Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees

Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for SEM for general pedigree data. We explored different SEM techniques using strum to analyze the multivariate longitudina...

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Main Authors:	Song, Yeunjoo E., Morris, Nathan J., Stein, Catherine M.
Format:	Online
Language:	English
Published:	BioMed Central 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133482/

id	pubmed-5133482
recordtype	oai_dc
spelling	pubmed-51334822016-12-15 Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees Song, Yeunjoo E. Morris, Nathan J. Stein, Catherine M. Proceedings Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for SEM for general pedigree data. We explored different SEM techniques using strum to analyze the multivariate longitudinal data and to ultimately test the association of genotypes on blood pressure traits. The quantitative blood pressure (BP) traits, systolic BP (SBP) and diastolic BP (DBP) were analyzed as the main traits of interest with age, sex, and smoking status as covariates. The single nucleotide polymorphism (SNP) genotype information from genome-wide association studies (GWAS) data was used for the test of association. The adjustment for hypertension treatment effect was done by the censored regression approach. Two different longitudinal data models, autoregressive model and latent growth curve model, were used to fit the longitudinal BP traits. The test of association for SNP was done using a novel score test within the SEM framework of strum. We found the 10 SNPs within the GWAS suggestive P value level, and among those 10, the most significant top 3 SNPs agreed in rank in both analysis models. The general SEM framework in strum is very useful to model and test for the association with massive genotype data and complex systems of multiple phenotypes with general pedigree data. BioMed Central 2016-10-18 /pmc/articles/PMC5133482/ /pubmed/27980653 http://dx.doi.org/10.1186/s12919-016-0047-4 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Song, Yeunjoo E. Morris, Nathan J. Stein, Catherine M.
spellingShingle	Song, Yeunjoo E. Morris, Nathan J. Stein, Catherine M. Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees
author_facet	Song, Yeunjoo E. Morris, Nathan J. Stein, Catherine M.
author_sort	Song, Yeunjoo E.
title	Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees
title_short	Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees
title_full	Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees
title_fullStr	Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees
title_full_unstemmed	Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees
title_sort	structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees
description	Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for SEM for general pedigree data. We explored different SEM techniques using strum to analyze the multivariate longitudinal data and to ultimately test the association of genotypes on blood pressure traits. The quantitative blood pressure (BP) traits, systolic BP (SBP) and diastolic BP (DBP) were analyzed as the main traits of interest with age, sex, and smoking status as covariates. The single nucleotide polymorphism (SNP) genotype information from genome-wide association studies (GWAS) data was used for the test of association. The adjustment for hypertension treatment effect was done by the censored regression approach. Two different longitudinal data models, autoregressive model and latent growth curve model, were used to fit the longitudinal BP traits. The test of association for SNP was done using a novel score test within the SEM framework of strum. We found the 10 SNPs within the GWAS suggestive P value level, and among those 10, the most significant top 3 SNPs agreed in rank in both analysis models. The general SEM framework in strum is very useful to model and test for the association with massive genotype data and complex systems of multiple phenotypes with general pedigree data.
publisher	BioMed Central
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133482/
_version_	1613752468760428544

Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees

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