Random forests for genetic association studies

Benjamin A. Goldstein, Eric Polley, Farren B S Briggs

Research output: Contribution to journalReview article

71 Scopus citations

Abstract

The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

Original languageEnglish (US)
Article number32
JournalStatistical Applications in Genetics and Molecular Biology
Volume10
Issue number1
DOIs
StatePublished - Aug 12 2011
Externally publishedYes

    Fingerprint

Keywords

  • genome wide association studies
  • machine learning
  • SNP

ASJC Scopus subject areas

  • Genetics
  • Molecular Biology
  • Statistics and Probability
  • Computational Mathematics

Cite this