Tang Rui, Sinnwell Jason P, Li Jia, Rider David N, de Andrade Mariza, Biernacka Joanna M
Department of Health Sciences Research, 200 First Street Southwest, Mayo Clinic, Rochester, Minnesota 55905, USA.
BMC Proc. 2009 Dec 15;3 Suppl 7(Suppl 7):S68. doi: 10.1186/1753-6561-3-s7-s68.
Random forest (RF) analysis of genetic data does not require specification of the mode of inheritance, and provides measures of variable importance that incorporate interaction effects. In this paper we describe RF-based approaches for assessment of gene and haplotype importance, and apply these approaches to a subset of the North American Rheumatoid Arthritis Consortium case-control data provided by Genetic Analysis Workshop 16. The RF analyses of 37 genes identified many of the same genes as logistic regression, but also suggested importance of certain single-nucleotide polymorphism and genes that were not ranked highly by logistic regression. A new permutation method did not reveal strong evidence of gene-gene interaction effects in these data. Although RFs are a promising approach for genetic data analysis, extensions beyond simple single-nucleotide polymorphism analyses and modifications to improve computational feasibility are needed.
对基因数据进行随机森林（RF）分析不需要指定遗传模式，并且能提供包含交互效应的变量重要性度量。在本文中，我们描述了基于RF的评估基因和单倍型重要性的方法，并将这些方法应用于遗传分析研讨会16提供的北美类风湿关节炎联盟病例对照数据的一个子集。对37个基因进行的RF分析识别出了许多与逻辑回归相同的基因，但也表明了某些单核苷酸多态性和未被逻辑回归高度排名的基因的重要性。一种新的置换方法并未在这些数据中揭示出基因-基因交互效应的有力证据。尽管RF是遗传数据分析的一种有前景的方法，但需要超越简单的单核苷酸多态性分析进行扩展，并进行修改以提高计算可行性。
