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Additional file 9: Network S2. CellDesigner diagram as pdf.

Additional file 2: Figure S1. Coverage of the Ras network with directed vs undirected interactions. Figure S2.. Network centrality analysis for hub and non-hub proteins. Figure S3. Crosstalk of the Ras-effector downstream proteins across the 12 effector classes. Figure S4. Shared proteins among the 12 effector classes. Figure S5. Subcellular localization and interaction compatibility. Figure S6. PANTHER GO pathways analysis of the 12 effector classes. Figure S7. Differential enrichment of the SysGO Processes (1) by effector class. Figure S8. Comparison of the enriched class-specific processes with literature reports.

Elucidating the role of genetic variation in the regulation of gene expression is key to understanding the pathobiology of complex diseases which, in consequence, is crucial in devising targeted treatment options. Expression quantitative trait locus (eQTL) analysis correlates a genetic variant with the strength of gene expression, thus defining thousands of regulated genes in a multitude of human cell types and tissues. Some eQTL may not act independently of each other but instead may be regulated in a coordinated fashion by seemingly independent genetic variants. To address this issue, we combined the approaches of eQTL analysis and colocalization studies. Gene expression was determined in datasets comprising 49 tissues from the Genotype-Tissue Expression (GTEx) project. From about 33,000 regulated genes, over 14,000 were found to be co-regulated in pairs and were assembled across all tissues to almost 15,000 unique clusters containing up to nine regulated genes affected by the same eQTL signal. The distance of co-regulated eGenes was, on average, 112 kilobase pairs. Of 713 genes known to express clinical symptoms upon haploinsufficiency, 231 (32.4%) are part of at least one of the identified clusters. This calls for caution should treatment approaches aim at an upregulation of a haploinsufficient gene. In conclusion, we present an unbiased approach to identifying co-regulated genes in and across multiple tissues. Knowledge of such common effects is crucial to appreciate implications on biological pathways involved, specifically when a treatment option targets a co-regulated disease gene.

Contains fulltext : 218949.pdf (Publisher’s version ) (Open Access) 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 cell-specific 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.

Over the last 15 years, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic landscape of complex phenotypes. Nevertheless, causal interpretations of GWAS data are challenging but crucial to understand underlying mechanisms and pathologies. In this review, we explore to what extend the research community follows up on GWAS data. We have traced the scientific activities responding to the two largest GWAS conducted on age-related macular degeneration (AMD) so far. Altogether 703 articles were manually categorized according to their study type. This demonstrates that follow-up studies mainly involve “Review articles” (33%) or “Genetic association studies” (33%), while 19% of publications report on findings from experimental work. It is striking to note that only three of 16 AMD-associated loci described de novo in 2016 were examined in the four-year follow-up period after publication. A comparative analysis of five studies on gene expression regulation in AMD-associated loci revealed consistent gene candidates for 15 of these loci. Our random survey highlights the fact that functional follow-up studies on GWAS results are still in its early stages hampering a significant refinement of the vast association data and thus a more accurate insight into mechanisms and pathways.

Additional file 3: Supplementary Figures.

Genome-wide association studies (GWAS) have identified an abundance of genetic loci associated with complex traits and diseases. In contrast, in-depth characterization of an individual genetic signal is rarely available. Here, we focus on the genetic variant rs2168518 in 15q24.1 previously associated with age-related macular degeneration (AMD), but only with suggestive evidence. In a two-step procedure, we initially conducted a series of association analyses to further delineate the association of rs2168518 with AMD but also with other complex phenotypes by using large independent datasets from the International AMD Genomics Consortium (IAMDGC) and the UK Biobank. We then performed a functional annotation with reference to gene expression regulation based on data from the Genotype-Tissue Expression (GTEx) project and RegulomeDB. Association analysis revealed a gender-specific association with male AMD patients and an association predominantly with choroidal neovascularization. Further, the AMD association colocalizes with an association signal of several blood pressure-related phenotypes and with the gene expression regulation of CYP1A1, a member of the cytochrome P450 superfamily of monooxygenases. Functional annotation revealed altered transcription factor (TF) binding sites for gender-specific TFs, including SOX9 and SRY. In conclusion, the pleiotropic 15q24.1 association signal suggests a shared mechanism between blood pressure regulation and choroidal neovascularization with a potential involvement of CYP1A1.

Additional file 2: Supplementary Note.

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., 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.