An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings.

ABSTRACT:

Background

As computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (GWA) datasets becomes possible. While most traditional statistical methods can only elucidate main effects of genetic variants on risk for disease, certain machine learning approaches are particularly suited to discover higher order and non-linear effects. One such approach is the Random Forests (RF) algorithm. The use of RF for SNP discovery related to human disease has grown in recent years; however, most work has focused on small datasets or simulation studies which are limited.

Results

Using a multiple sclerosis (MS) case-control dataset comprised of 300 K SNP genotypes across the genome, we outline an approach and som

SUBMITTER: Goldstein BA 

PROVIDER: S-EPMC2896336 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature