Use of a novel nonparametric version of DEPTH to identify genomic regions associated with prostate cancer risk

Abstract

Background: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis. Methods: We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array. Results: From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions. Conclusions: DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs. Impact: This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation.