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Structural equation modeling of gene-environment interactions in coronary heart disease

Xiaojuan Mi1, Kent M Eskridge, Varghese George

  • 1Department of Statistics, University of Nebraska, Lincoln, 68583-0963, USA.

Annals of Human Genetics
|January 19, 2011
PubMed

Insights

This study introduces a novel structural equation model (SEM) to uncover gene-environment (GE) interactions in coronary heart disease (CHD). The method identified a new GE interaction between SERPINE1 and body mass index (BMI) influencing CHD risk.

Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Coronary heart disease (CHD) is complex, influenced by genetics, environment, and gene-environment (GE) interactions.
  • Classical methods struggle to identify GE interactions due to intricate relationships between risk factors and CHD.
  • Mediating risk factors like hypertension, blood lipids, and glucose play a crucial role in CHD development.

Purpose of the Study:

  • To develop a two-level structural equation model (SEM) for identifying genes and GE interactions in CHD.
  • To account for causal structures among mediating risk factors and CHD (Level 1).
  • To incorporate hierarchical family structures (Level 2) into the analysis.

Main Methods:

  • Developed a two-level structural equation model (SEM).
  • Applied the SEM to the Framingham Heart Study (FHS) Offspring Cohort data.
  • Investigated direct, indirect, and total effects of genes and factors on CHD.

Main Results:

  • The SEM approach offers deeper insights into how genes and factors influence CHD compared to classical methods.
  • Successfully detected a previously unreported GE interaction between SERPINE1 and body mass index (BMI) on CHD.
  • The developed model aids in creating more realistic biological pathway models.

Conclusions:

  • SEM modeling is a powerful tool for analyzing GE interactions in complex epidemiological data.
  • This method enhances understanding of the multifactorial nature of CHD.
  • The findings highlight the potential of SEM in uncovering novel genetic and environmental risk factors for CHD.

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