comparison of pathway analysis approaches using lung cancer gwas data sets通路分析方法使用比较肺癌gwas数据集.pdf

Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets 1 2 1 3 4 Gordon Fehringer , Geoffrey Liu , Laurent Briollais , Paul Brennan , Christopher I. Amos , Margaret R. 4 ¨ 5 6 7 1 Spitz , Heike Bickeboller , H. Erich Wichmann , Angela Risch , Rayjean J. Hung * 1 Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada, 2 Department of Medicine and Medical Biophysics, Ontario Cancer Institute/Princess Margaret Hospital, Toronto, Ontario, Canada, 3 International Agency for Research on Cancer (IARC), Lyon, France, 4 Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America, 5 Department of Genetic Epidemiology, University Medical Center, University of Goettingen, Goettingen, Germany, 6 Institute of Epidemiology I, Helmholtz Center Munich, Neuherberg, Germany, 7 Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany 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 G