies, twin studies, genetic mutations, and genome-wide association studies. The genes implicated potentially include VSX1, miR-184, DOCK9, SOD1, RAB3GAP1, and HGF. Besides the coding mutations, we also highlight the potential contribution of DNA copy number variants in the pathogenesis of keratoconus. Finally, we present future directions for genetic research in the understanding of the complex genetics of keratoconus and its clinical significance. As new functional, candidate genes for keratoconus are being discovered at a rapid pace, the molecular genetic mechanisms underlying keratoconus pathogenesis will advance our understanding of keratoconus and promote the development of a novel therapy.

SUBMITTER: Wheeler J

PROVIDER: S-EPMC3686480 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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The Genetics of Keratoconus: A Review.

Wheeler Joshua J Hauser Michael A MA Afshari Natalie A NA Allingham R Rand RR Liu Yutao Y

Reproductive system & sexual disorders : current research 20120601 Suppl 6

Keratoconus is the most common ectatic disorder of the corneal. Genetic and environmental factors may contribute to its pathogenesis. The focus of this article is to summarize current research into the complex genetics of keratoconus. We discuss the evidence of genetic etiology including family-based linkage studies, twin studies, genetic mutations, and genome-wide association studies. The genes implicated potentially include VSX1, miR-184, DOCK9, SOD1, RAB3GAP1,</ ...[more]

PMID: 23795306

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Project description:BackgroundKeratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.ObjectiveThe aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.MethodsFor this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.ResultsWe compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study.ConclusionsMachine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

Project description:Stroke genetics includes several topics of clinical interest, including (1) molecular genetic variations affecting risk of monogenic stroke syndromes; (2) molecular genetic variations affecting risk of common stroke syndromes, sometimes with specific effects on risk of specific main types of stroke or subtypes of ischemic and hemorrhagic stroke; (3) genetics of conditions associated with stroke risk e.g. white matter hyperintensities, atrial fibrillation and hypertension; (4) hereditary causes of familial aggregation of stroke; (5) epigenetic impact on protein expression during acute brain injury; (6) genetic influence on stroke recovery; and (7) pharmacogenetics. Genetic research methods include candidate gene studies; Genome Wide Association Studies; family studies; RNA and protein analyses; and advanced computer-aided analytical methods to detect statistically significant associations. Several methods that could improve our knowledge of stroke genetics are being developed e.g.: Exome content analysis; Next-generation sequencing; Whole genome sequencing; and Epigenetics. During 2012-2014, several Single Nucleotide Polymorphisms (SNPs) have been related to common ischemic stroke risk. Certain SNPs have been associated with risk of specific ischemic stroke subtypes such as large vessel disease and cardiac embolism, particular subtypes of intracerebral hemorrhage (ICH), especially lobar ICH, and with prognosis after ICH. Large international studies on stroke recovery and exome content are ongoing. Advanced mathematical models have been used to study how several SNPs can act together and increase stroke risk burden. Such efforts require large numbers of patients and controls, which is achieved by co-operation in large international consortia such as the International Stroke Genetics Consortium. This overview includes an introduction to genetics, stroke genetics in general, and different genetic variations that may influence stroke risk. It presents some of the latest reports on stroke genetics published in high impact journals. The role of pharmacogenetics, the current clinical situation, and future prospects will also be discussed.

Dataset Information

The Genetics of Keratoconus: A Review.

Publications

The Genetics of Keratoconus: A Review.

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