Expression quantitative trait loci for PI3K/AKT pathway.
ABSTRACT: A genome-wide association study (GWAS) was conducted to identify expression quantitative trait loci (eQTLs) for the genes involved in phosphatidylinositol-3-kinase/v-akt murine thymoma viral oncogene homolog (PI3K/AKT) pathway.Data on mRNA expression of 341 genes in lymphoblastoid cell lines of 373 Europeans recruited by the 1000 Genomes Project using Illumina HiSeq2000 were utilized. We used their genotypes at 5,941,815 nucleotide variants obtained by Genome Analyzer II and SOLiD.The association analysis revealed 4166 nucleotide variants associated with expression of 85 genes (P?
Project description:This study aimed to explore the efficacy and mechanism of Chanling Gao (CLG), a compound Chinese medicine, on colorectal cancer (CRC). A model of transplanted CRC was established in nude mice. The mice were treated 7 days after CRC transplantation with either Capecitabine or CLG for 3 weeks. On the 28th day after the operation, CRC growth and liver metastasis were assessed by morphology, the changes in the expression of HIF-1? (hypoxia inducible factor-1?), stromal cell-derived factor-1 alpha (SDF-1?), CXCR4 (C-X-C chemokine receptor type 4), PI3K, and Akt in the transplanted tumor and SDF-1? and CXCR4 in the liver were detected by Western blot and immunohistochemistry. The protein contents of vascular endothelial growth factor (VEGF), matrix metalloproteinase (MMP)-2, and collagen IV in the serum and transplanted tumor and SDF-1? and CXCR4 in liver tissues were detected by enzyme-linked immunosorbent assay. In the Capecitabine and high dose CLG groups, the growth and liver metastasis of CRC were significantly inhibited, the protein levels of HIF-1?, SDF-1?, CXCR4, MMP-2, VEGF, PI3K, Akt, P-PI3K and P-Akt in the transplanted tumor were lower, while the content of collagen IV in the transplanted tumor was higher, than in Model group. A high dose of CLG inhibited the growth of transplanted tumor and liver metastasis of CRC in nude mice, probably by inhibiting the HIF-1?/SDF-1?-CXCR4/PI3K-Akt signaling pathway reducing the synthesis and release of VEGF and degradation of collagen IV.
Project description:BACKGROUND:Sex-differences in cancer occurrence and mortality are evident across tumor types; men exhibit higher rates of incidence and often poorer responses to treatment. Targeted approaches to the treatment of tumors that account for these sex-differences require the characterization and understanding of the fundamental biological mechanisms that differentiate them. Hepatocellular Carcinoma (HCC) is the second leading cause of cancer death worldwide, with the incidence rapidly rising. HCC exhibits a male-bias in occurrence and mortality, but previous studies have failed to explore the sex-specific dysregulation of gene expression in HCC. METHODS:Here, we characterize the sex-shared and sex-specific regulatory changes in HCC tumors in the TCGA LIHC cohort using combined and sex-stratified differential expression and eQTL analyses. RESULTS:By using a sex-specific differential expression analysis of tumor and tumor-adjacent samples, we uncovered etiologically relevant genes and pathways differentiating male and female HCC. While both sexes exhibited activation of pathways related to apoptosis and cell cycle, males and females differed in the activation of several signaling pathways, with females showing PPAR pathway enrichment while males showed PI3K, PI3K/AKT, FGFR, EGFR, NGF, GF1R, Rap1, DAP12, and IL-2 signaling pathway enrichment. Using eQTL analyses, we discovered germline variants with differential effects on tumor gene expression between the sexes. 24.3% of the discovered eQTLs exhibit differential effects between the sexes, illustrating the substantial role of sex in modifying the effects of eQTLs in HCC. The genes that showed sex-specific dysregulation in tumors and those that harbored a sex-specific eQTL converge in clinically relevant pathways, suggesting that the molecular etiologies of male and female HCC are partially driven by differential genetic effects on gene expression. CONCLUSIONS:Sex-stratified analyses detect sex-specific molecular etiologies of HCC. Overall, our results provide new insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and provide a framework for future studies on sex-biased cancers.
