Mixed effects structural equation models and phenotypic causal networks

Area of Science:

• * Biological systems analysis
• * Quantitative genetics
• * Systems biology

Background:

• * Complex biological networks exhibit pervasive causal relationships among variables.
• * Studying these networks requires specialized modeling approaches like Structural Equation Models (SEM).
• * SEM facilitates representing causal mechanisms among phenotypic traits and quantifying relationship magnitudes.

Purpose of the Study:

• * To present methodologies for applying SEM to investigate phenotypic trait systems.
• * To explore methods for searching causal structures within these systems.
• * To address confounding effects from genetic correlations during causal structure discovery.

Main Methods:

• * Application of Structural Equation Models (SEM) for causal inference in biological systems.
• * Development of approaches for searching and defining a priori causal structures among phenotypic traits.
• * Incorporation of methods to account for confounding effects from genetic correlations.

Main Results:

• * Demonstrated utility of SEM in elucidating causal pathways among biological traits.
• * Provided a framework for discovering underlying causal structures in complex biological networks.
• * Showcased the ability to infer causal relationships despite confounding genetic correlations.

Conclusions:

• * SEM is a powerful tool for understanding biological causality and predicting system responses to interventions.
• * The presented methods enable the discovery of causal structures in phenotypic trait systems.
• * Accounting for genetic correlations is crucial for accurate causal inference in biological networks.