Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms

Lee, Seungmook; Jhun, Mina A.; Lee, Eunkyung; Park, Taesung

doi:10.1186/1753-6561-1-s1-s76

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…Finally, tackling a completely different problem, Lee et al [2007] proposed using SEMs to help understand relationships in expression data. Although their models included only latent genetic factors controlling the expression of 12 genes, environmental factors could have been included, had they been available.…”

Section: Discussionmentioning

confidence: 99%

“…Structural equation models. Lee et al [2007] found that the optimal model involved correlation but not causation between the latent variables (representing RUNX1 and ETV6). Nor was there evidence for an additional latent variable controlling both RUNX1 and ETV6.…”

Section: Data Modelingmentioning

confidence: 97%

“…Lee et al [2007] worked with the expression data. Their goal was to use structural equation models (SEMs) [Bollen, 1989] to aid in the understanding of how RUNX1 and ETV6 (genes related to acute myeloid leukemia) are related to each other and to the expression of other genes in the genome.…”

Section: Data Modelingmentioning

confidence: 99%

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Gene by environment interactions

Culverhouse¹,

Suarez²

2007

Genet. Epidemiol.

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This paper summarizes the contributions of group 8 to the Genetic Analysis Workshop 15. Group 8 focused on ways to address the possibility that genetic and environmental effects on phenotype may not be independent, but instead may interact in ways that could play important roles in determining phenotype. Among the eight contributors to this group, all three data sets (expression data, rheumatoid arthritis data, and simulated data) were analyzed. Contributions to this section fell into the two broad categories of refining the data (e.g. stratifying or weighting based on a covariate value) and explicitly modeling the interactions. The contributions also illustrate that there are at least two possible goals for such studies. One goal is simply to identify factors contributing to phenotype in the presence of interactions that might mask the signal to univariate methods. A related but distinct goal is to characterize an interaction (e.g. to determine if the interaction is significant).

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Section: Discussionmentioning

confidence: 99%

Section: Data Modelingmentioning

confidence: 97%

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Gene by environment interactions

Culverhouse¹,

Suarez²

2007

Genet. Epidemiol.

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“…SEM has been successfully used to elucidate causal relationships in disparate fields such as econometrics, sociology and psychology 7–9. In addition, it has been applied to Quantify Trait Loci (QTLs) for association and linkage mapping in biology,10,11 as well as to identify genetic networks from microarray data or SNP data 12–14. The significant features of SEM are the inclusion of latent variables into the constructed model and the ability to infer the network, including the cycle structure.…”

Section: Introductionmentioning

confidence: 99%

“…7–9 In addition, it has been applied to Quantify Trait Loci (QTLs) for association and linkage mapping in biology, 10,11 as well as to identify genetic networks from microarray data or SNP data. 12–14 The significant features of SEM are the inclusion of latent variables into the constructed model and the ability to infer the network, including the cycle structure. Additionally, the linear relationships between the latent variables and the observed variables are assumed to minimize the differences between the fitted covariance matrix and the calculated sample covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Application of Structure Equation Modeling for Inferring a Serial Transcriptional Regulation in Yeast

Aburatani

2011

Gene�Regul Syst Bio

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Revealing the gene regulatory systems among DNA and proteins in living cells is one of the central aims of systems biology. In this study, I used Structural Equation Modeling (SEM) in combination with stepwise factor analysis to infer the protein-DNA interactions for gene expression control from only gene expression profiles, in the absence of protein information. I applied my approach to infer the causalities within the well-studied serial transcriptional regulation composed of GAL-related genes in yeast. This allowed me to reveal the hierarchy of serial transcriptional regulation, including previously unclear protein-DNA interactions. The validity of the constructed model was demonstrated by comparing the results with previous reports describing the regulation of the transcription factors. Furthermore, the model revealed combinatory regulation by Gal4p and Gal80p. In this study, the target genes were divided into three types: those regulated by one factor and those controlled by a combination of two factors.

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Latent variable modeling paradigms for genotype‐trait association studies

Liu

Foulkes

2011

Biometrical J

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Characterizing associations among multiple single-nucleotide polymorphisms (SNPs) within and across genes, and measures of disease progression or disease status will potentially offer new insight into disease etiology and disease progression. However, this presents a significant analytic challenge due to the existence of multiple potentially informative genetic loci, as well as environmental and demographic factors, and the generally uncharacterized and complex relationships among them. Latent variable modeling approaches offer a natural framework for analysis of data arising from these population-based genetic association investigations of complex diseases as they are well-suited to uncover simultaneous effects of multiple markers. In this manuscript we describe application and performance of two such latent variable methods, namely structural equation models (SEMs) and mixed effects models (MEMs), and highlight their theoretical overlap. The relative advantages of each paradigm are investigated through simulation studies and, finally, an application to data arising from a study of anti-retroviral-associated dyslipidemia in HIV-infected individuals is provided for illustration.

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Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms

Cited by 4 publications

References 13 publications

Gene by environment interactions

Gene by environment interactions

Application of Structure Equation Modeling for Inferring a Serial Transcriptional Regulation in Yeast

Latent variable modeling paradigms for genotype‐trait association studies

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