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Comparison of genome-wide association and genomic prediction methods for milk production traits in Korean Holstein cattle.

Authors
  • Lee, SeokHyun1
  • Dang, ChangGwon1
  • Choy, YunHo1
  • Do, ChangHee2
  • Cho, Kwanghyun3
  • Kim, Jongjoo4
  • Kim, Yousam4
  • Lee, Jungjae5
  • 1 Animal Breeding and Genetics Division, National Institute of Animal Science, RDA, Cheonan 31000, Korea. , (North Korea)
  • 2 Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea. , (North Korea)
  • 3 Department of Dairy Science, Korea National College of Agriculture and Fisheries, Jeonju 54874, Korea. , (North Korea)
  • 4 Division of Applied Life Science, Yeungnam University, Gyeongsan 38541, Korea. , (North Korea)
  • 5 Jun P&C Institute, INC., Yongin 16950, Korea. , (North Korea)
Type
Published Article
Journal
Asian-Australasian journal of animal sciences
Publication Date
Jul 01, 2019
Volume
32
Issue
7
Pages
913–921
Identifiers
DOI: 10.5713/ajas.18.0847
PMID: 30744323
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The objectives of this study were to compare identified informative regions through two genome-wide association study (GWAS) approaches and determine the accuracy and bias of the direct genomic value (DGV) for milk production traits in Korean Holstein cattle, using two genomic prediction approaches: single-step genomic best linear unbiased prediction (ss-GBLUP) and Bayesian Bayes-B. Records on production traits such as adjusted 305-day milk (MY305), fat (FY305), and protein (PY305) yields were collected from 265,271 first parity cows. After quality control, 50,765 single-nucleotide polymorphic genotypes were available for analysis. In GWAS for ss-GBLUP (ssGWAS) and Bayes-B (BayesGWAS), the proportion of genetic variance for each 1-Mb genomic window was calculated and used to identify informative genomic regions. Accuracy of the DGV was estimated by a five-fold cross-validation with random clustering. As a measure of accuracy for DGV, we also assessed the correlation between DGV and deregressed-estimated breeding value (DEBV). The bias of DGV for each method was obtained by determining regression coefficients. A total of nine and five significant windows (1 Mb) were identified for MY305 using ssGWAS and BayesGWAS, respectively. Using ssGWAS and BayesGWAS, we also detected multiple significant regions for FY305 (12 and 7) and PY305 (14 and 2), respectively. Both single-step DGV and Bayes DGV also showed somewhat moderate accuracy ranges for MY305 (0.32 to 0.34), FY305 (0.37 to 0.39), and PY305 (0.35 to 0.36) traits, respectively. The mean biases of DGVs determined using the single-step and Bayesian methods were 1.50±0.21 and 1.18±0.26 for MY305, 1.75±0.33 and 1.14±0.20 for FY305, and 1.59±0.20 and 1.14±0.15 for PY305, respectively. From the bias perspective, we believe that genomic selection based on the application of Bayesian approaches would be more suitable than application of ss-GBLUP in Korean Holstein populations.

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