Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA173725 | ENA |
REPOSITORIES: ENA
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Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. We have developed HEFT, a fast multivariate method that detect eQTLs by analyzing thousands of traits simultaneously in the presence of hidden factors. HEFT employs a combined regression factor analysis approach to analyze the gene expression and genotype data sets, looking for univariate or multivariate eQTLs that regulate gene expression, while simultaneously controlling for both orthogonal or non-orthogonal hidden factors. We show by extensive simulation that HEFT outperforms competing methods, and by applying HEFT to a study that included presumptive hidden factors, we identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis. HEFT analysis results file (includes all results, not just top hits) linked below as supplementary file.
2014-01-07 | E-GEOD-40364 | biostudies-arrayexpress
Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer.
2014-01-07 | GSE40364 | GEO
Project description:eQTL analysis of 5 DAP Zea mays (maize) kernels
2020-04-16 | E-GEOD-110315 | ExpressionAtlas
Project description:We performed expression quantitative trait locus (eQTL) mapping between two strains of Tetranychus urticae (the two-spotted spider mite), a generalist herbivore known for its rapid evolution of acaricide resistance. For parents, we used the inbred strain MR-VPi that is highly resistant to multiple acaricides in different classes, and the inbred strain ROS-ITi that is comparatively susceptible to many acaricides. The eQTL mapping experiment was performed with F3 samples, and thousands of trans and cis eQTL were identified, including for genes in families known (or suspected) to be involved in the metabolism of xenobiotics (plant produced secondary compounds and acaricides). One trans eQTL hotspot was identified that strongly impacted the expression of many detoxification genes in different gene families. Follow up studies using derived near isogenic lines validated the trans eQTL hotspot, and RNA interference (RNAi) knockdown of tandemly duplicated genes encoding products with homology to the ligand binding domains of nuclear hormone receptor 96 genes impacted many of the same detoxification genes controlled by the hotspot.
2023-07-14 | GSE221677 | GEO
Project description:<p>Schizophrenia is a common and severe psychotic disorder. While some common SNPs and rare copy number variants have been identified as being significantly associated with disease risk, the biological mechanisms remain undefined. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using microarrays on lymphoblastoid cell lines from a total of 413 cases and 446 controls. Regression analysis identified 95 transcripts differentially expressed by affection status at a genome-wide false discovery rate of 0.05, while simultaneously controlling for confounding effects. These transcripts represented 89 genes with functions such as neurotransmission, gene regulation, cell cycle progression, differentiation, apoptosis, and immunity. The observed differential expression of extended major histocompatibility complex region genes converges with the genetic evidence from schizophrenia genome-wide association studies, which find the same region to be the most significant schizophrenia susceptibility locus. Our analysis also provides novel candidate genes for further study to assess their potential contribution to schizophrenia.</p>
| phs000775 | dbGaP
Project description:Analyses of QTLs for expression levels (eQTLs) of the genes reveal genetic relationship between expression variation and the regulator, thus unlocking the information for identifying the regulatory network. Oligo-nucleotide expression microarrays hybridized with RNA can simultaneously provide data for molecular markers and transcript abundance. In this study, we used Affymetrix GeneChip Rice Genome Array to analyze eQTLs in rice shoots at 72 h after germination from 110 recombinant inbred lines (RILs) derived from a cross between Zhenshan 97 and Minghui 63. Totally 1,632 single feature polymorphisms (SFPs) plus 23 PCR markers were identified and placed into 601 recombinant bins, spanning 1,459 cM in length, which were used as markers to genotype the RILs. We obtained 16,372 expression traits (e-traits) each with at least one eQTL, resulting in 26,051 eQTLs in total, including both cis- and trans-eQTLs. We also identified 171 eQTL hotspots among rice genome, each of which controls transcript variations of many e-traits. Gene Ontology analysis revealed enrichment of certain functional categories of genes in some of the eQTL hotspots. In particular, eQTLs for e-traits involving DNA metabolic process was significantly enriched in several eQTL hotspots on chromosomes 3, 5 and 10. Several transcription factors colocalizing with cis-eQTLs showed significant correlations with hundreds of e-traits, indicating possible co-regulation. We also detected correlations between the QTLs for shoot dry weight and eQTLs, revealing possible candidate genes for the trait. These results provided the clues for identification and characterization of regulatory network in the whole genome at the transcriptional level.
2011-06-24 | GSE22564 | GEO
Project description:Microarray technologies,which can measure the expression of thousands of genes simultaneously are useful in understanding global gene networks and identifying novel genes and functional gene classes.The adipose depots may have various adipogenic state-specific genes and regulations of adipose accretion patterns in beef cattle.Therefore,the purpose of this study was to examine the molecular mechanisms of longissimus dorsi muscle,subcutaneous and abdominal adipose tissue depots in a native Chinese yellow breed by identifying differentially expressed genes using Bovine Genome Array.The GO and pathway analysis further validated differentially expressed genes identified in array analysis.
2016-08-18 | GSE49992 | GEO
Project description:Genome-wide gene expression profiling has been extensively used to generate biological hypotheses based on differential expression. Recently, many studies have used microarrays to measure gene expression levels across genetic mapping populations. These gene expression phenotypes have been used for genome-wide association analyses, an analysis referred to as expression QTL (eQTL) mapping. Here, eQTL analysis was performed in fat issue from 28 inbred strains of mice. We focused our analysis on “trans-eQTL bands”, defined as instances in which the expression patterns of many genes were all associated to a common genetic locus. Genes comprising trans-eQTL bands were screened for enrichments in functional gene sets representing known biological pathways, while genes located at associated trans-eQTL band loci were considered candidate transcriptional modulators. We demonstrate that these patterns reflect previously characterized relationships between known upstream transcriptional regulators and their downstream target genes. Moreover, we used this strategy to identify both novel regulators and novel members of known pathways. Finally, based on putative regulatory relationship identified in our analysis, we validated in vitro a previously uncharacterized role for cyclin H in the regulation of oxidative phosphorylation. Keywords: strain comparison
2008-05-01 | GSE8028 | GEO
Project description:Analysis of TBR1 downnstream target genes in embryonic forebrains
2015-07-21 | E-GEOD-49237 | ExpressionAtlas
Project description:Genome-wide analysis of genes regulated by ARR22 overexpression.
2015-06-05 | E-MEXP-3685 | ExpressionAtlas