Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk
Robert Macinnis, , Daniel F. Schmidt , Enes Makalic , Gianluca Severi , Liesel M. Fitzgerald , Matthias Reumann , Miroslaw K. Kapuscinski , Adam Kowalczyk , Zeyu Zhou , Benjamin Goudey , Guoqi Qian , Quang M. Bui , Daniel J. Park , Adam Freeman , Melissa C. Southey , Ali Amin Al Olama , Zsofia Kote-Jarai , Rosalind A. Eeles , John L. Hopper , Graham Giles
AbstractWe 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.
|Journal series||Cancer Epidemiology Biomarkers & Prevention, ISSN 1055-9965|
|Publication size in sheets||0.5|
|Score|| = 40.0, 05-02-2019, ArticleFromJournal|
= 40.0, 05-02-2019, ArticleFromJournal
|Publication indicators||: 2016 = 1.326; : 2016 = 4.142 (2) - 2016=4.202 (5); = 0|
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