scholarly journals Genetic effects on gene expression across human tissues

Nature ◽  
2017 ◽  
Vol 550 (7675) ◽  
pp. 204-213 ◽  
Author(s):  
Keyword(s):  
Gene Expression ◽  
Genetic Basis ◽  
Tissue Expression ◽  
Genetic Effects ◽  
Human Tissues ◽  
Cellular Mechanisms ◽  
Genetic Traits ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

scholarly journals Genetic Variants Associated mRNA Stability in Lung

2021 ◽  
Author(s):  
Jian-Rong Li ◽  
Mabel Tang ◽  
Yafang Li ◽  
Christopher I Amos ◽  
Chao Cheng
Keyword(s):  
Gene Expression ◽  
Mrna Stability ◽  
Genetic Variants ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Tissue Expression ◽  
Rna Seq ◽  
Genetic Traits ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background: Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).Results: Here, we presented a computational framework that take the advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3,942 genes and 186,132 eQTLs for 4,751 genes from 15,122,700 genetic variants for 13,476 genes, respectively. Interesting, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

Molecular characterization of rat gastric mucosal response to potent acid inhibition

2005 ◽  
Vol 22 (1) ◽  
pp. 24-32 ◽  
Author(s):  
Kristin G. Nørsett ◽  
Astrid Lægreid ◽  
Mette Langaas ◽  
Sara Wörlund ◽  
Reidar Fossmark ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stress Responses ◽  
Clinical Medicine ◽  
Defense Responses ◽  
Acid Inhibition ◽  
Expression Levels ◽  
Cellular Processes ◽  
Gastric Corpus ◽  
Gene Expression Levels

Potent acid inhibition with proton pump inhibitors (PPIs) is widely used in clinical medicine, especially for gastroesophageal reflux disease. PPIs cause profound changes in the intragastric environment with near-neutral pH and increase serum concentration of the gastric secretagogue hormone gastrin. Long-term hypergastrinemia increases mucosal thickness and enterochromaffin-like cell density in gastric corpus mucosa and results in development of gastric carcinoids in experimental animals. Our aim was to study responses to potent acid inhibition by characterizing genome-wide gene expression changes in gastric corpus mucosa in rats dosed with the PPI omeprazole. Nine rats received 400 μmol/kg omeprazole daily for 10 wk. Seven rats received vehicle only. Analysis of gastric corpus with microarrays representing 11,848 genes identified 134 genes with changed gene expression levels in omeprazole-dosed rats. Several of the identified genes were previously known to be affected by potent acid inhibition. Of the 62 genes with known functions that changed gene expression levels after PPI dosing, 27 are known to be involved in proliferation and apoptosis and immune, inflammatory, and stress responses. Our study indicates that microarray analysis can detect relevant gene expression changes in the complex gastric tissue, and that cellular processes involved in cell growth and defense responses are strongly affected by PPI dosing. Many genes are identified that were not previously known to be affected by inhibition of gastric acid secretion or that have unknown biological functions. Characterization of the roles of these genes may give new insight into molecular responses to treatment with PPIs.

scholarly journals Quantifying genetic effects on disease mediated by assayed gene expression levels

2020 ◽  
Vol 52 (6) ◽  
pp. 626-633 ◽  
Author(s):  
Douglas W. Yao ◽  
Luke J. O’Connor ◽  
Alkes L. Price ◽  
Alexander Gusev
Keyword(s):  
Gene Expression ◽  
Genetic Effects ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression

2021 ◽  
Vol 12 ◽  
Author(s):  
Cheng Gao ◽  
Hairong Wei ◽  
Kui Zhang
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Penalized Regression ◽  
Low Rank ◽  
Eqtl Mapping ◽  
Nucleotide Polymorphisms ◽  
Cellular Mechanisms ◽  
Expression Levels ◽  
Gene Expression Levels

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.

scholarly journals Variability in Gene Expression is Associated with Incomplete Penetrance in Inherited Eye Disorders

Genes ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 179 ◽  
Author(s):  
David J. Green ◽  
Shalaw R. Sallah ◽  
Jamie M. Ellingford ◽  
Simon C. Lovell ◽  
Panagiotis I. Sergouniotis
Keyword(s):  
Gene Expression ◽  
Human Gene ◽  
Tissue Expression ◽  
Incomplete Penetrance ◽  
Expression Levels ◽  
Mendelian Disorders ◽  
Eye Disorders ◽  
Mutation Database ◽  
Gene Expression Levels ◽  
Variable Penetrance