Project description:Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."
Project description:In recent years, multiple eQTL (expression quantitative trait loci) catalogs have become available that can help understand the functionality of complex trait-related single nucleotide polymorphisms (SNPs). In eQTL catalogs, gene expression is often strongly associated with multiple SNPs, which may reflect either one or multiple independent associations. Conditional eQTL analysis allows a distinction between dependent and independent eQTLs. We performed conditional eQTL analysis in 4,896 peripheral blood microarray gene expression samples. Our analysis showed that 35% of genes with a cis eQTL have at least two independent cis eQTLs; for several genes up to 13 independent cis eQTLs were identified. Also, 12% (671) of the independent cis eQTLs identified in conditional analyses were not significant in unconditional analyses. The number of GWAS catalog SNPs identified as eQTL in the conditional analyses increases with 24% as compared to unconditional analyses. We provide an online conditional cis eQTL mapping catalog for whole blood (https://eqtl.onderzoek.io/), which can be used to lookup eQTLs more accurately than in standard unconditional whole blood eQTL databases.
Project description:Understanding the complexity of the human brain transcriptome architecture is one of the most important human genetics study areas. Previous studies have applied expression quantitative trait loci (eQTL) analysis at the genome-wide level of the brain to understand the underlying mechanisms relating to neurodegenerative diseases, primarily at the transcript level. To increase the resolution of our understanding, the current study investigates multi/single-region, transcript/exon-level and cis versus trans-acting eQTL, across 10 regions of the human brain. Some of the key findings of this study are: (i) only a relatively small proportion of eQTLs will be detected, where the sensitivity is under 5%; (ii) when an eQTL is acting in multiple regions (MR-eQTL), it tends to have very similar effects on gene expression in each of these regions, as well as being cis-acting; (iii) trans-acting eQTLs tend to have larger effects on expression compared to cis-acting eQTLs and tend to be specific to a single region (SR-eQTL) of the brain; (iv) the cerebellum has a very large number of eQTLs that function exclusively in this region, compared with other regions of the brain; (v) importantly, an interactive visualisation tool (Shiny app) was developed to visualise the MR/SR-eQTL at transcript and exon levels.
Project description:BACKGROUND:Many genetic risk loci have been identified for inflammatory bowel disease and colorectal cancer; however, identifying the causal genes for each association signal remains a challenge. Expression quantitative trait loci (eQTL) studies have identified common variants that induce differential gene expression and eQTLs can be cross-referenced with disease association signals for gene prioritization. However, the genetics of gene expression are highly tissue-specific, and further eQTL datasets from primary tissues are needed. METHODS:We have conducted an eQTL discovery study using tissue extracted endoscopically from the terminal ileum and 4 colonic locations of non-inflamed bowel from 65 controls and patients with quiescent inflammatory bowel disease. A genome-wide cis-eQTL analysis was performed on >3,600,000 variants and 13,558 expressed probes. RESULTS:We identified 1312 independent eQTLs associated with the differential expression of 1222 genes in rectal mucosa. One hundred seventy-one, 211, 168, and 102 independent eQTLs were identified in the sigmoid, descending colon, ascending colon, and terminal ileum, respectively. Twenty-six percent of genes with rectal eQTLs were novel and unique compared with 7 published eQTL datasets. Rectal eQTLs were significantly enriched for genes expressed in the colon. Examining 163 inflammatory bowel disease risk loci identified 11 tag single-nucleotide polymorphisms that were rectal eQTLs. A colorectal cancer locus at 11q23 contained a rectal eQTL for COLCA2, a protein implicated in colon cancer pathogenesis. CONCLUSIONS:This study defines a catalog of ileal and colonic eQTLs. Our data reaffirm the tissue specificity of eQTLs and support the notion that identification of functional variants in relevant tissue can be effective in fine-mapping genetic risk loci.
Project description:Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.
