Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.

Journal: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Published Date: Aug 18, 2016

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.

Authors

  • Robert J MacInnis
    Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.
  • Daniel F Schmidt
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
  • Enes Makalic
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
  • Gianluca Severi
    Human Genetics Foundation, Torino, Italy.
  • Liesel M FitzGerald
    Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania.
  • Matthias Reumann
    IBM Research, Zurich, Switzerland.
  • Miroslaw K Kapuscinski
    Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.
  • Adam Kowalczyk
    Warsaw University of Technology, Warsaw, Poland.
  • Zeyu Zhou
    IBM Research - Australia, Carlton, Australia.
  • Benjamin Goudey
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
  • Guoqi Qian
    School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.
  • Quang M Bui
    Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.
  • Daniel J Park
    Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Victoria, Australia.
  • Adam Freeman
    Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.
  • Melissa C Southey
    Department of Pathology, University of Melbourne, Carlton, Victoria, Australia.
  • Ali Amin Al Olama
    Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
  • Zsofia Kote-Jarai
    The Institute of Cancer Research, London, United Kingdom.
  • Rosalind A Eeles
  • John L Hopper
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
  • Graham G Giles
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.

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