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Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation.
Chen, Y; Atashi, H; Mota, R R; Grelet, C; Vanderick, S; Hu, H; Gengler, N.
Afiliación
  • Chen Y; TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), Gembloux, Belgium.
  • Atashi H; TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), Gembloux, Belgium.
  • Mota RR; Department of Animal Science, Shiraz University, Shiraz, Iran.
  • Grelet C; Council on Dairy Cattle Breeding, Maryland, Bowie, USA.
  • Vanderick S; Walloon Agricultural Research Center (CRA-W), Gembloux, Belgium.
  • Hu H; TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), Gembloux, Belgium.
J Anim Breed Genet ; 140(6): 695-706, 2023 Nov.
Article en En | MEDLINE | ID: mdl-37571877
ABSTRACT
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.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Genoma / Genómica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Female / Humans / Male Idioma: En Revista: J Anim Breed Genet Asunto de la revista: GENETICA / MEDICINA VETERINARIA Año: 2023 Tipo del documento: Article País de afiliación: Bélgica

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Genoma / Genómica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Female / Humans / Male Idioma: En Revista: J Anim Breed Genet Asunto de la revista: GENETICA / MEDICINA VETERINARIA Año: 2023 Tipo del documento: Article País de afiliación: Bélgica