Project description:Most expression quantitative trait locus (eQTL) studies to date have been performed in heterogeneous tissues as opposed to specific cell types. To better understand the cell-type-specific regulatory landscape of human melanocytes, which give rise to melanoma but account for <5% of typical human skin biopsies, we performed an eQTL analysis in primary melanocyte cultures from 106 newborn males. We identified 597,335 cis-eQTL SNPs prior to linkage disequilibrium (LD) pruning and 4997 eGenes (FDR < 0.05). Melanocyte eQTLs differed considerably from those identified in the 44 GTEx tissue types, including skin. Over a third of melanocyte eGenes, including key genes in melanin synthesis pathways, were unique to melanocytes compared to those of GTEx skin tissues or TCGA melanomas. The melanocyte data set also identified trans-eQTLs, including those connecting a pigmentation-associated functional SNP with four genes, likely through cis-regulation of IRF4 Melanocyte eQTLs are enriched in cis-regulatory signatures found in melanocytes as well as in melanoma-associated variants identified through genome-wide association studies. Melanocyte eQTLs also colocalized with melanoma GWAS variants in five known loci. Finally, a transcriptome-wide association study using melanocyte eQTLs uncovered four novel susceptibility loci, where imputed expression levels of five genes (ZFP90, HEBP1, MSC, CBWD1, and RP11-383H13.1) were associated with melanoma at genome-wide significant P-values. Our data highlight the utility of lineage-specific eQTL resources for annotating GWAS findings, and present a robust database for genomic research of melanoma risk and melanocyte biology.
Project description:Understanding the causal processes that contribute to disease onset and progression is essential for developing novel therapies. Although trans-acting expression quantitative trait loci (trans-eQTLs) can directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes. Here, we analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We used co-expression modules inferred from gene expression data with five methods as traits in trans-eQTL analysis to limit multiple testing and improve interpretability. In addition to replicating three established associations, we discovered a novel trans-eQTL near SLC39A8 regulating a module of metallothionein genes in LPS-stimulated monocytes. Interestingly, this effect was mediated by a transient cis-eQTL present only in early LPS response and lost before the trans effect appeared. Our analyses highlight how co-expression combined with functional enrichment analysis improves the identification and prioritisation of trans-eQTLs when applied to emerging cell-type-specific datasets.
Project description:Expression quantitative trait loci (eQTLs) represent genetic control points of gene expression, and can be categorized as cis- and trans-acting, reflecting local and distant regulation of gene expression respectively. Although there is evidence of co-regulation within clusters of trans-eQTLs, the extent of co-expression patterns and their relationship with the genotypes at eQTLs are not fully understood. We have mapped thousands of cis- and trans-eQTLs in four tissues (fat, kidney, adrenal and left ventricle) in a large panel of rat recombinant inbred (RI) strains. Here we investigate the genome-wide correlation structure in expression levels of eQTL transcripts and underlying genotypes to elucidate the nature of co-regulation within cis- and trans-eQTL datasets. Across the four tissues, we consistently found statistically significant correlations of cis-regulated gene expression to be rare (<0.9% of all pairs tested). Most (>80%) of the observed significant correlations of cis-regulated gene expression are explained by correlation of the underlying genotypes. In comparison, co-expression of trans-regulated gene expression is more common, with significant correlation ranging from 2.9%-14.9% of all pairs of trans-eQTL transcripts. We observed a total of 81 trans-eQTL clusters (hot-spots), defined as consisting of > or =10 eQTLs linked to a common region, with very high levels of correlation between trans-regulated transcripts (77.2-90.2%). Moreover, functional analysis of large trans-eQTL clusters (> or =30 eQTLs) revealed significant functional enrichment among genes comprising 80% of the large clusters. The results of this genome-wide co-expression study show the effects of the eQTL genotypes on the observed patterns of correlation, and suggest that functional relatedness between genes underlying trans-eQTLs is reflected in the degree of co-expression observed in trans-eQTL clusters. Our results demonstrate the power of an integrative, systematic approach to the analysis of a large gene expression dataset to uncover underlying structure, and inform future eQTL studies.