Inherited eye disorders (IED) are a heterogeneous group of Mendelian conditions that are associated with visual impairment. Although these disorders often exhibit incomplete penetrance and variable expressivity, the scale and mechanisms of these phenomena remain largely unknown. Here, we utilize publicly-available genomic and transcriptomic datasets to gain insights into variable penetrance in IED. Variants in a curated set of 340 IED-implicated genes were extracted from the Human Gene Mutation Database (HGMD) 2019.1 and cross-checked with the Genome Aggregation Database (gnomAD) 2.1 control-only dataset. Genes for which >1 variants were encountered in both HGMD and gnomAD were considered to be associated with variable penetrance (n = 56). Variability in gene expression levels was then estimated for the subset of these genes that was found to be adequately expressed in two relevant resources: the Genotype-Tissue Expression (GTEx) and Eye Genotype Expression (EyeGEx) datasets. We found that genes suspected to be associated with variable penetrance tended to have significantly more variability in gene expression levels in the general population (p = 0.0000015); this finding was consistent across tissue types. The results of this study point to the possible influence of cis and/or trans-acting elements on the expressivity of variants causing Mendelian disorders. They also highlight the potential utility of quantifying gene expression as part of the investigation of families showing evidence of variable penetrance.

scholarly journals Quantifying genetic effects on disease mediated by assayed gene expression levels

2019 ◽  
Author(s):  
Douglas W. Yao ◽  
Luke J. O’Connor ◽  
Alkes L. Price ◽  
Alexander Gusev
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Genetic Effects ◽  
Genetic Component ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Gene Sets ◽  
Wide Range ◽  
Gene Expression Levels

AbstractDisease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs). However, it remains unclear whether this overlap is driven by mediation of genetic effects on disease by expression levels, or whether it primarily reflects pleiotropic relationships instead. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of assayed steady-state gene expression levels, using summary association statistics from GWAS and eQTL studies. We show that MESC produces robust estimates of expression-mediated heritability across a wide range of simulations. We applied MESC to GWAS summary statistics for 42 diseases and complex traits (average N = 323K) and cis-eQTL data across 48 tissues from the GTEx consortium. We determined that a statistically significant but low proportion of disease heritability (mean estimate 11% with S.E. 2%) is mediated by the cis-genetic component of assayed gene expression levels, with substantial variation across diseases (point estimates from 0% to 38%). We further partitioned expression-mediated heritability across various gene sets. We observed an inverse relationship between cis-heritability of expression and disease heritability mediated by expression, suggesting that genes with weaker eQTLs have larger causal effects on disease. Moreover, we observed broad patterns of expression-mediated heritability enrichment across functional gene sets that implicate specific gene sets in disease, including loss-of-function intolerant genes and FDA-approved drug targets. Our results demonstrate that eQTLs estimated from steady-state expression levels in bulk tissues are informative of regulatory disease mechanisms, but that such eQTLs are insufficient to explain the majority of disease heritability. Instead, additional assays are necessary to more fully capture the regulatory effects of GWAS variants.

scholarly journals Gene expression profiling ofLeishmania (Leishmania) donovani: overcoming technical variation and exploiting biological variation

Parasitology ◽  
2007 ◽  
Vol 135 (2) ◽  
pp. 183-194 ◽  
Author(s):  
S. DECUYPERE ◽  
M. VANAERSCHOT ◽  
S. RIJAL ◽  
V. YARDLEY ◽  
L. MAES ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Molecular Characterization ◽  
Expression Profiling ◽  
Leishmania Donovani ◽  
Expression Levels ◽  
Intracellular Amastigotes ◽  
Gene Expression Levels

SUMMARYGene expression profiling is increasingly used in the field of infectious diseases for characterization of host, pathogen and the nature of their interaction. The purpose of this study was to develop a robust, standardized method for comparative expression profiling and molecular characterization ofLeishmania donovaniclinical isolates. The limitations and possibilities associated with expression profiling in intracellular amastigotes and promastigotes were assessed through a series of comparative experiments in which technical and biological parameters were scrutinized. On a technical level, our results show that it is essential to use parasite harvesting procedures that involve minimal disturbance of the parasite's environment in order to ‘freeze’ gene expression levels instantly; this is particularly a delicate task for intracellular amastigotes and for specific ‘sensory’ genes. On the biological level, we demonstrate that gene expression levels fluctuate duringin vitrodevelopment of both intracellular amastigotes and promastigotes. We chose to use expression-curves rather than single, specific, time-point measurements to capture this biological variation. Intracellular amastigote protocols need further refinement, but we describe a first generation tool for high-throughput comparative molecular characterization of patients' isolates, based on the changing expression profiles of promastigotes duringin vitrodifferentiation.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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