NDLI: Improving the accuracy of genomic prediction in Chinese Holstein cattle by using one-step blending

Content Provider	Springer Nature : BioMed Central
Author	Li, Xiujin Wang, Sheng Huang, Ju Li, Leyi Zhang, Qin Ding, Xiangdong
Abstract	Background The one-step blending approach has been suggested for genomic prediction in dairy cattle. The core of this approach is to incorporate pedigree and phenotypic information of non-genotyped animals. The objective of this study was to investigate the improvement of the accuracy of genomic prediction using the one-step blending method in Chinese Holstein cattle. Findings Three methods, GBLUP (genomic best linear unbiased prediction), original one-step blending with a genomic relationship matrix, and adjusted one-step blending with an adjusted genomic relationship matrix, were compared with respect to the accuracy of genomic prediction for five milk production traits in Chinese Holstein. For the two one-step blending methods, de-regressed proofs of 17 509 non-genotyped cows, including 424 dams and 17 085 half-sisters of the validation cows, were incorporated in the prediction model. The results showed that, averaged over the five milk production traits, the one-step blending increased the accuracy of genomic prediction by about 0.12 compared to GBLUP. No further improvement in accuracies was obtained from the adjusted one-step blending over the original one-step blending in our situation. Improvements in accuracies obtained with both one-step blending methods were almost completely contributed by the non-genotyped dams. Conclusions Compared with GBLUP, the one-step blending approach can significantly improve the accuracy of genomic prediction for milk production traits in Chinese Holstein cattle. Thus, the one-step blending is a promising approach for practical genomic selection in Chinese Holstein cattle, where the reference population mainly consists of cows.
Related Links	https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-014-0066-4.pdf
Ending Page	5
Page Count	5
Starting Page	1
File Format	HTM / HTML
ISSN	12979686
DOI	10.1186/s12711-014-0066-4
Journal	Genetics Selection Evolution
Issue Number	1
Volume Number	46
Language	English
Publisher	BioMed Central
Publisher Date	2014-10-14
Access Restriction	Open
Subject Keyword	Animal Genetics and Genomics Evolutionary Biology Agriculture Reference Population Genomic Selection Genomic Prediction Estimate Breeding Value Validation Population
Content Type	Text
Resource Type	Article
Subject	Genetics Ecology, Evolution, Behavior and Systematics Medicine Animal Science and Zoology
Journal Impact Factor	3.6/2023
5-Year Journal Impact Factor	4.1/2023

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