The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo.

An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs / Hao, Xingjie; Liang, Aixin; Plastow, Graham; Zhang, Chunyan; Wang, Zhiquan; Liu, Jiajia; Salzano, Angela; Gasparrini, Bianca; Campanile, Giuseppe; Zhang, Shujun; Yang, Liguo. - In: GENES. - ISSN 2073-4425. - 13:8(2022), p. 1430. [10.3390/genes13081430]

An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs

Salzano, Angela;Gasparrini, Bianca;Campanile, Giuseppe;
2022

Abstract

The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo.
2022
An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs / Hao, Xingjie; Liang, Aixin; Plastow, Graham; Zhang, Chunyan; Wang, Zhiquan; Liu, Jiajia; Salzano, Angela; Gasparrini, Bianca; Campanile, Giuseppe; Zhang, Shujun; Yang, Liguo. - In: GENES. - ISSN 2073-4425. - 13:8(2022), p. 1430. [10.3390/genes13081430]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/896042
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