↓ Skip to main content

Identifying Causal Genes and Dysregulated Pathways in Complex Diseases

Overview of attention for article published in PLoS Computational Biology, March 2011
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users
f1000
1 research highlight platform

Citations

dimensions_citation
166 Dimensions

Readers on

mendeley
294 Mendeley
citeulike
28 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Identifying Causal Genes and Dysregulated Pathways in Complex Diseases
Published in
PLoS Computational Biology, March 2011
DOI 10.1371/journal.pcbi.1001095
Pubmed ID
Authors

Yoo-Ah Kim, Stefan Wuchty, Teresa M. Przytycka

Abstract

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 294 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 15 5%
Brazil 4 1%
France 3 1%
United Kingdom 3 1%
Korea, Republic of 2 <1%
Spain 2 <1%
Belgium 2 <1%
Germany 1 <1%
India 1 <1%
Other 6 2%
Unknown 255 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 90 31%
Student > Ph. D. Student 77 26%
Professor > Associate Professor 25 9%
Student > Master 24 8%
Professor 19 6%
Other 40 14%
Unknown 19 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 141 48%
Biochemistry, Genetics and Molecular Biology 45 15%
Computer Science 31 11%
Medicine and Dentistry 19 6%
Mathematics 5 2%
Other 22 7%
Unknown 31 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 January 2012.
All research outputs
#8,534,976
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#5,637
of 8,960 outputs
Outputs of similar age
#44,183
of 119,820 outputs
Outputs of similar age from PLoS Computational Biology
#31
of 64 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 119,820 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.