Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Area of Science:

• Animal Genomics
• Quantitative Genetics
• Livestock Breeding

Background:

• Xinjiang Brown cattle, the predominant breed in Xinjiang, require a robust genomic evaluation system, particularly for improving breeds with limited genetic advancement.
• Establishing a reliable genomic evaluation system is essential for enhancing milk production traits in Xinjiang Brown cattle.

Purpose of the Study:

• To develop a cross-breed joint reference population for genomic evaluation of Xinjiang Brown cattle by integrating Chinese Holstein cattle data.
• To determine the optimal number of Chinese Holstein cattle to include in the joint reference population for improved genomic prediction accuracy of milk traits.

Main Methods:

• Analysis of genetic structure using Illumina GeneSeek GGP bovine 150K chip data for 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle.
• Application of the Bayes method single-step genome-wide best linear unbiased prediction (ssGBLUP) for genomic evaluation.
• Randomly dividing Chinese Holstein cattle populations to construct joint reference populations and comparing prediction accuracy, bias, and inflation coefficients.

Main Results:

• Significant genetic structure differences were observed between Xinjiang Brown and Chinese Holstein cattle.
• Incorporating 600 to 900 Chinese Holstein cattle positively influenced the genomic prediction of milk traits in Xinjiang Brown cattle, with reliability ranging from 0.142-0.465.
• The feasibility of forming a multi-breed joint reference population was demonstrated, but optimal Chinese Holstein inclusion varies per Xinjiang Brown population.

Conclusions:

• Integrating Chinese Holstein cattle into a joint reference population is a viable strategy for enhancing genomic evaluation of Xinjiang Brown cattle milk traits.
• A pre-evaluation of genetic structure and kinship is crucial before incorporating foreign breeds to ensure the authenticity and reliability of genomic predictions.
• This approach offers a pathway to improve genomic selection accuracy in Xinjiang Brown cattle, contributing to breed improvement efforts.