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Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

Overview of attention for article published in BioData Mining, December 2016
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Mentioned by

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3 X users
wikipedia
1 Wikipedia page

Citations

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6 Dimensions

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30 Mendeley
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Title
Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies
Published in
BioData Mining, December 2016
DOI 10.1186/s13040-016-0119-z
Pubmed ID
Authors

Samantha Cheng, Angeline S. Andrew, Peter C. Andrews, Jason H. Moore

Abstract

Bladder cancer is common disease with a complex etiology that is likely due to many different genetic and environmental factors. The goal of this study was to embrace this complexity using a bioinformatics analysis pipeline designed to use machine learning to measure synergistic interactions between single nucleotide polymorphisms (SNPs) in two genome-wide association studies (GWAS) and then to assess their enrichment within functional groups defined by Gene Ontology. The significance of the results was evaluated using permutation testing and those results that replicated between the two GWAS data sets were reported. In the first step of our bioinformatics pipeline, we estimated the pairwise synergistic effects of SNPs on bladder cancer risk in both GWAS data sets using Multifactor Dimensionality Reduction (MDR) machine learning method that is designed specifically for this purpose. Statistical significance was assessed using a 1000-fold permutation test. Each single SNP was assigned a p-value based on its strongest pairwise association. Each SNP was then mapped to one or more genes using a window of 500 kb upstream and downstream from each gene boundary. This window was chosen to capture as many regulatory variants as possible. Using Exploratory Visual Analysis (EVA), we then carried out a gene set enrichment analysis at the gene level to identify those genes with an overabundance of significant SNPs relative to the size of their mapped regions. Each gene was assigned to a biological functional group defined by Gene Ontology (GO). We next used EVA to evaluate the overabundance of significant genes in biological functional groups. Our study yielded one GO category, carboxy-lysase activity (GO:0016831), that was significant in analyses from both GWAS data sets. Interestingly, only the gamma-glutamyl carboxylase (GGCX) gene from this GO group was significant in both the detection and replication data, highlighting the complexity of the pathway-level effects on risk. The GGCX gene is expressed in the bladder, but has not been previously associated with bladder cancer in univariate GWAS. However, there is some experimental evidence that carboxy-lysase activity might play a role in cancer and that genes in this pathway should be explored as drug targets. This study provides a genetic basis for that observation. Our machine learning analysis of genetic associations in two GWAS for bladder cancer identified numerous associations with pairs of SNPs. Gene set enrichment analysis found aggregation of risk-associated SNPs in genes and significant genes in GO functional groups. This study supports a role for decarboxylase protein complexes in bladder cancer susceptibility. Previous research has implicated decarboxylases in bladder cancer etiology; however, the genes that we found to be significant in the detection and replication data are not known to have direct influence on bladder cancer, suggesting some novel hypotheses. This study highlights the need for a complex systems approach to the genetic and genomic analysis of common diseases such as cancer.

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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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 3%
United States 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 23%
Researcher 5 17%
Student > Ph. D. Student 3 10%
Student > Postgraduate 2 7%
Student > Doctoral Student 1 3%
Other 4 13%
Unknown 8 27%
Readers by discipline Count As %
Computer Science 6 20%
Medicine and Dentistry 5 17%
Agricultural and Biological Sciences 4 13%
Biochemistry, Genetics and Molecular Biology 2 7%
Physics and Astronomy 1 3%
Other 3 10%
Unknown 9 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 May 2017.
All research outputs
#6,315,653
of 22,914,829 outputs
Outputs from BioData Mining
#136
of 308 outputs
Outputs of similar age
#116,562
of 418,945 outputs
Outputs of similar age from BioData Mining
#3
of 6 outputs
Altmetric has tracked 22,914,829 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 308 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 55% of its peers.
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 418,945 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.