title: r2VIM: A new variable selection method for random forests in genome-wide association studies.
abstract: BACKGROUND
  Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses.
  
  
  RESULTS
  We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS.
  
  
  CONCLUSIONS
  The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.
authors.affiliation: Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Dr, 21224 Baltimore, USA ; Current address: Institute of Medical Informatics and Statistics, University of Kiel, Brunswiker Str. 10, 24105 Kiel, Germany.
authors.forename: Silke
authors.initials: S
authors.lastname: Szymczak
authors.affiliation: Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Dr, 21224 Baltimore, USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Holzinger
authors.affiliation: Clinical Trials and Outcomes Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, 1 AMS Circle, 20892 Bethesda, USA.
authors.forename: Abhijit
authors.initials: A
authors.lastname: Dasgupta
authors.affiliation: Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, 12 South Dr, 20892 Bethesda, USA.
authors.forename: James D
authors.initials: JD
authors.lastname: Malley
authors.affiliation: Department of Clinical Medicine, School of Medicine, Trinity College Dublin, 152-160 Pearse Street, 2 Dublin, Ireland.
authors.forename: Anne M
authors.initials: AM
authors.lastname: Molloy
authors.affiliation: Division of Intramural Population Health Research, Eunice Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6100 Executive Blvd, 20892 Bethesda, USA.
authors.forename: James L
authors.initials: JL
authors.lastname: Mills
authors.affiliation: Molecular Pathogenesis Section, Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, 50 South Dr, 20892 Bethesda, USA.
authors.forename: Lawrence C
authors.initials: LC
authors.lastname: Brody
authors.affiliation: Department of Ophthalmology, University of Pennsylvania, 422 Curie Blvd, 19104 Philadelphia, USA.
authors.forename: Dwight
authors.initials: D
authors.lastname: Stambolian
authors.affiliation: Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Dr, 21224 Baltimore, USA.
authors.forename: Joan E
authors.initials: JE
authors.lastname: Bailey-Wilson
keywords: Genetic; Genome-wide association study; Machine learning; Random forest; SNP; Variable importance; Variable selection
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