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Asian-Australas J Anim Sci > Accepted Articles
DOI: https://doi.org/10.5713/ajas.18.0847    [Accepted] Published online February 9, 2019.
Comparison of genome-wide association and genomic prediction methods for milk production traits in Korean Holstein cattle
Seokhyun Lee1, ChangGwon Dang1, YunHo Choy1, ChangHee Do2, Kwanghyun Cho3, Jongju Kim4, Yousam Kim4, Jungjae Lee5,*
1Animal Breeding & Genetics Division, National Institute of Animal Science, RDA, Cheonan 31000, Korea
2Division of Animal & Dairy Science, Chungnam National University, Daejeon 34134, Korea
3Department of Dairy Science, Korea national College of Agriculture and Fisheries, Jeonju 54874, Korea
4Division of Applied Life Science, Yeungnam University, Gyeongsan 38541, Korea
5Jun P&C Institute, INC., Yongin-si, 16950, Korea
Correspondence:  Jungjae Lee, Tel: +82-10-4130-8678, Fax: +82-31-705-0296, Email: jungjae.ansc@gmail.com
Received: 12 November 2018   • Revised: 13 December 2018   • Accepted: 11 January 2019
Abstract
Objective: 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 GBLUP (ss-GBLUP) and Bayesian Bayes-B.

Methods

From 265,271 first parity cows, records on production traits such as adjusted 305-day milk (MY305), fat (FY305), and protein (PY305) yields were collected. After quality control, 50,765 SNP 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.

Results

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 & 7) and PY305 (14 & 2), respectively. Both single-step DGV and Bayes DGV also showed somewhat moderate accuracy ranges for MY305 (0.32-0.34), FY305 (0.37-0.39), and PY305 (0.35-0.36) 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.

Conclusions

From the bias perspective, we believe that genomic selection based on the application of Bayesian approaches would be more suitable than application of single-step GBLUP in Korean Holstein populations.
Keywords: Bayesian Approach; Genomic Selection; Holstein Cattle; Milk Production; Single-step GBLUP


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