scholarly journals Regulation of KLF4 Turnover Reveals an Unexpected Tissue-Specific Role of pVHL in Tumorigenesis

2012 ◽  
Vol 45 (2) ◽  
pp. 233-243 ◽  
Author(s):  
Armin M. Gamper ◽  
Xinxian Qiao ◽  
Jennifer Kim ◽  
Liyong Zhang ◽  
Michelle C. DeSimone ◽  
...  
2012 ◽  
Vol 288 (2) ◽  
pp. 1226-1237 ◽  
Author(s):  
Tatiana L. Radzyukevich ◽  
Jonathon C. Neumann ◽  
Tara N. Rindler ◽  
Naomi Oshiro ◽  
David J. Goldhamer ◽  
...  

2015 ◽  
Vol 23 ◽  
pp. A33
Author(s):  
P.K. Sacitharan ◽  
J. Zarebska ◽  
A. Chanalaris ◽  
G. Bou Gharios ◽  
E. J ◽  
...  

2015 ◽  
Vol 60 (2) ◽  
pp. 339
Author(s):  
Armin M. Gamper ◽  
Xinxian Qiao ◽  
Jennifer Kim ◽  
Liyong Zhang ◽  
Michelle C. DeSimone ◽  
...  

2019 ◽  
Vol 138 ◽  
pp. 53-62 ◽  
Author(s):  
Umapathy Dhamodharan ◽  
Amin Karan ◽  
Dornadula Sireesh ◽  
Alladi Vaishnavi ◽  
Arumugam Somasundar ◽  
...  

2019 ◽  
Author(s):  
Xingjie Shi ◽  
Xiaoran Chai ◽  
Yi Yang ◽  
Qing Cheng ◽  
Yuling Jiao ◽  
...  

AbstractTranscriptome-wide association studies (TWAS) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWAS in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. The major limitation of these methods is that they cannot be used to elucidate the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWAS, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make use of widely available GWAS summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and false positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWAS data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.


Author(s):  
Karin Tamm ◽  
Marina Suhorutshenko ◽  
Miia Rm ◽  
Jaak Simm ◽  
Madis Metsis

Endocrinology ◽  
2017 ◽  
Vol 158 (11) ◽  
pp. 4093-4104 ◽  
Author(s):  
Wojciech G Garbacz ◽  
Mengxi Jiang ◽  
Meishu Xu ◽  
Jun Yamauchi ◽  
H Henry Dong ◽  
...  

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Vishnu Hosur ◽  
Bonnie L. Lyons ◽  
Lisa M. Burzenski ◽  
Leonard D. Shultz

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Myoungsoo Lee ◽  
Yongsung Lee ◽  
Jihye Song ◽  
Junhyung Lee ◽  
Sun-Young Chang

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