Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Variable expression quantitative trait loci analysis of breast cancer risk variants.

ABSTRACT: Genome wide association studies (GWAS) have identified more than 180 variants associated with breast cancer risk, however the underlying functional mechanisms and biological pathways which confer disease susceptibility remain largely unknown. As gene expression traits are under genetic regulation we hypothesise that differences in gene expression variability may identify causal breast cancer susceptibility genes. We performed variable expression quantitative trait loci (veQTL) analysis using tissue-specific expression data from the Genotype-Tissue Expression (GTEx) Common Fund Project. veQTL analysis identified 70 associations (p < 5 × 10^-8) consisting of 60 genes and 27 breast cancer risk variants, including 55 veQTL that were observed in breast tissue only. Pathway analysis of genes associated with breast-specific veQTL revealed an enrichment of four genes (CYP11B1, CYP17A1 HSD3B2 and STAR) involved in the C21-steroidal biosynthesis pathway that converts cholesterol to breast-related hormones (e.g. oestrogen). Each of these four genes were significantly more variable in individuals homozygous for rs11075995 (A/A) breast cancer risk allele located in the FTO gene, which encodes an RNA demethylase. The A/A allele was also found associated with reduced expression of FTO, suggesting an epi-transcriptomic mechanism may underlie the dysregulation of genes involved in hormonal biosynthesis leading to an increased risk of breast cancer. These findings provide evidence that genetic variants govern high levels of expression variance in breast tissue, thus building a more comprehensive insight into the underlying biology of breast cancer risk loci.

SUBMITTER: Wiggins GAR

PROVIDER: S-EPMC8009949 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Variants at IRX4 as prostate cancer expression quantitative trait loci.

Project description:Genome-wide association studies (GWAS) have identified numerous prostate cancer-associated risk loci. Some variants at these loci may be regulatory and influence expression of nearby genes. Such loci are known as cis-expression quantitative trait loci (cis-eQTL). As cis-eQTLs are highly tissue-specific, we asked if GWAS-identified prostate cancer risk loci are cis-eQTLs in human prostate tumor tissues. We investigated 50 prostate cancer samples for their genotype at 59 prostate cancer risk-associated single-nucleotide polymorphisms (SNPs) and performed cis-eQTL analysis of transcripts from paired primary tumors within two megabase windows. We tested 586 transcript-genotype associations, of which 27 were significant (false discovery rate ≤10%). An equivalent eQTL analysis of the same prostate cancer risk loci in lymphoblastoid cell lines did not result in any significant associations. The top-ranked cis-eQTL involved the IRX4 (Iroquois homeobox protein 4) transcript and rs12653946, tagged by rs10866528 in our study (P=4.91 × 10(-5)). Replication studies, linkage disequilibrium, and imputation analyses highlight population specificity at this locus. We independently validated IRX4 as a potential prostate cancer risk gene through cis-eQTL analysis of prostate cancer risk variants. Cis-eQTL analysis in relevant tissues, even with a small sample size, can be a powerful method to expedite functional follow-up of GWAS.

| S-EPMC3953920 | biostudies-literature

Enrichment of inflammatory bowel disease and colorectal cancer risk variants in colon expression quantitative trait loci

Project description:Genome wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with diseases of the colon including inflammatory bowel diseases (IBD) and colorectal cancer (CRC). However, the functional role of many of these SNPs is largely unknown and tissue-specific resources are lacking. Expression quantitative trait loci (eQTL) mapping identifies target genes of disease-associated SNPs. Here, we comprehensively map eQTLs in the human colon, assess their relevance for GWAS of colonic diseases and provide functional characterization. Subjects included 40 healthy African American individuals who had undergone colonoscopy at the University of Illinois Chicago for screening purposes. Distal colonic biopsies were obtained in all subjects at 20 cm from the anal verge at the recto-sigmoid junction and were immediately dispensed in RNAlater. Total mRNA was extracted from manually ground tissue with the Promega Maxwell 16 Tissue LEV Total RNA Purification Kit for automated purification on the Maxwell 16 Instrument and mRNA analysis was performed on Illumina HumanHT-12v4 Expression BeadChip arrays. Genomic DNA was obtained from whole-blood samples from the same individuals and genotyped using the Affymetrix Axiom Genome-wide Pan-African array. Cis- and trans-eQTL analyses were performed on the dataset of 8.4 million imputed SNPs and 16,252 expression probes corresponding to 12,363 unique autosomal genes in 40 subjects. Associations between SNPs and gene expression levels were examined with Matrix eQTL using linear regression. False discovery rate calculations were performed separately for cis- and trans-eQTLs.

2014-04-15 | E-GEOD-56789 | biostudies-arrayexpress

Enrichment of inflammatory bowel disease and colorectal cancer risk variants in colon expression quantitative trait loci.

