Genome-wide association studies (GWAS) have identified numerous genetic variants in the human genome associated with diseases and traits. Nevertheless, for most loci the causative variant is still unknown. Expression quantitative trait loci (eQTL) in disease relevant tissues is an excellent approach to correlate genetic association with gene expression. While liver is the primary site of gene transcription for two pathways relevant to age-related macular degeneration (AMD), namely the complement system and cholesterol metabolism, we explored the contribution of AMD associated variants to modulate liver gene expression. We extracted publicly available data and computed the largest eQTL data set for liver tissue to date. Genotypes and expression data from all studies underwent rigorous quality control. Subsequently, Matrix eQTL was used to identify significant local eQTL. In total, liver samples from 588 individuals revealed 202,489 significant eQTL variants affecting 1,959 genes (Q-Value < 0.001). In addition, a further 101 independent eQTL signals were identified in 93 of the 1,959 eQTL genes. Importantly, our results independently reinforce the notion that high density lipoprotein metabolism plays a role in AMD pathogenesis. Taken together, our study generated a first comprehensive map reflecting the genetic regulatory landscape of gene expression in liver.
Worldwide, age-related macular degeneration (AMD) is a serious threat to vision loss in individuals over 50 years of age with a pooled prevalence of approximately 9%. For 2020, the number of people afflicted with this condition is estimated to reach 200 million. While AMD lesions presenting as geographic atrophy (GA) show high inter-individual variability, only little is known about prognostic factors. Here, we aimed to elucidate the contribution of clinical, demographic and genetic factors on GA progression. Analyzing the currently largest dataset on GA lesion growth (N = 388), our findings suggest a significant and independent contribution of three factors on GA lesion growth including at least two genetic factors (ARMS2_rs10490924 [P < 0.00088] and C3_rs2230199 [P < 0.00015]) as well as one clinical component (presence of GA in the fellow eye [P < 0.00023]). These correlations jointly explain up to 7.2% of the observed inter-individual variance in GA lesion progression and should be considered in strategy planning of interventional clinical trials aimed at evaluating novel treatment options in advanced GA due to AMD.
Background: Advanced age-related macular degeneration (AMD) is a leading cause of blindness. While around half of the genetic contribution to advanced AMD has been uncovered, little is known about the genetic architecture of early AMD. Methods: To identify genetic factors for early AMD, we conducted a genome-wide association study (GWAS) meta-analysis (14,034 cases, 91,214 controls, 11 sources of data including the International AMD Genomics Consortium, IAMDGC, and UK Biobank, UKBB). We ascertained early AMD via color fundus photographs by manual grading for 10 sources and via an automated machine learning approach for > 170,000 photographs from UKBB. We searched for early AMD loci via GWAS and via a candidate approach based on 14 previously suggested early AMD variants.
Genome-wide association studies (GWAS) for late stage age-related macular degeneration (AMD) have identified 52 independent genetic variants with genome-wide significance at 34 genomic loci. Typically, such an approach rarely results in the identification of functional variants implicating a defined gene in the disease process. We now performed a transcriptome-wide association study (TWAS) allowing the prediction of effects of AMD-associated genetic variants on gene expression. The TWAS was based on the genotypes of 16,144 late-stage AMD cases and 17,832 healthy controls, and gene expression was imputed for 27 different human tissues which were obtained from 134 to 421 individuals. A linear regression model including each individuals imputed gene expression data and the respective AMD status identified 106 genes significantly associated to AMD variants in at least one tissue (Q-value < 0.001). Gene enrichment analysis highlighted rather systemic than tissue-or cellspecific processes. Remarkably, 31 of the 106 genes overlapped with significant GWAS signals of other complex traits and diseases, such as neurological or autoimmune conditions. Taken together, our study highlights the fact that expression of genes associated with AMD is not restricted to retinal tissue as could be expected for an eye disease of the posterior pole, but instead is rather ubiquitous suggesting processes underlying AMD pathology to be of systemic nature. Age-related macular degeneration (AMD) is a frequent disease of the choroid, Bruch's membrane, retinal pigment epithelium and photoreceptor complex characterized by a progressive loss of vision in older individuals of industrialized countries. For the year 2016, it was estimated that worldwide approximately 162 million were affected by early AMD (prevalence 8.0%) and 10 million people by late AMD (prevalence 0.4%). These numbers were predicted to increase to 260 million patients with early and 19 million with late AMD by the year 2040 1. It is well established that AMD is a complex disease, involving environmental and genetic risk factors. The International AMD Genomics Consortium (IAMDGC) performed a genome-wide association study (GWAS) and reported 7,218 genetic variants to be associated with late-stage AMD at a genome-wide significance level (P-value ≤ 5 × 10 −8). Sequential forward selection finally identified 52 independent association signals, which are distributed over 34 genomic loci 2. An additional 11,768 variants (P-value ≤ 5 × 10 −4) failed to reach genome-wide significance in this study but may well play a role in AMD pathogenesis. These variants may become relevant only with an increase in sample-size in future GWAS, or by gathering additional information on the functional impact of these variants in relation to AMD pathology.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.