Human Lung Tissue eQTL Study
Ontology highlight
ABSTRACT: Genetic risks underlying respiratory diseases (e.g. COPD and asthma) are carefully studied by large genome-wide association studies (GWAS), where many loci were identified. In the post-GWAS era, the main challenge is to find the causal genes and pathways in GWAS-nominated chromosomal regions and to characterize etiology mechanism. The Lung eQTL Consortium is an international effort to systematically capture the genetic architecture of gene expression regulation in human lung. By studying lung specimens from 1,103 individuals of mostly European ancestry, we report a large number of genetic variants affecting gene expression in the lung, or lung expression quantitative trait loci (eQTL). These lung eQTLs will serve as an important resource to aid in the understanding of the molecular underpinnings of lung biology and its disruption in disease.
REPOSITORIES: dbGaP
Dataset's files
Items per page: 5 1 - 5 of 15 |
Similar Datasets
Project description:African Americans (AAs) are disproportionately affected by metabolic diseases and adverse drug events, with limited publicly available genomic and transcriptomic data to advance the knowledge of the molecular underpinnings or genetic associations to these diseases or drug response phenotypes. To fill this gap, we obtained 60 primary hepatocyte cultures from AA liver donors for genome-wide mapping of expression quantitative trait loci (eQTL) using LAMatrix. We identified 277 eGenes and 19,770 eQTLs, of which 67 eGenes and 7,415 eQTLs are not observed in the Genotype-Tissue Expression Project (GTEx) liver eQTL analysis. Of the eGenes found in GTEx only 25 share the same lead eQTL. These AA-specific eQTLs are less correlated to GTEx eQTL in effect sizes and have larger Fst values compared to eQTLs found in both cohorts (overlapping eQTLs). We assessed the overlap between GWAS variants and their tagging variants with AA hepatocyte eQTLs and demonstrated that AA hepatocyte eQTLs can decrease the number of potential causal variants at GWAS loci. Additionally, we identified 75,002 exon QTLs of which 48.8% are not eQTLs in AA hepatocytes. Our analysis provides the first comprehensive characterization of AA hepatocyte eQTLs and highlights the unique discoveries that are made possible due to the increased genetic diversity within the African ancestry genome.
2020-03-27 | GSE147628 | GEO
Project description:Genome-wide association studies (GWAS) have boosted our knowledge of genetic risk variants in autoimmune diseases (AIDs). Most of the risk variants are located within or near genes with immunological functions, and the majority is found to be non-coding, pointing towards a regulatory role. We have performed a cis expression quantitative trait locus (eQTL) screen to investigate whether single nucleotide polymorphisms (SNPs) associated with AIDs influence gene expression in thymus. Genotyping was performed using the Immunochip and 353 AID associated SNPs were tested against expression of surrounding genes (+/- 1 Mb) from human thymic tissue (N=42). We identified eight genes where the expression was associated with AID risk SNPs at a study-wide level of significance (P < 2.57x10-5). Five genes (FCRL3, RNASET2, C2orf74, SIRPG and SYS1) displayed cis eQTL signals also in other tissues, while for two loci (NPIPB8 and LOC388814), the eQTL signal appear to be thymus-specific. Since many AID risk variants from GWAS have been subsequently fine-mapped in recent Immunochip projects, we explored the overlap between these novel AID risk variants and the thymic eQTL regions. Moreover, we examined the functional annotation of the seven expression altering SNPs (eSNPs). Our study reveals autoimmune risk variants that act as eQTLs in thymus. We have highlighted functional variants within these genetic regions that potentially can represent causal autoimmune risk variants. Total RNA from 42 human thymic samples were obtained from children undergoing cardiac surgery.
