Genomic selection for meat quality traits in Pekin duck

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Poultry Breeding

Background:

• Genomic selection (GS) leverages genome-wide molecular markers to predict an animal's breeding value.
• Accurate prediction of meat quality traits is crucial for Pekin duck breeding programs.

Purpose of the Study:

• To estimate heritability and assess genomic prediction accuracy for meat quality traits in Pekin ducks.
• To compare different genomic prediction methods and population strategies.

Main Methods:

• Utilized genomic selection training populations of 639 fat-type and 540 lean-type Pekin ducks.
• Employed a single-trait animal model for heritability estimation and phenotype adjustment.
• Applied GBLUP and BayesR methods for SNP effect estimation and 5-fold cross-validation for accuracy assessment.

Main Results:

• Genomic prediction accuracy ranged from 0.235 to 0.501.
• Lowest accuracy was observed for abdominal fat weight in the combined population.
• Highest accuracy was achieved for abdominal fat percentage in the lean-type duck population.
• The BayesR method consistently yielded higher prediction accuracy compared to GBLUP.

Conclusions:

• BayesR is a superior method for genomic prediction of meat quality traits in Pekin ducks.
• Combining fat-type and lean-type populations for genomic prediction is beneficial only when breeding aims align for specific traits.
• Genomic selection offers a valuable tool for improving meat quality in Pekin ducks.