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.
Highlights
Numerous genome-wide association studies (GWAS) in diverse complex diseases have uncovered hundreds of genetic risk factors by determining hundred thousands of single nucleotide polymorphisms (SNPs) in cohorts of thousands of individuals in a hypothesisfree approach
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 genomewide 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
Downstream analysis was performed for 492,217 SNPs using the software package MOSGWA
Summary
Numerous genome-wide association studies (GWAS) in diverse complex diseases have uncovered hundreds of genetic risk factors by determining hundred thousands of single nucleotide polymorphisms (SNPs) in cohorts of thousands of individuals in a hypothesisfree approach. These findings provide valuable insights into the genetic architecture of common diseases they collectively account for a relatively small proportion of heritability [1]. The remaining CRCs evolve sporadically and are influenced by numerous genetic variants with low penetrance but of high prevalence in the population (>1%) This common disease-common variant hypothesis was formulated in the early days of GWAS, but was relativized when identified risk loci explained only a small fraction of genetic variance in complex traits. More refined concepts include the common disease-rare variant hypothesis [2], the infinitesimal and the broad sense heritability model (discussed in [3])
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