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Hofer, P.* ; Hagmann, M.* ; Brezina, S.* ; Dolejsi, E.* ; Mach, K.* ; Leeb, G.* ; Baierl, A.* ; Buch, S.* ; Sutterlüty-Fall, H.* ; Karner-Hanusch, J.* ; Bergmann, M.M.* ; Bachleitner-Hofmann, T.* ; Stift, A.* ; Gerger, A.* ; Rötzer, K.* ; Karner, J.* ; Stättner, S.* ; Waldenberger, M. ; Meitinger, T. ; Strauch, K. ; Linseisen, J. ; Gieger, C. ; Frommlet, F.* ; Gsur, A.*

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

Oncotarget 8, 98623-98634 (2017)

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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=5.43x10(-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 (alpha=8.9x10(-4)), the most significant associations were observed for SNPs rs10505477 (P=6.08x10(-4)) and rs6983267 (P=7.35x10(-4)) of CASC8, rs3802842 (P=8.98x10(-5), COLCA1,2), and rs12953717 (P=4.64x10(-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.

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