Dataset Information

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.

ABSTRACT:

Motivation

Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohn's disease. This may partly be due to the low power of standard statistical approaches to detect gene-gene and gene-environment interactions when small marginal effects are present. A promising alternative is Random Forests, which have already been successfully applied in candidate gene analyses. Important single nucleotide polymorphisms are detected by permutation importance measures. To this day, the application to GWA data was highly cumbersome with existing implementations because of the high computational burden.

Results

Here, we present the new freely available software package Random Jungle (RJ), which facilitates the rapid analysis of GWA data. The program yields valid results and computes up to 159 times faster than the fastest alternative implementation, while still maintaining all options of other programs. Specifically, it offers the different permutation importance measures available. It includes new options such as the backward elimination method. We illustrate the application of RJ to a GWA of Crohn's disease. The most important single nucleotide polymorphisms (SNPs) validate recent findings in the literature and reveal potential interactions.

Availability

The RJ software package is freely available at http://www.randomjungle.org

SUBMITTER: Schwarz DF

PROVIDER: S-EPMC2894507 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Publications

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.

Schwarz Daniel F DF König Inke R IR Ziegler Andreas A

Bioinformatics (Oxford, England) 20100526 14

<h4>Motivation</h4>Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohn's disease. This may partly be due to the low power of standard statistical approaches to detect gene-gene and gene-environment interactions ...[more]

PMID: 20505004

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Dataset Information

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.

Motivation

Results

Availability

Publications

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.

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