June 2020
Volume 61, Issue 7
ARVO Annual Meeting Abstract  |   June 2020
A multiomics investigation of risk factors associated with intraocular pressure in the general population
Author Affiliations & Notes
  • Pirro G Hysi
    King's College London, Richmond, United Kingdom
  • Anthony P Khawaja
    Moorfields Eye Hospital, London, United Kingdom
  • Christopher Hammond
    King's College London, Richmond, United Kingdom
  • Footnotes
    Commercial Relationships   Pirro Hysi, None; Anthony Khawaja, None; Christopher Hammond, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 979. doi:
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      Pirro G Hysi, Anthony P Khawaja, Christopher Hammond; A multiomics investigation of risk factors associated with intraocular pressure in the general population. Invest. Ophthalmol. Vis. Sci. 2020;61(7):979.

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

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Purpose : The intraocular pressure (IOP) is an important risk factor for primary open angle glaucoma (POAG). Although genome-wide association studies (GWAS) have found many genetic loci associated with both, the link between genetic polymorphisms and downstream alterations of the epigenetic, transcription and metabolic profiles is poorly understood. The purpose of this study is to use integrative information (genome, epigenome, transcriptome and metabolome) to identify mechanisms that lead to elevated IOP in the general population.

Methods : IOP measurements were available in 681 subjects with epigenomic, 718 with multiple tissue transcriptomic and 1,785 with metabolomic information. Genotyping information was also available for all subjects. In addition, genetic summary statistics for association with IOP from the UK Biobank (N=103,382), as well as previously published eQTLs from different tissues (N=3,551 and N=581) and mQTL (N=1,980) were also used. When measurements in multiple tissues were available, colocalization with the GTEx expression profiles was used to identify the most appropriate tissue for the analyses.
Linear regression were used to initially test associations between genetic and other biomarkers and IOP. Mendelian Randomization models were subsequently used to confirm causality.

Results : Genes associated with IOP are preferentially expressed in adipose and vascular and endothelial tissues. The statistically strongest relationship was observed between IOP and the LMX1B peripheral blood methylation (p=1.7x10-28) and adipocyte (p=1.6x10-07) expression levels. In addition, strong associations were observed between adipocyte (p=8.8x10-06) and blood (p=8.3x10-12) ALDH9A1 expression levels and IOP, suggesting that this gene and not the TMCO1 where most association is observed within this locus mediate the effects over IOP. Finally, we identified novel metabolites whose peripheral levels was causatively associated to IOP.

Conclusions : These results illustrate the power of integrative methods for disease biomarker identification. In addition to casting light over putative molecular mechanisms mediating the effects of genetic loci associated with IOP, this study also identified several novel associations with other biomarkers.

This is a 2020 ARVO Annual Meeting abstract.


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