Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel risk loci in colorectal cancer

BackgroundDEPendency of association on the number of Top Hits (DEPTH) is an approach to identify candidate risk regions by considering the risk signals from over-lapping groups of sequential variants across the genome. MethodsWe conducted a DEPTH analysis using a sliding window of 200 SNPs to colorectal cancer (CRC) data from the Colon Cancer Family Registry (CCFR) (5,735 cases and 3,688 controls), and GECCO (8,865 cases and 10,285 controls) studies. A DEPTH score >1 was used to identify risk regions common to both studies. We compared DEPTH results against those from conventional GWAS analyses of these two studies as well as against 132 published risk regions. ResultsInitial DEPTH analysis revealed 2,622 (CCFR) and 3,686 (GECCO) risk regions, of which 569 were common to both studies. Bootstrapping revealed 40 and 49 likely risk regions in the CCFR and GECCO data sets, respectively. Notably, DEPTH identified at least 82 likely risk regions that would not be detected using conventional GWAS methods, nor had they been identified in previous CRC GWASs. We found four reproducible risk regions (2q22.2, 2q33.1, 6p21.32, 13q14.3), with the HLA locus at 6p21 having the highest DEPTH score. The strongest associated SNPs were rs762216297, rs149490268, rs114741460, and rs199707618 for the CCFR data, and rs9270761 for the GECCO data. ConclusionDEPTH can identify novel likely risk regions for CRC not identified using conventional analyses of much larger datasets. ImpactDEPTH has potential as a powerful complementary tool to conventional GWAS analyses for identifying risk regions within the genome.