The objective of this study was to compare the accuracies of genomic prediction for milk yield, fat yield, and protein yield from Philippine dairy buffaloes using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with the accuracies based on pedigree BLUP (pBLUP). To also assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted breeding values (BVs) was also calculated. Two data sets were analyzed. The GENO data consisting of all female buffaloes that have both phenotypes and genotypes ( = 904 with 1,773,305-days lactation records) were analyzed using pBLUP and GBLUP. The ALL data, consisting of the GENO data plus females with phenotypes but not genotyped ( = 1,975 with 3,821,305-days lactation records), were analyzed using pBLUP and ssGBLUP. Animals were genotyped with the Affymetrix 90k buffalo genotyping array. After quality control, 60,827 single-nucleotide polymorphisms were used for downward analysis. A pedigree file containing 2,642 animals was used for pBLUP and ssGBLUP. Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set. To assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated. Results showed that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP. Average GBLUP and ssGBLUP accuracies were 0.24 and 0.29, respectively, whereas average pBLUP accuracies (for GENO and ALL data) were 0.21 and 0.22, respectively. Slopes of the two genomic methods were also closer to one, indicating lesser bias, compared to pBLUP. Average GBLUP and ssGBLUP slopes were 0.89 and 0.84, respectively, whereas the average pBLUP (for GENO and ALL data) slopes were 0.80 and 0.54, respectively.
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http://dx.doi.org/10.3389/fgene.2021.682576 | DOI Listing |
Infect Genet Evol
January 2025
Institute of Animal Breeding and Genetics, Justus-Liebig-University Gießen, 35390 Gießen, Germany.
Infections with the liver fluke (Fasciola hepatica) cause economic losses in cattle production worldwide. Also, infections with rumen flukes (Calicophoron/Paramphistomum spp.) are gaining importance in grazing cattle in Europe.
View Article and Find Full Text PDFJ Dairy Sci
January 2025
Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602; Council on Dairy Cattle Breeding, Bowie, MD 20716.
The US dairy cattle genetic evaluation is currently a multistep process, including multibreed traditional BLUP estimations followed by single-breed SNP effects estimation. Single-step GBLUP (ssGBLUP) combines pedigree and genomic data for all breeds in one analysis. Unknown parent groups (UPG) or metafounders (MF) can be used to address missing pedigree information.
View Article and Find Full Text PDFJ Anim Sci
January 2024
Zoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.
PLoS One
November 2024
Department of Biotechnology, Yeungnam University, Gyeongsan, Republic of Korea.
J Anim Breed Genet
September 2024
Institute of Animal Science, University of Hohenheim, Stuttgart, Germany.
Genomic selection is widely implemented in livestock breeding programmes across species. Its potential is also evident for sheep breeding; however, it has several limitations, particularly because of the high genetic diversity across and within sheep breeds. In Germany, the predominant sheep breed is the Merino sheep.
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