Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

In silico pathway analysis and tissue specific cis-eQTL for colorectal cancer GWAS risk variants.

ABSTRACT: Genome-wide association studies have identified 55 genetic variants associated with colorectal cancer risk to date. However, potential causal genes and pathways regulated by these risk variants remain to be characterized. Therefore, we performed gene ontology enrichment and pathway analyses to determine if there was an enrichment of genes in proximity to the colorectal cancer risk variants that could further elucidate the probable causal genes and pathways involved in colorectal cancer biology.For the 65 unique genes that either contained, or were immediately neighboring up- and downstream, of these variants there was a significant enrichment for the KEGG pathway, Pathways in Cancer (p-value?=?2.67 × 10-5) and an enrichment for multiple biological processes (FDR?

SUBMITTER: Loo LWM

PROVIDER: S-EPMC5432975 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

In silico pathway analysis and tissue specific cis-eQTL for colorectal cancer GWAS risk variants.

Loo Lenora W M LWM Lemire Mathieu M Le Marchand Loïc L

BMC genomics 20170515 1

<h4>Background</h4>Genome-wide association studies have identified 55 genetic variants associated with colorectal cancer risk to date. However, potential causal genes and pathways regulated by these risk variants remain to be characterized. Therefore, we performed gene ontology enrichment and pathway analyses to determine if there was an enrichment of genes in proximity to the colorectal cancer risk variants that could further elucidate the probable causal genes and pathways involved in colorect ...[more]

PMID: 28506205

Similar Datasets

cis-Expression QTL Analysis of Established Risk Variants for Colorectal Cancer

2012-01-31 | E-GEOD-31737 | biostudies-arrayexpress

cis-Expression QTL analysis of established colorectal cancer risk variants in colon tumors and adjacent normal tissue.

Project description:Genome-wide association studies (GWAS) have identified 19 risk variants associated with colorectal cancer. As most of these risk variants reside outside the coding regions of genes, we conducted cis-expression quantitative trait loci (cis-eQTL) analyses to investigate possible regulatory functions on the expression of neighboring genes. Forty microsatellite stable and CpG island methylator phenotype-negative colorectal tumors and paired adjacent normal colon tissues were used for genome-wide SNP and gene expression profiling. We found that three risk variants (rs10795668, rs4444235 and rs9929218, using near perfect proxies rs706771, rs11623717 and rs2059252, respectively) were significantly associated (FDR q-value ≤0.05) with expression levels of nearby genes (<2 Mb up- or down-stream). We observed an association between the low colorectal cancer risk allele (A) for rs10795668 at 10p14 and increased expression of ATP5C1 (q = 0.024) and between the colorectal cancer high risk allele (C) for rs4444235 at 14q22.2 and increased expression of DLGAP5 (q = 0.041), both in tumor samples. The colorectal cancer low risk allele (A) for rs9929218 at 16q22.1 was associated with a significant decrease in expression of both NOL3 (q = 0.017) and DDX28 (q = 0.046) in the adjacent normal colon tissue samples. Of the four genes, DLGAP5 and NOL3 have been previously reported to play a role in colon carcinogenesis and ATP5C1 and DDX28 are mitochondrial proteins involved in cellular metabolism and division, respectively. The combination of GWAS findings, prior functional studies, and the cis-eQTL analyses described here suggest putative functional activities for three of the colorectal cancer GWAS identified risk loci as regulating the expression of neighboring genes.

| S-EPMC3281844 | biostudies-literature

cis-Expression QTL Analysis of Established Risk Variants for Colorectal Cancer

2012-01-31 | GSE31737 | GEO

Common variants conferring risk of schizophrenia: a pathway analysis of GWAS data.

Project description:Unlike the typical analysis of single markers in genome-wide association studies (GWAS), we incorporated Gene Set Enrichment Analysis (GSEA) and hypergeometric test and combined them using Fisher's combined method to perform pathway-based analysis in order to detect genes' combined effects on mediating schizophrenia. A few pathways were consistently found to be top ranked and likely associated with schizophrenia by these methods; they are related to metabolism of glutamate, the process of apoptosis, inflammation, and immune system (e.g., glutamate metabolism pathway, TGF-beta signaling pathway, and TNFR1 pathway). The genes involved in these pathways had not been detected by single marker analysis, suggesting this approach may complement the original analysis of GWAS dataset.

| S-EPMC2933424 | biostudies-literature

Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci.

Project description:Migraine-a painful, throbbing headache disorder-is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models-MASHR, elastic net, and SMultiXcan-to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.

| S-EPMC10449685 | biostudies-literature

Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia.

Project description:Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS.

| S-EPMC5851843 | biostudies-literature

Genetic variants and risk of esophageal squamous cell carcinoma: a GWAS-based pathway analysis.

Project description:This study was designed to identify candidate single-nucleotide polymorphisms (SNPs) that may affect the susceptibility to esophageal squamous cell carcinoma (ESCC) and elucidate their potential mechanisms to generate SNP-to-gene-to-pathway hypotheses. A genome-wide association study (GWAS) dataset for ESCC, which included 453,852 SNPs from 1898 ESCC patients and 2100 control subjects of Chinese population, was reviewed. The identify candidate causal SNPs and pathways (ICSNPathway) analysis identified seven candidate SNPs, five genes, and seven pathways, which together revealed seven hypothetical biological mechanisms. The three strongest hypothetical biological mechanisms were as follows: rs4135113→TDG→BASE EXCISION REPAIR; rs1800450→MBL2→MONOSACCHARIDE BINDING; and rs3769823→CASP8→d4gdiPathway. The GWAS dataset was evaluated using the ICSNPathway, which showed seven candidate SNPs, five genes, and seven pathways that may contribute to the susceptibility of patients to ESCC.

| S-EPMC4696063 | biostudies-literature

Allele-specific expression and eQTL analysis in mouse adipose tissue.

