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Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles

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This study investigated the reliability of genomic prediction (GP) using breed origin of alleles (BOA) approach in the Nordic Red (RDC) population, which has an admixed population structure. The RDC population consists of animals with varying degrees of genetic materials from the Danish Red (RDM), Swedish Red (SRB), Finnish Ayrshire (FAY), and Holstein (HOL) because bulls have been used across the breeds. The BOA approach was tested using 39,550 RDC animals in the reference population and 11,786 in the validation population. Deregressed proofs (DRP) of milk, fat and protein were used as response variable for GP. Direct genomic breeding values (DGV) for animals in the validation population were calculated with (BOA model) or without (joint model) considering breed origin of alleles. The joint model assumed homogeneous marker effects and a single set of marker effects were estimated, whereas BOA model assumed heterogeneous marker effects, and different sets of marker effects were estimated across the breeds. For the BOA approach, we tested scenarios assuming both correlated (BOA_cor) and uncorrelated (BOA_uncor) marker effects between the breeds. Additionally, we investigated GP using a standard Illumina 50K chip and including SNP selected from imputed whole-genome sequencing (50K+WGS). We also studied the effect of estimating (co)variances for genome regions of different sizes to exploit the information of the genome regions contributing to the (co)variance between the breeds. Region sizes were set as 1 SNP, a group of 30 or 100 adjacent SNP, or the whole genome. Reliability of DGV was measured as squared correlations between DGV and DRP divided by the reliability of DRP. Across the 3 traits, in general, RS30 and RS100 SNP yielded the highest reliabilities. Including WGS SNP improved reliabilities in almost all scenarios (0.297 on average for 50K and 0.307 on average for 50K+WGS). The BOA_uncor (0.233 on average) was inferior to the joint model (0.339 on average), but the reliabilities obtained using BOA_cor (0.334 on average) in most cases were not significantly different from those obtained using the joint model. The results indicate that both including additional whole-genome sequencing SNP and dividing the genome into fixed regions improve GP in the RDC. The BOA models have the potential to increase the reliability of GP, but the benefit is limited in populations with a high exchange of genetic material for a long time, as is the case for RDC.

Original languageEnglish
JournalJournal of Dairy Science
Volume105
Issue3
Pages (from-to)2426-2438
Number of pages13
ISSN0022-0302
DOIs
Publication statusPublished - Mar 2022

Bibliographical note

Funding Information:
This work has received funding from a PhD scholarship from the National Agency of Research and Innovation of Uruguay (INIA, Montevideo, Uruguay, POS_EXT_2018_1_154296) and also from European Union's Horizon 2020 research and innovation programme?GenTORE?under grant agreement no. 727213. The authors are grateful to Aoxing Liu (Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark) and Egill Gautason (Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark) who helped with initial data preparation and with the new assembly map (ARSUCDI1.2) used in the analysis. The authors thank Gert Pedersen Aamand (SEGES, Aarhus, Denmark) and Ulrik Sander Nielsen (SEGES, Aarhus, Denmark) for assistance with data and valuable discussion. The authors also acknowledge Didier Boichard (INRAE, Animal Genetics and Integrative Biology, Jouy-en-Josas, France) for providing the FR SNP. Finally, the authors thank the editor and 2 anonymous referees for the valuable comments. The authors have not stated any conflicts of interest.

Funding Information:
This work has received funding from a PhD scholarship from the National Agency of Research and Innovation of Uruguay (INIA, Montevideo, Uruguay, POS_EXT_2018_1_154296) and also from European Union's Horizon 2020 research and innovation programme–GenTORE–under grant agreement no. 727213. The authors are grateful to Aoxing Liu (Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark) and Egill Gautason (Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark) who helped with initial data preparation and with the new assembly map (ARSUCDI1.2) used in the analysis. The authors thank Gert Pedersen Aamand (SEGES, Aarhus, Denmark) and Ulrik Sander Nielsen (SEGES, Aarhus, Denmark) for assistance with data and valuable discussion. The authors also acknowledge Didier Boichard (INRAE, Animal Genetics and Integrative Biology, Jouy-en-Josas, France) for providing the FR SNP. Finally, the authors thank the editor and 2 anonymous referees for the valuable comments. The authors have not stated any conflicts of interest.

Publisher Copyright:
© 2022 American Dairy Science Association

    Research areas

  • admixed cattle population, breed-specific effects, genomic selection

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