A univariate perspective of multivariate genome-wide association analysis
A univariate perspective of multivariate genome-wide association analysis
复制标题
多变量全基因组关联分析的单变量视角
DOI：
10.1002/gepi.22128
复制
发表时间：
2018-07-01
影响因子：
2.1
通讯作者：
Zhang, Heping
中科院分区：
文献类型：
--
作者：
Guo, Xiaobo;Zhu, Junxian;Zhang, Heping
在全基因组关联研究（GWAS）中经常收集多个相关的表型，并且系统地、同时地分析多个表型可以整合来自单个表型的信号，从而增加检测遗传信号的能力。然而，基本的问题仍然是开放的，包括条件和原因下，多变量分析是有益的，如何在多变量分析中出现一个高度显着的信号。为了理解这些问题，我们建议将多变量模型分解为一系列简单的单变量模型。这种转换提供了一个更清晰的定量分析的情况下，多变量的方法可以是有益的双变量表型的情况下。一个真实的数据分析来说明如何解释信号所产生的多元GWAS。
Multiple correlated phenotypes are frequently collected in genome-wide association studies (GWASs), and a systematic, simultaneous analysis of multiple phenotypes can integrate the signals from single phenotypes, therefore increasing the power of detecting genetic signals. However, fundamental questions remain open, including the conditions and reasons under which the multivariate analysis is beneficial, how a highly significant signal arises in the multivariate analysis. To understand these issues, we propose to decompose the multivariate model into a series of simple univariate models. This transformation offers a clearer quantitative analysis of the circumstances under which a multivariate approach can be beneficial for the bivariate phenotypes case. A real data analysis is employed to illustrate how to interpret how the signals arising from multivariate GWASs.