Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation.

Chen, Yansen; Atashi, Hadi; Mota, R R; Grelet, C; Vanderick, Sylvie; Hu, Hongqing; GplusE Consortium; Gengler, Nicolas

doi:10.1111/jbg.12819

Article (Scientific journals)

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation.

Chen, Yansen; Atashi, Hadi; Mota, R R et al.

2023 • In Journal of Animal Breeding and Genetics

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/305942

DOI
10.1111/jbg.12819

PubMed
37571877

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Keywords :

SNP effect; mid-infrared spectra; nitrogen use efficiency; Animal Science and Zoology; Food Animals; General Medicine

Abstract :

[en] Nitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs.

Disciplines :

Zoology

Author, co-author :

Chen, Yansen ; Université de Liège - ULiège > TERRA Research Centre

Atashi, Hadi ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS) ; Department of Animal Science, Shiraz University, Shiraz, Iran

Mota, R R ; Council on Dairy Cattle Breeding, Maryland, Bowie, USA

Grelet, C ; Walloon Agricultural Research Center (CRA-W), Gembloux, Belgium

Vanderick, Sylvie ; Université de Liège - ULiège > TERRA Research Centre > Animal Sciences (AS)

Hu, Hongqing ; Université de Liège - ULiège > TERRA Research Centre

GplusE Consortium

Gengler, Nicolas ; Université de Liège - ULiège > TERRA Research Centre > Animal Sciences (AS)

Language :

English

Title :

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation.

Publication date :

12 August 2023

Journal title :

Journal of Animal Breeding and Genetics

ISSN :

0931-2668

eISSN :

1439-0388

Publisher :

Wiley, Germany

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jbg.12819

Funders :

CSC - China Scholarship Council [CN]
SPW DG03-DGARNE - Service Public de Wallonie. Direction Générale Opérationnelle Agriculture, Ressources naturelles et Environnement [BE]

Data Set :

10.1111/jbg.12819

Available on ORBi :

since 23 August 2023

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