Bayesian and frequentist analysis of an Austrian genome-wide association study of colorectal cancer and advanced adenomas

• Published in: Oncotarget 2017-11, Vol.8 (58), p.98623-98634
• Main Authors: Hofer, Philipp, Hagmann, Michael, Brezina, Stefanie, Dolejsi, Erich, Mach, Karl, Leeb, Gernot, Baierl, Andreas, Buch, Stephan, Sutterlüty-Fall, Hedwig, Karner-Hanusch, Judith, Bergmann, Michael M, Bachleitner-Hofmann, Thomas, Stift, Anton, Gerger, Armin, Rötzer, Katharina, Karner, Josef, Stättner, Stefan, Waldenberger, Melanie, Meitinger, Thomas, Strauch, Konstantin, Linseisen, Jakob, Gieger, Christian, Frommlet, Florian, Gsur, Andrea
• Format: Article
• Language: English
• Subjects: Research Paper
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.21697

Most genome-wide association studies (GWAS) were analyzed using single marker tests in combination with stringent correction procedures for multiple testing. Thus, a substantial proportion of associated single nucleotide polymorphisms (SNPs) remained undetected and may account for missing heritability in complex traits. Model selection procedures present a powerful alternative to identify associated SNPs in high-dimensional settings. In this GWAS including 1060 colorectal cancer cases, 689 cases of advanced colorectal adenomas and 4367 controls we pursued a dual approach to investigate genome-wide associations with disease risk applying both, single marker analysis and model selection based on the modified Bayesian information criterion, mBIC2, implemented in the software package MOSGWA. For different case-control comparisons, we report models including between 1-14 candidate SNPs. A genome-wide significant association of rs17659990 (P=5.43×10 , , chromosome 3p21.2) with colorectal cancer risk was observed. Furthermore, 56 SNPs known to influence susceptibility to colorectal cancer and advanced adenoma were tested in a hypothesis-driven approach and several of them were found to be relevant in our Austrian cohort. After correction for multiple testing (α=8.9×10 ), the most significant associations were observed for SNPs rs10505477 (P=6.08×10 ) and rs6983267 (P=7.35×10 ) of , rs3802842 (P=8.98×10 , ), and rs12953717 (P=4.64×10 , ). All previously unreported SNPs demand replication in additional samples. Reanalysis of existing GWAS datasets using model selection as tool to detect SNPs associated with a complex trait may present a promising resource to identify further genetic risk variants not only for colorectal cancer.