ArticleHuman heredity2010
A framework for structural equation models in general pedigrees.
Article in Human heredity, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A robust and powerful GWAS method for family trios supporting within-family Mendelian randomization analysis.Research square · 2025Article
- Heritability and longitudinal outcomes of spelling skills in individuals with histories of early speech and language disorders.Learning and individual differences · 2018Article
- Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19.BMC genetics · 2016Article
- Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees.BMC proceedings · 2016Article
- A tutorial on structural equation modeling for analysis of overlapping symptoms in co-occurring conditions using MPlus.Statistics in medicine · 2015Article
- strum: an R package for structural modeling of latent variables for general pedigrees.BMC genetics · 2015Article
- The household contact study design for genetic epidemiological studies of infectious diseases.Frontiers in genetics · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
BACKGROUND/
aimsStructural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. In this paper, a framework for implementing structural equation models (SEMs) in family data is proposed.
methodsThis framework includes both a latent measurement model and a structural model with covariates. It allows for a wide variety of models, including latent growth curve models. Environmental, polygenic and other genetic variance components can be included in the SEM. Kronecker notation makes it easy to separate the SEM process from a familial correlation model. A limited information method of model fitting is discussed. We show how missing data and ascertainment may be handled. We give several examples of how the framework may be used.
resultsA simulation study shows that our method is computationally feasible, and has good statistical properties.
conclusionOur framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage tests. A preliminary Matlab program is available, and we are currently implementing a more complete and user-friendly R package.
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