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

// Philipp Hofer 1 , Michael Hagmann 2 , Stefanie Brezina 1 , Erich Dolejsi 2 , Karl Mach 3 , Gernot Leeb 3 , Andreas Baierl 4 , Stephan Buch 5 , Hedwig Sutterlüty-Fall 1 , Judith Karner-Hanusch 6 , Michael M. Bergmann 6 , Thomas Bachleitner-Hofmann 6 , Anton Stift 6 , Armin Gerger 7 , Katharina Rötzer 7 , Josef Karner 8 , Stefan Stättner 8 , Melanie Waldenberger 9 , Thomas Meitinger 9 , Konstantin Strauch 9, 10 , Jakob Linseisen 9 , Christian Gieger 9 , Florian Frommlet 2 and Andrea Gsur 1 1 Institute of Cancer Research, Medical University of Vienna, Vienna, Austria 2 Center for Medical Statistics, Informatics, and Intelligent Systems, Section for Medical Statistics, Medical University of Vienna, Vienna, Austria 3 Hospital Oberpullendorf, Oberpullendorf, Austria 4 Department of Statistics and Operations Research, University of Vienna, Vienna, Austria 5 University Hospital Dresden, Dresden, Germany 6 Department of Surgery, Medical University of Vienna, Vienna, Austria 7 Division of Oncology, Medical University of Graz, Graz, Austria 8 Sozialmedizinisches Zentrum Süd, Vienna, Austria 9 Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany 10 Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany Correspondence to: Andrea Gsur, email: andrea.gsur@meduniwien.ac.at Keywords: advanced colorectal adenomas; colorectal cancer; GWAS; model selection; MOSGWA Received: July 26, 2017 Accepted: September 03, 2017 Published: October 09, 2017 ABSTRACT 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 -9 , DOCK3 , 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 -4 ), the most significant associations were observed for SNPs rs10505477 (P=6.08×10 -4 ) and rs6983267 (P=7.35×10 -4 ) of CASC8, rs3802842 (P=8.98×10 -5 , COLCA1,2 ), and rs12953717 (P=4.64×10 -4 , SMAD7 ). 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.