Genomic Prediction Using Imputed Whole-Genome Sequence Data in Australian Angus Cattle

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Genomic Prediction

Background:

• Genomic breeding values are crucial for selecting superior livestock.
• Estimating genomic breeding values requires accurate and dense genetic marker data.
• The utility of whole-genome sequence (WGS) data versus lower-density markers for prediction accuracy is an ongoing research question.

Purpose of the Study:

• To compare the accuracy and bias of genomic predictions for growth and carcass traits in Australian Angus cattle using three marker densities: 50K, high-density (HD), and WGS.
• To evaluate the impact of marker density on genomic prediction accuracy across varying levels of relatedness between reference and validation animals.

Main Methods:

• Whole-genome sequence (WGS) data was utilized to estimate genomic breeding values.
• Genomic Best Linear Unbiased Prediction (GBLUP) was employed for prediction.
• Cross-validation was used to assess prediction accuracy and bias.
• Animals were categorized into subgroups based on relatedness to the reference population.

Main Results:

• Prediction accuracies were similar across 50K, HD, and WGS marker densities.
• Accuracies ranged from 0.61 to 0.68 for body weight traits and 0.40 to 0.52 for carcass traits.
• Marginal decreases in accuracy were observed with increased marker density, with no substantial improvement from WGS.

Conclusions:

• Whole-genome sequence data did not provide a substantial improvement in genomic prediction accuracy for the studied traits in this Australian Angus population.
• Population structure likely influenced the lack of significant differences in prediction accuracy across marker densities.
• Lower-density marker panels may be sufficient for genomic prediction in this context.