ArticleVeterinary world2021
Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population.
Article in Veterinary world, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 5 citations in OpenAlex.
- Genetic Parameter Estimation and Accuracy of Selection for Milk Production Traits in Romanian Black and White Cattle: A Bayesian Multi-Trait Animal Model Using MCMCglmm.Animals : an open access journal from MDPI · 2026Article
- Potential Applications of Genome-Wide Association Studies in Establishing Climate Resilience in Livestock: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Genomic Selection Using Single-Step Genomic BLUP on the Number of Services per Conception Trait in Thai-Holstein Crossbreeds.Animals : an open access journal from MDPI · 2023Article
- Accounting for population structure in genomic predictions of Eucalyptus globulus.G3 (Bethesda, Md.) · 2022Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimGenomic selection improves accuracy and decreases the generation interval, increasing the selection response. This study was conducted to assess the benefits of using single-step genomic best linear unbiased prediction (ssGBLUP) for genomic evaluations of milk yield and heat tolerance in Thai-Holstein cows and to test the value of old phenotypic data to maintain the accuracy of predictions. MATERIALS AND
methodsThe dataset included 104,150 milk yield records collected from 1999 to 2018 from 15,380 cows. The pedigree contained 33,799 animals born between 1944 and 2016, of which 882 were genotyped. Analyses were performed with and without genomic information using ssGBLUP and BLUP, respectively. Statistics for bias, dispersion, the ratio of accuracies, and the accuracy of estimated breeding values were calculated using the linear regression (LR) method. A partial dataset excluded the phenotypes of the last generation, and 66 bulls were identified as validation individuals.
resultsBias was considerable for BLUP (0.44) but negligible (-0.04) for ssGBLUP; dispersion was similar for both techniques (0.84 vs. 1.06 for BLUP and ssGBLUP, respectively). The ratio of accuracies was 0.33 for BLUP and 0.97 for ssGBLUP, indicating more stable predictions for ssGBLUP. The accuracy of predictions was 0.18 for BLUP and 0.36 for ssGBLUP. Excluding the first 10 years of phenotypic data (i.e., 1999-2008) decreased the accuracy to 0.09 for BLUP and 0.32 for ssGBLUP. Genomic information doubled the accuracy and increased the persistence of genomic estimated breeding values when old phenotypes were removed.
conclusionThe LR method is useful for estimating accuracies and bias in complex models. When the population size is small, old data are useful, and even a small amount of genomic information can substantially improve the accuracy. The effect of heat stress on first parity milk yield is small.
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