2016-01-21 | E-GEOD-77052 | biostudies-arrayexpress
Project description:Multiple gene expression studies have been performed in lung or airway tissues from subjects with chronic obstructive pulmonary disease (COPD). However, in comparison to genome-wide association studies (GWAS), there has been poor replication across these studies. We sought to perform gene expression profiling on a large sample of severe COPD cases and control smokers and use network methods to identify interacting genes and pathways. We collected surgically-resected lung tissue samples from subjects with severe COPD and controls with normal lung function; all subjects were former smokers. Gene expression profiling on homogenized lung tissue was performed using Illumina HumanHT-12 BeadChips. Protein and RNA binding studies, RNA-interference, a murine smoking model, and expression quantitative trait locus analyses were used to identify genes and proteins that may interact with three known COPD GWAS genes: IREB2, HHIP and FAM13A. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to define co-expressed gene modules. Comparing 111 COPD cases and 40 control smokers, 214 genes were differentially expressed; none of these genes were at significant GWAS loci. However, the top differentially expressed gene was HMGB1, which interacts with AGER, a gene implicated through COPD GWAS. The genes differentially expressed in lung tissue showed strong enrichment for putative interactors of IREB2 and HHIP, with less support for FAM13A, based on the gene sets from the in vitro, in vivo, and in silico studies. In the WGCNA, the module most highly associated for COPD was enriched for B-cell pathways, which shared seventeen genes with the results of the Hhip+/- mouse smoking model. As in other common diseases, genes at COPD GWAS loci were not differentially expressed with COPD status; however, using a combination of network methods, experimental studies of interaction and careful phenotype definition, we found differential expression of putative interactors of these genes, and we replicated previous human and mouse microarray results involving B cell pathways.
2017-03-29 | GSE76925 | GEO
Project description:Genome-wide association studies (GWAS) have identified over 300 loci associated with the inflammatory bowel diseases (IBD), but putative causal genes for most are unknown. We conducted the largest disease-focused expression quantitative trait loci (eQTL) analysis using colon tissue from 252 IBD patients to determine genetic effects on gene expression and potential contribution to IBD. Combined with two non-IBD colon eQTL studies, we identified 194 potential target genes for 108 GWAS loci. eQTL in IBD tissue were enriched for IBD GWAS loci colocalizations, provided novel evidence for IBD-associated genes such as ABO and TNFRSF14, and identified additional target genes compared to non-IBD tissue eQTL. IBD-associated eQTL unique to diseased tissue had distinct regulatory and functional characteristics with increased effect sizes. Together, these highlight the importance of eQTL studies in diseased tissue for understanding functional consequences of genetic variants, and elucidating molecular mechanisms and regulation of key genes involved in IBD.
2024-10-16 | GSE279302 | GEO
Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (?eQTLs?) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3?UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3?UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Illumina gene expression microarrays for 60 normal human livers. Expression quantitative trait loci were identified by genome wide association mapping.
2011-06-27 | E-GEOD-28893 | biostudies-arrayexpress
Project description:Profiles of sequence variants that influence gene transcription are very important for understanding mechanisms that affect phenotypic variation and disease susceptibility. Using genotypes at 1.4 million SNPs and a comprehensive transcriptional profile of 15,454 coding genes and 6,113 lincRNA genes obtained from peripheral blood cells of 298 Japanese individuals, we mapped expression quantitative trait loci (eQTLs). We identified 3,804 cis-eQTLs (within 500 kb from target genes) and 165 trans-eQTLs (>500 kb away or on different chromosomes). Cis-eQTLs were often located in transcribed or adjacent regions of genes; among these regions, 5’ untranslated regions and 5’ flanking regions had the largest effects. Epigenetic evidence for regulatory potential accumulated in public databases explained the magnitude of the effects of our eQTLs. Cis-eQTLs were often located near the respective target genes, if not within genes. Large effect sizes were observed with eQTLs near target genes, and effect sizes were obviously attenuated as the eQTL distance from the gene increased. Using a very stringent significance threshold, we identified 165 large-effect trans-eQTLs. We used our eQTL map to assess 8,069 disease-associated SNPs identified in 1,436 genome-wide association studies (GWAS). We identified genes that might be truly causative, but GWAS might have failed to identify for 148 out of the GWAS-identified SNPs; for example, TUFM (P=3.3E-48) was identified for inflammatory bowel disease (early onset); ZFP90 (P=4.4E-34) for ulcerative colitis; and IDUA (P=2.2E-11) for Parkinson's disease. We identified four genes (P<2.0E-14) that might be related to three diseases and two hematological traits; each expression is regulated by trans-eQTLs on a different chromosome than the gene. The study subjects were 301 apparently healthy individuals residing in Nagahama City, Japan. Whole blood was collected from each participant when in a non-stimulated state.
