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Genome Selection for Fleece Traits in Inner Mongolia Cashmere Goats Based on GWAS Prior Marker Information
Huanfeng Yao1, Na Wang2, Yu Li2
1College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Animals : an Open Access Journal From MDPI
|November 13, 2025
Summary
This study enhances genomic prediction accuracy for Inner Mongolia Cashmere goat (IMCG) fleece traits by integrating genome-wide association study (GWAS) markers. This improved selection accuracy accelerates genetic progress in breeding programs.
Area of Science:
- Animal Genetics and Breeding
- Genomics
- Quantitative Genetics
Background:
- The Inner Mongolia Cashmere goat (IMCG) industry relies on fleece traits (cashmere yield, wool length, cashmere length, cashmere diameter) for economic viability.
- Traditional animal models for genomic prediction assume uniform SNP effect distributions, which may not reflect the genetic complexity of different traits.
- Accurate genomic prediction is crucial for effective selection and sustainable development of the IMCG industry.
Purpose of the Study:
- To improve the accuracy of genomic prediction for key fleece traits in IMCGs.
- To investigate the utility of incorporating genome-wide association study (GWAS)-derived prior marker information into genomic evaluation models.
- To enhance genetic selection strategies for IMCGs to accelerate genetic gain.
Main Methods:
- Conducted a genome-wide association study (GWAS) on 2299 IMCGs using 67,021 imputed SNPs.
- Utilized significant SNPs (top 5%-20%) from GWAS as prior biological information, weighting them by their contribution to genetic variance.
- Integrated weighted prior markers with remaining loci to construct a kinship matrix for estimating genetic parameters and genomic breeding values using a modified GBLUP approach.
Main Results:
- Heritability estimates for cashmere yield (CY), wool length (WL), cashmere length (CL), and cashmere diameter (CD) were 0.26, 0.37, 0.09, and 0.35, respectively.
- Integrating top 5% markers improved genomic prediction accuracy by 16.67% for CY and 19.75% for CL.
- Integrating top 10% markers improved accuracy by 9.81% for WL and 10.14% for CD, demonstrating significant enhancements over conventional GBLUP.
Conclusions:
- Integrating GWAS-derived prior marker information significantly improves genomic prediction accuracy for IMCG fleece traits.
- This approach enables more precise genetic selection, leading to accelerated genetic progress in long-term breeding programs.
- The refined genomic evaluation method offers a valuable tool for enhancing the sustainability and economic productivity of the IMCG industry.
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