Project description:Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with diseases of the colon including inflammatory bowel diseases (IBD) and colorectal cancer (CRC). However, the functional role of many of these SNPs is largely unknown and tissue-specific resources are lacking. Expression quantitative trait loci (eQTL) mapping identifies target genes of disease-associated SNPs. This study provides a comprehensive eQTL map of distal colonic samples obtained from 40 healthy African Americans and demonstrates their relevance for GWAS of colonic diseases.8.4 million imputed SNPs were tested for their associations with 16,252 expression probes representing 12,363 unique genes. 1,941 significant cis-eQTL, corresponding to 122 independent signals, were identified at a false discovery rate (FDR) of 0.01. Overall, among colon cis-eQTL, there was significant enrichment for GWAS variants for IBD (Crohn's disease [CD] and ulcerative colitis [UC]) and CRC as well as type 2 diabetes and body mass index. ERAP2, ADCY3, INPP5E, UBA7, SFMBT1, NXPE1 and REXO2 were identified as target genes for IBD-associated variants. The CRC-associated eQTL rs3802842 was associated with the expression of C11orf93 (COLCA2). Enrichment of colon eQTL near transcription start sites and for active histone marks was demonstrated, and eQTL with high population differentiation were identified.Through the comprehensive study of eQTL in the human colon, this study identified novel target genes for IBD- and CRC-associated genetic variants. Moreover, bioinformatic characterization of colon eQTL provides a tissue-specific tool to improve understanding of biological differences in diseases between different ethnic groups.

| S-EPMC4351699 | biostudies-literature

Enrichment of inflammatory bowel disease and colorectal cancer risk variants in colon expression quantitative trait loci

2014-04-15 | GSE56789 | GEO

Integration analysis of methylation quantitative trait loci and GWAS identify three schizophrenia risk variants.

Project description:Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with schizophrenia (SCZ). However, prioritizing risk variants and regulatory elements for follow-up functional studies remains a major challenge. Therefore, we performed an integrated analysis to identify variants who affect methylation levels of nearby genes and contribute to the risk of SCZ, and to explore the potential role of these variants in SCZ pathogenesis. First, we used the Summary data-based Mendelian Randomization (SMR) method to integrate GWAS and methylation quantitative trait loci data. Then, the SNP-methylation combinations as associated with SCZ were replicated across multiple samples. Totally, we identified and replicated 14 and one SNP-methylation combinations in blood and brain tissues, respectively, that significantly associated with SCZ. Furthermore, our expression quantitative trait loci analysis, differential methylation analysis, neuroimaging genetics, and cognitive genetics analysis consistently supported the potential roles of these 15 SNPs in the pathogenesis of SCZ. Finally, using the convergent functional genomics method, we prioritized three risk SNPs, including rs3765971 (RERE, PSMR = 3.87 × 10-8), rs55742290 (ARL6IP4, PSMR = 1.50 × 10-7), and rs7293091 (CENPM, PSMR = 5.09 × 10-7), may represent promising risk variants in SCZ. These convergent lines of evidence suggest that three risk variants may be involved in the pathogenesis of SCZ. Further investigation of the roles of these variants in the pathogenesis of SCZ is warranted.

| S-EPMC7235211 | biostudies-literature

Single-cell expression quantitative trait loci (eQTL) analysis of SLE-risk loci in lupus patient monocytes.

Project description:BackgroundWe performed expression quantitative trait locus (eQTL) analysis in single classical (CL) and non-classical (NCL) monocytes from patients with systemic lupus erythematosus (SLE) to quantify the impact of well-established genetic risk alleles on transcription at single-cell resolution.MethodsSingle-cell gene expression was quantified using qPCR in purified monocyte subpopulations (CD14++CD16- CL and CD14dimCD16+ NCL) from SLE patients. Novel analysis methods were used to control for the within-person correlations observed, and eQTLs were compared between cell types and risk alleles.ResultsThe SLE-risk alleles demonstrated significantly more eQTLs in NCLs as compared to CLs (p = 0.0004). There were 18 eQTLs exclusive to NCL cells, 5 eQTLs exclusive to CL cells, and only one shared eQTL, supporting large differences in the impact of the risk alleles between these monocyte subsets. The SPP1 and TNFAIP3 loci were associated with the greatest number of transcripts. Patterns of shared influence in which different SNPs impacted the same transcript also differed between monocyte subsets, with greater evidence for synergy in NCL cells. IRF1 expression demonstrated an on/off pattern, in which expression was zero in all of the monocytes studied from some individuals, and this pattern was associated with a number of SLE risk alleles. We observed corroborating evidence of this IRF1 expression pattern in public data sets.ConclusionsWe document multiple SLE-risk allele eQTLs in single monocytes which differ greatly between CL and NCL subsets. These data support the importance of the SPP1 and TNFAIP3 risk variants and the IRF1 transcript in SLE patient monocyte function.

| S-EPMC8630910 | biostudies-literature

Genetic susceptibility variants for lung cancer: replication study and assessment as expression quantitative trait loci.

