Keratoconus is characterised by reduced rigidity of the cornea with distortion and focal thinning that causes blurred vision, however, the pathogenetic mechanisms are unknown. It can lead to severe visual morbidity in children and young adults and is a common indication for corneal transplantation worldwide. Here we report the first large scale genome-wide association study of keratoconus including 4,669 cases and 116,547 controls. We have identified significant association with 36 genomic loci that, for the first time, implicate both dysregulation of corneal collagen matrix integrity and cell differentiation pathways as primary disease-causing mechanisms. The results also suggest pleiotropy, with some disease mechanisms shared with other corneal diseases, such as Fuchs endothelial corneal dystrophy. The common variants associated with keratoconus explain 12.5% of the genetic variance, which shows potential for the future development of a diagnostic test to detect susceptibility to disease.

Purpose.: We assessed the impact of keratoconus disease indicators in the better eye and worse eye on quality of life (QoL) using the Vision and Quality of Life Index (VisQoL) multi-attribute utility instrument (MAUI). Methods.: ?tlsb -.01w?>Patients with keratoconus completed the six-item VisQoL utility measure. Visual acuity was assessed using a logMAR chart, and corneal thickness and the keratometric values were measured by using Scheimpflug imaging (Pentacam). Four indicators of keratoconus disease status were considered in this study, namely best corrected visual acuity (BCVA), average front corneal curvature (Front Km), thinnest corneal location (TCL), and spherical equivalent refractive error (SE). As keratoconus is an asymmetric condition, we considered the disease parameters separately for the better eye and the worse eye. The association between the four keratoconus indicators and VisQoL utilities was assessed using multivariate linear regression. Results.: A total of 170 patients with keratoconus completed the VisQoL. Patients' median age was 33 (IQR = 18) years (range, 14–75 years) and 58% (n = 99) were males. The median VisQoL utility value was 0.60 (IQR, 0.46; range, 0.02–0.99). The VisQoL utilities reduced with increasing age (ρ = −0.18, P = 0.02) and were higher for males (median = 0.65, IQR = 0.49) than females (median = 0.51, IQR = 0.40). In univariate analyses, BCVA in the better and worse eye, and Front Km and TCL in the worse eye were associated with lower VisQol utilities. However, after adjusting for relevant covariates, only BCVA in the better eye remained associated significantly with reduced VisQoL utilities (β = −0.20, P = 0.018). Conclusions.: Using a vision-specific MAUI, our study demonstrated substantial disutility relating to keratoconus. Worse vision in the better eye (but not the worse eye) was associated independently with a reduction in VisQoL utilities, suggesting that considering VisQoL utilities based on vision in the better eye is an important estimate of the impact of keratoconus from the patients' perspective. Treatment and rehabilitation interventions to retard the progression of vision impairment in the better eye resulting from keratoconus would be most efficacious at an early stage to improve QoL outcomes for patients with this disease.

Purpose. A recent genome-wide association study (GWAS) identified six loci associated with central corneal thickness that also conferred associated risk of keratoconus (KC). We aimed to assess whether genetic associations existed for these loci with KC or corneal curvature in an independent cohort of European ancestry. Methods. In total, 157 patients with KC were recruited from public and private clinics in Melbourne, Australia, and 673 individuals without KC were identified through the Genes in Myopia study from Australia. The following six single-nucleotide polymorphisms (SNPs) that showed a statistically significant association with KC in a recent GWAS study were selected for genotyping in our cohort: rs4894535 (FNDC3B), rs1324183 (MPDZ-NF1B), rs1536482 (RXRA-COL5A1), rs7044529 (COL5A), rs2721051 (FOXO1), and rs9938149 (BANP-ZNF469). The SNPs were assessed for their association with KC or corneal curvature using logistic or linear regression methods, with age and sex included as covariates. Bonferroni corrections were applied to account for multiple testing. Results. Genotyping data were available for five of the SNPs. Statistically significant associations with KC were found for the SNPs rs1324183 (P = 0.001; odds ratio [OR], 1.68) and rs9938149 (P = 0.010; OR, 1.47). Meta-analysis of previous studies yielded genome-wide significant evidence of an association for rs1324183, firmly establishing it as a KC risk variant. None of the SNPs were significantly associated with corneal curvature. Conclusions. The SNPs rs1324183 in the MPDZ-NF1B gene and rs9938149 (between BANP and ZNF4659) were associated with KC in this independent cohort, but their association was via a non–corneal curvature route.

A previous study has indicated suggestive association of the hepatocyte growth factor (HGF) gene with Keratoconus. We wished to assess this association in an independent Caucasian cohort as well as assess its association with corneal curvature. Keratoconus patients were recruited from private and public clinics in Melbourne, Australia. Non-keratoconic individuals were identified from the Genes in Myopia (GEM) study from Australia. A total of 830 individuals were used for the analysis including 157 keratoconic and 673 non keratoconic subjects. Tag single nucleotide polymorphisms (tSNPs) were chosen to encompass the hepatocyte growth factor gene as well as 2 kb upstream of the start codon through to 2 kb downstream of the stop codon. Logistic and linear regression including age and gender as covariates were applied in statistical analysis with subsequent Bonferroni correction. Ten tSNPs were genotyped. Following statistical analysis and multiple testing correction, a statistically significant association was found for the tSNP rs2286194 {p = 1.1×10-³ Odds Ratio 0.52, 95% CI - 0.35, 0.77} for keratoconus. No association was found between the 10 tSNPs and corneal curvature. These findings provide additional evidence of significant association of the HGF gene with Keratoconus. This association does not appear to act through the corneal curvature route.

(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.