Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation...

Area of Science:

• Genetics
• Statistical Genomics
• Animal Breeding

Background:

• Genome-Wide Association Studies (GWAS) are crucial for identifying genetic variants associated with traits.
• Understanding complex genotype-phenotype relationships requires models that account for causal pathways among multiple traits.
• Traditional multi-trait association analyses (MTM-GWAS) offer limited insight into the mechanisms of single-nucleotide polymorphism (SNP) effects.

Purpose of the Study:

• To apply Structural Equation Modeling to Genome-Wide Association Studies (SEM-GWAS) in chickens.
• To investigate causal relationships among breast meat (BM) yield, body weight (BW), hen-house production (HHP), and SNPs.
• To compare the performance of SEM-GWAS with traditional MTM-GWAS for a more comprehensive understanding of SNP effects.

Main Methods:

• Application of Structural Equation Modeling (SEM) within a GWAS framework (SEM-GWAS).
• Inference of causal path diagrams using the inductive causation algorithm with highest posterior density (HPD) intervals.
• Comparison of SEM-GWAS results with traditional multi-trait association analyses (MTM-GWAS).

Main Results:

• SEM-GWAS successfully inferred causal relationships among BM, BW, HHP, and SNPs.
• Positive path coefficient for BM → BW and negative coefficients for BM → HHP and BW → HHP were consistently estimated.
• SEM-GWAS decomposed SNP effects into direct, indirect, and total components, unlike MTM-GWAS which only captured overall effects.

Conclusions:

• SEM-GWAS provides a more comprehensive understanding of SNP effects by partitioning them into direct and indirect components.
• This approach reveals the mechanisms by which SNPs influence traits, offering deeper biological insights than MTM-GWAS.
• SEM-GWAS is a valuable tool for dissecting complex genotype-phenotype relationships and causal mediation in genetic studies.