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Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets

Overview of attention for article published in PLOS ONE, February 2012
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Title
Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0031816
Pubmed ID
Authors

Gordon Fehringer, Geoffrey Liu, Laurent Briollais, Paul Brennan, Christopher I. Amos, Margaret R. Spitz, Heike Bickeböller, H. Erich Wichmann, Angela Risch, Rayjean J. Hung

Abstract

Pathway analysis has been proposed as a complement to single SNP analyses in GWAS. This study compared pathway analysis methods using two lung cancer GWAS data sets based on four studies: one a combined data set from Central Europe and Toronto (CETO); the other a combined data set from Germany and MD Anderson (GRMD). We searched the literature for pathway analysis methods that were widely used, representative of other methods, and had available software for performing analysis. We selected the programs EASE, which uses a modified Fishers Exact calculation to test for pathway associations, GenGen (a version of Gene Set Enrichment Analysis (GSEA)), which uses a Kolmogorov-Smirnov-like running sum statistic as the test statistic, and SLAT, which uses a p-value combination approach. We also included a modified version of the SUMSTAT method (mSUMSTAT), which tests for association by averaging χ(2) statistics from genotype association tests. There were nearly 18000 genes available for analysis, following mapping of more than 300,000 SNPs from each data set. These were mapped to 421 GO level 4 gene sets for pathway analysis. Among the methods designed to be robust to biases related to gene size and pathway SNP correlation (GenGen, mSUMSTAT and SLAT), the mSUMSTAT approach identified the most significant pathways (8 in CETO and 1 in GRMD). This included a highly plausible association for the acetylcholine receptor activity pathway in both CETO (FDR≤0.001) and GRMD (FDR = 0.009), although two strong association signals at a single gene cluster (CHRNA3-CHRNA5-CHRNB4) drive this result, complicating its interpretation. Few other replicated associations were found using any of these methods. Difficulty in replicating associations hindered our comparison, but results suggest mSUMSTAT has advantages over the other approaches, and may be a useful pathway analysis tool to use alongside other methods such as the commonly used GSEA (GenGen) approach.

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Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Germany 1 2%
Italy 1 2%
Norway 1 2%
Spain 1 2%
United Kingdom 1 2%
Unknown 48 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 30%
Researcher 12 21%
Professor > Associate Professor 5 9%
Student > Master 5 9%
Student > Bachelor 3 5%
Other 9 16%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 38%
Biochemistry, Genetics and Molecular Biology 8 14%
Medicine and Dentistry 8 14%
Computer Science 6 11%
Veterinary Science and Veterinary Medicine 1 2%
Other 4 7%
Unknown 8 14%