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


Autoři: Tobias Strunz aff001;  Christina Kiel aff001;  Felix Grassmann aff001;  Rinki Ratnapriya aff003;  Madeline Kwicklis aff003;  Marcus Karlstetter aff004;  Sascha Fauser aff005;  Nicole Arend aff006;  Anand Swaroop aff003;  Thomas Langmann aff004;  Armin Wolf aff007;  Bernhard H. F. Weber aff001
Působiště autorů: Institute of Human Genetics, University of Regensburg, Regensburg, Germany aff001;  Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom aff002;  Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, Bethesda, United States of America aff003;  Laboratory for Experimental Immunology of the Eye, Department of Ophthalmology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany aff004;  Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland aff005;  Department of Ophthalmology, Ludwig-Maximilians-University, Munich, Germany aff006;  Department of Ophthalmology, University of Ulm, Ulm, Germany aff007;  Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany aff008
Vyšlo v časopise: A mega-analysis of expression quantitative trait loci in retinal tissue. PLoS Genet 16(9): e1008934. doi:10.1371/journal.pgen.1008934
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008934

Souhrn

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.

Klíčová slova:

Eye diseases – Gene expression – Gene regulation – Genetics of disease – Genome-wide association studies – Genomic signal processing – Genomics – Retina


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PLOS Genetics


2020 Číslo 9

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