Dataset Information

Genetic associations for keratoconus: a systematic review and meta-analysis.

ABSTRACT: Genetic associations for keratoconus could be useful for understanding disease pathogenesis and discovering biomarkers for early detection of the disease. We conducted a systematic review and meta-analysis to summarize all reported genetic associations for the disease. We searched in the MEDLINE, Embase, Web of Science, and HuGENET databases for genetic studies of keratoconus published from 1950 to June 2016. The summary odds ratio and 95% confidence intervals of all polymorphisms were estimated using the random-effect model. Among 639 reports that were retrieved, 24 fulfilled required criteria as eligible studies for meta-analysis, involving a total of 53 polymorphisms in 28 genes/loci. Results of our meta-analysis lead to the prioritization of 8 single-nucleotide polymorphisms (SNPs) in 6 genes/loci for keratoconus in Whites. Of them 5 genes/loci were originally detected in genome-wide association studies, including FOXO1 (rs2721051, P?=?5.6?×?10-11), RXRA-COL5A1 (rs1536482, P?=?2.5?×?10-9), FNDC3B (rs4894535, P?=?1.4?×?10-8), IMMP2L (rs757219, P?=?6.1?×?10-7; rs214884, P?=?2.3?×?10-5), and BANP-ZNF469 (rs9938149, P?=?1.3?×?10-5). The gene COL4A4 (rs2229813, P?=?1.3?×?10-12; rs2228557, P?=?4.5?×?10-7) was identified in previous candidate gene studies. We also found SNPs in 10 genes/loci that had a summary P value?

SUBMITTER: Rong SS

PROVIDER: S-EPMC5496893 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Genetic associations for keratoconus: a systematic review and meta-analysis.

Rong Shi Song SS Ma Sarah Tsz Ue STU Yu Xin Ting XT Ma Li L Chu Wai Kit WK Chan Tommy Chung Yan TCY Wang Yu Meng YM Young Alvin L AL Pang Chi Pui CP Jhanji Vishal V Chen Li Jia LJ

Scientific reports 20170704 1

Genetic associations for keratoconus could be useful for understanding disease pathogenesis and discovering biomarkers for early detection of the disease. We conducted a systematic review and meta-analysis to summarize all reported genetic associations for the disease. We searched in the MEDLINE, Embase, Web of Science, and HuGENET databases for genetic studies of keratoconus published from 1950 to June 2016. The summary odds ratio and 95% confidence intervals of all polymorphisms were estimated ...[more]

PMID: 28676647

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Project description:(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949-0.982), with a pooled specificity of 0.985 (95% CI 0.971-0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822-0.923), with a pooled specificity of 0.947 (95% CI 0.914-0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.

Dataset Information

Genetic associations for keratoconus: a systematic review and meta-analysis.

Publications

Genetic associations for keratoconus: a systematic review and meta-analysis.

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