Author
Listed:
- 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 χ2 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.
Suggested Citation
Gordon Fehringer & Geoffrey Liu & Laurent Briollais & Paul Brennan & Christopher I Amos & Margaret R Spitz & Heike Bickeböller & H Erich Wichmann & Angela Risch & Rayjean J Hung, 2012.
"Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets,"
PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
Handle:
RePEc:plo:pone00:0031816
DOI: 10.1371/journal.pone.0031816
Download full text from publisher
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Peng Wei & Hongwei Tang & Donghui Li, 2012.
"Insights into Pancreatic Cancer Etiology from Pathway Analysis of Genome-Wide Association Study Data,"
PLOS ONE, Public Library of Science, vol. 7(10), pages 1-10, October.
- Albert Rosenberger & Melanie Sohns & Stefanie Friedrichs & Rayjean J Hung & Gord Fehringer & John McLaughlin & Christopher I Amos & Paul Brennan & Angela Risch & Irene Brüske & Neil E Caporaso & Maria, 2017.
"Gene-set meta-analysis of lung cancer identifies pathway related to systemic lupus erythematosus,"
PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0031816. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.