Pathway-guided identification of gene-gene interactions.
Pathway-guided identification of gene-gene interactions.
复制标题
基因-基因相互作用的路径引导识别。
DOI：
10.1111/ahg.12080
复制
发表时间：
2014
影响因子：
1.9
通讯作者：
Tzeng,Jung-Ying
中科院分区：
文献类型：
--
作者：
Wang,Xin;Zhang,Daowen;Tzeng,Jung-Ying
在基因水平上评估基因-基因互作(GXG)可以利用来自标记-标记对的放大的相互作用信号来检查生物功能单位的上位性。虽然目前基于基因的GXG方法往往是针对两个或几个基因设计的，但对于复杂的性状，通常会有许多候选基因的列表来探索GXG。我们提出了一个路径引导正则化的回归模型来检测基因之间的相互作用。具体地说，我们使用主成分来总结基因对之间的SNP-SNP相互作用，并使用L1惩罚，该惩罚结合了基于生物指导和特征监督的适应性权重来识别重要的主效应和交互效应。我们的方法旨在结合生物学指导和数据适应性，并产生可信的发现，这些发现可能会提供见解，以便为进一步的分子研究制定生物学假说。所提出的方法可以用来探索具有许多候选基因的GXG，并且即使在样本量小于所研究的预测因子的数量的情况下也适用。通过仿真和实际数据分析，对该方法的有效性进行了评估。结果表明，与不使用路径和特征指导的方法相比，性能有所改善。
Assessing gene‐gene interactions (GxG) at the gene level can permit examination of epistasis at biologically functional units with amplified interaction signals from marker‐marker pairs. While current gene‐based GxG methods tend to be designed for two or a few genes, for complex traits, it is often common to have a list of many candidate genes to explore GxG. We propose a regression model with pathway‐guided regularization for detecting interactions among genes. Specifically, we use the principal components to summarize the SNP‐SNP interactions between a gene pair, and use an L1 penalty that incorporates adaptive weights based on biological guidance and trait supervision to identify important main and interaction effects. Our approach aims to combine biological guidance and data adaptiveness, and yields credible findings that may be likely to shed insights in order to formulate biological hypotheses for further molecular studies. The proposed approach can be used to explore the GxG with a list of many candidate genes and is applicable even when sample size is smaller than the number of predictors studied. We evaluate the utility of the proposed method using simulation and real data analysis. The results suggest improved performance over methods not utilizing pathway and trait guidance.