Project description:Many single nucleotide polymorphisms (SNPs) have been associated with lung cancer but lack confirmation and functional characterization. We retested the association of 56 candidate SNPs with lung adenocarcinoma risk and overall survival in a cohort of 823 Italian patients and 779 healthy controls, and assessed their function as expression quantitative trait loci (eQTLs). In the replication study, eight SNPs (rs401681, rs3019885, rs732765, rs2568494, rs16969968, rs6495309, rs11634351, and rs4105144) associated with lung adenocarcinoma risk and three (rs9557635, rs4105144, and rs735482) associated with survival. Five of these SNPs acted as cis-eQTLs, being associated with the transcription of IREB2 (rs2568494, rs16969968, rs11634351, rs6495309), PSMA4 (rs6495309) and ERCC1 (rs735482), out of 10,821 genes analyzed in lung. For these three genes, we obtained experimental evidence of differential allelic expression in lung tissue, pointing to the existence of in-cis genomic variants that regulate their transcription. These results suggest that these SNPs exert their effects on cancer risk/outcome through the modulation of mRNA levels of their target genes.

| S-EPMC5299838 | biostudies-literature

Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants.

Project description:Expression quantitative trait loci (eQTLs) studies provide associations of genetic variants with gene expression but fall short of pinpointing functionally important eQTLs. Here, using H3K27ac HiChIP assays, we mapped eQTLs overlapping active cis-regulatory elements that interact with their target gene promoters (promoter-interacting eQTLs, pieQTLs) in five common immune cell types (Database of Immune Cell Expression, Expression quantitative trait loci and Epigenomics (DICE) cis-interactome project). This approach allowed us to identify functionally important eQTLs and show mechanisms that explain their cell-type restriction. We also devised an approach to eQTL discovery that relies on HiChIP-based promoter interaction maps as a structural framework for deciding which SNPs to test for association with gene expression, and observe ultra-long-distance pieQTLs (>1 megabase away), including several disease-risk variants. We validated the functional role of pieQTLs using reporter assays, CRISPRi, dCas9-tiling guides and Cas9-mediated base-pair editing. In this article we present a method for functional eQTL discovery and provide insights into relevance of noncoding variants for cell-specific gene regulation and for disease association beyond conventional eQTL mapping.

| S-EPMC8053422 | biostudies-literature

Influence analysis in quantitative trait loci detection.

Project description:This paper presents systematic methods for the detection of influential individuals that affect the log odds (LOD) score curve. We derive general formulas of influence functions for profile likelihoods and introduce them into two standard quantitative trait locus detection methods-the interval mapping method and single marker analysis. Besides influence analysis on specific LOD scores, we also develop influence analysis methods on the shape of the LOD score curves. A simulation-based method is proposed to assess the significance of the influence of the individuals. These methods are shown useful in the influence analysis of a real dataset of an experimental population from an F2 mouse cross. By receiver operating characteristic analysis, we confirm that the proposed methods show better performance than existing diagnostics.

| S-EPMC4282084 | biostudies-other

Pan-cancer analysis of promoter activity quantitative trait loci.

Project description:Altered promoter activity has been generally observed in diverse biological processes, including tumorigenesis. Accumulating evidence suggests that employing a quantitative trait locus mapping approach is effective in comprehending the genetic basis of promoter activity. By utilizing genotype data from The Cancer Genome Atlas and calculating corresponding promoter activity values using proActiv, we systematically evaluated the impact of genetic variants on promoter activity and identified >1.0 million promoter activity quantitative trait loci (paQTLs) as both cis- and trans-acting. Additionally, leveraging data from the genome-wide association study (GWAS) catalog, we discovered >1.3 million paQTLs that overlap with known GWAS linkage disequilibrium regions. Remarkably, ∼9324 paQTLs exhibited significant associations with patient prognosis. Moreover, investigating the impact of promoter activity on >1000 imputed antitumor therapy responses among pan-cancer patients revealed >43 000 million significant associations. Furthermore, ∼25 000 significant associations were identified between promoter activity and immune cell abundance. Finally, a user-friendly data portal, Pancan-paQTL (https://www.hbpding.com/PancanPaQTL/), was constructed for users to browse, search and download data of interest. Pancan-paQTL serves as a comprehensive multidimensional database, enabling functional and clinical investigations into genetic variants associated with promoter activity, drug responses and immune infiltration across multiple cancer types.

| S-EPMC10644876 | 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