Song Yeunjoo E, Morris Nathan J, Stein Catherine M
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106 USA.
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106 USA ; Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106 USA.
BMC Proc. 2016 Oct 18;10(Suppl 7):303-307. doi: 10.1186/s12919-016-0047-4. eCollection 2016.
Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, implements a framework for SEM for general pedigree data. We explored different SEM techniques using 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 . We found the 10 SNPs within the GWAS suggestive value level, and among those 10, the most significant top 3 SNPs agreed in rank in both analysis models. The general SEM framework in is very useful to model and test for the association with massive genotype data and complex systems of multiple phenotypes with general pedigree data.
结构方程模型（SEM）已被广泛应用于包括遗传分析在内的众多应用科学领域。最近开发的R软件包为一般系谱数据实现了一个SEM框架。我们使用该软件包探索了不同的SEM技术，以分析多变量纵向数据，并最终检验基因型与血压性状之间的关联。将定量血压（BP）性状，即收缩压（SBP）和舒张压（DBP）作为主要关注性状进行分析，并将年龄、性别和吸烟状况作为协变量。利用全基因组关联研究（GWAS）数据中的单核苷酸多态性（SNP）基因型信息进行关联检验。采用删失回归方法对高血压治疗效果进行调整。使用两种不同的纵向数据模型，即自回归模型和潜在增长曲线模型，对纵向血压性状进行拟合。在该软件包的SEM框架内，使用一种新颖的计分检验对SNP进行关联检验。我们在GWAS提示性P值水平内发现了10个SNP，在这10个SNP中，最显著的前3个SNP在两个分析模型中的排名一致。该软件包中的通用SEM框架对于利用大量基因型数据以及具有一般系谱数据的多个复杂表型系统进行关联建模和检验非常有用。
