scholarly journals G6PC2 Modulates Fasting Blood Glucose In Male Mice in Response to Stress

Endocrinology ◽  
2016 ◽  
Vol 157 (8) ◽  
pp. 3002-3008 ◽  
Author(s):  
Kayla A. Boortz ◽  
Kristen E. Syring ◽  
Chunhua Dai ◽  
Lynley D. Pound ◽  
James K. Oeser ◽  
...  

The glucose-6-phosphatase catalytic 2 (G6PC2) gene is expressed specifically in pancreatic islet beta cells. Genome-wide association studies have shown that single nucleotide polymorphisms in the G6PC2 gene are associated with variations in fasting blood glucose (FBG) but not fasting plasma insulin. Molecular analyses examining the functional effects of these single nucleotide polymorphisms demonstrate that elevated G6PC2 expression is associated with elevated FBG. Studies in mice complement these genome-wide association data and show that deletion of the G6pc2 gene lowers FBG without affecting fasting plasma insulin. This suggests that, together with glucokinase, G6PC2 forms a substrate cycle that determines the glucose sensitivity of insulin secretion. Because genome-wide association studies and mouse studies demonstrate that elevated G6PC2 expression raises FBG and because chronically elevated FBG is detrimental to human health, increasing the risk of type 2 diabetes, it is unclear why G6PC2 evolved. We show here that the synthetic glucocorticoid dexamethasone strongly induces human G6PC2 promoter activity and endogenous G6PC2 expression in isolated human islets. Acute treatment with dexamethasone selectively induces endogenous G6pc2 expression in 129SvEv but not C57BL/6J mouse pancreas and isolated islets. The difference is due to a single nucleotide polymorphism in the C57BL/6J G6pc2 promoter that abolishes glucocorticoid receptor binding. In 6-hour fasted, nonstressed 129SvEv mice, deletion of G6pc2 lowers FBG. In response to the stress of repeated physical restraint, which is associated with elevated plasma glucocorticoid levels, G6pc2 gene expression is induced and the difference in FBG between wild-type and knockout mice is enhanced. These data suggest that G6PC2 may have evolved to modulate FBG in response to stress.

Author(s):  
Yingjie Guo ◽  
Chenxi Wu ◽  
Zhian Yuan ◽  
Yansu Wang ◽  
Zhen Liang ◽  
...  

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.


2014 ◽  
Vol 26 (2) ◽  
pp. 567-582 ◽  
Author(s):  
Zhongxue Chen ◽  
Hon Keung Tony Ng ◽  
Jing Li ◽  
Qingzhong Liu ◽  
Hanwen Huang

In the past decade, hundreds of genome-wide association studies have been conducted to detect the significant single-nucleotide polymorphisms that are associated with certain diseases. However, most of the data from the X chromosome were not analyzed and only a few significant associated single-nucleotide polymorphisms from the X chromosome have been identified from genome-wide association studies. This is mainly due to the lack of powerful statistical tests. In this paper, we propose a novel statistical approach that combines the information of single-nucleotide polymorphisms on the X chromosome from both males and females in an efficient way. The proposed approach avoids the need of making strong assumptions about the underlying genetic models. Our proposed statistical test is a robust method that only makes the assumption that the risk allele is the same for both females and males if the single-nucleotide polymorphism is associated with the disease for both genders. Through simulation study and a real data application, we show that the proposed procedure is robust and have excellent performance compared to existing methods. We expect that many more associated single-nucleotide polymorphisms on the X chromosome will be identified if the proposed approach is applied to current available genome-wide association studies data.


2020 ◽  
Vol 36 (19) ◽  
pp. 4957-4959
Author(s):  
David B Blumenthal ◽  
Lorenzo Viola ◽  
Markus List ◽  
Jan Baumbach ◽  
Paolo Tieri ◽  
...  

Abstract Summary Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes. Availability and implementation EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary information Supplementary data are available at Bioinformatics online.


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