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 sho

SUBMITTER: Szymczak S 

PROVIDER: S-EPMC4736152 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature