Structural Equation Modeling of Gene-Environment Interactions in Coronary Heart Disease

Xiaojuan Mi, Kent M Eskridge, Varghese George, Dong Wang

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.

Original languageEnglish (US)
Pages (from-to)255-265
Number of pages11
JournalAnnals of Human Genetics
Volume75
Issue number2
DOIs
StatePublished - Mar 1 2011

Fingerprint

Gene-Environment Interaction
Coronary Disease
Structural Models
Biological Models
Genes
Blood Glucose
Body Mass Index
Cohort Studies
Hypertension
Lipids

Keywords

  • Complex human disease
  • Gene-environment interactions
  • Multiple traits
  • Two-level structural equation model

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

Structural Equation Modeling of Gene-Environment Interactions in Coronary Heart Disease. / Mi, Xiaojuan; Eskridge, Kent M; George, Varghese; Wang, Dong.

In: Annals of Human Genetics, Vol. 75, No. 2, 01.03.2011, p. 255-265.

Research output: Contribution to journalArticle

Mi, Xiaojuan ; Eskridge, Kent M ; George, Varghese ; Wang, Dong. / Structural Equation Modeling of Gene-Environment Interactions in Coronary Heart Disease. In: Annals of Human Genetics. 2011 ; Vol. 75, No. 2. pp. 255-265.
@article{6e43325a352047eb813cd739c89d439d,
title = "Structural Equation Modeling of Gene-Environment Interactions in Coronary Heart Disease",
abstract = "Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.",
keywords = "Complex human disease, Gene-environment interactions, Multiple traits, Two-level structural equation model",
author = "Xiaojuan Mi and Eskridge, {Kent M} and Varghese George and Dong Wang",
year = "2011",
month = "3",
day = "1",
doi = "10.1111/j.1469-1809.2010.00634.x",
language = "English (US)",
volume = "75",
pages = "255--265",
journal = "Annals of Human Genetics",
issn = "0003-4800",
publisher = "Wiley-Blackwell",
number = "2",

}

TY - JOUR

T1 - Structural Equation Modeling of Gene-Environment Interactions in Coronary Heart Disease

AU - Mi, Xiaojuan

AU - Eskridge, Kent M

AU - George, Varghese

AU - Wang, Dong

PY - 2011/3/1

Y1 - 2011/3/1

N2 - Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.

AB - Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.

KW - Complex human disease

KW - Gene-environment interactions

KW - Multiple traits

KW - Two-level structural equation model

UR - http://www.scopus.com/inward/record.url?scp=79851509271&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79851509271&partnerID=8YFLogxK

U2 - 10.1111/j.1469-1809.2010.00634.x

DO - 10.1111/j.1469-1809.2010.00634.x

M3 - Article

VL - 75

SP - 255

EP - 265

JO - Annals of Human Genetics

JF - Annals of Human Genetics

SN - 0003-4800

IS - 2

ER -