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Comparative Accuracy of Machine Learning and GBLUP for Predicting Genomic Estimated Breeding Values in Chickens
Haoxiang Chai1, Yuqi Yang2, Dan Wang1
1Shandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China.
Machine learning models like Random Forest (RF) and Genomic Best Linear Unbiased Prediction (GBLUP) show promise for predicting genomic breeding values in laying hens. RF excels with high-density data for egg quality, while GBLUP is effective for high heritability traits using low-density markers.
Area of Science:
- Animal Genomics
- Quantitative Genetics
- Poultry Science
Background:
- Machine learning (ML) applications in genomic breeding value prediction are limited in layer breeding.
- Genomic selection is crucial for improving economically important traits in poultry.
Purpose of the Study:
- To compare ML models (MLP, RF) and GBLUP for predicting genomic breeding values in Wenshui Luhua Green-Shelled laying hens.
- To evaluate the impact of data type (whole-genome resequencing vs. 50K chip) and SNP density on prediction accuracy.
- To identify optimal models for different egg production and quality traits.
Main Methods:
- Utilized whole-genome resequencing data from 834 laying hens.
- Applied Multilayer Perceptron (MLP), Random Forest (RF), and Genomic Best Linear Unbiased Prediction (GBLUP) models.
- Performed 10-fold cross-validation to assess model performance and prediction accuracy.
Main Results:
- Heritability estimates varied across traits, with egg weight traits showing high heritability (0.570-0.631).
- Random Forest (RF) performed best for egg shape index and most egg quality traits.
- Genomic Best Linear Unbiased Prediction (GBLUP) was optimal for egg weight traits, achieving higher prediction accuracy with whole-genome data.
- RF showed the most significant advantage at high SNP densities, while GBLUP remained stable at low densities.
Conclusions:
- GBLUP is suitable for high heritability traits and low-density marker scenarios.
- RF demonstrates superior predictive performance for egg production and quality traits under high-density conditions.
- This study provides a scientific basis for selecting appropriate genomic selection models for laying hens.
