Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats
Goats are essential to the dairy industry in Shaanxi, China, with udder traits playing a critical role in determining milk production and economic value for breeding programs. However, the direct measurement of these traits in dairy goats is challenging and resource-intensive. This study leveraged g...
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2025-01-01
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| author | Xiaoting Yao Jiaxin Li Jiaqi Fu Xingquan Wang Longgang Ma Hojjat Asadollahpour Nanaei Ali Mujtaba Shah Zhuangbiao Zhang Peipei Bian Shishuo Zhou Ao Wang Xihong Wang Yu Jiang |
| author_facet | Xiaoting Yao Jiaxin Li Jiaqi Fu Xingquan Wang Longgang Ma Hojjat Asadollahpour Nanaei Ali Mujtaba Shah Zhuangbiao Zhang Peipei Bian Shishuo Zhou Ao Wang Xihong Wang Yu Jiang |
| author_sort | Xiaoting Yao |
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| description | Goats are essential to the dairy industry in Shaanxi, China, with udder traits playing a critical role in determining milk production and economic value for breeding programs. However, the direct measurement of these traits in dairy goats is challenging and resource-intensive. This study leveraged genotyping imputation to explore the genetic parameters and architecture of udder traits and assess the efficiency of genomic prediction methods. Using data from 635 Saanen dairy goats, genotyped for over 14,717,075 SNP markers and phenotyped for three udder traits, heritability was 0.16 for udder width, 0.32 for udder depth, and 0.13 for teat spacing, with genetic correlations of 0.79, 0.70, and 0.45 observed among the traits. Genome-wide association studies (GWAS) revealed four candidate genes with selection signatures linked to udder traits. Predictive models, including GBLUP, kernel ridge regression (KRR), and Adaboost.RT, were evaluated for genomic estimated breeding value (GEBV) prediction. Machine learning models (KRR and Adaboost.RT) outperformed GBLUP by 20% and 11% in predictive accuracy, showing superior stability and reliability. These results underscore the potential of machine learning approaches to enhance genomic prediction accuracy in dairy goats, providing valuable insights that could contribute to improvements in animal health, productivity, and economic outcomes within the dairy goat industry. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Animals |
| spelling | doaj-art-916ced6ad3a149d2ba3dfcc3e4edaf702025-01-24T13:18:20ZengMDPI AGAnimals2076-26152025-01-0115226110.3390/ani15020261Genomic Landscape and Prediction of Udder Traits in Saanen Dairy GoatsXiaoting Yao0Jiaxin Li1Jiaqi Fu2Xingquan Wang3Longgang Ma4Hojjat Asadollahpour Nanaei5Ali Mujtaba Shah6Zhuangbiao Zhang7Peipei Bian8Shishuo Zhou9Ao Wang10Xihong Wang11Yu Jiang12Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaAnimal Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz 7155863511, IranKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, ChinaGoats are essential to the dairy industry in Shaanxi, China, with udder traits playing a critical role in determining milk production and economic value for breeding programs. However, the direct measurement of these traits in dairy goats is challenging and resource-intensive. This study leveraged genotyping imputation to explore the genetic parameters and architecture of udder traits and assess the efficiency of genomic prediction methods. Using data from 635 Saanen dairy goats, genotyped for over 14,717,075 SNP markers and phenotyped for three udder traits, heritability was 0.16 for udder width, 0.32 for udder depth, and 0.13 for teat spacing, with genetic correlations of 0.79, 0.70, and 0.45 observed among the traits. Genome-wide association studies (GWAS) revealed four candidate genes with selection signatures linked to udder traits. Predictive models, including GBLUP, kernel ridge regression (KRR), and Adaboost.RT, were evaluated for genomic estimated breeding value (GEBV) prediction. Machine learning models (KRR and Adaboost.RT) outperformed GBLUP by 20% and 11% in predictive accuracy, showing superior stability and reliability. These results underscore the potential of machine learning approaches to enhance genomic prediction accuracy in dairy goats, providing valuable insights that could contribute to improvements in animal health, productivity, and economic outcomes within the dairy goat industry.https://www.mdpi.com/2076-2615/15/2/261genotyping imputationgenomic predictionheritabilitybreeding programsmachine learning |
| spellingShingle | Xiaoting Yao Jiaxin Li Jiaqi Fu Xingquan Wang Longgang Ma Hojjat Asadollahpour Nanaei Ali Mujtaba Shah Zhuangbiao Zhang Peipei Bian Shishuo Zhou Ao Wang Xihong Wang Yu Jiang Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats Animals genotyping imputation genomic prediction heritability breeding programs machine learning |
| title | Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats |
| title_full | Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats |
| title_fullStr | Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats |
| title_full_unstemmed | Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats |
| title_short | Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats |
| title_sort | genomic landscape and prediction of udder traits in saanen dairy goats |
| topic | genotyping imputation genomic prediction heritability breeding programs machine learning |
| url | https://www.mdpi.com/2076-2615/15/2/261 |
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