scholarly journals Gene expression profiling reveals candidate genes related to residual feed intake in duodenum of laying ducks1,2

2017 ◽  
Vol 95 (12) ◽  
pp. 5270-5277 ◽  
Author(s):  
T. Zeng ◽  
L. Huang ◽  
J. Ren ◽  
L. Chen ◽  
Y. Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Candidate Genes ◽  
Feed Intake ◽  
Expression Profiling ◽  
Residual Feed Intake

scholarly journals Analysis of meat quality traits and gene expression profiling of pigs divergent in residual feed intake

Meat Science ◽  
2018 ◽  
Vol 137 ◽  
pp. 265-274 ◽  
Author(s):  
Justyna Horodyska ◽  
Michael Oster ◽  
Henry Reyer ◽  
Anne Maria Mullen ◽  
Peadar G. Lawlor ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Meat Quality ◽  
Feed Intake ◽  
Expression Profiling ◽  
Residual Feed Intake ◽  
Quality Traits ◽  
Meat Quality Traits

scholarly journals Gene expression profiling reveals candidate genes for defining spider silk gland types

2021 ◽  
pp. 103594
Author(s):  
R. Crystal Chaw ◽  
Thomas H. Clarke ◽  
Peter Arensburger ◽  
Nadia A. Ayoub ◽  
Cheryl Y. Hayashi
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Candidate Genes ◽  
Expression Profiling ◽  
Spider Silk ◽  
Silk Gland

The Use of Gene-Expression Profiling to Identify Candidate Genes in Human Sepsis

2007 ◽  
Vol 176 (7) ◽  
pp. 676-684 ◽  
Author(s):  
Benjamin M. P. Tang ◽  
Anthony S. McLean ◽  
Ian W. Dawes ◽  
Stephen J. Huang ◽  
Ruby C. Y. Lin
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Candidate Genes ◽  
Expression Profiling ◽  
Human Sepsis

scholarly journals Integrative analysis of transcriptome-wide association study and gene expression profiling identifies candidate genes associated with stroke

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7435
Author(s):  
Jian Yang ◽  
Bin Yan ◽  
Yajuan Fan ◽  
Lihong Yang ◽  
Binbin Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Candidate Genes ◽  
Expression Profiling ◽  
Enrichment Analysis ◽  
Integrative Analysis ◽  
Functional Gene ◽  
Gene Sets ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Stroke is a major public health burden worldwide. Although genetic variation is known to play a role in the pathogenesis of stroke, the specific pathogenic mechanisms are still unclear. Transcriptome-wide association studies (TWAS) is a powerful approach to prioritize candidate risk genes underlying complex traits. However, this approach has not been applied in stroke. Methods We conducted an integrative analysis of TWAS using data from the MEGASTROKE Consortium and gene expression profiling to identify candidate genes for the pathogenesis of stroke. Gene ontology (GO) enrichment analysis was also conducted to detect functional gene sets. Results The TWAS identified 515 transcriptome-wide significant tissue-specific genes, among which SLC25A44 (P = 5.46E−10) and LRCH1 (P = 1.54E−6) were significant by Bonferroni test for stroke. After validation with gene expression profiling, 19 unique genes were recognized. GO enrichment analysis identified eight significant GO functional gene sets, including regulation of cell shape (P = 0.0059), face morphogenesis (P = 0.0247), and positive regulation of ATPase activity (P = 0.0256). Conclusions Our study identified multiple stroke-associated genes and gene sets, and this analysis provided novel insights into the genetic mechanisms underlying stroke.

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