Genomic prediction of milk production traits for Thai dairy cattle using single-step approach with random regression test-day model
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Abstract
In genomic prediction, the single-step genomic BLUP (ssGBLUP) has been demonstrated to outperform multi-step methods. In statistical analyses of milk production traits, the random regression test-day model (RR-TDM) has clear advantages over other models. This study aimed to evaluate the feasibility of using the single-step random regression test-day model (SS-RR-TDM) in genomic prediction of milk production traits, in comparison with the pedigree-based RR-TDM, and to investigate an effect of genotyped cows on the accuracy of genomic prediction for young bulls. Data of milk yield (n=170,666) and milk components (n= 160,526) were from 24,858 and 23,201 cows in first lactation, calving between November 1993 and March 2018, respectively. Additionally, 876 and 868 bulls and cows of each data set were genotyped using Illumina Bovine SNP50 BeadChip. We cut off the data in the last six years, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. Estimated breeding values (EBVs) were obtained with the traditional pedigree-based RR-TDM, and genomic estimated breeding values (GEBVs) were estimated with SS-RR-TDM. The prediction methods were compared with the genetic predictive ability for young bulls, namely theoretical accuracy, validation accuracy, and unbiasedness. Theory accuracies were obtained by inverting the coefficient matrix of the mixed model equations (MME) whereas validation accuracies were measured by the Pearson correlation between de-regressed EBV from the full dataset and (G)EBV predicted with the reduced dataset. The unbiasedness is determined by the regression coefficient calculated according to the linear regression model (closed to 1). For prediction of all milk production traits using only bull genotypes, on average, SS-RR-TDM increased theoretical accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the extra increase was 0.02 and 0.07 for theoretical accuracies and validation accuracies, respectively.
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References
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