Yang Qiong, Wang Yuanjia
Department of Biostatistics, Boston University School of Public Health, 810 Mass Avenue, Boston, MA 02118, USA.
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10027, USA.
J Probab Stat. 2012 May 1;2012:652569. doi: 10.1155/2012/652569.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.
这是一篇根据知识共享署名许可协议分发的开放获取文章，允许在任何媒介中进行不受限制的使用、分发和复制，前提是对原始作品进行适当引用。多变量表型在基因关联研究中经常遇到。分析多变量表型的目的通常包括发现具有多效性效应的新基因变异，即影响多种表型，以及揭示潜在遗传机制的最终目标。近年来，针对此类表型出现了新方法的开发以及现有统计方法的应用。在本文中，我们综述了用于分析单个标记与由相同类型成分（例如，全为连续型或全为分类型）或不同类型成分（例如，一些是连续型而另一些是分类型）组成的多变量表型之间关联的可用方法。我们还综述了因果推断方法，这些方法旨在检验检测到的与多变量表型的关联是真正的多效性，还是基因标记通过影响其他表型而对某些表型发挥作用。
