A mega-analysis of expression quantitative trait loci in retinal tissue

URN to cite this document:: urn:nbn:de:bvb:355-epub-441591
DOI to cite this document:: 10.5283/epub.44159

Strunz, Tobias

; Kiel, Christina

; Grassmann, Felix

; Ratnapriya, Rinki

; Kwicklis, Madeline ; Karlstetter, Marcus ; Fauser, Sascha ; Arend, Nicole ; Swaroop, Anand

; Langmann, Thomas ; Wolf, Armin ; Weber, Bernhard H. F.

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Date of publication of this fulltext: 12 Nov 2020 13:21

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Author summary The retina is a multilayered and highly specified neural tissue crucial for high-resolution visual perception and spatial orientation. Environmental and genetic insults to the retina result in many blinding diseases, such as age-related macular degeneration or glaucoma. Commonly, many of these diseases are age-related suggesting that minor changes are accumulating over a life-time, with little or no contribution of strong individual effects. Specifically, this is true for genetic factors known to underlie the etiology of complex diseases including the prevalent eye diseases. In our study, we searched for effects on gene expression due to genetic variation using 311 healthy post-mortem retinal tissue samples. We show that 3,007 of the 16,766 genes investigated are regulated in the retina by genetic variations. Of these, 80 genes are potentially associated to one or more of twelve complex eye diseases or retinal traits tested. Interestingly, 10 genes appear to be involved in the development of several eye traits suggesting that cellular mechanisms may act at a common point in the disease process. Consequently, our study provides the basis to further explore retinal disease pathways and is likely to highlight target molecules for future therapeutic applications. Significant association signals from genome-wide association studies (GWAS) point to genomic regions of interest. However, for most loci the causative genetic variant remains undefined. Determining expression quantitative trait loci (eQTL) in a disease relevant tissue is an excellent approach to zoom in on disease- or trait-associated association signals and hitherto on relevant disease mechanisms. To this end, we explored regulation of gene expression in healthy retina (n = 311) and generated the largest cis-eQTL data set available to date. Genotype- and RNA-Seq data underwent rigorous quality control protocols before FastQTL was applied to assess the influence of genetic markers on local (cis) gene expression. Our analysis identified 403,151 significant eQTL variants (eVariants) that regulate 3,007 genes (eGenes) (Q-Value < 0.05). A conditional analysis revealed 744 independent secondary eQTL signals for 598 of the 3,007 eGenes. Interestingly, 99,165 (24.71%) of all unique eVariants regulate the expression of more than one eGene. Filtering the dataset for eVariants regulating three or more eGenes revealed 96 potential regulatory clusters. Of these, 31 harbour 130 genes which are partially regulated by the same genetic signal. To correlate eQTL and association signals, GWAS data from twelve complex eye diseases or traits were included and resulted in identification of 80 eGenes with potential association. Remarkably, expression of 10 genes is regulated by eVariants associated with multiple eye diseases or traits. In conclusion, we generated a unique catalogue of gene expression regulation in healthy retinal tissue and applied this resource to identify potentially pleiotropic effects in highly prevalent human eye diseases. Our study provides an excellent basis to further explore mechanisms of various retinal disease etiologies.

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