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Editors Selection IGR 7-1

Basic Science: GWAS and Glaucoma

Comment by Tin Aung & Eranga Vithana on:

78212 Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma, Macgregor S; Ong JS; An J et al., Nature Genetics, 2018; 50: 1067-1071

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Genetic factors behind a complex disease such as primary open-angle glaucoma (POAG) can be resolved by the analysis of quantitative traits, or endophenotypes, that are associated with the disease such as intra ocular pressure (IOP) or vertical cup-to-disc ratio (VCDR). These traits are heritable and primarily state-independent, i.e., they manifest in the individuals regardless of the disease state but the disease risk is correlated genetically with the endophenotype.¹ By identifying the genes that govern these quantitative traits, it is possible to unravel the genetic determinants that confer individual susceptibility to POAG. Khawaja and Colleagues proved this point unequivocally in one of the largest Genome-wide Association Studies (GWAS) conducted to date on IOP, which included 139,555 European participants, the majority of whom were derived from the UK Bio Bank (n = 103,382). They identified a remarkable 112 genomic loci associated with IOP, 68 of which are novel. In addition, 48 of the loci were nominally associated with glaucoma, with 14 of the loci being significant at a Bonferroni-corrected threshold, highlighting the high genetic correlation between IOP and glaucoma. The data in this study was replicated and corroborated by another, which was published back to back in the same issue of Nature Genetics.²

Several important insights were brought forth by this study that may be of clinical significance

Several important insights were brought forth by the Khawaja study that may be of clinical significance. Several of the IOP loci discovered supported an important role for angiopoietin receptor tyrosine kinase (ANG-TEK) signaling in IOP regulation. Functional validation of these genes in appropriate model systems would indicate if ANG-TEK signaling would be a good therapeutic target for POAG. Indicating that risk stratification for POAG would also be a possible reality in the future, the regression-based POAG-prediction model built using the 123 significant SNPs along with age and sex predicted a substantial portion of POAG cases in two independent cohorts. Area under the receiver operating characteristic curve (AUROC) of 0.76 and 0.74 were observed in US NEIGHBORHOOD study participants and in independent glaucoma cases from the UK Biobank, respectively. In future additional SNPs and other clinical risk factors could be added to further improve discriminatory power (AUC) of these genetic prediction models. Certainly, there are more variants to be found for IOP.

This study brings closer the translation of the genetic findings to clinical prediction and diagnosis

The SNPs discovered in this study collectively explained between 17%-19% of the IOP variance The SNPs discovered in this study collectively explained between 17%-19% of the IOP variance, indicating that GWAS has not yet reached its limit in identifying genetic loci for IOP given the estimated heritability of IOP at 55%.³ Expanding the sample size would identify more loci, albeit of increasingly low effect sizes. One of the limitations of this study is the fact that genetic data was derived from only Caucasian samples, excluding individuals of Asian and African ancestry. This questions the transferability of this data to the other races. Recent studies have found that POAG genetic susceptibility alleles associated in Caucasians appear to play a greatly reduced role in populations of African ancestry, indicating the need for separate studies.⁴ Nevertheless, findings in this study bring close the translation of the genetic findings to clinical prediction and diagnosis and now direct the research community towards the more challenging task of functional validation and assessment of clinical utility of these risk variants.

This study brings closer the translation of the genetic findings to clinical prediction and diagnosis

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