Expression quantitative trait loci regulate HNF4A and PTBP1 expression in human brains.
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ABSTRACT:
SUBMITTER: Liu G
PROVIDER: S-EPMC4522784 | biostudies-literature | 2015 Jul
REPOSITORIES: biostudies-literature
Publications
Expression quantitative trait loci regulate HNF4A and PTBP1 expression in human brains.
Liu Guiyou G Bao Xinjie X Wang Renzhi R
Proceedings of the National Academy of Sciences of the United States of America 20150629 30
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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.
| S-EPMC4345969 | biostudies-literature
Project description:A genome-wide association study was conducted to identify expression quantitative trait loci (eQTL) for human telomerase.We tested the genetic associations of nucleotide variants with expression of the genes encoding human telomerase reverse transcriptase (hTERT) and telomerase RNA components (TERC) in lymphoblastoid cell lines derived from 373 Europeans.Our results revealed 6 eQTLs associated with hTERT (P < 5 × 10). One eQTL (rs17755753) was located in the intron 1 of the gene encoding R-spondin-3 (RSPO3), a well-known Wnt signaling regulator. Transcriptome-wide association analysis for these eQTLs revealed their additional associations with the expression of 29 genes (P < 4.75 × 10), including prickle planar cell polarity protein 2 (PRICKLE2) gene important for the Wnt signaling pathway. This concurs with previous studies in which significant expressional relationships between hTERT and some genes (β-catenin and Wnt-3a) in the Wnt signaling pathway have been observed.This study suggested 6 novel eQTLs for hTERT and the association of hTERT with the Wnt signaling pathway. Further studies are needed to understand their underlying mechanisms to improve our understanding of the role of hTERT in cancer.
| S-EPMC5079342 | biostudies-literature
Project description:Mendelian loci that control the expression levels of transcripts are called expression quantitative trait loci (eQTL). When mapping eQTL, we often deal with thousands of expression traits simultaneously, which complicates the statistical model and data analysis. Two simple approaches may be taken in eQTL analysis: (1) individual transcript analysis in which a single expression trait is mapped at a time and the entire eQTL mapping involves separate analysis of thousands of traits and (2) individual marker analysis where differentially expressed transcripts are detected on the basis of their association with the segregation pattern of an individual marker and the entire analysis requires scanning markers of the entire genome. Neither approach is optimal because data are not analyzed jointly. We develop a Bayesian clustering method that analyzes all expressed transcripts and markers jointly in a single model. A transcript may be simultaneously associated with multiple markers. Additionally, a marker may simultaneously alter the expression of multiple transcripts. This is a model-based method that combines a Gaussian mixture of expression data with segregation of multiple linked marker loci. Parameter estimation for each variable is obtained via the posterior mean drawn from a Markov chain Monte Carlo sample. The method allows a regular quantitative trait to be included as an expression trait and subject to the same clustering assignment. If an expression trait links to a locus where a quantitative trait also links, the expressed transcript is considered to be associated with the quantitative trait. The method is applied to a microarray experiment with 60 F(2) mice measured for 25 different obesity-related quantitative traits. In the experiment, approximately 40,000 transcripts and 145 codominant markers are investigated for their associations. A program written in SAS/IML is available from the authors on request.
| S-EPMC1893048 | biostudies-literature
Project description:Most of the risk loci identified from genome-wide association (GWA) studies do not provide direct information on the biological basis of a disease or on the underlying mechanisms. Recent expression quantitative trait locus (eQTL) association studies have provided information on genetic factors associated with gene expression variation. These eQTLs might contribute to phenotype diversity and disease susceptibility, but interpretation is handicapped by low reproducibility of the expression results. To address this issue, we have generated a set of consensus eQTLs by integrating publicly available data for specific human populations and cell types. Overall, we find over 4000 genes that are involved in high-confidence eQTL relationships. To elucidate the role that eQTLs play in human common diseases, we matched the high-confidence eQTLs to a set of 335 disease risk loci identified from the Wellcome Trust Case Control Consortium GWA study and follow-up studies for 7 human complex trait diseases-bipolar disorder (BD), coronary artery disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). The results show that the data are consistent with ∼50% of these disease loci arising from an underlying expression change mechanism.
