Stein Catherine M, Morris Nathan J, Nock Nora L
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
Methods Mol Biol. 2012;850:495-512. doi: 10.1007/978-1-61779-555-8_27.
Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of general pedigrees. Here, we review the theory of SEM for both unrelated and family data, the available software for SEM, and provide an example of SEM analysis.
结构方程模型（SEM）是一种多元统计框架，用于对直接和间接观测（潜在）变量之间的复杂关系进行建模。SEM是一个通用框架，涉及同时求解线性方程组，并涵盖回归、因子分析、路径分析和潜在增长曲线建模等其他技术。最近，SEM在复杂遗传性状分析中受到欢迎，因为它可用于更好地分析相关变量（性状）之间的关系，将基因建模为多个观测到的遗传变异的函数的潜在变量，并评估多个遗传变异与多个感兴趣的相关表型之间的关联。虽然一般的SEM框架仅允许对独立观测进行分析，但最近的工作已将SEM扩展用于一般家系的分析。在此，我们回顾了用于无关数据和家系数据的SEM理论、SEM可用软件，并提供一个SEM分析示例。
