Genomic Selection for Economic Traits in Inner Mongolia Cashmere Goats by Integrating GWAS Prior Information

Area of Science:

• Animal Genetics and Breeding
• Genomic Selection
• Quantitative Genetics

Background:

• Accurate genomic selection is crucial for identifying superior livestock individuals.
• Integrating genome-wide association study (GWAS) information has shown potential to enhance genomic prediction accuracy.

Purpose of the Study:

• To evaluate the impact of integrating GWAS prior information on the accuracy of genomic prediction for key economic traits in Inner Mongolia cashmere goats.
• To estimate the contribution of significant loci to genetic variance and heritability for cashmere yield, diameter, body weight, and length.

Main Methods:

• Utilized phenotypic, environmental, and genotypic data from Inner Mongolia cashmere goats.
• Extracted top 5-20% significant loci from a previous GWAS as prior marker information.
• Estimated genomic breeding values using the GBLUP-GA method with GWAS prior information.
• Assessed genomic prediction accuracy via five-fold cross-validation.

Main Results:

• Significant loci contributed substantially to genetic variance (e.g., 64-71% for cashmere yield, 76-82% for body weight).
• Additive heritability estimates increased when incorporating GWAS prior information compared to traditional methods.
• Optimal genomic prediction accuracy was achieved by integrating 5% GWAS information for cashmere yield, body weight, and length, and 20% for cashmere diameter.
• Dominance effects were found to be negligible for most traits studied.

Conclusions:

• Integrating GWAS prior information effectively improves genomic prediction accuracy for economically important traits in cashmere goats.
• The contribution of significant loci to genetic variance and heritability is substantial.
• Dominance effects can be disregarded when applying GWAS prior information for genomic selection of these traits, simplifying selection strategies.