| S-EPMC4960478 | biostudies-literature
Project description:BackgroundExpression quantitative trait loci (eQTL) are genomic regions regulating RNA transcript expression levels. Genome-wide Association Studies (GWAS) have identified many variants, often in non-coding regions, with unknown functions and eQTL provide a possible mechanism by which these variants may influence observable phenotypes. Limited access and availability of tissues such as brain has led to the use of blood as a substitute for eQTL analyses.MethodsHere, we evaluate the overlap of eQTL reported in published studies conducted in blood and brain tissues to assess the utility of blood as an alternative to brain tissue in the study of neurological and psychiatric conditions. Expression QTL results from eight published brain studies were compared to blood eQTL identified in from a meta-analysis involving 5,311 individuals. We accounted for differences in SNP platforms and study design by using SNP proxies in high linkage disequilibrium with reported eQTL. The degree of overlap between studies was calculated by ascertaining if an eQTL identified in one study was also identified in the other study.ResultsThe percentage of eQTL overlapping for brain and blood expression after adjusting for differences in sample size ranged from 13 - 23% (mean 19.2%). Amongst pairs of brain studies eQTL overlap ranged from 0 - 35%, with higher degrees of overlap found for studies using expression data collected from the same brain region.ConclusionOur results suggest that whenever possible tissue specific to the pathophysiology of the disease being studied should be used for transcription analysis.
| S-EPMC4066287 | biostudies-literature
Project description:Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.
| S-EPMC10767806 | biostudies-literature
Project description:In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. The underlying trait-associated variants likely reside in regulatory regions and exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying these cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. For a total of 129 left ventricular samples that were collected from non-diseased human donor hearts, genome-wide transcript abundance and genotyping was determined using microarrays. Each of the 18,402 transcripts and 897,683 SNP genotypes that remained after pre-processing and stringent quality control were tested for eQTL effects. We identified 771 eQTLs, regulating 429 unique transcripts. Overlaying these eQTLs with cardiac GWAS loci identified novel candidates for studies aimed at elucidating the functional and transcriptional impact of these loci. Thus, this work provides for the first time a comprehensive eQTL map of human heart: a powerful and unique resource that enables systems genetics approaches for the study of cardiac traits.
| S-EPMC4028258 | biostudies-literature
Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the âbiological signatureâ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either a custom long oligonucleotide microarray for the interrogation of 231 genes encompassed by QTL1 and QTL2, or a TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL genes. A âflip-dyeâ or âbalanced blockâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Six âbalanced blockâ normalized files are submitted for the long oligonucleotide array interrogating the hearts from S versus S.LEW(5)x6x9 animals, fourteen âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x9 animals, and twelve âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x11 animals. GPR and MEV files cannot be located.
2010-06-25 | E-GEOD-7628 | biostudies-arrayexpress
Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the ‘biological signature’ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Keywords: rat, hypertension, genetics, polygenic trait, microarray, gene expression
2008-03-04 | GSE7628 | GEO
Project description: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 < 5 × 10). A total of 73 eQTLs were identified as association signals for the expression of multiple genes. They included 9 eQTLs for both of the genes encoding collagen type I alpha 1 (COL1A1) and integrin alpha 11 (ITGA11), which synthesize a major complex of plasma membrane. They also included eQTLs for type IV collagen molecules; 13 eQTLs for both collagen type IV alpha 1 (COL4A1) and collagen type IV alpha 2 (COL4A2) and 18 eQTLs for both collagen type IV alpha 5 (COL4A5) and collagen type IV alpha 6 (COL4A6). Some genes expressed by the eQTLs might induce expression of the genes encoding type IV collagen. One eQTL (rs16871986) was located in the promoter of palladin (PALLD) gene which might synthesize collagen by activating fibroblasts through the PI3K/AKT pathway. Another eQTL (rs34845474) was located in an enhancer of cadherin related family member 3 (CDHR3) gene which can mediate cell adhesion.This study showed a profile of eQTLs for the genes involved in the PI3K/AKT pathway using a healthy population, revealing 73 eQTLs associated with expression of multiple genes. They might be candidates of common variants in predicting genetic susceptibility to cancer and in targeting cancer therapy. Further studies are required to examine their underlying mechanisms for regulating expression of the genes.
| S-EPMC5228698 | biostudies-literature