2014-06-23 | E-GEOD-53351 | biostudies-arrayexpress
Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (‘eQTLs’) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3’UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3’UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Agilent gene expression microarrays for 206 normal human livers (464 unique arrays corresponding to those samples). Genotypes were also derived from 224 of these samples using Illumina SNP chips. Expression quantitative trait loci were identified by genome wide association mapping. This submission represents the primary transcriptome component of study.
2011-06-27 | E-GEOD-25935 | biostudies-arrayexpress
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.
2014-04-15 | GSE56789 | GEO
Project description:Coronary artery disease (CAD) is the leading cause of death worldwide. A recent meta-analysis of genome-wide association studies (GWAS) identified 175 loci associated with CAD. The majority of these loci are in non-coding regions and are predicted to regulate gene expression. We hypothesized that a subset of the CAD GWAS loci was associated with the regulation of transcription in vascular smooth muscle cells (SMCs), which play critical roles in the development of CAD. We measured gene expression in SMCs isolated from the ascending aortas of 151 ethnically diverse heart transplant donors in quiescent or proliferative conditions and calculated the association of their expression and splicing with ~6.3 million imputed single nucleotide polymorphism (SNP) markers across the genome. We identified 4,910 expression and 4,412 splice quantitative trait loci (sQTL) that represent regions of the genome associated with transcript abundance and splicing. 3,660 of the expression quantitative trait loci (eQTL) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 of the eQTLs were SMC- and sex-specific, respectively. To identify the effector transcripts regulated by the CAD GWAS loci, we used four distinct colocalization approaches and identified 84 eQTL and 164 sQTLs that colocalized with CAD loci, suggesting a more significant role for the genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. Two of the CAD loci colocolized with SMC eQTL showed sex-specific (AL160313.1 and TERF2IP) and one of the loci colocalized with SMC-specific eQTLs (ALKBH8). 27% and 37% of the sQTLs were unique to quiescent or proliferative SMCs, respectively. The most significantly associated CAD locus, 9p21, was an sQTL for the long non-coding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. Collectively, these results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in vascular smooth muscle cells.'
2022-10-03 | GSE198544 | GEO
Project description:Coronary artery disease (CAD) is the leading cause of death worldwide. A recent meta-analysis of genome-wide association studies (GWAS) identified 175 loci associated with CAD. The majority of these loci are in non-coding regions and are predicted to regulate gene expression. We hypothesized that a subset of the CAD GWAS loci was associated with the regulation of transcription in vascular smooth muscle cells (SMCs), which play critical roles in the development of CAD. We measured gene expression in SMCs isolated from the ascending aortas of 151 ethnically diverse heart transplant donors in quiescent or proliferative conditions and calculated the association of their expression and splicing with ~6.3 million imputed single nucleotide polymorphism (SNP) markers across the genome. We identified 4,910 expression and 4,412 splice quantitative trait loci (sQTL) that represent regions of the genome associated with transcript abundance and splicing. 3,660 of the expression quantitative trait loci (eQTL) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 of the eQTLs were SMC- and sex-specific, respectively. To identify the effector transcripts regulated by the CAD GWAS loci, we used four distinct colocalization approaches and identified 84 eQTL and 164 sQTLs that colocalized with CAD loci, suggesting a more significant role for the genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. Two of the CAD loci colocolized with SMC eQTL showed sex-specific (AL160313.1 and TERF2IP) and one of the loci colocalized with SMC-specific eQTLs (ALKBH8). 27% and 37% of the sQTLs were unique to quiescent or proliferative SMCs, respectively. The most significantly associated CAD locus, 9p21, was an sQTL for the long non-coding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. Collectively, these results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in vascular smooth muscle cells.'
2022-01-20 | GSE193817 | GEO