Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China.
PLoS One. 2013 Apr 19;8(4):e62129. doi: 10.1371/journal.pone.0062129. Print 2013.
For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.
对于全基因组关联数据分析，一个通路上的两个基因、两个连锁基因区域内的两个 SNP 或一个基因内的两个连锁外显子内的 SNP 通常是相互关联的。因此，我们提出了基因-基因共关联的概念，它不仅指由于传统的近乎独立条件下的相互作用，还指两个基因之间的相关性。此外，我们基于偏最小二乘路径建模 (PLSPM) 构建了一种用于检测基因-基因共关联的新统计量。通过模拟，在三种不同类型的共关联下，突出了传统相互作用和共关联之间的关系。模拟和真实数据分析都表明，基于 PLSPM 的提出的统计方法比基于单 SNP 的逻辑模型、基于 PCA 的逻辑模型和其他基于基因的方法具有更好的性能。
