Including measured genotypes in statistical models to study the interplay of multiple factors affecting complex...

Area of Science:

• Quantitative genetics
• Statistical genetics
• Behavioral genetics

Background:

• Complex traits result from intricate interactions between multiple genetic and environmental factors.
• Existing methods often limit the comprehensive study of these gene-environment interplay aspects.

Purpose of the Study:

• To propose a flexible structural equation model for analyzing complex traits.
• To provide a general approach for fitting such models to empirical data without restrictive assumptions.

Main Methods:

• Developed a model with observed and latent variables accommodating unrestricted numbers and causal directions.
• The model allows for genetic effects on means, variances, and variable relationships, controlling for stratification.
• Incorporates various genetic data types including di-/multi-allelic loci, haplotypes, and parental genotypes, fitted using Mx software.

Main Results:

• Simulation studies confirmed the model's accuracy with correct Type I errors and unbiased parameter estimates.
• The model demonstrated satisfactory power in discriminating between alternative hypotheses.
• The approach is applicable to nuclear families of any size and uses interpretable parameterization.

Conclusions:

• The proposed flexible structural equation model offers a robust framework for dissecting the etiology of complex traits.
• It enhances the ability to study gene-environment interactions and genetic influences on various phenotypic components.
• The model is accessible to researchers through provided software, facilitating broader application in genetic studies.