On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
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DOI：
10.1093/bioinformatics/btq257
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发表时间：
2010-07-15
期刊：
影响因子：
5.8
通讯作者：
Ziegler, Andreas
中科院分区：
文献类型：
--
作者：
Schwarz, Daniel F.;Koenig, Inke R.;Ziegler, Andreas
动机：全基因组关联（GWA）研究已被证明是一种成功的方法，有助于解开复杂遗传疾病的遗传基础。然而，所确定的关联并不适合用于疾病预测，并且对于大多数疾病，例如2型糖尿病或克罗恩病，只能解释一小部分遗传性。这可能部分是由于低功率的标准统计方法来检测基因-基因和基因-环境的相互作用时，小的边际效应。一个有前途的替代方法是随机森林，它已经成功地应用于候选基因分析。重要的单核苷酸多态性通过排列重要性测量来检测。到这一天，GWA数据的应用程序是非常繁琐的现有的实现，因为高的计算burner.Results：在这里，我们提出了新的免费软件包随机丛林（RJ），这有利于快速分析GWA数据。该程序产生有效的结果，计算速度比最快的替代实现快159倍，同时仍然保持其他程序的所有选项。具体来说，它提供了不同的排列重要性措施。它包括新的选择，如向后淘汰法。我们说明RJ的应用程序的GWA克罗恩病。最重要的单核苷酸多态性（SNPs）验证了文献中的最新发现，并揭示了潜在的相互作用。
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.