Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants.
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ABSTRACT:
SUBMITTER: Chandra V
PROVIDER: S-EPMC8053422 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:Pharmacogenomics has employed candidate gene studies and, more recently, genome-wide association studies (GWAS) in efforts to identify loci associated with drug response and/or toxicity. The advantage of GWAS is the simultaneous, unbiased testing of millions of SNPs; the challenge is that functional information is absent for the vast majority of loci that are implicated. In the present study, we systematically evaluated SNPs associated with chemotherapeutic agent-induced cytotoxicity for six different anticancer agents and evaluated whether these SNPs were disproportionately likely to be within a functional class such as coding (consisting of missense, nonsense, or frameshift polymorphisms), noncoding (such as 3'UTRs or splice sites), or expression quantitative trait loci (eQTLs; indicating that a SNP genotype is associated with the transcript abundance level of a gene). We found that the chemotherapeutic drug susceptibility-associated SNPs are more likely to be eQTLs, and, in fact, more likely to be associated with the transcriptional expression level of multiple genes (n > or = 10) as potential master regulators, than a random set of SNPs in the genome, conditional on minor allele frequency. Furthermore, we observed that this enrichment compared with random expectation is not present for other traditionally important coding and noncoding SNP functional categories. This research therefore has significant implications as a general approach for the identification of genetic predictors of drug response and provides important insights into the likely function of SNPs identified in GWAS analysis of pharmacologic studies.
| S-EPMC2889115 | biostudies-literature
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
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
Project description: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.
| S-EPMC8009949 | biostudies-literature
Project description:BACKGROUND:Expression quantitative trait loci (eQTL) analysis is a powerful method to detect correlations between gene expression and genomic variants and is widely used to interpret the biological mechanism underlying identified genome wide association studies (GWAS) risk loci. Numerous eQTL studies have been performed on different cell types and tissues of which the majority has been based on microarray technology. METHODS:We present here an eQTL analysis based on cap analysis gene expression sequencing (CAGEseq) data created from human postmortem frontal lobe tissue combined with genotypes obtained through genotyping arrays, exome sequencing, and CAGEseq. Using CAGEseq as an expression profiling technique combined with these different genotyping techniques allows measurement of the molecular effect of variants on individual transcription start sites and increases the resolution of eQTL analysis by also including the non-annotated parts of the genome. RESULTS:We identified 2410 eQTLs and show that non-coding transcripts are more likely to contain an eQTL than coding transcripts, in particular antisense transcripts. We provide evidence for how previously identified GWAS loci for schizophrenia (NRGN), Parkinson's disease, and Alzheimer's disease (PARK16 and MAPT loci) could increase the risk for disease at a molecular level. Furthermore, we demonstrate that CAGEseq improves eQTL analysis because variants obtained from CAGEseq are highly enriched for having a functional effect and thus are an efficient method towards the identification of causal variants. CONCLUSION:Our data contain both coding and non-coding transcripts and has the added value that we have identified eQTLs for variants directly adjacent to TSS. Future eQTL studies would benefit from combining CAGEseq with RNA sequencing for a more complete interpretation of the transcriptome and increased understanding of eQTL signals.
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Project description:Clinical studies of non-communicable diseases identify multimorbidities that suggest a common set of predisposing factors. Despite the fact that humans have ~24,000 genes, we do not understand the genetic pathways that contribute to the development of multimorbid non-communicable disease. Here we create a multimorbidity atlas of traits based on pleiotropy of spatially regulated genes. Using chromatin interaction and expression Quantitative Trait Loci (eQTL) data, we analyse 20,782 variants (p?<?5?×?10-6) associated with 1351 phenotypes to identify 16,248 putative spatial eQTL-eGene pairs that are involved in 76,013 short- and long-range regulatory interactions (FDR?<?0.05) in different human tissues. Convex biclustering of spatial eGenes that are shared among phenotypes identifies complex interrelationships between nominally different phenotype-associated SNPs. Our approach enables the simultaneous elucidation of variant interactions with target genes that are drivers of multimorbidity, and those that contribute to unique phenotype associated characteristics.
| S-EPMC6281603 | biostudies-literature
Project description:Meta-analysis is an important tool in linkage analysis. The pooling of results across primary linkage studies allows greater statistical power to detect quantitative-trait loci (QTLs) and more-precise estimation of their genetic effects and, hence, yields conclusions that are stronger relative to those of individual studies. Previous methods for the meta-analysis of linkage studies have been proposed, and, although some methods address the problem of between-study heterogeneity, most methods still require linkage analysis at the same marker or set of markers across studies, whereas others do not result in an estimate of genetic variance. In this study, we present a meta-analytic procedure to evaluate evidence from several studies that report Haseman-Elston statistics for linkage to a QTL at multiple, possibly distinct, markers on a chromosome. This technique accounts for between-study heterogeneity and estimates both the location of the QTL and the magnitude of the genetic effect more precisely than does an individual study. We also provide standard errors for the genetic effect and for the location (in cM) of the QTL, using a resampling method. The approach can be applied under other conditions, provided that the various studies use the same linkage statistic.
| S-EPMC384993 | biostudies-other
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