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A Causality Perspective of Genomic Breed Composition for Composite Animals
Xiao-Lin Wu1,2, Zhi Li1, Yangfan Wang2,3
1Biostatistics and Bioinformatics, Neogen GeneSeek Operations, Lincoln, NE, United States.
Frontiers in Genetics
|November 16, 2020
Summary
This study introduces a new path analysis method for estimating genomic breed composition (GBC) in cattle. The method accurately estimates GBC even when ancestral breeds have highly correlated genomic information.
Area of Science:
- Animal Genetics
- Quantitative Genetics
- Bioinformatics
Background:
- Genomic breed composition (GBC) is crucial for managing crossbreeding programs and understanding animal inheritance.
- Existing statistical methods for GBC estimation have limitations in interpretation and reliability, especially with correlated ancestral breeds.
Purpose of the Study:
- To propose and validate a causality-based interpretation of GBC using path analysis.
- To apply path analysis for estimating GBC in Brangus and Beefmaster composite cattle breeds.
- To compare the performance of path analysis with traditional methods like linear regression and admixture models.
Main Methods:
- Path analysis was employed to decompose ancestral-progeny relationships into direct and indirect effects.
- Genomic breed composition was measured using direct GBC (D-GBC) and combined GBC (C-GBC) coefficients.
- Three SNP panels (1K, 5K, 10K) and various genotyping platforms were utilized for GBC estimation.
Main Results:
- Estimated GBC showed minimal variation across different SNP panels and genotyping platforms.
- In Brangus cattle, path analysis results (D-GBC and C-GBC) aligned with linear regression and admixture models due to distant ancestral relationships.
- Path analysis (D-GBC) provided robust GBC estimates in Beefmaster cattle, overcoming challenges posed by high genomic correlations between ancestral breeds (Hereford and Shorthorn).
Conclusions:
- Path analysis offers a reliable and interpretable method for estimating GBC in composite animal populations.
- The D-GBC derived from path analysis is robust to biases caused by high genomic correlations among ancestral breeds.
- This causality-based approach enhances the accuracy of GBC estimation for informed crossbreeding management decisions.
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