Project description:BackgroundThe simplest definition of cis-eQTLs versus trans, refers to genetic variants that affect expression in an allele specific manner, with implications on underlying mechanism. Yet, due to technical limitations of expression microarrays, the vast majority of eQTL studies performed in the last decade used a genomic distance based definition as a surrogate for cis, therefore exploring local rather than cis-eQTLs.ResultsIn this study we use RNAseq to explore allele specific expression (ASE) in adipose tissue of male and female F1 mice, produced from reciprocal crosses of C57BL/6J and DBA/2J strains. Comparison of the identified cis-eQTLs, to local-eQTLs, that were obtained from adipose tissue expression in two previous population based studies in our laboratory, yields poor overlap between the two mapping approaches, while both local-eQTL studies show highly concordant results. Specifically, local-eQTL studies show ~60% overlap between themselves, while only 15-20% of local-eQTLs are identified as cis by ASE, and less than 50% of ASE genes are recovered in local-eQTL studies. Utilizing recently published ENCODE data, we also find that ASE genes show significant bias for SNPs prevalence in DNase I hypersensitive sites that is ASE direction specific.ConclusionsWe suggest a new approach to analysis of allele specific expression that is more sensitive and accurate than the commonly used fisher or chi-square statistics. Our analysis indicates that technical differences between the cis and local-eQTL approaches, such as differences in genomic background or sex specificity, account for relatively small fraction of the discrepancy. Therefore, we suggest that the differences between two eQTL mapping approaches may facilitate sorting of SNP-eQTL interactions into true cis and trans, and that a considerable portion of local-eQTL may actually represent trans interactions.

| S-EPMC4089026 | biostudies-literature

A cis-eQTL genetic variant in PLK4 confers high risk of hepatocellular carcinoma.

Project description:PurposeThe overexpression and knockdown of PLK4 were both reported to generate aneuploidy. Thus, we aimed to investigate whether genetic variants in PLK4 contribute to the development of hepatocellular carcinoma (HCC).MethodsWe evaluated associations of common variants in PLK4 and its promoter for the risk of HCC in our association study (1300 cases and 1344 controls). The genotype-tissue expression (GTEx) and The cancer genome atlas (TCGA) databases were used to quantify the expression of PLK4. Cell proliferation and migration affected by PLK4 in HCC were assessed in vitro. Drug susceptibility testing (DST) model was used to assess the sensibility of PLK4-activated HCC to CFI-400945, a small molecule inhibitor of PLK4.ResultsHerein, we found a significant association between rs3811741, located in the PLK4 intron, and liver cancer risk (OR = 1.26, P = 9.81 × 10-5 ). Although PLK4 expressed at lower levels in somatic tissues compared to the testis, the risk allele A of rs3811741 was associated with increased PLK4 expression in liver cancer tissues. Additionally, PLK4 high expression was remarkably associated with shortened survival of HCC (HR = 1.97, P = .001). Furthermore, overexpression of PLK4 promoted, while knockdown of PLK4 suppressed cancer cell proliferation, migration, and invasion. DST model demonstrated that CFI-400945 can effectively suppress rampant proliferation of HCC with highly expressed PLK4.ConclusionTaken together, our study demonstrated that PLK4 is a susceptibility gene and plays an oncogenic role in HCC. Furthermore, we identified that PLK4 sensitives HCC to CFI-400945, which may be an ideal therapy target for HCC.

| S-EPMC6797585 | biostudies-literature

A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer.

Project description:Genome-wide association studies (GWAS) of colorectal cancer (CRC) have identified 23 susceptibility loci thus far. Analyses of previously conducted GWAS indicate additional risk loci are yet to be discovered. To identify novel CRC susceptibility loci, we conducted a new GWAS and performed a meta-analysis with five published GWAS (totalling 7,577 cases and 9,979 controls of European ancestry), imputing genotypes utilising the 1000 Genomes Project. The combined analysis identified new, significant associations with CRC at 1p36.2 marked by rs72647484 (minor allele frequency [MAF] = 0.09) near CDC42 and WNT4 (P = 1.21 × 10(-8), odds ratio [OR] = 1.21 ) and at 16q24.1 marked by rs16941835 (MAF = 0.21, P = 5.06 × 10(-8); OR = 1.15) within the long non-coding RNA (lncRNA) RP11-58A18.1 and ~500 kb from the nearest coding gene FOXL1. Additionally we identified a promising association at 10p13 with rs10904849 intronic to CUBN (MAF = 0.32, P = 7.01 × 10(-8); OR = 1.14). These findings provide further insights into the genetic and biological basis of inherited genetic susceptibility to CRC. Additionally, our analysis further demonstrates that imputation can be used to exploit GWAS data to identify novel disease-causing variants.

| S-EPMC4438486 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data