Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population
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Published:2021-12-15
Issue:
Volume:
Page:3119-3125
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ISSN:2231-0916
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Container-title:Veterinary World
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language:en
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Short-container-title:Vet World
Author:
Sungkhapreecha Piriyaporn1ORCID, Misztal Ignacy2ORCID, Hidalgo Jorge2ORCID, Lourenco Daniela2ORCID, Buaban Sayan3ORCID, Chankitisakul Vibuntita4ORCID, Boonkum Wuttigrai4ORCID
Affiliation:
1. Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Thailand. 2. Department of Animal and Dairy Science, University of Georgia, USA. 3. The Bureau of Animal Husbandry and Genetic Improvement, Pathum Thani, Thailand. 4. Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Thailand; Network Center for Animal Breeding and Omics Research, Khon Kaen University, Thailand.
Abstract
Background and Aim: Genomic 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 Methods: The 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.
Results: Bias 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.
Conclusion: The 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.
Funder
Thailand Research Fund
Publisher
Veterinary World
Subject
General Veterinary
Reference36 articles.
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