Estimating genomic breeding values and detecting QTL using univariate and bivariate models

Area of Science:

• Animal breeding and genetics
• Quantitative genetics
• Genomic selection

Background:

• Genomic selection is valuable for traits that are difficult or expensive to measure.
• Multi-trait selection is crucial for managing such traits.
• The added value of multi-trait genomic selection requires investigation.

Purpose of the Study:

• To evaluate the added value of multi-trait genomic selection.
• To compare univariate and bivariate genomic selection models.
• To assess the performance of different Bayesian models (BayesA, BayesC) and relationship matrices (A, G).

Main Methods:

• Analyzed simulated quantitative and binary traits using four univariate and bivariate linear models.
• Employed REML and SNP-based Bayesian models (BayesA, BayesC) to predict breeding values.
• Utilized genotype permutations for significance thresholds in QTL probability sampling.

Main Results:

• Bivariate models increased breeding value accuracies by up to 0.08 compared to univariate models.
• The BayesC model showed the highest accuracies for both quantitative and binary traits.
• The bivariate BayesC model identified a significant number of QTL for both traits.

Conclusions:

• Bivariate genomic selection models enhance accuracy of estimated breeding values (EBV) for both quantitative and binary traits.
• The BayesC model offers high accuracy and identifies numerous QTL.
• Genotype permutation is an effective method for determining significance thresholds for QTL probabilities.