Structural Equation Modeling

  • Catherine M. Stein
  • Nathan J. Morris
  • Noémi B. Hall
  • Nora L. Nock
Part of the Methods in Molecular Biology book series (MIMB, volume 1666)


Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed 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 to 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 data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Key words

Multivariate analysis Latent variables Modeling Candidate gene analysis Complex traits Path analysis Structural equation modeling Association Population studies Family studies SEM 


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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Catherine M. Stein
    • 1
    • 2
  • Nathan J. Morris
    • 1
  • Noémi B. Hall
    • 1
  • Nora L. Nock
    • 1
  1. 1.Department of Population and Quantitative Health SciencesCase Western Reserve University School of MedicineClevelandUSA
  2. 2.Center for Proteomics and BioinformaticsCase Western Reserve UniversityClevelandUSA

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