Lee SeokHyun, Dang ChangGwon, Choy YunHo, Do ChangHee, Cho Kwanghyun, Kim Jongjoo, Kim Yousam, Lee Jungjae
Animal Breeding and Genetics Division, National Institute of Animal Science, RDA, Cheonan 31000, Korea.
Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea.
Asian-Australas J Anim Sci. 2019 Jul;32(7):913-921. doi: 10.5713/ajas.18.0847. Epub 2019 Feb 9.
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.
本研究的目的是通过两种全基因组关联研究(GWAS)方法比较识别出的信息区域,并使用两种基因组预测方法:单步基因组最佳线性无偏预测(ss-GBLUP)和贝叶斯Bayes-B,来确定韩国荷斯坦奶牛产奶性状直接基因组值(DGV)的准确性和偏差。
收集了265,271头头胎奶牛的生产性状记录,如校正的305天产奶量(MY305)、乳脂量(FY305)和乳蛋白量(PY305)。经过质量控制后,有50,765个单核苷酸多态性基因型可用于分析。在针对ss-GBLUP的GWAS(ssGWAS)和Bayes-B的GWAS(BayesGWAS)中,计算每个1兆碱基基因组窗口的遗传方差比例,并用于识别信息基因组区域。通过随机聚类的五重交叉验证来估计DGV的准确性。作为DGV准确性的衡量标准,我们还评估了DGV与去回归估计育种值(DEBV)之间的相关性。通过确定回归系数来获得每种方法的DGV偏差。
使用ssGWAS和BayesGWAS分别为MY305鉴定出总共9个和5个显著窗口(1兆碱基)。使用ssGWAS和BayesGWAS,我们还分别检测到FY305(12个和7个)和PY305(14个和2个)的多个显著区域。单步DGV和贝叶斯DGV对于MY305(0.32至0.34)、FY305(0.37至0.39)和PY305(0.35至0.36)性状也都显示出一定程度的中等准确性范围。使用单步和贝叶斯方法确定的DGV的平均偏差,对于MY305分别为1.50±0.21和1.18±0.26,对于FY305分别为1.75±0.33和1.14±0.20,对于PY305分别为1.59±0.20和1.14±0.15。
从偏差角度来看,我们认为在韩国荷斯坦牛群体中,基于贝叶斯方法应用的基因组选择比应用ss-GBLUP更合适。