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


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.
Author Robert Macinnis,
Robert Macinnis,,,
, Daniel F. Schmidt
Daniel F. Schmidt,,
, Enes Makalic
Enes Makalic,,
, Gianluca Severi
Gianluca Severi,,
, Liesel M. Fitzgerald
Liesel M. Fitzgerald,,
, Matthias Reumann
Matthias Reumann,,
, Miroslaw K. Kapuscinski
Miroslaw K. Kapuscinski,,
, Adam Kowalczyk (FMIS / DCSDCAM) - Univ Melbourne, Ctr Epidemiol & Biostatist, Melbourne, Australia; Univ Melbourne, School Math & Stat, Parkville, Vic, Australia
Adam Kowalczyk,,
- Department of CAD/CAM Systems Design and Computer-Aided Medicine
, Zeyu Zhou
Zeyu Zhou,,
, Benjamin Goudey
Benjamin Goudey,,
et al.`
Journal seriesCancer Epidemiology Biomarkers & Prevention, ISSN 1055-9965
Issue year2016
Publication size in sheets0.5
ASJC Classification2730 Oncology; 2713 Epidemiology
Languageen angielski
Score (nominal)40
ScoreMinisterial score = 40.0, 05-02-2019, ArticleFromJournal
Ministerial score (2013-2016) = 40.0, 05-02-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.326; WoS Impact Factor: 2016 = 4.142 (2) - 2016=4.202 (5); WoS Citations = 0
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