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.

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.

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.

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.'

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Human Lung Tissue eQTL Study

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