Using gene expression data to identify causal pathways between genotype and phenotype in a complex disease:...

Area of Science:

• Genetics
• Systems Biology
• Bioinformatics

Background:

• Understanding the causal relationships between genotype, gene expression, and phenotype is crucial in genetics.
• The Genetic Analysis Workshop 19 (GAW19) provided a valuable dataset for exploring these complex interactions.

Purpose of the Study:

• To compare structural equation modeling (SEM) and a Bayesian unified framework (BUF) for inferring causal models.
• To identify the most probable causal pathways influencing traits within the GAW19 data.

Main Methods:

• Employed SEM and BUF approaches to infer causal models.
• Utilized pairwise association filtering to reduce computational complexity.
• Applied weighted gene correlation network analysis (WGCNA) to cluster genes into functional modules, simplifying causal inference.

Main Results:

• Both SEM and BUF approaches were effective in inferring causal relationships.
• WGCNA successfully reduced the number of gene relationships, facilitating more tractable causal modeling.
• Identified key functional modules that represent underlying biological mechanisms.

Conclusions:

• The integration of WGCNA with causal modeling frameworks like SEM and BUF offers a powerful strategy for dissecting complex genotype-phenotype relationships.
• This approach significantly enhances the feasibility and interpretability of causal inference in large-scale genetic datasets.