Comparison of genome-wide association and genomic prediction methods for milk production traits in Korean Holstein cattle
| Autor: | Jungjae Lee, You-Sam Kim, Yunho Choy, Seokhyun Lee, Kwanghyun Cho, Jong-Joo Kim, ChangHee Do, ChangGwon Dang |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Holstein Cattle
Bayesian probability lcsh:Animal biochemistry Genome-wide association study Best linear unbiased prediction Biology Article Correlation Bayes' theorem Linear regression Genetic variation Statistics lcsh:QP501-801 lcsh:SF1-1100 Bayesian Approach 0402 animal and dairy science Milk Production Single-step Genomic Best Linear Unbiased Prediction 04 agricultural and veterinary sciences Animal Breeding and Genetics 040201 dairy & animal science Genomic Selection Animal Science and Zoology lcsh:Animal culture Food Science |
| Zdroj: | Asian-Australasian Journal of Animal Sciences Asian-Australasian Journal of Animal Sciences, Vol 32, Iss 7, Pp 913-921 (2019) |
| ISSN: | 1976-5517 1011-2367 |
| Popis: | 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 genomic best linear unbiased prediction (ss-GBLUP) and Bayesian Bayes-B. METHODS 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. 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 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. CONCLUSION 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. |
| Databáze: | OpenAIRE |
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