Using Simultaneous Equation Modeling for Defining Complex Phenotypes

Sample size: 100 publication Evidence: moderate

Author Information

Author(s): Terri M. King, Laura Almasy, Christopher I Amos, Joan E Bailey-Wilson, Rita M Cantor, Cashell E Jaquish, Maria Martinez, Rosalind J Neuman, Jane M Olson, Lyle J Palmer, Stephen S Rich, M Anne Spence, Jean W MacCluer

Primary Institution: The University of Texas – Houston Medical School

Hypothesis

Can simultaneous equation modeling (SEM) techniques effectively detect complex relationships among interrelated phenotypes?

Conclusion

The SEM procedure using empirically developed structural equations was able to partially recover the underlying simulation relationship better than generalized linear modeling.

Supporting Evidence

The SEM method was more effective at recovering relationships than generalized linear models.
Significant predictors of glucose included alcohol consumption, triglycerides, and weight.
Cholesterol was not included in the SEM analysis as it was not a component of the structural models.

Takeaway

This study shows that a special math method called SEM can help us understand how different health factors are related to each other, like cholesterol and glucose.

Methodology

Generalized linear models were used to derive structural equations, which were then applied using SEM to analyze the relationships among cholesterol, glucose, triglycerides, and HDL-C.