ArticleBMC genetics2015
strum: an R package for structural modeling of latent variables for general pedigrees.
Article in BMC genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Heritability and longitudinal outcomes of spelling skills in individuals with histories of early speech and language disorders.Learning and individual differences · 2018Article
- Behavioral and Molecular Genetics of Reading-Related AM and FM Detection Thresholds.Behavior genetics · 2017Article
- 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
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Authors and funding
3 authors.
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Abstract
backgroundStructural equation modeling (SEM) is an extremely general and powerful approach to account for measurement error and causal pathways when analyzing data, and it has been used in wide range of applied sciences. There are many commercial and freely available software packages for SEM. However, it is difficult to use any of the packages to analyze general pedigree data, and SEM packages for genetics are limited in their application.
resultsWe present the new R package strum to serve the need of a suitable SEM software tool for genetic analysis. It implements a general framework for SEM within the context of general pedigree data. This context requires specialized considerations such as familial correlations and ascertainment. Our package is an extraordinarily flexible tool capable of modeling genetic association, linkage analysis, polygenic effects, shared environment, and ascertainment combined with confirmatory factor analysis and general SEM. It also provides a convenient tool for model visualization, and integrates tools for simulating pedigree data. The various features of this package are tested through a simulation study to evaluate performance, and our results show that strum is very reliable and robust in terms of the accuracy and coverage of parameter estimates.
conclusionsstrum is a valuable new tool for genetic analysis. It can be easily used with general pedigree data, incorporating both measurement and structural models, giving it some significant advantages over other software packages. It also includes a built-in approach for handling ascertainment, a helpful integrated tool for genetic data simulation, and built-in tools for model visualization, providing a significant addition to biomedical research.
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Registered trials
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