scholarly journals Genetic Variants Contribute to Gene Expression Variability in Humans

Genetics ◽  
2012 ◽  
Vol 193 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Amanda M. Hulse ◽  
James J. Cai
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Expression Variability

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

scholarly journals Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease

2021 ◽  
Author(s):  
Andrew A. Crawford ◽  
◽  
Sean Bankier ◽  
Elisabeth Altmaier ◽  
Catriona L. K. Barnes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cardiovascular Disease ◽  
Genetic Variants ◽  
Plasma Cortisol ◽  
Statistical Power ◽  
Mendelian Randomisation ◽  
Eqtl Analysis ◽  
Causative Role ◽  
Peripheral Tissues ◽  
Corticosteroid Binding Globulin

AbstractThe stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variations in CBG levels have an effect on delivery of cortisol to peripheral tissues. Two-sample Mendelian randomisation analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with increased odds of chronic ischaemic heart disease (0.32, 95% CI 0.06–0.59) and myocardial infarction (0.21, 95% CI 0.00–0.43) in UK Biobank and similarly in CARDIoGRAMplusC4D. These findings reveal a causative pathway for CBG in determining cortisol action in peripheral tissues and thereby contributing to the aetiology of cardiovascular disease.

scholarly journals Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration

2018 ◽  
Vol 115 (50) ◽  
pp. E11701-E11710 ◽  
Author(s):  
Yoong Wearn Lim ◽  
Haiyin Chen-Harris ◽  
Oleg Mayba ◽  
Steve Lianoglou ◽  
Arthur Wuster ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Immunotherapy ◽  
Genetic Variants ◽  
Immune Cell ◽  
Response To Therapy ◽  
Cell Infiltration ◽  
Cancer Type ◽  
Immune Cell Infiltration ◽  
Durable Response ◽  
The Impact

Cancer immunotherapy has emerged as an effective therapy in a variety of cancers. However, a key challenge in the field is that only a subset of patients who receive immunotherapy exhibit durable response. It has been hypothesized that host genetics influences the inherent immune profiles of patients and may underlie their differential response to immunotherapy. Herein, we systematically determined the association of common germline genetic variants with gene expression and immune cell infiltration of the tumor. We identified 64,094 expression quantitative trait loci (eQTLs) that associated with 18,210 genes (eGenes) across 24 human cancers. Overall, eGenes were enriched for their being involved in immune processes, suggesting that expression of immune genes can be shaped by hereditary genetic variants. We identified the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene as a pan-cancer type eGene whose expression levels stratified overall survival in a subset of patients with bladder cancer receiving anti–PD-L1 (atezolizumab) therapy. Finally, we identified 103 gene signature QTLs (gsQTLs) that were associated with predicted immune cell abundance within the tumor microenvironment. Our findings highlight the impact of germline SNPs on cancer-immune phenotypes and response to therapy; and these analyses provide a resource for integration of germline genetics as a component of personalized cancer immunotherapy.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals Inferring gene regulation dynamics from static snapshots of gene expression variability

2021 ◽  
Vol 104 (4) ◽  
Author(s):  
Euan Joly-Smith ◽  
Zitong Jerry Wang ◽  
Andreas Hilfinger
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Expression Variability
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